From b3b260a12b02f6f24280bfbf12a7a594934ba9ad Mon Sep 17 00:00:00 2001 From: andrewliao11 Date: Tue, 2 Feb 2016 20:28:16 +0800 Subject: [PATCH 01/39] training imagenet on faster rcnn --- .../scripts/faster_rcnn_end2end_imagenet.sh | 48 +++ .../test_faster_rcnn_end2end_imagenet.sh | 37 +++ lib/datasets/factory_imagenet.py | 50 +++ lib/datasets/imagenet.py | 297 ++++++++++++++++++ tools/test_net_imagenet.py | 85 +++++ tools/train_net_imagenet.py | 112 +++++++ 6 files changed, 629 insertions(+) create mode 100755 experiments/scripts/faster_rcnn_end2end_imagenet.sh create mode 100755 experiments/scripts/test_faster_rcnn_end2end_imagenet.sh create mode 100644 lib/datasets/factory_imagenet.py create mode 100644 lib/datasets/imagenet.py create mode 100755 tools/test_net_imagenet.py create mode 100755 tools/train_net_imagenet.py diff --git a/experiments/scripts/faster_rcnn_end2end_imagenet.sh b/experiments/scripts/faster_rcnn_end2end_imagenet.sh new file mode 100755 index 000000000..db0e5d849 --- /dev/null +++ b/experiments/scripts/faster_rcnn_end2end_imagenet.sh @@ -0,0 +1,48 @@ +#!/bin/bash +# Usage: +# ./experiments/scripts/default_faster_rcnn.sh GPU NET [--set ...] +# Example: +# ./experiments/scripts/default_faster_rcnn.sh 0 ZF \ +# --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400,500,600,700]" + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +NET=$2 +NET_lc=${NET,,} +ITERS=100000 +DATASET_TRAIN=imagenet_val1 +DATASET_TEST=imagenet_val2 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:2:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +LOG="experiments/logs/faster_rcnn_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" +exec &> >(tee -a "$LOG") +echo Logging output to "$LOG" + +NET_INIT=data/imagenet_models/${NET}.v2.caffemodel + +time ./tools/train_net_imagenet.py --gpu ${GPU_ID} \ + --solver models/${NET}/faster_rcnn_end2end/solver.prototxt \ + --weights ${NET_INIT} \ + --imdb ${DATASET_TRAIN} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/faster_rcnn_end2end.yml \ + ${EXTRA_ARGS} + +set +x +NET_FINAL=`grep -B 1 "done solving" ${LOG} | grep "Wrote snapshot" | awk '{print $4}'` +set -x + +time ./tools/test_net_imagenet.py --gpu ${GPU_ID} \ + --def models/${NET}/faster_rcnn_end2end/test.prototxt \ + --net ${NET_FINAL} \ + --imdb ${DATASET_TEST} \ + --cfg experiments/cfgs/faster_rcnn_end2end.yml \ + ${EXTRA_ARGS} diff --git a/experiments/scripts/test_faster_rcnn_end2end_imagenet.sh b/experiments/scripts/test_faster_rcnn_end2end_imagenet.sh new file mode 100755 index 000000000..341e4ad50 --- /dev/null +++ b/experiments/scripts/test_faster_rcnn_end2end_imagenet.sh @@ -0,0 +1,37 @@ +#!/bin/bash +# Usage: +# ./experiments/scripts/default_faster_rcnn.sh GPU NET [--set ...] +# Example: +# ./experiments/scripts/default_faster_rcnn.sh 0 ZF \ +# --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400,500,600,700]" + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +NET=$2 +NET_lc=${NET,,} +ITERS=100000 +DATASET_TRAIN=imagenet_val1 +DATASET_TEST=imagenet_val2 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:2:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +LOG="experiments/logs/faster_rcnn_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" +exec &> >(tee -a "$LOG") +echo Logging output to "$LOG" + +NET_INIT=data/imagenet_models/${NET}.v2.caffemode +NET_FINAL=/home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_70000.caffemodel + +time ./tools/test_net_imagenet.py --gpu ${GPU_ID} \ + --def models/${NET}/faster_rcnn_end2end/test.prototxt \ + --net ${NET_FINAL} \ + --imdb ${DATASET_TEST} \ + --cfg experiments/cfgs/faster_rcnn_end2end.yml \ + ${EXTRA_ARGS} diff --git a/lib/datasets/factory_imagenet.py b/lib/datasets/factory_imagenet.py new file mode 100644 index 000000000..c1acd805c --- /dev/null +++ b/lib/datasets/factory_imagenet.py @@ -0,0 +1,50 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Factory method for easily getting imdbs by name.""" + +__sets = {} + +import datasets.imagenet +import numpy as np + +def _selective_search_IJCV_top_k(split, year, top_k): + """Return an imdb that uses the top k proposals from the selective search + IJCV code. + """ + imdb = datasets.pascal_voc(split, year) + imdb.roidb_handler = imdb.selective_search_IJCV_roidb + imdb.config['top_k'] = top_k + return imdb + +# Set up voc__ using selective search "fast" mode +for split in ['train', 'val', 'val1', 'val2', 'test']: + name = 'imagenet_{}'.format(split) + devkit_path = '/media/VSlab2/imagenet/ILSVRC13' + __sets[name] = (lambda split=split, devkit_path = devkit_path:datasets.imagenet.imagenet(split,devkit_path)) + print name + print __sets[name] + +''' +# Set up voc___top_ using selective search "quality" mode +# but only returning the first k boxes +for top_k in np.arange(1000, 11000, 1000): + for year in ['2007', '2012']: + for split in ['train', 'val', 'trainval', 'test']: + name = 'voc_{}_{}_top_{:d}'.format(year, split, top_k) + __sets[name] = (lambda split=split, year=year, top_k=top_k:_selective_search_IJCV_top_k(split, year, top_k)) +''' + +def get_imdb(name): + """Get an imdb (image database) by name.""" + if not __sets.has_key(name): + raise KeyError('Unknown dataset: {}'.format(name)) + return __sets[name]() + +def list_imdbs(): + """List all registered imdbs.""" + return __sets.keys() diff --git a/lib/datasets/imagenet.py b/lib/datasets/imagenet.py new file mode 100644 index 000000000..729d5801f --- /dev/null +++ b/lib/datasets/imagenet.py @@ -0,0 +1,297 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import datasets +import datasets.imagenet +import os, sys +import datasets.imdb +import xml.dom.minidom as minidom +import numpy as np +import scipy.sparse +import scipy.io as sio +import utils.cython_bbox +import cPickle +import subprocess + +class imagenet(datasets.imdb): + def __init__(self, image_set, devkit_path): + datasets.imdb.__init__(self, image_set) + self._image_set = image_set + self._devkit_path = devkit_path + self._data_path = os.path.join(self._devkit_path, 'ILSVRC2013_DET_' + self._image_set[:-1]) + synsets = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat')) + self._classes = ('__background__',) + self._wnid = (0,) + for i in xrange(200): + self._classes = self._classes + (synsets['synsets'][0][i][2][0],) + self._wnid = self._wnid + (synsets['synsets'][0][i][1][0],) + self._wnid_to_ind = dict(zip(self._wnid, xrange(self.num_classes))) + self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) + self._image_ext = ['.JPEG'] + self._image_index = self._load_image_set_index() + # Default to roidb handler + self._roidb_handler = self.selective_search_roidb + + # Specific config options + self.config = {'cleanup' : True, + 'use_salt' : True, + 'top_k' : 2000} + + assert os.path.exists(self._devkit_path), \ + 'Devkit path does not exist: {}'.format(self._devkit_path) + assert os.path.exists(self._data_path), \ + 'Path does not exist: {}'.format(self._data_path) + + def image_path_at(self, i): + """ + Return the absolute path to image i in the image sequence. + """ + return self.image_path_from_index(self._image_index[i]) + + def image_path_from_index(self, index): + """ + Construct an image path from the image's "index" identifier. + """ + image_path = os.path.join(self._data_path, + index[:23] + self._image_ext[0]) + assert os.path.exists(image_path), \ + 'Path does not exist: {}'.format(image_path) + return image_path + + def _load_image_set_index(self): + """ + Load the indexes listed in this dataset's image set file. + """ + # Example path to image set file: + # self._data_path + /ImageSets/val.txt + image_set_file = os.path.join(self._devkit_path, 'data', 'det_lists', self._image_set + '.txt') + assert os.path.exists(image_set_file), \ + 'Path does not exist: {}'.format(image_set_file) + with open(image_set_file) as f: + image_index = [x.strip() for x in f.readlines()] + return image_index + + def gt_roidb(self): + """ + Return the database of ground-truth regions of interest. + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') + if os.path.exists(cache_file): + with open(cache_file, 'rb') as fid: + roidb = cPickle.load(fid) + print '{} gt roidb loaded from {}'.format(self.name, cache_file) + return roidb + + gt_roidb = [self._load_imagenet_annotation(index) + for index in self.image_index] + with open(cache_file, 'wb') as fid: + cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) + print 'wrote gt roidb to {}'.format(cache_file) + + return gt_roidb + + def selective_search_roidb(self): + """ + Return the database of selective search regions of interest. + Ground-truth ROIs are also included. + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = os.path.join(self.cache_path, + self.name + '_selective_search_roidb.pkl') + + if os.path.exists(cache_file): + with open(cache_file, 'rb') as fid: + roidb = cPickle.load(fid) + print '{} ss roidb loaded from {}'.format(self.name, cache_file) + return roidb + + if self._image_set != 'val2': + gt_roidb = self.gt_roidb() + ss_roidb = self._load_selective_search_roidb(gt_roidb) + roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb) + else: + roidb = self._load_selective_search_roidb(None) + print len(roidb) + with open(cache_file, 'wb') as fid: + cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) + print 'wrote ss roidb to {}'.format(cache_file) + return roidb + + def rpn_roidb(self): + if self._image_set != 'val2': + gt_roidb = self.gt_roidb() + rpn_roidb = self._load_rpn_roidb(gt_roidb) + roidb = datasets.imdb.merge_roidbs(gt_roidb, rpn_roidb) + else: + roidb = self._load_rpn_roidb(None) + + return roidb + + def _load_rpn_roidb(self, gt_roidb): + filename = self.config['rpn_file'] + print 'loading {}'.format(filename) + assert os.path.exists(filename), \ + 'rpn data not found at: {}'.format(filename) + with open(filename, 'rb') as f: + box_list = cPickle.load(f) + return self.create_roidb_from_box_list(box_list, gt_roidb) + + def _load_selective_search_roidb(self, gt_roidb): + filename = os.path.abspath(os.path.join(self._devkit_path, 'selective_search_data', + self.name + '.mat')) + assert os.path.exists(filename), \ + 'Selective search data not found at: {}'.format(filename) + raw_data = sio.loadmat(filename)['boxes'].ravel() + + box_list = [] + for i in xrange(raw_data.shape[0]): + box_list.append(raw_data[i][:, (1, 0, 3, 2)] - 1) + #box_list.append(raw_data[i][:, (1, 0, 3, 2)]) + + return self.create_roidb_from_box_list(box_list, gt_roidb) + + def selective_search_IJCV_roidb(self): + """ + eturn the database of selective search regions of interest. + Ground-truth ROIs are also included. + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = os.path.join(self.cache_path, + '{:s}_selective_search_IJCV_top_{:d}_roidb.pkl'. + format(self.name, self.config['top_k'])) + + if os.path.exists(cache_file): + with open(cache_file, 'rb') as fid: + roidb = cPickle.load(fid) + print '{} ss roidb loaded from {}'.format(self.name, cache_file) + return roidb + + gt_roidb = self.gt_roidb() + ss_roidb = self._load_selective_search_IJCV_roidb(gt_roidb) + roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb) + with open(cache_file, 'wb') as fid: + cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) + print 'wrote ss roidb to {}'.format(cache_file) + + return roidb + + def _load_selective_search_IJCV_roidb(self, gt_roidb): + IJCV_path = os.path.abspath(os.path.join(self.cache_path, '..', + 'selective_search_IJCV_data', + self.name)) + assert os.path.exists(IJCV_path), \ + 'Selective search IJCV data not found at: {}'.format(IJCV_path) + + top_k = self.config['top_k'] + box_list = [] + for i in xrange(self.num_images): + filename = os.path.join(IJCV_path, self.image_index[i] + '.mat') + raw_data = sio.loadmat(filename) + box_list.append((raw_data['boxes'][:top_k, :]-1).astype(np.uint16)) + + return self.create_roidb_from_box_list(box_list, gt_roidb) + + def _load_imagenet_annotation(self, index): + """ + Load image and bounding boxes info from txt files of imagenet. + """ + filename = os.path.join(self._devkit_path, 'ILSVRC2013_DET_bbox_' + self._image_set[:-1], index[:23] + '.xml') + # print 'Loading: {}'.format(filename) + def get_data_from_tag(node, tag): + return node.getElementsByTagName(tag)[0].childNodes[0].data + + with open(filename) as f: + data = minidom.parseString(f.read()) + + objs = data.getElementsByTagName('object') + num_objs = len(objs) + + boxes = np.zeros((num_objs, 4), dtype=np.uint16) + gt_classes = np.zeros((num_objs), dtype=np.int32) + overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) + + # Load object bounding boxes into a data frame. + for ix, obj in enumerate(objs): + # Make pixel indexes 0-based + #x1 = float(get_data_from_tag(obj, 'xmin')) - 1 + #y1 = float(get_data_from_tag(obj, 'ymin')) - 1 + #x2 = float(get_data_from_tag(obj, 'xmax')) - 1 + #y2 = float(get_data_from_tag(obj, 'ymax')) - 1 + x1 = float(get_data_from_tag(obj, 'xmin')) + y1 = float(get_data_from_tag(obj, 'ymin')) + x2 = float(get_data_from_tag(obj, 'xmax')) + y2 = float(get_data_from_tag(obj, 'ymax')) + cls = self._wnid_to_ind[ + str(get_data_from_tag(obj, "name")).lower().strip()] + boxes[ix, :] = [x1, y1, x2, y2] + gt_classes[ix] = cls + overlaps[ix, cls] = 1.0 + + overlaps = scipy.sparse.csr_matrix(overlaps) + + return {'boxes' : boxes, + 'gt_classes': gt_classes, + 'gt_overlaps' : overlaps, + 'flipped' : False} + + def _write_imagenet_results_file(self, all_boxes): + use_salt = self.config['use_salt'] + comp_id = 'comp4' + if use_salt: + comp_id += '-{}'.format(os.getpid()) + + # VOCdevkit/results/comp4-44503_det_test_aeroplane.txt + path = os.path.join(self._devkit_path, 'results', comp_id + '_') + print 'Writing {} results file'.format(self.name) + filename = path + 'det_' + self._image_set + '.txt' + with open(filename, 'wt') as f: + for im_ind, index in enumerate(self.image_index): + for cls_ind, cls in enumerate(self.classes): + if cls == '__background__': + continue + dets = all_boxes[cls_ind][im_ind] + if dets == []: + continue + # the VOCdevkit expects 1-based indices + for k in xrange(dets.shape[0]): + f.write('{:d} {:d} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. + format(im_ind + 1, cls_ind, dets[k, -1], + dets[k, 0] + 1, dets[k, 1] + 1, + dets[k, 2] + 1, dets[k, 3] + 1)) + return comp_id + + def _do_matlab_eval(self, comp_id, output_dir='output'): + rm_results = self.config['cleanup'] + + path = os.path.join(os.path.dirname(__file__), + 'VOCdevkit-matlab-wrapper') + cmd = 'cd {} && '.format(path) + cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB) + cmd += '-r "dbstop if error; ' + cmd += 'setenv(\'LC_ALL\',\'C\'); imagenet_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \ + .format(self._devkit_path, comp_id, + self._image_set, output_dir, int(rm_results)) + print('Running:\n{}'.format(cmd)) + status = subprocess.call(cmd, shell=True) + + def evaluate_detections(self, all_boxes, output_dir): + comp_id = self._write_imagenet_results_file(all_boxes) + self._do_matlab_eval(comp_id, output_dir) + + def competition_mode(self, on): + if on: + self.config['use_salt'] = False + self.config['cleanup'] = False + else: + self.config['use_salt'] = True + self.config['cleanup'] = True + +if __name__ == '__main__': + d = datasets.imagenet('val1', '') + res = d.roidb + from IPython import embed; embed() diff --git a/tools/test_net_imagenet.py b/tools/test_net_imagenet.py new file mode 100755 index 000000000..f5a7ef5b1 --- /dev/null +++ b/tools/test_net_imagenet.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Test a Fast R-CNN network on an image database.""" + +import _init_paths +from fast_rcnn.test import test_net +from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list +from datasets.factory_imagenet import get_imdb +import caffe +import argparse +import pprint +import time, os, sys + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Test a Fast R-CNN network') + parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use', + default=0, type=int) + parser.add_argument('--def', dest='prototxt', + help='prototxt file defining the network', + default=None, type=str) + parser.add_argument('--net', dest='caffemodel', + help='model to test', + default=None, type=str) + parser.add_argument('--cfg', dest='cfg_file', + help='optional config file', default=None, type=str) + parser.add_argument('--wait', dest='wait', + help='wait until net file exists', + default=True, type=bool) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to test', + default='voc_2007_test', type=str) + parser.add_argument('--comp', dest='comp_mode', help='competition mode', + action='store_true') + parser.add_argument('--set', dest='set_cfgs', + help='set config keys', default=None, + nargs=argparse.REMAINDER) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + +if __name__ == '__main__': + args = parse_args() + + print('Called with args:') + print(args) + + if args.cfg_file is not None: + cfg_from_file(args.cfg_file) + if args.set_cfgs is not None: + cfg_from_list(args.set_cfgs) + + cfg.GPU_ID = args.gpu_id + + print('Using config:') + pprint.pprint(cfg) + + while not os.path.exists(args.caffemodel) and args.wait: + print('Waiting for {} to exist...'.format(args.caffemodel)) + time.sleep(10) + + caffe.set_mode_gpu() + caffe.set_device(args.gpu_id) + net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST) + net.name = os.path.splitext(os.path.basename(args.caffemodel))[0] + + imdb = get_imdb(args.imdb_name) + imdb.competition_mode(args.comp_mode) + if not cfg.TEST.HAS_RPN: + imdb.set_proposal_method(cfg.TEST.PROPOSAL_METHOD) + + test_net(net, imdb) diff --git a/tools/train_net_imagenet.py b/tools/train_net_imagenet.py new file mode 100755 index 000000000..f22716181 --- /dev/null +++ b/tools/train_net_imagenet.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Train a Fast R-CNN network on a region of interest database.""" + +import _init_paths +from fast_rcnn.train import get_training_roidb, train_net +from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir +from datasets.factory_imagenet import get_imdb +import datasets.imdb +import caffe +import argparse +import pprint +import numpy as np +import sys + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') + parser.add_argument('--gpu', dest='gpu_id', + help='GPU device id to use [0]', + default=0, type=int) + parser.add_argument('--solver', dest='solver', + help='solver prototxt', + default=None, type=str) + parser.add_argument('--iters', dest='max_iters', + help='number of iterations to train', + default=40000, type=int) + parser.add_argument('--weights', dest='pretrained_model', + help='initialize with pretrained model weights', + default=None, type=str) + parser.add_argument('--cfg', dest='cfg_file', + help='optional config file', + default=None, type=str) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to train on', + default='voc_2007_trainval', type=str) + parser.add_argument('--rand', dest='randomize', + help='randomize (do not use a fixed seed)', + action='store_true') + parser.add_argument('--set', dest='set_cfgs', + help='set config keys', default=None, + nargs=argparse.REMAINDER) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + +def combined_roidb(imdb_names): + def get_roidb(imdb_name): + imdb = get_imdb(imdb_name) + print 'Loaded dataset `{:s}` for training'.format(imdb.name) + imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) + print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD) + roidb = get_training_roidb(imdb) + return roidb + + roidbs = [get_roidb(s) for s in imdb_names.split('+')] + roidb = roidbs[0] + if len(roidbs) > 1: + for r in roidbs[1:]: + roidb.extend(r) + imdb = datasets.imdb(imdb_names) + else: + imdb = get_imdb(imdb_names) + return imdb, roidb + +if __name__ == '__main__': + args = parse_args() + + print('Called with args:') + print(args) + + if args.cfg_file is not None: + cfg_from_file(args.cfg_file) + if args.set_cfgs is not None: + cfg_from_list(args.set_cfgs) + + cfg.GPU_ID = args.gpu_id + + print('Using config:') + pprint.pprint(cfg) + + if not args.randomize: + # fix the random seeds (numpy and caffe) for reproducibility + np.random.seed(cfg.RNG_SEED) + caffe.set_random_seed(cfg.RNG_SEED) + + # set up caffe + caffe.set_mode_gpu() + caffe.set_device(args.gpu_id) + + imdb, roidb = combined_roidb(args.imdb_name) + print '{:d} roidb entries'.format(len(roidb)) + + output_dir = get_output_dir(imdb, None) + print 'Output will be saved to `{:s}`'.format(output_dir) + + train_net(args.solver, roidb, output_dir, + pretrained_model=args.pretrained_model, + max_iters=args.max_iters) From 520d2f7cab3384d4046409a60c17e561845f1d04 Mon Sep 17 00:00:00 2001 From: andrewliao11 Date: Wed, 3 Feb 2016 15:58:57 +0800 Subject: [PATCH 02/39] upload README --- README.md | 244 ++++++++++----------------------------------- original_README.md | 201 +++++++++++++++++++++++++++++++++++++ 2 files changed, 254 insertions(+), 191 deletions(-) create mode 100644 original_README.md diff --git a/README.md b/README.md index 23434547e..be32112dd 100644 --- a/README.md +++ b/README.md @@ -1,201 +1,63 @@ -### Disclaimer +# Training Faster RCNN on Imagenet -The official Faster R-CNN code (written in MATLAB) is available [here](https://github.com/ShaoqingRen/faster_rcnn). -If your goal is to reproduce the results in our NIPS 2015 paper, please use the [official code](https://github.com/ShaoqingRen/faster_rcnn). +## preparing data -This repository contains a Python *reimplementation* of the MATLAB code. -This Python implementation is built on a fork of [Fast R-CNN](https://github.com/rbgirshick/fast-rcnn). -There are slight differences between the two implementations. -In particular, this Python port - - is ~10% slower at test-time, because some operations execute on the CPU in Python layers (e.g., 220ms / image vs. 200ms / image for VGG16) - - gives similar, but not exactly the same, mAP as the MATLAB version - - is *not compatible* with models trained using the MATLAB code due to the minor implementation differences - - **includes approximate joint training** that is 1.5x faster than alternating optimization (for VGG16) -- see these [slides](https://www.dropbox.com/s/xtr4yd4i5e0vw8g/iccv15_tutorial_training_rbg.pdf?dl=0) for more information - -# *Faster* R-CNN: Towards Real-Time Object Detection with Region Proposal Networks - -By Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun (Microsoft Research) - -This Python implementation contains contributions from Sean Bell (Cornell) written during an MSR internship. - -Please see the official [README.md](https://github.com/ShaoqingRen/faster_rcnn/blob/master/README.md) for more details. - -Faster R-CNN was initially described in an [arXiv tech report](http://arxiv.org/abs/1506.01497) and was subsequently published in NIPS 2015. - -### License - -Faster R-CNN is released under the MIT License (refer to the LICENSE file for details). - -### Citing Faster R-CNN - -If you find Faster R-CNN useful in your research, please consider citing: - - @inproceedings{renNIPS15fasterrcnn, - Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun}, - Title = {Faster {R-CNN}: Towards Real-Time Object Detection - with Region Proposal Networks}, - Booktitle = {Advances in Neural Information Processing Systems ({NIPS})}, - Year = {2015} - } - -### Contents -1. [Requirements: software](#requirements-software) -2. [Requirements: hardware](#requirements-hardware) -3. [Basic installation](#installation-sufficient-for-the-demo) -4. [Demo](#demo) -5. [Beyond the demo: training and testing](#beyond-the-demo-installation-for-training-and-testing-models) -6. [Usage](#usage) - -### Requirements: software - -1. Requirements for `Caffe` and `pycaffe` (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html)) - - **Note:** Caffe *must* be built with support for Python layers! - - ```make - # In your Makefile.config, make sure to have this line uncommented - WITH_PYTHON_LAYER := 1 - ``` - - You can download my [Makefile.config](http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/Makefile.config) for reference. -2. Python packages you might not have: `cython`, `python-opencv`, `easydict` -3. [optional] MATLAB (required for PASCAL VOC evaluation only) - -### Requirements: hardware - -1. For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices -2. For training with VGG16, you'll need a K40 (~11G of memory) - -### Installation (sufficient for the demo) - -1. Clone the Faster R-CNN repository - ```Shell - # Make sure to clone with --recursive - git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git - ``` - -2. We'll call the directory that you cloned Faster R-CNN into `FRCN_ROOT` - - *Ignore notes 1 and 2 if you followed step 1 above.* - - **Note 1:** If you didn't clone Faster R-CNN with the `--recursive` flag, then you'll need to manually clone the `caffe-fast-rcnn` submodule: - ```Shell - git submodule update --init --recursive - ``` - **Note 2:** The `caffe-fast-rcnn` submodule needs to be on the `faster-rcnn` branch (or equivalent detached state). This will happen automatically *if you followed step 1 instructions*. - -3. Build the Cython modules - ```Shell - cd $FRCN_ROOT/lib - make - ``` - -4. Build Caffe and pycaffe - ```Shell - cd $FRCN_ROOT/caffe-fast-rcnn - # Now follow the Caffe installation instructions here: - # http://caffe.berkeleyvision.org/installation.html - - # If you're experienced with Caffe and have all of the requirements installed - # and your Makefile.config in place, then simply do: - make -j8 && make pycaffe - ``` - -5. Download pre-computed Faster R-CNN detectors - ```Shell - cd $FRCN_ROOT - ./data/scripts/fetch_faster_rcnn_models.sh - ``` - - This will populate the `$FRCN_ROOT/data` folder with `faster_rcnn_models`. See `data/README.md` for details. - These models were trained on VOC 2007 trainval. - -### Demo - -*After successfully completing [basic installation](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo. - -**Python** - -To run the demo -```Shell -cd $FRCN_ROOT -./tools/demo.py ``` -The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007. - -### Beyond the demo: installation for training and testing models -1. Download the training, validation, test data and VOCdevkit - - ```Shell - wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtrainval_06-Nov-2007.tar - wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtest_06-Nov-2007.tar - wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCdevkit_08-Jun-2007.tar - ``` - -2. Extract all of these tars into one directory named `VOCdevkit` - - ```Shell - tar xvf VOCtrainval_06-Nov-2007.tar - tar xvf VOCtest_06-Nov-2007.tar - tar xvf VOCdevkit_08-Jun-2007.tar - ``` - -3. It should have this basic structure - - ```Shell - $VOCdevkit/ # development kit - $VOCdevkit/VOCcode/ # VOC utility code - $VOCdevkit/VOC2007 # image sets, annotations, etc. - # ... and several other directories ... - ``` - -4. Create symlinks for the PASCAL VOC dataset - - ```Shell - cd $FRCN_ROOT/data - ln -s $VOCdevkit VOCdevkit2007 - ``` - Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects. -5. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012 -6. Follow the next sections to download pre-trained ImageNet models - -### Download pre-trained ImageNet models - -Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16. - -```Shell -cd $FRCN_ROOT -./data/scripts/fetch_imagenet_models.sh +ILSVRC13 +└─── LSVRC2013_DET_val + │ *.JPEG (Image files) +└─── data + │ meta_det.mat ``` -VGG16 comes from the [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo), but is provided here for your convenience. -ZF was trained at MSRA. - -### Usage - -To train and test a Faster R-CNN detector using the **alternating optimization** algorithm from our NIPS 2015 paper, use `experiments/scripts/faster_rcnn_alt_opt.sh`. -Output is written underneath `$FRCN_ROOT/output`. - -```Shell -cd $FRCN_ROOT -./experiments/scripts/faster_rcnn_alt_opt.sh [GPU_ID] [NET] [--set ...] -# GPU_ID is the GPU you want to train on -# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use -# --set ... allows you to specify fast_rcnn.config options, e.g. -# --set EXP_DIR seed_rng1701 RNG_SEED 1701 +Load the meta_det.mat file by +``` +classes = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat')) ``` -("alt opt" refers to the alternating optimization training algorithm described in the NIPS paper.) +## Construct IMDB file +There's are several file you need to modify. -To train and test a Faster R-CNN detector using the **approximate joint training** method, use `experiments/scripts/faster_rcnn_end2end.sh`. -Output is written underneath `$FRCN_ROOT/output`. +#### factory_imagenet.py +This file is in the directory **$FRCNN_ROOT/lib/datasets**($FRCNN_ROOT is the where your faster rcnn locate) and is called by train_net_imagenet.py. +It is the interface loading the imdb file. +``` +for split in ['train', 'val', 'val1', 'val2', 'test']: + name = 'imagenet_{}'.format(split) + devkit_path = '/media/VSlab2/imagenet/ILSVRC13' + __sets[name] = (lambda split=split, devkit_path=devkit_path:datasets.imagenet.imagenet(split,devkit_path)) +``` +#### imagenet.py +##### In function __ __init__ __(self, image_set, devkit_path) +we have to enlarge the number of category from 20+1 into 200+1 categories. Note that in imagenet dataset, the object category is something like "n02691156", instead of "airplane" +``` +self._data_path = os.path.join(self._devkit_path, 'ILSVRC2013_DET_' + self._image_set[:-1]) +synsets = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat')) +self._classes = ('__background__',) +self._wnid = (0,) +for i in xrange(200): + self._classes = self._classes + (synsets['synsets'][0][i][2][0],) + self._wnid = self._wnid + (synsets['synsets'][0][i][1][0],) +self._wnid_to_ind = dict(zip(self._wnid, xrange(self.num_classes))) +self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) +``` +self._class denotes the class name +self._wnid denotes the id of the category -```Shell -cd $FRCN_ROOT -./experiments/scripts/faster_rcnn_end2end.sh [GPU_ID] [NET] [--set ...] -# GPU_ID is the GPU you want to train on -# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use -# --set ... allows you to specify fast_rcnn.config options, e.g. -# --set EXP_DIR seed_rng1701 RNG_SEED 1701 +##### In function _load_imagenet_annotation(self, index) +This is because in the pascal voc dataset, all coordinates start from one, so in order to make them start from 0, we need to minus 1. But this is not true for imagenet, so we should not minus 1. +So we need to modify these lines to: +``` +for ix, obj in enumerate(objs): + x1 = float(get_data_from_tag(obj, 'xmin')) + y1 = float(get_data_from_tag(obj, 'ymin')) + x2 = float(get_data_from_tag(obj, 'xmax')) + y2 = float(get_data_from_tag(obj, 'ymax')) + cls = self._wnid_to_ind[str(get_data_from_tag(obj, "name")).lower().strip()] ``` +Noted that in faster rcnnn, we don't need to run the selective-search, which is the main difference from fast rcnn. +## Start to Train On Imagenet! +Run the **$FRCNN/experiments/scripts/faster_rcnn_end2end_imagenet.sh**. +The use of .sh file is just the same as the original [faster rcnn ](https://github.com/rbgirshick/py-faster-rcnn) +## Reference +[How to train fast rcnn on imagenet](http://sunshineatnoon.github.io/Train-fast-rcnn-model-on-imagenet-without-matlab/) -This method trains the RPN module jointly with the Fast R-CNN network, rather than alternating between training the two. It results in faster (~ 1.5x speedup) training times and similar detection accuracy. See these [slides](https://www.dropbox.com/s/xtr4yd4i5e0vw8g/iccv15_tutorial_training_rbg.pdf?dl=0) for more details. diff --git a/original_README.md b/original_README.md new file mode 100644 index 000000000..23434547e --- /dev/null +++ b/original_README.md @@ -0,0 +1,201 @@ +### Disclaimer + +The official Faster R-CNN code (written in MATLAB) is available [here](https://github.com/ShaoqingRen/faster_rcnn). +If your goal is to reproduce the results in our NIPS 2015 paper, please use the [official code](https://github.com/ShaoqingRen/faster_rcnn). + +This repository contains a Python *reimplementation* of the MATLAB code. +This Python implementation is built on a fork of [Fast R-CNN](https://github.com/rbgirshick/fast-rcnn). +There are slight differences between the two implementations. +In particular, this Python port + - is ~10% slower at test-time, because some operations execute on the CPU in Python layers (e.g., 220ms / image vs. 200ms / image for VGG16) + - gives similar, but not exactly the same, mAP as the MATLAB version + - is *not compatible* with models trained using the MATLAB code due to the minor implementation differences + - **includes approximate joint training** that is 1.5x faster than alternating optimization (for VGG16) -- see these [slides](https://www.dropbox.com/s/xtr4yd4i5e0vw8g/iccv15_tutorial_training_rbg.pdf?dl=0) for more information + +# *Faster* R-CNN: Towards Real-Time Object Detection with Region Proposal Networks + +By Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun (Microsoft Research) + +This Python implementation contains contributions from Sean Bell (Cornell) written during an MSR internship. + +Please see the official [README.md](https://github.com/ShaoqingRen/faster_rcnn/blob/master/README.md) for more details. + +Faster R-CNN was initially described in an [arXiv tech report](http://arxiv.org/abs/1506.01497) and was subsequently published in NIPS 2015. + +### License + +Faster R-CNN is released under the MIT License (refer to the LICENSE file for details). + +### Citing Faster R-CNN + +If you find Faster R-CNN useful in your research, please consider citing: + + @inproceedings{renNIPS15fasterrcnn, + Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun}, + Title = {Faster {R-CNN}: Towards Real-Time Object Detection + with Region Proposal Networks}, + Booktitle = {Advances in Neural Information Processing Systems ({NIPS})}, + Year = {2015} + } + +### Contents +1. [Requirements: software](#requirements-software) +2. [Requirements: hardware](#requirements-hardware) +3. [Basic installation](#installation-sufficient-for-the-demo) +4. [Demo](#demo) +5. [Beyond the demo: training and testing](#beyond-the-demo-installation-for-training-and-testing-models) +6. [Usage](#usage) + +### Requirements: software + +1. Requirements for `Caffe` and `pycaffe` (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html)) + + **Note:** Caffe *must* be built with support for Python layers! + + ```make + # In your Makefile.config, make sure to have this line uncommented + WITH_PYTHON_LAYER := 1 + ``` + + You can download my [Makefile.config](http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/Makefile.config) for reference. +2. Python packages you might not have: `cython`, `python-opencv`, `easydict` +3. [optional] MATLAB (required for PASCAL VOC evaluation only) + +### Requirements: hardware + +1. For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices +2. For training with VGG16, you'll need a K40 (~11G of memory) + +### Installation (sufficient for the demo) + +1. Clone the Faster R-CNN repository + ```Shell + # Make sure to clone with --recursive + git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git + ``` + +2. We'll call the directory that you cloned Faster R-CNN into `FRCN_ROOT` + + *Ignore notes 1 and 2 if you followed step 1 above.* + + **Note 1:** If you didn't clone Faster R-CNN with the `--recursive` flag, then you'll need to manually clone the `caffe-fast-rcnn` submodule: + ```Shell + git submodule update --init --recursive + ``` + **Note 2:** The `caffe-fast-rcnn` submodule needs to be on the `faster-rcnn` branch (or equivalent detached state). This will happen automatically *if you followed step 1 instructions*. + +3. Build the Cython modules + ```Shell + cd $FRCN_ROOT/lib + make + ``` + +4. Build Caffe and pycaffe + ```Shell + cd $FRCN_ROOT/caffe-fast-rcnn + # Now follow the Caffe installation instructions here: + # http://caffe.berkeleyvision.org/installation.html + + # If you're experienced with Caffe and have all of the requirements installed + # and your Makefile.config in place, then simply do: + make -j8 && make pycaffe + ``` + +5. Download pre-computed Faster R-CNN detectors + ```Shell + cd $FRCN_ROOT + ./data/scripts/fetch_faster_rcnn_models.sh + ``` + + This will populate the `$FRCN_ROOT/data` folder with `faster_rcnn_models`. See `data/README.md` for details. + These models were trained on VOC 2007 trainval. + +### Demo + +*After successfully completing [basic installation](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo. + +**Python** + +To run the demo +```Shell +cd $FRCN_ROOT +./tools/demo.py +``` +The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007. + +### Beyond the demo: installation for training and testing models +1. Download the training, validation, test data and VOCdevkit + + ```Shell + wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtrainval_06-Nov-2007.tar + wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtest_06-Nov-2007.tar + wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCdevkit_08-Jun-2007.tar + ``` + +2. Extract all of these tars into one directory named `VOCdevkit` + + ```Shell + tar xvf VOCtrainval_06-Nov-2007.tar + tar xvf VOCtest_06-Nov-2007.tar + tar xvf VOCdevkit_08-Jun-2007.tar + ``` + +3. It should have this basic structure + + ```Shell + $VOCdevkit/ # development kit + $VOCdevkit/VOCcode/ # VOC utility code + $VOCdevkit/VOC2007 # image sets, annotations, etc. + # ... and several other directories ... + ``` + +4. Create symlinks for the PASCAL VOC dataset + + ```Shell + cd $FRCN_ROOT/data + ln -s $VOCdevkit VOCdevkit2007 + ``` + Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects. +5. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012 +6. Follow the next sections to download pre-trained ImageNet models + +### Download pre-trained ImageNet models + +Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16. + +```Shell +cd $FRCN_ROOT +./data/scripts/fetch_imagenet_models.sh +``` +VGG16 comes from the [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo), but is provided here for your convenience. +ZF was trained at MSRA. + +### Usage + +To train and test a Faster R-CNN detector using the **alternating optimization** algorithm from our NIPS 2015 paper, use `experiments/scripts/faster_rcnn_alt_opt.sh`. +Output is written underneath `$FRCN_ROOT/output`. + +```Shell +cd $FRCN_ROOT +./experiments/scripts/faster_rcnn_alt_opt.sh [GPU_ID] [NET] [--set ...] +# GPU_ID is the GPU you want to train on +# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use +# --set ... allows you to specify fast_rcnn.config options, e.g. +# --set EXP_DIR seed_rng1701 RNG_SEED 1701 +``` + +("alt opt" refers to the alternating optimization training algorithm described in the NIPS paper.) + +To train and test a Faster R-CNN detector using the **approximate joint training** method, use `experiments/scripts/faster_rcnn_end2end.sh`. +Output is written underneath `$FRCN_ROOT/output`. + +```Shell +cd $FRCN_ROOT +./experiments/scripts/faster_rcnn_end2end.sh [GPU_ID] [NET] [--set ...] +# GPU_ID is the GPU you want to train on +# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use +# --set ... allows you to specify fast_rcnn.config options, e.g. +# --set EXP_DIR seed_rng1701 RNG_SEED 1701 +``` + +This method trains the RPN module jointly with the Fast R-CNN network, rather than alternating between training the two. It results in faster (~ 1.5x speedup) training times and similar detection accuracy. See these [slides](https://www.dropbox.com/s/xtr4yd4i5e0vw8g/iccv15_tutorial_training_rbg.pdf?dl=0) for more details. From 1be564fae5d89f6c7d1a7dd88a7d9298cc893ba1 Mon Sep 17 00:00:00 2001 From: andrewliao11 Date: Wed, 3 Feb 2016 16:07:24 +0800 Subject: [PATCH 03/39] upload new README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index be32112dd..ae33730a1 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +if you want to refer to the original README.md, read the original_README.md # Training Faster RCNN on Imagenet ## preparing data From 92a55b6ad186bec637e1a80b0cfa81a28c082dc3 Mon Sep 17 00:00:00 2001 From: andrewliao11 Date: Wed, 3 Feb 2016 21:55:15 +0800 Subject: [PATCH 04/39] add prototxt --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index ae33730a1..5dcb447ef 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,17 @@ for ix, obj in enumerate(objs): cls = self._wnid_to_ind[str(get_data_from_tag(obj, "name")).lower().strip()] ``` Noted that in faster rcnnn, we don't need to run the selective-search, which is the main difference from fast rcnn. +## Modify the prototxt +**train.prototxt** +Change the number of classes into 200+1 +``` +param_str: "'num_classes': 201" +``` +In layer "bbox_pred", change the number of output into (200+1)*4 +``` +num_output: 804 +``` +You can modify the **test.prototxt** in the same way. ## Start to Train On Imagenet! Run the **$FRCNN/experiments/scripts/faster_rcnn_end2end_imagenet.sh**. The use of .sh file is just the same as the original [faster rcnn ](https://github.com/rbgirshick/py-faster-rcnn) From 90cf42dc319c83f676b8c44e1c00e984d80d6f83 Mon Sep 17 00:00:00 2001 From: andrewliao11 Date: Wed, 3 Feb 2016 22:23:31 +0800 Subject: [PATCH 05/39] modify demo.py --- tools/demo.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/tools/demo.py b/tools/demo.py index 4c8e78874..b350c2305 100755 --- a/tools/demo.py +++ b/tools/demo.py @@ -24,12 +24,10 @@ import caffe, os, sys, cv2 import argparse -CLASSES = ('__background__', - 'aeroplane', 'bicycle', 'bird', 'boat', - 'bottle', 'bus', 'car', 'cat', 'chair', - 'cow', 'diningtable', 'dog', 'horse', - 'motorbike', 'person', 'pottedplant', - 'sheep', 'sofa', 'train', 'tvmonitor') +CLASSES = ('__background__',) +synsets = sio.loadmat(os.path.join('/media/VSlab2/imagenet/ILSVRC13', 'data', 'meta_det.mat')) +for i in xrange(200): + CLASSES = CLASSES + (synsets['synsets'][0][i][2][0],) NETS = {'vgg16': ('VGG16', 'VGG16_faster_rcnn_final.caffemodel'), @@ -117,10 +115,8 @@ def parse_args(): args = parse_args() - prototxt = os.path.join(cfg.ROOT_DIR, 'models', NETS[args.demo_net][0], - 'faster_rcnn_alt_opt', 'faster_rcnn_test.pt') - caffemodel = os.path.join(cfg.ROOT_DIR, 'data', 'faster_rcnn_models', - NETS[args.demo_net][1]) + prototxt = os.path.join(cfg.ROOT_DIR, 'models/VGG16/faster_rcnn_end2end/test.prototxt') + caffemodel = '/home/andrewliao11/Tracker/vgg16_faster_rcnn_iter_100000.caffemodel' if not os.path.isfile(caffemodel): raise IOError(('{:s} not found.\nDid you run ./data/script/' From d9dac048f83e6cfbeab5c75bc1f749a99aaf5b5c Mon Sep 17 00:00:00 2001 From: andrewliao11 Date: Wed, 3 Feb 2016 23:38:50 +0800 Subject: [PATCH 06/39] upload demo image and modify demo.py --- data/demo/demo_01.jpg | Bin 0 -> 63950 bytes data/demo/demo_02.jpg | Bin 0 -> 74089 bytes data/demo/demo_03.jpg | Bin 0 -> 102208 bytes tools/demo.py | 15 +++++++++------ tools/output_demo_01.jpg.png 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vis_detections(image_name, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: @@ -66,6 +65,10 @@ def vis_detections(im, class_name, dets, thresh=0.5): plt.axis('off') plt.tight_layout() plt.draw() + # save the image + fig = plt.gcf() + fig.savefig("output_"+image_name+".png") + def demo(net, image_name): """Detect object classes in an image using pre-computed object proposals.""" @@ -83,7 +86,7 @@ def demo(net, image_name): '{:d} object proposals').format(timer.total_time, boxes.shape[0]) # Visualize detections for each class - CONF_THRESH = 0.8 + CONF_THRESH = 0.7 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background @@ -93,7 +96,7 @@ def demo(net, image_name): cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] - vis_detections(im, cls, dets, thresh=CONF_THRESH) + vis_detections(image_name, im, cls, dets, thresh=CONF_THRESH) def parse_args(): """Parse input arguments.""" @@ -137,8 +140,8 @@ def parse_args(): for i in xrange(2): _, _= im_detect(net, im) - im_names = ['000456.jpg', '000542.jpg', '001150.jpg', - '001763.jpg', '004545.jpg'] + im_names = ['demo_01.jpg', 'demo_02.jpg', 'demo_03.jpg', 'demo_04.jpg', 'demo_05.jpg'] + #im_names = ['demo_01.jpg'] for im_name in im_names: print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~' print 'Demo for data/demo/{}'.format(im_name) diff --git a/tools/output_demo_01.jpg.png b/tools/output_demo_01.jpg.png new file mode 100644 index 0000000000000000000000000000000000000000..495a950be66390d7c78c8ddc21174e2d40888ae1 GIT binary patch literal 708449 zcmeFYgv3ZkzK>rQ273=k9fnnE>wZ?wNZ2vK6?kfVDSGNuGo+Z zK3FQ6ZyB3z@T=cJGz%B%FL1qU|LWscwp>;b53W^QH(Kbr%48=eB8n|4?_CkL$$uC`_Px+i4YY+mCsFk0WbTNbnAM?hn{luvwYLf24CHt4JJt9g162gAp$ zfx|k|5ST8Qx-nSusdDIA{yLj5%#o;oalx%_ewB0|U0kgh82z_7Ya*N;aJ)OF z1+xv?RLk~5%YG7bikqCAlnB{*rjspa!nXfQqc;5bAjYZ85~T?iActG$Q$bqVEl(8I 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173465 bytes tools/output_demo_02.jpg | Bin 0 -> 223162 bytes tools/output_demo_03.jpg | Bin 0 -> 213125 bytes tools/output_demo_04.jpg | Bin 0 -> 546223 bytes tools/output_demo_05.jpg | Bin 0 -> 201866 bytes 6 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 tools/output_demo_01.jpg create mode 100644 tools/output_demo_02.jpg create mode 100644 tools/output_demo_03.jpg create mode 100644 tools/output_demo_04.jpg create mode 100644 tools/output_demo_05.jpg diff --git a/tools/demo.py b/tools/demo.py index f9b3b2b5f..4a35161de 100755 --- a/tools/demo.py +++ b/tools/demo.py @@ -67,7 +67,7 @@ def vis_detections(image_name, im, class_name, dets, thresh=0.5): plt.draw() # save the image fig = plt.gcf() - fig.savefig("output_"+image_name+".png") + fig.savefig("output_"+image_name) def demo(net, image_name): diff --git a/tools/output_demo_01.jpg b/tools/output_demo_01.jpg new file mode 100644 index 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Sep 17 00:00:00 2001 From: Andrew Date: Thu, 4 Feb 2016 23:35:37 +0800 Subject: [PATCH 13/39] Update README(demo image and videos) --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5dcb447ef..e0f173de0 100644 --- a/README.md +++ b/README.md @@ -67,9 +67,18 @@ In layer "bbox_pred", change the number of output into (200+1)*4 num_output: 804 ``` You can modify the **test.prototxt** in the same way. -## Start to Train On Imagenet! +## Start to Train Faster RCNN On Imagenet! Run the **$FRCNN/experiments/scripts/faster_rcnn_end2end_imagenet.sh**. The use of .sh file is just the same as the original [faster rcnn ](https://github.com/rbgirshick/py-faster-rcnn) +## Demo +Just run the **demo.py** to visualize pictures! +![demo_02](https://github.com/andrewliao11/py-faster-rcnn/blob/master/tools/output_demo_02.jpg?raw=true) +![demo_03](https://github.com/andrewliao11/py-faster-rcnn/blob/master/tools/output_demo_03.jpg?raw=true) + +### faster rcnn with tracker on videos +[![IMAGE ALT TEXT HERE](http://img.youtube.com/vi/wY7LADoEuFs/0.jpg)](http://www.youtube.com/watch?v=wY7LADoEuFs) + +Original video "https://www.jukinmedia.com/videos/view/5655" ## Reference [How to train fast rcnn on imagenet](http://sunshineatnoon.github.io/Train-fast-rcnn-model-on-imagenet-without-matlab/) From 7877474fa0c2091693724358417c9b33188f2182 Mon Sep 17 00:00:00 2001 From: Andrew Date: Tue, 16 Feb 2016 10:06:09 +0800 Subject: [PATCH 14/39] Update README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e0f173de0..4dce17cb6 100644 --- a/README.md +++ b/README.md @@ -82,3 +82,4 @@ Original video "https://www.jukinmedia.com/videos/view/5655" ## Reference [How to train fast rcnn on imagenet](http://sunshineatnoon.github.io/Train-fast-rcnn-model-on-imagenet-without-matlab/) +If you have any advance question, feel free to contact me by andrewliao11@gmail.com From 915518c3d85a4d5b0a6233f96656871cee2ff47c Mon Sep 17 00:00:00 2001 From: Andrew Date: Tue, 16 Feb 2016 10:06:36 +0800 Subject: [PATCH 15/39] Update README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 4dce17cb6..dcd9411b2 100644 --- a/README.md +++ b/README.md @@ -82,4 +82,5 @@ Original video "https://www.jukinmedia.com/videos/view/5655" ## Reference [How to train fast rcnn on imagenet](http://sunshineatnoon.github.io/Train-fast-rcnn-model-on-imagenet-without-matlab/) +## Othres If you have any advance question, feel free to contact me by andrewliao11@gmail.com From be501593496e3726f16f2c11628e4faddf5da9f8 Mon Sep 17 00:00:00 2001 From: Andrew Date: Tue, 16 Feb 2016 10:06:53 +0800 Subject: [PATCH 16/39] typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index dcd9411b2..d5ac0290b 100644 --- a/README.md +++ b/README.md @@ -82,5 +82,5 @@ Original video "https://www.jukinmedia.com/videos/view/5655" ## Reference [How to train fast rcnn on imagenet](http://sunshineatnoon.github.io/Train-fast-rcnn-model-on-imagenet-without-matlab/) -## Othres +## Others If you have any advance question, feel free to contact me by andrewliao11@gmail.com From b7906b91f15c4f02445ad39cdd881ac64f8fd7b5 Mon Sep 17 00:00:00 2001 From: Andrew Date: Sat, 5 Mar 2016 10:17:56 +0800 Subject: [PATCH 17/39] update shell script part --- README.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d5ac0290b..8b4c7f0ce 100644 --- a/README.md +++ b/README.md @@ -6,9 +6,11 @@ if you want to refer to the original README.md, read the original_README.md ``` ILSVRC13 └─── LSVRC2013_DET_val - │ *.JPEG (Image files) + │ *.JPEG (Image files, ex:ILSVRC2013_val_00000565.JPEG) └─── data │ meta_det.mat + └─── det_lists + │ val1.txt, val2.txt ``` Load the meta_det.mat file by ``` @@ -57,7 +59,8 @@ for ix, obj in enumerate(objs): ``` Noted that in faster rcnnn, we don't need to run the selective-search, which is the main difference from fast rcnn. ## Modify the prototxt -**train.prototxt** +Under the directory **$FRCNN_ROOT/** +#### train.prototxt Change the number of classes into 200+1 ``` param_str: "'num_classes': 201" @@ -67,6 +70,17 @@ In layer "bbox_pred", change the number of output into (200+1)*4 num_output: 804 ``` You can modify the **test.prototxt** in the same way. + +## [Last step] Modify the shell script +Under the dircetory **$FRCNN_ROOT/experiments/scripts** +#### faster_rcnn_end2end_imagenet.sh +You can specify which dataset to train/test on and your what pre-trainded model is +``` +ITERS=100000 +DATASET_TRAIN=imagenet_val1 +DATASET_TEST=imagenet_val2 +NET_INIT=data/imagenet_models/${NET}.v2.caffemodel +``` ## Start to Train Faster RCNN On Imagenet! Run the **$FRCNN/experiments/scripts/faster_rcnn_end2end_imagenet.sh**. The use of .sh file is just the same as the original [faster rcnn ](https://github.com/rbgirshick/py-faster-rcnn) From c1897141bed693834a25a19b2c0888662e83acdf Mon Sep 17 00:00:00 2001 From: Andrew Date: Sat, 19 Mar 2016 23:47:29 +0800 Subject: [PATCH 18/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8b4c7f0ce..f17777d1e 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -if you want to refer to the original README.md, read the original_README.md +Hope this tutorial will help you a lot! and I appreciate that you can **star** it! # Training Faster RCNN on Imagenet ## preparing data From bcb724175f2f62e67d30dfc8f4473ad007ce6bb1 Mon Sep 17 00:00:00 2001 From: Andrew Date: Wed, 4 May 2016 09:10:39 +0800 Subject: [PATCH 19/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f17777d1e..6c8dd8451 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -Hope this tutorial will help you a lot! and I appreciate that you can **star** it! + # Training Faster RCNN on Imagenet ## preparing data From 3c0371cb36b7bef94581a8f77709e066746e31dc Mon Sep 17 00:00:00 2001 From: Andrew Date: Mon, 20 Jun 2016 14:44:22 +0800 Subject: [PATCH 20/39] add my log file --- log | 33365 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 33365 insertions(+) create mode 100644 log diff --git a/log b/log new file mode 100644 index 000000000..a2caa3d5d --- /dev/null +++ b/log @@ -0,0 +1,33365 @@ ++ set -e ++ export PYTHONUNBUFFERED=True ++ PYTHONUNBUFFERED=True ++ GPU_ID=3 ++ NET=VGG16 ++ NET_lc=vgg16 ++ ITERS=100000 ++ DATASET_TRAIN=imagenet_val1 ++ DATASET_TEST=imagenet_val2 ++ array=($@) ++ len=0 ++ EXTRA_ARGS= ++ EXTRA_ARGS_SLUG= +++ date +%Y-%m-%d_%H-%M-%S ++ LOG=experiments/logs/faster_rcnn_VGG16_.txt.2016-06-16_02-05-17 ++ exec +++ tee -a experiments/logs/faster_rcnn_VGG16_.txt.2016-06-16_02-05-17 ++ echo Logging output to experiments/logs/faster_rcnn_VGG16_.txt.2016-06-16_02-05-17 +Logging output to experiments/logs/faster_rcnn_VGG16_.txt.2016-06-16_02-05-17 ++ NET_INIT=data/imagenet_models/VGG16.v2.caffemodel ++ ./tools/train_net_imagenet.py --gpu 3 --solver models/VGG16/faster_rcnn_end2end/solver.prototxt --weights data/imagenet_models/VGG16.v2.caffemodel --imdb imagenet_val1 --iters 100000 --cfg experiments/cfgs/faster_rcnn_end2end.yml +imagenet_train + at 0x7f36b8183848> +imagenet_val + at 0x7f36b81838c0> +imagenet_val1 + at 0x7f36b8183938> +imagenet_val2 + at 0x7f36b81839b0> +imagenet_test + at 0x7f36b8183a28> +Called with args: +Namespace(cfg_file='experiments/cfgs/faster_rcnn_end2end.yml', gpu_id=3, imdb_name='imagenet_val1', max_iters=100000, pretrained_model='data/imagenet_models/VGG16.v2.caffemodel', randomize=False, set_cfgs=None, solver='models/VGG16/faster_rcnn_end2end/solver.prototxt') +Using config: +{'DEDUP_BOXES': 0.0625, + 'EPS': 1e-14, + 'EXP_DIR': 'faster_rcnn_end2end', + 'GPU_ID': 3, + 'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]), + 'RNG_SEED': 3, + 'ROOT_DIR': '/home/andrewliao11/py-faster-rcnn', + 'TEST': {'BBOX_REG': True, + 'HAS_RPN': True, + 'MAX_SIZE': 1000, + 'NMS': 0.3, + 'PROPOSAL_METHOD': 'selective_search', + 'RPN_MIN_SIZE': 16, + 'RPN_NMS_THRESH': 0.7, + 'RPN_POST_NMS_TOP_N': 300, + 'RPN_PRE_NMS_TOP_N': 6000, + 'SCALES': [600], + 'SVM': False}, + 'TRAIN': {'ASPECT_GROUPING': True, + 'BATCH_SIZE': 128, + 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], + 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0], + 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2], + 'BBOX_NORMALIZE_TARGETS': True, + 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True, + 'BBOX_REG': True, + 'BBOX_THRESH': 0.5, + 'BG_THRESH_HI': 0.5, + 'BG_THRESH_LO': 0.1, + 'FG_FRACTION': 0.25, + 'FG_THRESH': 0.5, + 'HAS_RPN': True, + 'IMS_PER_BATCH': 1, + 'MAX_SIZE': 1000, + 'PROPOSAL_METHOD': 'gt', + 'RPN_BATCHSIZE': 256, + 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], + 'RPN_CLOBBER_POSITIVES': False, + 'RPN_FG_FRACTION': 0.5, + 'RPN_MIN_SIZE': 16, + 'RPN_NEGATIVE_OVERLAP': 0.3, + 'RPN_NMS_THRESH': 0.7, + 'RPN_POSITIVE_OVERLAP': 0.7, + 'RPN_POSITIVE_WEIGHT': -1.0, + 'RPN_POST_NMS_TOP_N': 2000, + 'RPN_PRE_NMS_TOP_N': 12000, + 'SCALES': [600], + 'SNAPSHOT_INFIX': '', + 'SNAPSHOT_ITERS': 10000, + 'USE_FLIPPED': True, + 'USE_PREFETCH': False}, + 'USE_GPU_NMS': True} +Loaded dataset `val1` for training +Set proposal method: gt +Appending horizontally-flipped training examples... +val1 gt roidb loaded from /home/andrewliao11/py-faster-rcnn/data/cache/val1_gt_roidb.pkl +done +Preparing training data... +/usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2652: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. 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+ [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2] + [ 0.1 0.1 0.2 0.2]] +[ 0.1 0.1 0.2 0.2] +Normalizing targets +done +WARNING: Logging before InitGoogleLogging() is written to STDERR +I0616 02:05:48.575042 9857 solver.cpp:54] Initializing solver from parameters: +train_net: "models/VGG16/faster_rcnn_end2end/train.prototxt" +base_lr: 0.001 +display: 20 +lr_policy: "step" +gamma: 0.1 +momentum: 0.9 +weight_decay: 0.0005 +stepsize: 50000 +snapshot: 0 +snapshot_prefix: "vgg16_faster_rcnn" +average_loss: 100 +iter_size: 2 +I0616 02:05:48.575085 9857 solver.cpp:86] Creating training net from train_net file: models/VGG16/faster_rcnn_end2end/train.prototxt +I0616 02:05:48.575848 9857 net.cpp:50] Initializing net from parameters: +name: "VGG_ILSVRC_16_layers" +state { + phase: TRAIN +} +layer { + name: "input-data" + type: "Python" + top: "data" + top: "im_info" + top: "gt_boxes" + python_param { + module: "roi_data_layer.layer" + layer: "RoIDataLayer" + param_str: "\'num_classes\': 201" + } +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 18 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 36 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "rpn_cls_score_reshape" + type: "Reshape" + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + reshape_param { + shape { + dim: 0 + dim: 2 + dim: -1 + dim: 0 + } + } +} +layer { + name: "rpn-data" + type: "Python" + bottom: "rpn_cls_score" + bottom: "gt_boxes" + bottom: "im_info" + bottom: "data" + top: "rpn_labels" + top: "rpn_bbox_targets" + top: "rpn_bbox_inside_weights" + top: "rpn_bbox_outside_weights" + python_param { + module: "rpn.anchor_target_layer" + layer: "AnchorTargetLayer" + param_str: "\'feat_stride\': 16" + } +} +layer { + name: "rpn_loss_cls" + type: "SoftmaxWithLoss" + bottom: "rpn_cls_score_reshape" + bottom: "rpn_labels" + top: "rpn_cls_loss" + loss_weight: 1 + propagate_down: true + propagate_down: false + loss_param { + ignore_label: -1 + normalize: true + } +} +layer { + name: "rpn_loss_bbox" + type: "SmoothL1Loss" + bottom: "rpn_bbox_pred" + bottom: "rpn_bbox_targets" + bottom: "rpn_bbox_inside_weights" + bottom: "rpn_bbox_outside_weights" + top: "rpn_loss_bbox" + loss_weight: 1 + smooth_l1_loss_param { + sigma: 3 + } +} +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} +layer { + name: "rpn_cls_prob_reshape" + type: "Reshape" + bottom: "rpn_cls_prob" + top: "rpn_cls_prob_reshape" + reshape_param { + shape { + dim: 0 + dim: 18 + dim: -1 + dim: 0 + } + } +} +layer { + name: "proposal" + type: "Python" + bottom: "rpn_cls_prob_reshape" + bottom: "rpn_bbox_pred" + bottom: "im_info" + top: "rpn_rois" + python_param { + module: "rpn.proposal_layer" + layer: "ProposalLayer" + param_str: "\'feat_stride\': 16" + } +} +layer { + name: "roi-data" + type: "Python" + bottom: "rpn_rois" + bottom: "gt_boxes" + top: "rois" + top: "labels" + top: "bbox_targets" + top: "bbox_inside_weights" + top: "bbox_outside_weights" + python_param { + module: "rpn.proposal_target_layer" + layer: "ProposalTargetLayer" + param_str: "\'num_classes\': 201" + } +} +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_h: 7 + pooled_w: 7 + spatial_scale: 0.0625 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 201 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 804 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "loss_cls" + type: "SoftmaxWithLoss" + bottom: "cls_score" + bottom: "labels" + top: "loss_cls" + loss_weight: 1 + propagate_down: true + propagate_down: false +} +layer { + name: "loss_bbox" + type: "SmoothL1Loss" + bottom: "bbox_pred" + bottom: "bbox_targets" + bottom: "bbox_inside_weights" + bottom: "bbox_outside_weights" + top: "loss_bbox" + loss_weight: 1 +} +I0616 02:05:48.576035 9857 layer_factory.hpp:76] Creating layer input-data +I0616 02:05:48.598570 9857 net.cpp:110] Creating Layer input-data +I0616 02:05:48.598608 9857 net.cpp:433] input-data -> data +I0616 02:05:48.598614 9857 net.cpp:433] input-data -> im_info +I0616 02:05:48.598619 9857 net.cpp:433] input-data -> gt_boxes +RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1} +I0616 02:05:48.599010 9857 net.cpp:155] Setting up input-data +I0616 02:05:48.599020 9857 net.cpp:163] Top shape: 1 3 600 1000 (1800000) +I0616 02:05:48.599035 9857 net.cpp:163] Top shape: 1 3 (3) +I0616 02:05:48.599037 9857 net.cpp:163] Top shape: 1 4 (4) +I0616 02:05:48.599041 9857 layer_factory.hpp:76] Creating layer data_input-data_0_split +I0616 02:05:48.599062 9857 net.cpp:110] Creating Layer data_input-data_0_split +I0616 02:05:48.599066 9857 net.cpp:477] data_input-data_0_split <- data +I0616 02:05:48.599069 9857 net.cpp:433] data_input-data_0_split -> data_input-data_0_split_0 +I0616 02:05:48.599076 9857 net.cpp:433] data_input-data_0_split -> data_input-data_0_split_1 +I0616 02:05:48.599082 9857 net.cpp:155] Setting up data_input-data_0_split +I0616 02:05:48.599086 9857 net.cpp:163] Top shape: 1 3 600 1000 (1800000) +I0616 02:05:48.599089 9857 net.cpp:163] Top shape: 1 3 600 1000 (1800000) +I0616 02:05:48.599092 9857 layer_factory.hpp:76] Creating layer im_info_input-data_1_split +I0616 02:05:48.599095 9857 net.cpp:110] Creating Layer im_info_input-data_1_split +I0616 02:05:48.599098 9857 net.cpp:477] im_info_input-data_1_split <- im_info +I0616 02:05:48.599102 9857 net.cpp:433] im_info_input-data_1_split -> im_info_input-data_1_split_0 +I0616 02:05:48.599105 9857 net.cpp:433] im_info_input-data_1_split -> im_info_input-data_1_split_1 +I0616 02:05:48.599108 9857 net.cpp:155] Setting up im_info_input-data_1_split +I0616 02:05:48.599112 9857 net.cpp:163] Top shape: 1 3 (3) +I0616 02:05:48.599114 9857 net.cpp:163] Top shape: 1 3 (3) +I0616 02:05:48.599117 9857 layer_factory.hpp:76] Creating layer gt_boxes_input-data_2_split +I0616 02:05:48.599119 9857 net.cpp:110] Creating Layer gt_boxes_input-data_2_split +I0616 02:05:48.599123 9857 net.cpp:477] gt_boxes_input-data_2_split <- gt_boxes +I0616 02:05:48.599125 9857 net.cpp:433] gt_boxes_input-data_2_split -> gt_boxes_input-data_2_split_0 +I0616 02:05:48.599130 9857 net.cpp:433] gt_boxes_input-data_2_split -> gt_boxes_input-data_2_split_1 +I0616 02:05:48.599134 9857 net.cpp:155] Setting up gt_boxes_input-data_2_split +I0616 02:05:48.599138 9857 net.cpp:163] Top shape: 1 4 (4) +I0616 02:05:48.599140 9857 net.cpp:163] Top shape: 1 4 (4) +I0616 02:05:48.599143 9857 layer_factory.hpp:76] Creating layer conv1_1 +I0616 02:05:48.599148 9857 net.cpp:110] Creating Layer conv1_1 +I0616 02:05:48.599150 9857 net.cpp:477] conv1_1 <- data_input-data_0_split_0 +I0616 02:05:48.599154 9857 net.cpp:433] conv1_1 -> conv1_1 +I0616 02:05:48.821652 9857 net.cpp:155] Setting up conv1_1 +I0616 02:05:48.821693 9857 net.cpp:163] Top shape: 1 64 600 1000 (38400000) +I0616 02:05:48.821705 9857 layer_factory.hpp:76] Creating layer relu1_1 +I0616 02:05:48.821714 9857 net.cpp:110] Creating Layer relu1_1 +I0616 02:05:48.821717 9857 net.cpp:477] relu1_1 <- conv1_1 +I0616 02:05:48.821722 9857 net.cpp:419] relu1_1 -> conv1_1 (in-place) +I0616 02:05:48.821734 9857 net.cpp:155] Setting up relu1_1 +I0616 02:05:48.821738 9857 net.cpp:163] Top shape: 1 64 600 1000 (38400000) +I0616 02:05:48.821740 9857 layer_factory.hpp:76] Creating layer conv1_2 +I0616 02:05:48.821745 9857 net.cpp:110] Creating Layer conv1_2 +I0616 02:05:48.821748 9857 net.cpp:477] conv1_2 <- conv1_1 +I0616 02:05:48.821751 9857 net.cpp:433] conv1_2 -> conv1_2 +I0616 02:05:48.823266 9857 net.cpp:155] Setting up conv1_2 +I0616 02:05:48.823289 9857 net.cpp:163] Top shape: 1 64 600 1000 (38400000) +I0616 02:05:48.823297 9857 layer_factory.hpp:76] Creating layer relu1_2 +I0616 02:05:48.823302 9857 net.cpp:110] Creating Layer relu1_2 +I0616 02:05:48.823303 9857 net.cpp:477] relu1_2 <- conv1_2 +I0616 02:05:48.823308 9857 net.cpp:419] relu1_2 -> conv1_2 (in-place) +I0616 02:05:48.823313 9857 net.cpp:155] Setting up relu1_2 +I0616 02:05:48.823318 9857 net.cpp:163] Top shape: 1 64 600 1000 (38400000) +I0616 02:05:48.823319 9857 layer_factory.hpp:76] Creating layer pool1 +I0616 02:05:48.823324 9857 net.cpp:110] Creating Layer pool1 +I0616 02:05:48.823328 9857 net.cpp:477] pool1 <- conv1_2 +I0616 02:05:48.823330 9857 net.cpp:433] pool1 -> pool1 +I0616 02:05:48.823339 9857 net.cpp:155] Setting up pool1 +I0616 02:05:48.823343 9857 net.cpp:163] Top shape: 1 64 300 500 (9600000) +I0616 02:05:48.823345 9857 layer_factory.hpp:76] Creating layer conv2_1 +I0616 02:05:48.823351 9857 net.cpp:110] Creating Layer conv2_1 +I0616 02:05:48.823354 9857 net.cpp:477] conv2_1 <- pool1 +I0616 02:05:48.823359 9857 net.cpp:433] conv2_1 -> conv2_1 +I0616 02:05:48.824000 9857 net.cpp:155] Setting up conv2_1 +I0616 02:05:48.824008 9857 net.cpp:163] Top shape: 1 128 300 500 (19200000) +I0616 02:05:48.824015 9857 layer_factory.hpp:76] Creating layer relu2_1 +I0616 02:05:48.824020 9857 net.cpp:110] Creating Layer relu2_1 +I0616 02:05:48.824023 9857 net.cpp:477] relu2_1 <- conv2_1 +I0616 02:05:48.824026 9857 net.cpp:419] relu2_1 -> conv2_1 (in-place) +I0616 02:05:48.824031 9857 net.cpp:155] Setting up relu2_1 +I0616 02:05:48.824034 9857 net.cpp:163] Top shape: 1 128 300 500 (19200000) +I0616 02:05:48.824038 9857 layer_factory.hpp:76] Creating layer conv2_2 +I0616 02:05:48.824041 9857 net.cpp:110] Creating Layer conv2_2 +I0616 02:05:48.824044 9857 net.cpp:477] conv2_2 <- conv2_1 +I0616 02:05:48.824048 9857 net.cpp:433] conv2_2 -> conv2_2 +I0616 02:05:48.825235 9857 net.cpp:155] Setting up conv2_2 +I0616 02:05:48.825244 9857 net.cpp:163] Top shape: 1 128 300 500 (19200000) +I0616 02:05:48.825248 9857 layer_factory.hpp:76] Creating layer relu2_2 +I0616 02:05:48.825253 9857 net.cpp:110] Creating Layer relu2_2 +I0616 02:05:48.825256 9857 net.cpp:477] relu2_2 <- conv2_2 +I0616 02:05:48.825259 9857 net.cpp:419] relu2_2 -> conv2_2 (in-place) +I0616 02:05:48.825263 9857 net.cpp:155] Setting up relu2_2 +I0616 02:05:48.825266 9857 net.cpp:163] Top shape: 1 128 300 500 (19200000) +I0616 02:05:48.825269 9857 layer_factory.hpp:76] Creating layer pool2 +I0616 02:05:48.825274 9857 net.cpp:110] Creating Layer pool2 +I0616 02:05:48.825278 9857 net.cpp:477] pool2 <- conv2_2 +I0616 02:05:48.825280 9857 net.cpp:433] pool2 -> pool2 +I0616 02:05:48.825285 9857 net.cpp:155] Setting up pool2 +I0616 02:05:48.825289 9857 net.cpp:163] Top shape: 1 128 150 250 (4800000) +I0616 02:05:48.825291 9857 layer_factory.hpp:76] Creating layer conv3_1 +I0616 02:05:48.825296 9857 net.cpp:110] Creating Layer conv3_1 +I0616 02:05:48.825299 9857 net.cpp:477] conv3_1 <- pool2 +I0616 02:05:48.825302 9857 net.cpp:433] conv3_1 -> conv3_1 +I0616 02:05:48.826033 9857 net.cpp:155] Setting up conv3_1 +I0616 02:05:48.826041 9857 net.cpp:163] Top shape: 1 256 150 250 (9600000) +I0616 02:05:48.826047 9857 layer_factory.hpp:76] Creating layer relu3_1 +I0616 02:05:48.826052 9857 net.cpp:110] Creating Layer relu3_1 +I0616 02:05:48.826056 9857 net.cpp:477] relu3_1 <- conv3_1 +I0616 02:05:48.826058 9857 net.cpp:419] relu3_1 -> conv3_1 (in-place) +I0616 02:05:48.826063 9857 net.cpp:155] Setting up relu3_1 +I0616 02:05:48.826066 9857 net.cpp:163] Top shape: 1 256 150 250 (9600000) +I0616 02:05:48.826068 9857 layer_factory.hpp:76] Creating layer conv3_2 +I0616 02:05:48.826074 9857 net.cpp:110] Creating Layer conv3_2 +I0616 02:05:48.826077 9857 net.cpp:477] conv3_2 <- conv3_1 +I0616 02:05:48.826081 9857 net.cpp:433] conv3_2 -> conv3_2 +I0616 02:05:48.827100 9857 net.cpp:155] Setting up conv3_2 +I0616 02:05:48.827108 9857 net.cpp:163] Top shape: 1 256 150 250 (9600000) +I0616 02:05:48.827113 9857 layer_factory.hpp:76] Creating layer relu3_2 +I0616 02:05:48.827118 9857 net.cpp:110] Creating Layer relu3_2 +I0616 02:05:48.827121 9857 net.cpp:477] relu3_2 <- conv3_2 +I0616 02:05:48.827126 9857 net.cpp:419] relu3_2 -> conv3_2 (in-place) +I0616 02:05:48.827129 9857 net.cpp:155] Setting up relu3_2 +I0616 02:05:48.827133 9857 net.cpp:163] Top shape: 1 256 150 250 (9600000) +I0616 02:05:48.827136 9857 layer_factory.hpp:76] Creating layer conv3_3 +I0616 02:05:48.827139 9857 net.cpp:110] Creating Layer conv3_3 +I0616 02:05:48.827142 9857 net.cpp:477] conv3_3 <- conv3_2 +I0616 02:05:48.827145 9857 net.cpp:433] conv3_3 -> conv3_3 +I0616 02:05:48.828148 9857 net.cpp:155] Setting up conv3_3 +I0616 02:05:48.828156 9857 net.cpp:163] Top shape: 1 256 150 250 (9600000) +I0616 02:05:48.828161 9857 layer_factory.hpp:76] Creating layer relu3_3 +I0616 02:05:48.828166 9857 net.cpp:110] Creating Layer relu3_3 +I0616 02:05:48.828169 9857 net.cpp:477] relu3_3 <- conv3_3 +I0616 02:05:48.828172 9857 net.cpp:419] relu3_3 -> conv3_3 (in-place) +I0616 02:05:48.828176 9857 net.cpp:155] Setting up relu3_3 +I0616 02:05:48.828181 9857 net.cpp:163] Top shape: 1 256 150 250 (9600000) +I0616 02:05:48.828182 9857 layer_factory.hpp:76] Creating layer pool3 +I0616 02:05:48.828186 9857 net.cpp:110] Creating Layer pool3 +I0616 02:05:48.828189 9857 net.cpp:477] pool3 <- conv3_3 +I0616 02:05:48.828192 9857 net.cpp:433] pool3 -> pool3 +I0616 02:05:48.828197 9857 net.cpp:155] Setting up pool3 +I0616 02:05:48.828202 9857 net.cpp:163] Top shape: 1 256 75 125 (2400000) +I0616 02:05:48.828203 9857 layer_factory.hpp:76] Creating layer conv4_1 +I0616 02:05:48.828208 9857 net.cpp:110] Creating Layer conv4_1 +I0616 02:05:48.828212 9857 net.cpp:477] conv4_1 <- pool3 +I0616 02:05:48.828214 9857 net.cpp:433] conv4_1 -> conv4_1 +I0616 02:05:48.830829 9857 net.cpp:155] Setting up conv4_1 +I0616 02:05:48.830850 9857 net.cpp:163] Top shape: 1 512 75 125 (4800000) +I0616 02:05:48.830858 9857 layer_factory.hpp:76] Creating layer relu4_1 +I0616 02:05:48.830868 9857 net.cpp:110] Creating Layer relu4_1 +I0616 02:05:48.830871 9857 net.cpp:477] relu4_1 <- conv4_1 +I0616 02:05:48.830878 9857 net.cpp:419] relu4_1 -> conv4_1 (in-place) +I0616 02:05:48.830885 9857 net.cpp:155] Setting up relu4_1 +I0616 02:05:48.830890 9857 net.cpp:163] Top shape: 1 512 75 125 (4800000) +I0616 02:05:48.830894 9857 layer_factory.hpp:76] Creating layer conv4_2 +I0616 02:05:48.830901 9857 net.cpp:110] Creating Layer conv4_2 +I0616 02:05:48.830905 9857 net.cpp:477] conv4_2 <- conv4_1 +I0616 02:05:48.830911 9857 net.cpp:433] conv4_2 -> conv4_2 +I0616 02:05:48.833928 9857 net.cpp:155] Setting up conv4_2 +I0616 02:05:48.833953 9857 net.cpp:163] Top shape: 1 512 75 125 (4800000) +I0616 02:05:48.833963 9857 layer_factory.hpp:76] Creating layer relu4_2 +I0616 02:05:48.833971 9857 net.cpp:110] Creating Layer relu4_2 +I0616 02:05:48.833976 9857 net.cpp:477] relu4_2 <- conv4_2 +I0616 02:05:48.833981 9857 net.cpp:419] relu4_2 -> conv4_2 (in-place) +I0616 02:05:48.833986 9857 net.cpp:155] Setting up relu4_2 +I0616 02:05:48.833989 9857 net.cpp:163] Top shape: 1 512 75 125 (4800000) +I0616 02:05:48.833992 9857 layer_factory.hpp:76] Creating layer conv4_3 +I0616 02:05:48.833997 9857 net.cpp:110] Creating Layer conv4_3 +I0616 02:05:48.833999 9857 net.cpp:477] conv4_3 <- conv4_2 +I0616 02:05:48.834003 9857 net.cpp:433] conv4_3 -> conv4_3 +I0616 02:05:48.837036 9857 net.cpp:155] Setting up conv4_3 +I0616 02:05:48.837061 9857 net.cpp:163] Top shape: 1 512 75 125 (4800000) +I0616 02:05:48.837069 9857 layer_factory.hpp:76] Creating layer relu4_3 +I0616 02:05:48.837077 9857 net.cpp:110] Creating Layer relu4_3 +I0616 02:05:48.837080 9857 net.cpp:477] relu4_3 <- conv4_3 +I0616 02:05:48.837085 9857 net.cpp:419] relu4_3 -> conv4_3 (in-place) +I0616 02:05:48.837090 9857 net.cpp:155] Setting up relu4_3 +I0616 02:05:48.837095 9857 net.cpp:163] Top shape: 1 512 75 125 (4800000) +I0616 02:05:48.837096 9857 layer_factory.hpp:76] Creating layer pool4 +I0616 02:05:48.837101 9857 net.cpp:110] Creating Layer pool4 +I0616 02:05:48.837105 9857 net.cpp:477] pool4 <- conv4_3 +I0616 02:05:48.837108 9857 net.cpp:433] pool4 -> pool4 +I0616 02:05:48.837115 9857 net.cpp:155] Setting up pool4 +I0616 02:05:48.837118 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.837121 9857 layer_factory.hpp:76] Creating layer conv5_1 +I0616 02:05:48.837126 9857 net.cpp:110] Creating Layer conv5_1 +I0616 02:05:48.837128 9857 net.cpp:477] conv5_1 <- pool4 +I0616 02:05:48.837132 9857 net.cpp:433] conv5_1 -> conv5_1 +I0616 02:05:48.840199 9857 net.cpp:155] Setting up conv5_1 +I0616 02:05:48.840222 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.840229 9857 layer_factory.hpp:76] Creating layer relu5_1 +I0616 02:05:48.840236 9857 net.cpp:110] Creating Layer relu5_1 +I0616 02:05:48.840240 9857 net.cpp:477] relu5_1 <- conv5_1 +I0616 02:05:48.840245 9857 net.cpp:419] relu5_1 -> conv5_1 (in-place) +I0616 02:05:48.840251 9857 net.cpp:155] Setting up relu5_1 +I0616 02:05:48.840255 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.840260 9857 layer_factory.hpp:76] Creating layer conv5_2 +I0616 02:05:48.840263 9857 net.cpp:110] Creating Layer conv5_2 +I0616 02:05:48.840266 9857 net.cpp:477] conv5_2 <- conv5_1 +I0616 02:05:48.840270 9857 net.cpp:433] conv5_2 -> conv5_2 +I0616 02:05:48.843602 9857 net.cpp:155] Setting up conv5_2 +I0616 02:05:48.843627 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.843636 9857 layer_factory.hpp:76] Creating layer relu5_2 +I0616 02:05:48.843643 9857 net.cpp:110] Creating Layer relu5_2 +I0616 02:05:48.843648 9857 net.cpp:477] relu5_2 <- conv5_2 +I0616 02:05:48.843653 9857 net.cpp:419] relu5_2 -> conv5_2 (in-place) +I0616 02:05:48.843660 9857 net.cpp:155] Setting up relu5_2 +I0616 02:05:48.843664 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.843667 9857 layer_factory.hpp:76] Creating layer conv5_3 +I0616 02:05:48.843675 9857 net.cpp:110] Creating Layer conv5_3 +I0616 02:05:48.843678 9857 net.cpp:477] conv5_3 <- conv5_2 +I0616 02:05:48.843683 9857 net.cpp:433] conv5_3 -> conv5_3 +I0616 02:05:48.846734 9857 net.cpp:155] Setting up conv5_3 +I0616 02:05:48.846763 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.846771 9857 layer_factory.hpp:76] Creating layer relu5_3 +I0616 02:05:48.846777 9857 net.cpp:110] Creating Layer relu5_3 +I0616 02:05:48.846781 9857 net.cpp:477] relu5_3 <- conv5_3 +I0616 02:05:48.846788 9857 net.cpp:419] relu5_3 -> conv5_3 (in-place) +I0616 02:05:48.846808 9857 net.cpp:155] Setting up relu5_3 +I0616 02:05:48.846812 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.846815 9857 layer_factory.hpp:76] Creating layer conv5_3_relu5_3_0_split +I0616 02:05:48.846818 9857 net.cpp:110] Creating Layer conv5_3_relu5_3_0_split +I0616 02:05:48.846822 9857 net.cpp:477] conv5_3_relu5_3_0_split <- conv5_3 +I0616 02:05:48.846825 9857 net.cpp:433] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_0 +I0616 02:05:48.846830 9857 net.cpp:433] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_1 +I0616 02:05:48.846835 9857 net.cpp:155] Setting up conv5_3_relu5_3_0_split +I0616 02:05:48.846839 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.846843 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.846844 9857 layer_factory.hpp:76] Creating layer rpn_conv/3x3 +I0616 02:05:48.846849 9857 net.cpp:110] Creating Layer rpn_conv/3x3 +I0616 02:05:48.846853 9857 net.cpp:477] rpn_conv/3x3 <- conv5_3_relu5_3_0_split_0 +I0616 02:05:48.846856 9857 net.cpp:433] rpn_conv/3x3 -> rpn/output +I0616 02:05:48.899034 9857 net.cpp:155] Setting up rpn_conv/3x3 +I0616 02:05:48.899070 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.899077 9857 layer_factory.hpp:76] Creating layer rpn_relu/3x3 +I0616 02:05:48.899085 9857 net.cpp:110] Creating Layer rpn_relu/3x3 +I0616 02:05:48.899087 9857 net.cpp:477] rpn_relu/3x3 <- rpn/output +I0616 02:05:48.899092 9857 net.cpp:419] rpn_relu/3x3 -> rpn/output (in-place) +I0616 02:05:48.899101 9857 net.cpp:155] Setting up rpn_relu/3x3 +I0616 02:05:48.899103 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.899106 9857 layer_factory.hpp:76] Creating layer rpn/output_rpn_relu/3x3_0_split +I0616 02:05:48.899109 9857 net.cpp:110] Creating Layer rpn/output_rpn_relu/3x3_0_split +I0616 02:05:48.899111 9857 net.cpp:477] rpn/output_rpn_relu/3x3_0_split <- rpn/output +I0616 02:05:48.899114 9857 net.cpp:433] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_0 +I0616 02:05:48.899119 9857 net.cpp:433] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_1 +I0616 02:05:48.899124 9857 net.cpp:155] Setting up rpn/output_rpn_relu/3x3_0_split +I0616 02:05:48.899127 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.899130 9857 net.cpp:163] Top shape: 1 512 38 63 (1225728) +I0616 02:05:48.899132 9857 layer_factory.hpp:76] Creating layer rpn_cls_score +I0616 02:05:48.899138 9857 net.cpp:110] Creating Layer rpn_cls_score +I0616 02:05:48.899142 9857 net.cpp:477] rpn_cls_score <- rpn/output_rpn_relu/3x3_0_split_0 +I0616 02:05:48.899144 9857 net.cpp:433] rpn_cls_score -> rpn_cls_score +I0616 02:05:48.899418 9857 net.cpp:155] Setting up rpn_cls_score +I0616 02:05:48.899423 9857 net.cpp:163] Top shape: 1 18 38 63 (43092) +I0616 02:05:48.899441 9857 layer_factory.hpp:76] Creating layer rpn_cls_score_rpn_cls_score_0_split +I0616 02:05:48.899446 9857 net.cpp:110] Creating Layer rpn_cls_score_rpn_cls_score_0_split +I0616 02:05:48.899447 9857 net.cpp:477] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score +I0616 02:05:48.899452 9857 net.cpp:433] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0 +I0616 02:05:48.899459 9857 net.cpp:433] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1 +I0616 02:05:48.899463 9857 net.cpp:155] Setting up rpn_cls_score_rpn_cls_score_0_split +I0616 02:05:48.899467 9857 net.cpp:163] Top shape: 1 18 38 63 (43092) +I0616 02:05:48.899471 9857 net.cpp:163] Top shape: 1 18 38 63 (43092) +I0616 02:05:48.899472 9857 layer_factory.hpp:76] Creating layer rpn_bbox_pred +I0616 02:05:48.899477 9857 net.cpp:110] Creating Layer rpn_bbox_pred +I0616 02:05:48.899478 9857 net.cpp:477] rpn_bbox_pred <- rpn/output_rpn_relu/3x3_0_split_1 +I0616 02:05:48.899484 9857 net.cpp:433] rpn_bbox_pred -> rpn_bbox_pred +I0616 02:05:48.899950 9857 net.cpp:155] Setting up rpn_bbox_pred +I0616 02:05:48.899955 9857 net.cpp:163] Top shape: 1 36 38 63 (86184) +I0616 02:05:48.899960 9857 layer_factory.hpp:76] Creating layer rpn_bbox_pred_rpn_bbox_pred_0_split +I0616 02:05:48.899965 9857 net.cpp:110] Creating Layer rpn_bbox_pred_rpn_bbox_pred_0_split +I0616 02:05:48.899967 9857 net.cpp:477] rpn_bbox_pred_rpn_bbox_pred_0_split <- rpn_bbox_pred +I0616 02:05:48.899971 9857 net.cpp:433] rpn_bbox_pred_rpn_bbox_pred_0_split -> rpn_bbox_pred_rpn_bbox_pred_0_split_0 +I0616 02:05:48.899976 9857 net.cpp:433] rpn_bbox_pred_rpn_bbox_pred_0_split -> rpn_bbox_pred_rpn_bbox_pred_0_split_1 +I0616 02:05:48.899979 9857 net.cpp:155] Setting up rpn_bbox_pred_rpn_bbox_pred_0_split +I0616 02:05:48.899983 9857 net.cpp:163] Top shape: 1 36 38 63 (86184) +I0616 02:05:48.899986 9857 net.cpp:163] Top shape: 1 36 38 63 (86184) +I0616 02:05:48.899989 9857 layer_factory.hpp:76] Creating layer rpn_cls_score_reshape +I0616 02:05:48.899996 9857 net.cpp:110] Creating Layer rpn_cls_score_reshape +I0616 02:05:48.899998 9857 net.cpp:477] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0 +I0616 02:05:48.900002 9857 net.cpp:433] rpn_cls_score_reshape -> rpn_cls_score_reshape +I0616 02:05:48.900007 9857 net.cpp:155] Setting up rpn_cls_score_reshape +I0616 02:05:48.900012 9857 net.cpp:163] Top shape: 1 2 342 63 (43092) +I0616 02:05:48.900014 9857 layer_factory.hpp:76] Creating layer rpn_cls_score_reshape_rpn_cls_score_reshape_0_split +I0616 02:05:48.900017 9857 net.cpp:110] Creating Layer rpn_cls_score_reshape_rpn_cls_score_reshape_0_split +I0616 02:05:48.900020 9857 net.cpp:477] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split <- rpn_cls_score_reshape +I0616 02:05:48.900023 9857 net.cpp:433] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split -> rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_0 +I0616 02:05:48.900028 9857 net.cpp:433] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split -> rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_1 +I0616 02:05:48.900032 9857 net.cpp:155] Setting up rpn_cls_score_reshape_rpn_cls_score_reshape_0_split +I0616 02:05:48.900037 9857 net.cpp:163] Top shape: 1 2 342 63 (43092) +I0616 02:05:48.900038 9857 net.cpp:163] Top shape: 1 2 342 63 (43092) +I0616 02:05:48.900041 9857 layer_factory.hpp:76] Creating layer rpn-data +I0616 02:05:48.900382 9857 net.cpp:110] Creating Layer rpn-data +I0616 02:05:48.900389 9857 net.cpp:477] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1 +I0616 02:05:48.900394 9857 net.cpp:477] rpn-data <- gt_boxes_input-data_2_split_0 +I0616 02:05:48.900398 9857 net.cpp:477] rpn-data <- im_info_input-data_1_split_0 +I0616 02:05:48.900401 9857 net.cpp:477] rpn-data <- data_input-data_0_split_1 +I0616 02:05:48.900406 9857 net.cpp:433] rpn-data -> rpn_labels +I0616 02:05:48.900411 9857 net.cpp:433] rpn-data -> rpn_bbox_targets +I0616 02:05:48.900418 9857 net.cpp:433] rpn-data -> rpn_bbox_inside_weights +I0616 02:05:48.900423 9857 net.cpp:433] rpn-data -> rpn_bbox_outside_weights +I0616 02:05:48.900877 9857 net.cpp:155] Setting up rpn-data +I0616 02:05:48.900888 9857 net.cpp:163] Top shape: 1 1 342 63 (21546) +I0616 02:05:48.900892 9857 net.cpp:163] Top shape: 1 36 38 63 (86184) +I0616 02:05:48.900895 9857 net.cpp:163] Top shape: 1 36 38 63 (86184) +I0616 02:05:48.900898 9857 net.cpp:163] Top shape: 1 36 38 63 (86184) +I0616 02:05:48.900902 9857 layer_factory.hpp:76] Creating layer rpn_loss_cls +I0616 02:05:48.900907 9857 net.cpp:110] Creating Layer rpn_loss_cls +I0616 02:05:48.900910 9857 net.cpp:477] rpn_loss_cls <- rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_0 +I0616 02:05:48.900914 9857 net.cpp:477] rpn_loss_cls <- rpn_labels +I0616 02:05:48.900918 9857 net.cpp:433] rpn_loss_cls -> rpn_cls_loss +I0616 02:05:48.900929 9857 layer_factory.hpp:76] Creating layer rpn_loss_cls +I0616 02:05:48.901016 9857 net.cpp:155] Setting up rpn_loss_cls +I0616 02:05:48.901021 9857 net.cpp:163] Top shape: (1) +I0616 02:05:48.901024 9857 net.cpp:168] with loss weight 1 +I0616 02:05:48.901033 9857 layer_factory.hpp:76] Creating layer rpn_loss_bbox +I0616 02:05:48.901041 9857 net.cpp:110] Creating Layer rpn_loss_bbox +I0616 02:05:48.901044 9857 net.cpp:477] rpn_loss_bbox <- rpn_bbox_pred_rpn_bbox_pred_0_split_0 +I0616 02:05:48.901047 9857 net.cpp:477] rpn_loss_bbox <- rpn_bbox_targets +I0616 02:05:48.901051 9857 net.cpp:477] rpn_loss_bbox <- rpn_bbox_inside_weights +I0616 02:05:48.901053 9857 net.cpp:477] rpn_loss_bbox <- rpn_bbox_outside_weights +I0616 02:05:48.901057 9857 net.cpp:433] rpn_loss_bbox -> rpn_loss_bbox +I0616 02:05:48.901486 9857 net.cpp:155] Setting up rpn_loss_bbox +I0616 02:05:48.901491 9857 net.cpp:163] Top shape: (1) +I0616 02:05:48.901494 9857 net.cpp:168] with loss weight 1 +I0616 02:05:48.901499 9857 layer_factory.hpp:76] Creating layer rpn_cls_prob +I0616 02:05:48.901502 9857 net.cpp:110] Creating Layer rpn_cls_prob +I0616 02:05:48.901505 9857 net.cpp:477] rpn_cls_prob <- rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_1 +I0616 02:05:48.901509 9857 net.cpp:433] rpn_cls_prob -> rpn_cls_prob +I0616 02:05:48.901530 9857 net.cpp:155] Setting up rpn_cls_prob +I0616 02:05:48.901533 9857 net.cpp:163] Top shape: 1 2 342 63 (43092) +I0616 02:05:48.901536 9857 layer_factory.hpp:76] Creating layer rpn_cls_prob_reshape +I0616 02:05:48.901541 9857 net.cpp:110] Creating Layer rpn_cls_prob_reshape +I0616 02:05:48.901545 9857 net.cpp:477] rpn_cls_prob_reshape <- rpn_cls_prob +I0616 02:05:48.901548 9857 net.cpp:433] rpn_cls_prob_reshape -> rpn_cls_prob_reshape +I0616 02:05:48.901554 9857 net.cpp:155] Setting up rpn_cls_prob_reshape +I0616 02:05:48.901558 9857 net.cpp:163] Top shape: 1 18 38 63 (43092) +I0616 02:05:48.901561 9857 layer_factory.hpp:76] Creating layer proposal +I0616 02:05:48.901963 9857 net.cpp:110] Creating Layer proposal +I0616 02:05:48.901971 9857 net.cpp:477] proposal <- rpn_cls_prob_reshape +I0616 02:05:48.901976 9857 net.cpp:477] proposal <- rpn_bbox_pred_rpn_bbox_pred_0_split_1 +I0616 02:05:48.901979 9857 net.cpp:477] proposal <- im_info_input-data_1_split_1 +I0616 02:05:48.901984 9857 net.cpp:433] proposal -> rpn_rois +I0616 02:05:48.902438 9857 net.cpp:155] Setting up proposal +I0616 02:05:48.902448 9857 net.cpp:163] Top shape: 1 5 (5) +I0616 02:05:48.902452 9857 layer_factory.hpp:76] Creating layer roi-data +I0616 02:05:48.902549 9857 net.cpp:110] Creating Layer roi-data +I0616 02:05:48.902555 9857 net.cpp:477] roi-data <- rpn_rois +I0616 02:05:48.902560 9857 net.cpp:477] roi-data <- gt_boxes_input-data_2_split_1 +I0616 02:05:48.902565 9857 net.cpp:433] roi-data -> rois +I0616 02:05:48.902570 9857 net.cpp:433] roi-data -> labels +I0616 02:05:48.902575 9857 net.cpp:433] roi-data -> bbox_targets +I0616 02:05:48.902580 9857 net.cpp:433] roi-data -> bbox_inside_weights +I0616 02:05:48.902585 9857 net.cpp:433] roi-data -> bbox_outside_weights +I0616 02:05:48.902909 9857 net.cpp:155] Setting up roi-data +I0616 02:05:48.902918 9857 net.cpp:163] Top shape: 1 5 (5) +I0616 02:05:48.902922 9857 net.cpp:163] Top shape: 1 1 (1) +I0616 02:05:48.902925 9857 net.cpp:163] Top shape: 1 804 (804) +I0616 02:05:48.902928 9857 net.cpp:163] Top shape: 1 804 (804) +I0616 02:05:48.902930 9857 net.cpp:163] Top shape: 1 804 (804) +I0616 02:05:48.902933 9857 layer_factory.hpp:76] Creating layer roi_pool5 +I0616 02:05:48.902938 9857 net.cpp:110] Creating Layer roi_pool5 +I0616 02:05:48.902941 9857 net.cpp:477] roi_pool5 <- conv5_3_relu5_3_0_split_1 +I0616 02:05:48.902945 9857 net.cpp:477] roi_pool5 <- rois +I0616 02:05:48.902950 9857 net.cpp:433] roi_pool5 -> pool5 +I0616 02:05:48.902956 9857 roi_pooling_layer.cpp:30] Spatial scale: 0.0625 +I0616 02:05:48.902961 9857 net.cpp:155] Setting up roi_pool5 +I0616 02:05:48.902966 9857 net.cpp:163] Top shape: 1 512 7 7 (25088) +I0616 02:05:48.902968 9857 layer_factory.hpp:76] Creating layer fc6 +I0616 02:05:48.902972 9857 net.cpp:110] Creating Layer fc6 +I0616 02:05:48.902976 9857 net.cpp:477] fc6 <- pool5 +I0616 02:05:48.902979 9857 net.cpp:433] fc6 -> fc6 +I0616 02:05:49.028956 9857 net.cpp:155] Setting up fc6 +I0616 02:05:49.028995 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.029008 9857 layer_factory.hpp:76] Creating layer relu6 +I0616 02:05:49.029016 9857 net.cpp:110] Creating Layer relu6 +I0616 02:05:49.029019 9857 net.cpp:477] relu6 <- fc6 +I0616 02:05:49.029023 9857 net.cpp:419] relu6 -> fc6 (in-place) +I0616 02:05:49.029031 9857 net.cpp:155] Setting up relu6 +I0616 02:05:49.029034 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.029037 9857 layer_factory.hpp:76] Creating layer drop6 +I0616 02:05:49.029042 9857 net.cpp:110] Creating Layer drop6 +I0616 02:05:49.029044 9857 net.cpp:477] drop6 <- fc6 +I0616 02:05:49.029047 9857 net.cpp:419] drop6 -> fc6 (in-place) +I0616 02:05:49.029053 9857 net.cpp:155] Setting up drop6 +I0616 02:05:49.029057 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.029059 9857 layer_factory.hpp:76] Creating layer fc7 +I0616 02:05:49.029064 9857 net.cpp:110] Creating Layer fc7 +I0616 02:05:49.029067 9857 net.cpp:477] fc7 <- fc6 +I0616 02:05:49.029070 9857 net.cpp:433] fc7 -> fc7 +I0616 02:05:49.049935 9857 net.cpp:155] Setting up fc7 +I0616 02:05:49.049973 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.049980 9857 layer_factory.hpp:76] Creating layer relu7 +I0616 02:05:49.049988 9857 net.cpp:110] Creating Layer relu7 +I0616 02:05:49.049991 9857 net.cpp:477] relu7 <- fc7 +I0616 02:05:49.049995 9857 net.cpp:419] relu7 -> fc7 (in-place) +I0616 02:05:49.050001 9857 net.cpp:155] Setting up relu7 +I0616 02:05:49.050005 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.050007 9857 layer_factory.hpp:76] Creating layer drop7 +I0616 02:05:49.050011 9857 net.cpp:110] Creating Layer drop7 +I0616 02:05:49.050014 9857 net.cpp:477] drop7 <- fc7 +I0616 02:05:49.050019 9857 net.cpp:419] drop7 -> fc7 (in-place) +I0616 02:05:49.050024 9857 net.cpp:155] Setting up drop7 +I0616 02:05:49.050027 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.050029 9857 layer_factory.hpp:76] Creating layer fc7_drop7_0_split +I0616 02:05:49.050034 9857 net.cpp:110] Creating Layer fc7_drop7_0_split +I0616 02:05:49.050037 9857 net.cpp:477] fc7_drop7_0_split <- fc7 +I0616 02:05:49.050041 9857 net.cpp:433] fc7_drop7_0_split -> fc7_drop7_0_split_0 +I0616 02:05:49.050045 9857 net.cpp:433] fc7_drop7_0_split -> fc7_drop7_0_split_1 +I0616 02:05:49.050050 9857 net.cpp:155] Setting up fc7_drop7_0_split +I0616 02:05:49.050053 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.050056 9857 net.cpp:163] Top shape: 1 4096 (4096) +I0616 02:05:49.050060 9857 layer_factory.hpp:76] Creating layer cls_score +I0616 02:05:49.050065 9857 net.cpp:110] Creating Layer cls_score +I0616 02:05:49.050067 9857 net.cpp:477] cls_score <- fc7_drop7_0_split_0 +I0616 02:05:49.050071 9857 net.cpp:433] cls_score -> cls_score +I0616 02:05:49.068348 9857 net.cpp:155] Setting up cls_score +I0616 02:05:49.068359 9857 net.cpp:163] Top shape: 1 201 (201) +I0616 02:05:49.068364 9857 layer_factory.hpp:76] Creating layer bbox_pred +I0616 02:05:49.068372 9857 net.cpp:110] Creating Layer bbox_pred +I0616 02:05:49.068374 9857 net.cpp:477] bbox_pred <- fc7_drop7_0_split_1 +I0616 02:05:49.068378 9857 net.cpp:433] bbox_pred -> bbox_pred +I0616 02:05:49.140568 9857 net.cpp:155] Setting up bbox_pred +I0616 02:05:49.140605 9857 net.cpp:163] Top shape: 1 804 (804) +I0616 02:05:49.140611 9857 layer_factory.hpp:76] Creating layer loss_cls +I0616 02:05:49.140620 9857 net.cpp:110] Creating Layer loss_cls +I0616 02:05:49.140624 9857 net.cpp:477] loss_cls <- cls_score +I0616 02:05:49.140628 9857 net.cpp:477] loss_cls <- labels +I0616 02:05:49.140635 9857 net.cpp:433] loss_cls -> loss_cls +I0616 02:05:49.140643 9857 layer_factory.hpp:76] Creating layer loss_cls +I0616 02:05:49.140704 9857 net.cpp:155] Setting up loss_cls +I0616 02:05:49.140722 9857 net.cpp:163] Top shape: (1) +I0616 02:05:49.140723 9857 net.cpp:168] with loss weight 1 +I0616 02:05:49.140744 9857 layer_factory.hpp:76] Creating layer loss_bbox +I0616 02:05:49.140749 9857 net.cpp:110] Creating Layer loss_bbox +I0616 02:05:49.140751 9857 net.cpp:477] loss_bbox <- bbox_pred +I0616 02:05:49.140754 9857 net.cpp:477] loss_bbox <- bbox_targets +I0616 02:05:49.140758 9857 net.cpp:477] loss_bbox <- bbox_inside_weights +I0616 02:05:49.140759 9857 net.cpp:477] loss_bbox <- bbox_outside_weights +I0616 02:05:49.140763 9857 net.cpp:433] loss_bbox -> loss_bbox +I0616 02:05:49.140805 9857 net.cpp:155] Setting up loss_bbox +I0616 02:05:49.140810 9857 net.cpp:163] Top shape: (1) +I0616 02:05:49.140811 9857 net.cpp:168] with loss weight 1 +I0616 02:05:49.140815 9857 net.cpp:236] loss_bbox needs backward computation. +I0616 02:05:49.140817 9857 net.cpp:236] loss_cls needs backward computation. +I0616 02:05:49.140820 9857 net.cpp:236] bbox_pred needs backward computation. +I0616 02:05:49.140822 9857 net.cpp:236] cls_score needs backward computation. +I0616 02:05:49.140825 9857 net.cpp:236] fc7_drop7_0_split needs backward computation. +I0616 02:05:49.140826 9857 net.cpp:236] drop7 needs backward computation. +I0616 02:05:49.140830 9857 net.cpp:236] relu7 needs backward computation. +I0616 02:05:49.140831 9857 net.cpp:236] fc7 needs backward computation. +I0616 02:05:49.140833 9857 net.cpp:236] drop6 needs backward computation. +I0616 02:05:49.140836 9857 net.cpp:236] relu6 needs backward computation. +I0616 02:05:49.140837 9857 net.cpp:236] fc6 needs backward computation. +I0616 02:05:49.140839 9857 net.cpp:236] roi_pool5 needs backward computation. +I0616 02:05:49.140842 9857 net.cpp:236] roi-data needs backward computation. +I0616 02:05:49.140846 9857 net.cpp:236] proposal needs backward computation. +I0616 02:05:49.140851 9857 net.cpp:236] rpn_cls_prob_reshape needs backward computation. +I0616 02:05:49.140852 9857 net.cpp:236] rpn_cls_prob needs backward computation. +I0616 02:05:49.140856 9857 net.cpp:236] rpn_loss_bbox needs backward computation. +I0616 02:05:49.140859 9857 net.cpp:236] rpn_loss_cls needs backward computation. +I0616 02:05:49.140863 9857 net.cpp:236] rpn-data needs backward computation. +I0616 02:05:49.140867 9857 net.cpp:236] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split needs backward computation. +I0616 02:05:49.140871 9857 net.cpp:236] rpn_cls_score_reshape needs backward computation. +I0616 02:05:49.140873 9857 net.cpp:236] rpn_bbox_pred_rpn_bbox_pred_0_split needs backward computation. +I0616 02:05:49.140877 9857 net.cpp:236] rpn_bbox_pred needs backward computation. +I0616 02:05:49.140879 9857 net.cpp:236] rpn_cls_score_rpn_cls_score_0_split needs backward computation. +I0616 02:05:49.140882 9857 net.cpp:236] rpn_cls_score needs backward computation. +I0616 02:05:49.140885 9857 net.cpp:236] rpn/output_rpn_relu/3x3_0_split needs backward computation. +I0616 02:05:49.140887 9857 net.cpp:236] rpn_relu/3x3 needs backward computation. +I0616 02:05:49.140890 9857 net.cpp:236] rpn_conv/3x3 needs backward computation. +I0616 02:05:49.140893 9857 net.cpp:236] conv5_3_relu5_3_0_split needs backward computation. +I0616 02:05:49.140897 9857 net.cpp:236] relu5_3 needs backward computation. +I0616 02:05:49.140898 9857 net.cpp:236] conv5_3 needs backward computation. +I0616 02:05:49.140900 9857 net.cpp:236] relu5_2 needs backward computation. +I0616 02:05:49.140903 9857 net.cpp:236] conv5_2 needs backward computation. +I0616 02:05:49.140905 9857 net.cpp:236] relu5_1 needs backward computation. +I0616 02:05:49.140908 9857 net.cpp:236] conv5_1 needs backward computation. +I0616 02:05:49.140910 9857 net.cpp:236] pool4 needs backward computation. +I0616 02:05:49.140914 9857 net.cpp:236] relu4_3 needs backward computation. +I0616 02:05:49.140916 9857 net.cpp:236] conv4_3 needs backward computation. +I0616 02:05:49.140918 9857 net.cpp:236] relu4_2 needs backward computation. +I0616 02:05:49.140921 9857 net.cpp:236] conv4_2 needs backward computation. +I0616 02:05:49.140923 9857 net.cpp:236] relu4_1 needs backward computation. +I0616 02:05:49.140925 9857 net.cpp:236] conv4_1 needs backward computation. +I0616 02:05:49.140928 9857 net.cpp:236] pool3 needs backward computation. +I0616 02:05:49.140930 9857 net.cpp:236] relu3_3 needs backward computation. +I0616 02:05:49.140933 9857 net.cpp:236] conv3_3 needs backward computation. +I0616 02:05:49.140935 9857 net.cpp:236] relu3_2 needs backward computation. +I0616 02:05:49.140938 9857 net.cpp:236] conv3_2 needs backward computation. +I0616 02:05:49.140940 9857 net.cpp:236] relu3_1 needs backward computation. +I0616 02:05:49.140943 9857 net.cpp:236] conv3_1 needs backward computation. +I0616 02:05:49.140945 9857 net.cpp:240] pool2 does not need backward computation. +I0616 02:05:49.140947 9857 net.cpp:240] relu2_2 does not need backward computation. +I0616 02:05:49.140950 9857 net.cpp:240] conv2_2 does not need backward computation. +I0616 02:05:49.140954 9857 net.cpp:240] relu2_1 does not need backward computation. +I0616 02:05:49.140955 9857 net.cpp:240] conv2_1 does not need backward computation. +I0616 02:05:49.140957 9857 net.cpp:240] pool1 does not need backward computation. +I0616 02:05:49.140960 9857 net.cpp:240] relu1_2 does not need backward computation. +I0616 02:05:49.140962 9857 net.cpp:240] conv1_2 does not need backward computation. +I0616 02:05:49.140965 9857 net.cpp:240] relu1_1 does not need backward computation. +I0616 02:05:49.140967 9857 net.cpp:240] conv1_1 does not need backward computation. +I0616 02:05:49.140970 9857 net.cpp:240] gt_boxes_input-data_2_split does not need backward computation. +I0616 02:05:49.140974 9857 net.cpp:240] im_info_input-data_1_split does not need backward computation. +I0616 02:05:49.140977 9857 net.cpp:240] data_input-data_0_split does not need backward computation. +I0616 02:05:49.140981 9857 net.cpp:240] input-data does not need backward computation. +I0616 02:05:49.140985 9857 net.cpp:283] This network produces output loss_bbox +I0616 02:05:49.140986 9857 net.cpp:283] This network produces output loss_cls +I0616 02:05:49.140990 9857 net.cpp:283] This network produces output rpn_cls_loss +I0616 02:05:49.140992 9857 net.cpp:283] This network produces output rpn_loss_bbox +I0616 02:05:49.141021 9857 net.cpp:297] Network initialization done. +I0616 02:05:49.141024 9857 net.cpp:298] Memory required for data: 1423516636 +I0616 02:05:49.141114 9857 solver.cpp:65] Solver scaffolding done. +Loading pretrained model weights from data/imagenet_models/VGG16.v2.caffemodel +[libprotobuf WARNING google/protobuf/io/coded_stream.cc:505] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. +[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 553432430 +Solving... +/home/andrewliao11/py-faster-rcnn/tools/../lib/rpn/proposal_target_layer.py:138: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future + bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] +/home/andrewliao11/py-faster-rcnn/tools/../lib/rpn/proposal_target_layer.py:139: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future + bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS +I0616 02:05:50.234966 9857 solver.cpp:242] Iteration 0, loss = 6.77537 +I0616 02:05:50.235009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.577054 (* 1 = 0.577054 loss) +I0616 02:05:50.235015 9857 solver.cpp:258] Train net output #1: loss_cls = 5.37939 (* 1 = 5.37939 loss) +I0616 02:05:50.235019 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.815551 (* 1 = 0.815551 loss) +I0616 02:05:50.235023 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0469038 (* 1 = 0.0469038 loss) +I0616 02:05:50.235028 9857 solver.cpp:571] Iteration 0, lr = 0.001 +/home/andrewliao11/py-faster-rcnn/tools/../lib/rpn/proposal_target_layer.py:177: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future + fg_inds = npr.choice(fg_inds, size=fg_rois_per_this_image, replace=False) +/home/andrewliao11/py-faster-rcnn/tools/../lib/rpn/proposal_target_layer.py:188: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future + bg_inds = npr.choice(bg_inds, size=bg_rois_per_this_image, replace=False) +/home/andrewliao11/py-faster-rcnn/tools/../lib/rpn/proposal_target_layer.py:195: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future + labels[fg_rois_per_this_image:] = 0 +I0616 02:06:00.745822 9857 solver.cpp:242] Iteration 20, loss = 2.09071 +I0616 02:06:00.745865 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190078 (* 1 = 0.190078 loss) +I0616 02:06:00.745872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.90063 (* 1 = 0.90063 loss) +I0616 02:06:00.745875 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.425971 (* 1 = 0.425971 loss) +I0616 02:06:00.745879 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0377344 (* 1 = 0.0377344 loss) +I0616 02:06:00.745883 9857 solver.cpp:571] Iteration 20, lr = 0.001 +I0616 02:06:11.352269 9857 solver.cpp:242] Iteration 40, loss = 2.62083 +I0616 02:06:11.352313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.620421 (* 1 = 0.620421 loss) +I0616 02:06:11.352319 9857 solver.cpp:258] Train net output #1: loss_cls = 2.397 (* 1 = 2.397 loss) +I0616 02:06:11.352322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.330095 (* 1 = 0.330095 loss) +I0616 02:06:11.352326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.109856 (* 1 = 0.109856 loss) +I0616 02:06:11.352330 9857 solver.cpp:571] Iteration 40, lr = 0.001 +I0616 02:06:22.134336 9857 solver.cpp:242] Iteration 60, loss = 2.37951 +I0616 02:06:22.134380 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342147 (* 1 = 0.342147 loss) +I0616 02:06:22.134387 9857 solver.cpp:258] Train net output #1: loss_cls = 1.24404 (* 1 = 1.24404 loss) +I0616 02:06:22.134390 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.276211 (* 1 = 0.276211 loss) +I0616 02:06:22.134394 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14065 (* 1 = 0.14065 loss) +I0616 02:06:22.134413 9857 solver.cpp:571] Iteration 60, lr = 0.001 +I0616 02:06:39.872661 9857 solver.cpp:242] Iteration 80, loss = 1.79846 +I0616 02:06:39.872706 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.43031 (* 1 = 0.43031 loss) +I0616 02:06:39.872711 9857 solver.cpp:258] Train net output #1: loss_cls = 1.3428 (* 1 = 1.3428 loss) +I0616 02:06:39.872715 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.383536 (* 1 = 0.383536 loss) +I0616 02:06:39.872720 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103547 (* 1 = 0.103547 loss) +I0616 02:06:39.872725 9857 solver.cpp:571] Iteration 80, lr = 0.001 +I0616 02:06:59.259312 9857 solver.cpp:242] Iteration 100, loss = 1.20309 +I0616 02:06:59.259357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271897 (* 1 = 0.271897 loss) +I0616 02:06:59.259362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.60214 (* 1 = 0.60214 loss) +I0616 02:06:59.259366 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201218 (* 1 = 0.201218 loss) +I0616 02:06:59.259371 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029102 (* 1 = 0.029102 loss) +I0616 02:06:59.259377 9857 solver.cpp:571] Iteration 100, lr = 0.001 +I0616 02:07:13.280014 9857 solver.cpp:242] Iteration 120, loss = 1.54033 +I0616 02:07:13.280061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290894 (* 1 = 0.290894 loss) +I0616 02:07:13.280066 9857 solver.cpp:258] Train net output #1: loss_cls = 0.551511 (* 1 = 0.551511 loss) +I0616 02:07:13.280069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127393 (* 1 = 0.127393 loss) +I0616 02:07:13.280073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169772 (* 1 = 0.0169772 loss) +I0616 02:07:13.280078 9857 solver.cpp:571] Iteration 120, lr = 0.001 +I0616 02:07:28.897116 9857 solver.cpp:242] Iteration 140, loss = 2.96301 +I0616 02:07:28.897163 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.612077 (* 1 = 0.612077 loss) +I0616 02:07:28.897168 9857 solver.cpp:258] Train net output #1: loss_cls = 1.48433 (* 1 = 1.48433 loss) +I0616 02:07:28.897172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.24454 (* 1 = 0.24454 loss) +I0616 02:07:28.897176 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.034237 (* 1 = 0.034237 loss) +I0616 02:07:28.897181 9857 solver.cpp:571] Iteration 140, lr = 0.001 +I0616 02:07:44.217461 9857 solver.cpp:242] Iteration 160, loss = 1.98915 +I0616 02:07:44.217504 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334905 (* 1 = 0.334905 loss) +I0616 02:07:44.217510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.891246 (* 1 = 0.891246 loss) +I0616 02:07:44.217514 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125475 (* 1 = 0.125475 loss) +I0616 02:07:44.217519 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.016599 (* 1 = 0.016599 loss) +I0616 02:07:44.217524 9857 solver.cpp:571] Iteration 160, lr = 0.001 +I0616 02:08:00.471386 9857 solver.cpp:242] Iteration 180, loss = 1.54887 +I0616 02:08:00.471428 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.602162 (* 1 = 0.602162 loss) +I0616 02:08:00.471434 9857 solver.cpp:258] Train net output #1: loss_cls = 1.1052 (* 1 = 1.1052 loss) +I0616 02:08:00.471438 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103742 (* 1 = 0.103742 loss) +I0616 02:08:00.471441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0685997 (* 1 = 0.0685997 loss) +I0616 02:08:00.471446 9857 solver.cpp:571] Iteration 180, lr = 0.001 +speed: 0.731s / iter +I0616 02:08:16.519829 9857 solver.cpp:242] Iteration 200, loss = 1.62162 +I0616 02:08:16.519873 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.380274 (* 1 = 0.380274 loss) +I0616 02:08:16.519879 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16786 (* 1 = 1.16786 loss) +I0616 02:08:16.519883 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0493288 (* 1 = 0.0493288 loss) +I0616 02:08:16.519887 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00797124 (* 1 = 0.00797124 loss) +I0616 02:08:16.519891 9857 solver.cpp:571] Iteration 200, lr = 0.001 +I0616 02:08:33.234566 9857 solver.cpp:242] Iteration 220, loss = 1.36487 +I0616 02:08:33.234612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.561802 (* 1 = 0.561802 loss) +I0616 02:08:33.234618 9857 solver.cpp:258] Train net output #1: loss_cls = 0.862595 (* 1 = 0.862595 loss) +I0616 02:08:33.234622 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111902 (* 1 = 0.111902 loss) +I0616 02:08:33.234627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0376711 (* 1 = 0.0376711 loss) +I0616 02:08:33.234632 9857 solver.cpp:571] Iteration 220, lr = 0.001 +I0616 02:08:47.925559 9857 solver.cpp:242] Iteration 240, loss = 2.29269 +I0616 02:08:47.925601 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.439866 (* 1 = 0.439866 loss) +I0616 02:08:47.925606 9857 solver.cpp:258] Train net output #1: loss_cls = 1.37892 (* 1 = 1.37892 loss) +I0616 02:08:47.925611 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.174202 (* 1 = 0.174202 loss) +I0616 02:08:47.925616 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00491831 (* 1 = 0.00491831 loss) +I0616 02:08:47.925619 9857 solver.cpp:571] Iteration 240, lr = 0.001 +I0616 02:09:03.326001 9857 solver.cpp:242] Iteration 260, loss = 1.93904 +I0616 02:09:03.326045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263314 (* 1 = 0.263314 loss) +I0616 02:09:03.326051 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10391 (* 1 = 1.10391 loss) +I0616 02:09:03.326056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00790091 (* 1 = 0.00790091 loss) +I0616 02:09:03.326059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0670183 (* 1 = 0.0670183 loss) +I0616 02:09:03.326062 9857 solver.cpp:571] Iteration 260, lr = 0.001 +I0616 02:09:22.481637 9857 solver.cpp:242] Iteration 280, loss = 1.4182 +I0616 02:09:22.481680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.482346 (* 1 = 0.482346 loss) +I0616 02:09:22.481685 9857 solver.cpp:258] Train net output #1: loss_cls = 1.50249 (* 1 = 1.50249 loss) +I0616 02:09:22.481689 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13 (* 1 = 0.13 loss) +I0616 02:09:22.481693 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220927 (* 1 = 0.0220927 loss) +I0616 02:09:22.481698 9857 solver.cpp:571] Iteration 280, lr = 0.001 +I0616 02:09:37.090031 9857 solver.cpp:242] Iteration 300, loss = 1.03755 +I0616 02:09:37.090077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106637 (* 1 = 0.106637 loss) +I0616 02:09:37.090082 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350268 (* 1 = 0.350268 loss) +I0616 02:09:37.090086 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158549 (* 1 = 0.158549 loss) +I0616 02:09:37.090090 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254919 (* 1 = 0.0254919 loss) +I0616 02:09:37.090095 9857 solver.cpp:571] Iteration 300, lr = 0.001 +I0616 02:09:53.437249 9857 solver.cpp:242] Iteration 320, loss = 2.43636 +I0616 02:09:53.437309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379475 (* 1 = 0.379475 loss) +I0616 02:09:53.437319 9857 solver.cpp:258] Train net output #1: loss_cls = 1.14947 (* 1 = 1.14947 loss) +I0616 02:09:53.437327 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.2222 (* 1 = 0.2222 loss) +I0616 02:09:53.437340 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160786 (* 1 = 0.0160786 loss) +I0616 02:09:53.437350 9857 solver.cpp:571] Iteration 320, lr = 0.001 +I0616 02:10:09.425061 9857 solver.cpp:242] Iteration 340, loss = 1.51078 +I0616 02:10:09.425108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435945 (* 1 = 0.435945 loss) +I0616 02:10:09.425114 9857 solver.cpp:258] Train net output #1: loss_cls = 1.30454 (* 1 = 1.30454 loss) +I0616 02:10:09.425118 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.394776 (* 1 = 0.394776 loss) +I0616 02:10:09.425122 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0922642 (* 1 = 0.0922642 loss) +I0616 02:10:09.425127 9857 solver.cpp:571] Iteration 340, lr = 0.001 +I0616 02:10:26.346233 9857 solver.cpp:242] Iteration 360, loss = 1.48237 +I0616 02:10:26.346277 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390996 (* 1 = 0.390996 loss) +I0616 02:10:26.346283 9857 solver.cpp:258] Train net output #1: loss_cls = 0.6382 (* 1 = 0.6382 loss) +I0616 02:10:26.346287 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.268668 (* 1 = 0.268668 loss) +I0616 02:10:26.346292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0657484 (* 1 = 0.0657484 loss) +I0616 02:10:26.346297 9857 solver.cpp:571] Iteration 360, lr = 0.001 +I0616 02:10:42.868648 9857 solver.cpp:242] Iteration 380, loss = 1.53553 +I0616 02:10:42.868693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.48943 (* 1 = 0.48943 loss) +I0616 02:10:42.868698 9857 solver.cpp:258] Train net output #1: loss_cls = 0.448966 (* 1 = 0.448966 loss) +I0616 02:10:42.868703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0134593 (* 1 = 0.0134593 loss) +I0616 02:10:42.868707 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0203292 (* 1 = 0.0203292 loss) +I0616 02:10:42.868711 9857 solver.cpp:571] Iteration 380, lr = 0.001 +speed: 0.771s / iter +I0616 02:10:58.894328 9857 solver.cpp:242] Iteration 400, loss = 2.00252 +I0616 02:10:58.894382 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384817 (* 1 = 0.384817 loss) +I0616 02:10:58.894390 9857 solver.cpp:258] Train net output #1: loss_cls = 1.38376 (* 1 = 1.38376 loss) +I0616 02:10:58.894394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.54831 (* 1 = 0.54831 loss) +I0616 02:10:58.894413 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.248493 (* 1 = 0.248493 loss) +I0616 02:10:58.894418 9857 solver.cpp:571] Iteration 400, lr = 0.001 +I0616 02:11:14.888159 9857 solver.cpp:242] Iteration 420, loss = 2.4401 +I0616 02:11:14.888201 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.46147 (* 1 = 0.46147 loss) +I0616 02:11:14.888207 9857 solver.cpp:258] Train net output #1: loss_cls = 1.39927 (* 1 = 1.39927 loss) +I0616 02:11:14.888211 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0990181 (* 1 = 0.0990181 loss) +I0616 02:11:14.888216 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014632 (* 1 = 0.014632 loss) +I0616 02:11:14.888219 9857 solver.cpp:571] Iteration 420, lr = 0.001 +I0616 02:11:31.604925 9857 solver.cpp:242] Iteration 440, loss = 1.36021 +I0616 02:11:31.604970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346903 (* 1 = 0.346903 loss) +I0616 02:11:31.604976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.947341 (* 1 = 0.947341 loss) +I0616 02:11:31.604980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134022 (* 1 = 0.134022 loss) +I0616 02:11:31.604984 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00639372 (* 1 = 0.00639372 loss) +I0616 02:11:31.604989 9857 solver.cpp:571] Iteration 440, lr = 0.001 +I0616 02:11:47.438356 9857 solver.cpp:242] Iteration 460, loss = 2.32784 +I0616 02:11:47.438402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.686171 (* 1 = 0.686171 loss) +I0616 02:11:47.438408 9857 solver.cpp:258] Train net output #1: loss_cls = 1.35726 (* 1 = 1.35726 loss) +I0616 02:11:47.438413 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.33084 (* 1 = 0.33084 loss) +I0616 02:11:47.438417 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129893 (* 1 = 0.129893 loss) +I0616 02:11:47.438421 9857 solver.cpp:571] Iteration 460, lr = 0.001 +I0616 02:12:05.296767 9857 solver.cpp:242] Iteration 480, loss = 2.77718 +I0616 02:12:05.296810 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.514054 (* 1 = 0.514054 loss) +I0616 02:12:05.296816 9857 solver.cpp:258] Train net output #1: loss_cls = 0.849854 (* 1 = 0.849854 loss) +I0616 02:12:05.296820 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.624191 (* 1 = 0.624191 loss) +I0616 02:12:05.296823 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.505368 (* 1 = 0.505368 loss) +I0616 02:12:05.296828 9857 solver.cpp:571] Iteration 480, lr = 0.001 +I0616 02:12:21.188433 9857 solver.cpp:242] Iteration 500, loss = 2.31821 +I0616 02:12:21.188477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.500784 (* 1 = 0.500784 loss) +I0616 02:12:21.188483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45784 (* 1 = 0.45784 loss) +I0616 02:12:21.188488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0880757 (* 1 = 0.0880757 loss) +I0616 02:12:21.188491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390839 (* 1 = 0.0390839 loss) +I0616 02:12:21.188496 9857 solver.cpp:571] Iteration 500, lr = 0.001 +I0616 02:12:37.293354 9857 solver.cpp:242] Iteration 520, loss = 1.87751 +I0616 02:12:37.293401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.601548 (* 1 = 0.601548 loss) +I0616 02:12:37.293406 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15385 (* 1 = 1.15385 loss) +I0616 02:12:37.293411 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.33409 (* 1 = 0.33409 loss) +I0616 02:12:37.293414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.109273 (* 1 = 0.109273 loss) +I0616 02:12:37.293419 9857 solver.cpp:571] Iteration 520, lr = 0.001 +I0616 02:12:54.378501 9857 solver.cpp:242] Iteration 540, loss = 1.36045 +I0616 02:12:54.378546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.534715 (* 1 = 0.534715 loss) +I0616 02:12:54.378552 9857 solver.cpp:258] Train net output #1: loss_cls = 1.021 (* 1 = 1.021 loss) +I0616 02:12:54.378556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.277673 (* 1 = 0.277673 loss) +I0616 02:12:54.378561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193868 (* 1 = 0.0193868 loss) +I0616 02:12:54.378564 9857 solver.cpp:571] Iteration 540, lr = 0.001 +I0616 02:13:07.316303 9857 solver.cpp:242] Iteration 560, loss = 1.28588 +I0616 02:13:07.316357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.566973 (* 1 = 0.566973 loss) +I0616 02:13:07.316367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.635858 (* 1 = 0.635858 loss) +I0616 02:13:07.316375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113396 (* 1 = 0.113396 loss) +I0616 02:13:07.316381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0771614 (* 1 = 0.0771614 loss) +I0616 02:13:07.316387 9857 solver.cpp:571] Iteration 560, lr = 0.001 +I0616 02:13:21.452976 9857 solver.cpp:242] Iteration 580, loss = 1.61856 +I0616 02:13:21.453027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.457469 (* 1 = 0.457469 loss) +I0616 02:13:21.453035 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281093 (* 1 = 0.281093 loss) +I0616 02:13:21.453042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127737 (* 1 = 0.127737 loss) +I0616 02:13:21.453047 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00520998 (* 1 = 0.00520998 loss) +I0616 02:13:21.453055 9857 solver.cpp:571] Iteration 580, lr = 0.001 +speed: 0.777s / iter +I0616 02:13:38.507870 9857 solver.cpp:242] Iteration 600, loss = 2.07628 +I0616 02:13:38.507913 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307282 (* 1 = 0.307282 loss) +I0616 02:13:38.507920 9857 solver.cpp:258] Train net output #1: loss_cls = 1.01712 (* 1 = 1.01712 loss) +I0616 02:13:38.507923 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.871024 (* 1 = 0.871024 loss) +I0616 02:13:38.507927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.711932 (* 1 = 0.711932 loss) +I0616 02:13:38.507931 9857 solver.cpp:571] Iteration 600, lr = 0.001 +I0616 02:13:53.591863 9857 solver.cpp:242] Iteration 620, loss = 2.25891 +I0616 02:13:53.591907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424268 (* 1 = 0.424268 loss) +I0616 02:13:53.591912 9857 solver.cpp:258] Train net output #1: loss_cls = 0.873251 (* 1 = 0.873251 loss) +I0616 02:13:53.591917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18324 (* 1 = 0.18324 loss) +I0616 02:13:53.591922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0713855 (* 1 = 0.0713855 loss) +I0616 02:13:53.591925 9857 solver.cpp:571] Iteration 620, lr = 0.001 +I0616 02:14:09.352033 9857 solver.cpp:242] Iteration 640, loss = 3.24209 +I0616 02:14:09.352082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.590474 (* 1 = 0.590474 loss) +I0616 02:14:09.352089 9857 solver.cpp:258] Train net output #1: loss_cls = 1.36576 (* 1 = 1.36576 loss) +I0616 02:14:09.352095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.538858 (* 1 = 0.538858 loss) +I0616 02:14:09.352102 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.326854 (* 1 = 0.326854 loss) +I0616 02:14:09.352110 9857 solver.cpp:571] Iteration 640, lr = 0.001 +I0616 02:14:22.924765 9857 solver.cpp:242] Iteration 660, loss = 2.22941 +I0616 02:14:22.924811 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.377163 (* 1 = 0.377163 loss) +I0616 02:14:22.924818 9857 solver.cpp:258] Train net output #1: loss_cls = 1.01397 (* 1 = 1.01397 loss) +I0616 02:14:22.924821 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.216394 (* 1 = 0.216394 loss) +I0616 02:14:22.924825 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136708 (* 1 = 0.136708 loss) +I0616 02:14:22.924829 9857 solver.cpp:571] Iteration 660, lr = 0.001 +I0616 02:14:38.468968 9857 solver.cpp:242] Iteration 680, loss = 2.8063 +I0616 02:14:38.469013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.643237 (* 1 = 0.643237 loss) +I0616 02:14:38.469019 9857 solver.cpp:258] Train net output #1: loss_cls = 1.59446 (* 1 = 1.59446 loss) +I0616 02:14:38.469024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.323146 (* 1 = 0.323146 loss) +I0616 02:14:38.469027 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.146632 (* 1 = 0.146632 loss) +I0616 02:14:38.469046 9857 solver.cpp:571] Iteration 680, lr = 0.001 +I0616 02:14:54.390502 9857 solver.cpp:242] Iteration 700, loss = 0.930338 +I0616 02:14:54.390549 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0690473 (* 1 = 0.0690473 loss) +I0616 02:14:54.390555 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158177 (* 1 = 0.158177 loss) +I0616 02:14:54.390559 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205236 (* 1 = 0.205236 loss) +I0616 02:14:54.390563 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254163 (* 1 = 0.0254163 loss) +I0616 02:14:54.390568 9857 solver.cpp:571] Iteration 700, lr = 0.001 +I0616 02:15:08.256120 9857 solver.cpp:242] Iteration 720, loss = 2.19852 +I0616 02:15:08.256165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.26852 (* 1 = 0.26852 loss) +I0616 02:15:08.256170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.793403 (* 1 = 0.793403 loss) +I0616 02:15:08.256175 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.231635 (* 1 = 0.231635 loss) +I0616 02:15:08.256178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0353535 (* 1 = 0.0353535 loss) +I0616 02:15:08.256182 9857 solver.cpp:571] Iteration 720, lr = 0.001 +I0616 02:15:25.244637 9857 solver.cpp:242] Iteration 740, loss = 1.73502 +I0616 02:15:25.244684 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.538856 (* 1 = 0.538856 loss) +I0616 02:15:25.244689 9857 solver.cpp:258] Train net output #1: loss_cls = 0.64627 (* 1 = 0.64627 loss) +I0616 02:15:25.244694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0545523 (* 1 = 0.0545523 loss) +I0616 02:15:25.244699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0542126 (* 1 = 0.0542126 loss) +I0616 02:15:25.244717 9857 solver.cpp:571] Iteration 740, lr = 0.001 +I0616 02:15:39.945852 9857 solver.cpp:242] Iteration 760, loss = 1.80842 +I0616 02:15:39.945896 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.657686 (* 1 = 0.657686 loss) +I0616 02:15:39.945901 9857 solver.cpp:258] Train net output #1: loss_cls = 1.08952 (* 1 = 1.08952 loss) +I0616 02:15:39.945905 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183239 (* 1 = 0.183239 loss) +I0616 02:15:39.945909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0429751 (* 1 = 0.0429751 loss) +I0616 02:15:39.945914 9857 solver.cpp:571] Iteration 760, lr = 0.001 +I0616 02:15:54.575979 9857 solver.cpp:242] Iteration 780, loss = 1.43926 +I0616 02:15:54.576028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.559102 (* 1 = 0.559102 loss) +I0616 02:15:54.576036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.923873 (* 1 = 0.923873 loss) +I0616 02:15:54.576042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.037878 (* 1 = 0.037878 loss) +I0616 02:15:54.576048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184902 (* 1 = 0.0184902 loss) +I0616 02:15:54.576055 9857 solver.cpp:571] Iteration 780, lr = 0.001 +speed: 0.776s / iter +I0616 02:16:10.743752 9857 solver.cpp:242] Iteration 800, loss = 2.65282 +I0616 02:16:10.743811 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.223356 (* 1 = 0.223356 loss) +I0616 02:16:10.743821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.810161 (* 1 = 0.810161 loss) +I0616 02:16:10.743829 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.930315 (* 1 = 0.930315 loss) +I0616 02:16:10.743841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.945128 (* 1 = 0.945128 loss) +I0616 02:16:10.743849 9857 solver.cpp:571] Iteration 800, lr = 0.001 +I0616 02:16:25.175056 9857 solver.cpp:242] Iteration 820, loss = 1.15248 +I0616 02:16:25.175099 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188985 (* 1 = 0.188985 loss) +I0616 02:16:25.175106 9857 solver.cpp:258] Train net output #1: loss_cls = 0.456765 (* 1 = 0.456765 loss) +I0616 02:16:25.175109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0985536 (* 1 = 0.0985536 loss) +I0616 02:16:25.175113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122515 (* 1 = 0.0122515 loss) +I0616 02:16:25.175117 9857 solver.cpp:571] Iteration 820, lr = 0.001 +I0616 02:16:41.118854 9857 solver.cpp:242] Iteration 840, loss = 1.36085 +I0616 02:16:41.118906 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371735 (* 1 = 0.371735 loss) +I0616 02:16:41.118913 9857 solver.cpp:258] Train net output #1: loss_cls = 0.869635 (* 1 = 0.869635 loss) +I0616 02:16:41.118921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179053 (* 1 = 0.179053 loss) +I0616 02:16:41.118927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.059841 (* 1 = 0.059841 loss) +I0616 02:16:41.118935 9857 solver.cpp:571] Iteration 840, lr = 0.001 +I0616 02:16:55.866158 9857 solver.cpp:242] Iteration 860, loss = 1.44913 +I0616 02:16:55.866200 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.713686 (* 1 = 0.713686 loss) +I0616 02:16:55.866206 9857 solver.cpp:258] Train net output #1: loss_cls = 1.37526 (* 1 = 1.37526 loss) +I0616 02:16:55.866210 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175954 (* 1 = 0.175954 loss) +I0616 02:16:55.866214 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0677906 (* 1 = 0.0677906 loss) +I0616 02:16:55.866219 9857 solver.cpp:571] Iteration 860, lr = 0.001 +I0616 02:17:10.360617 9857 solver.cpp:242] Iteration 880, loss = 1.50433 +I0616 02:17:10.360661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224712 (* 1 = 0.224712 loss) +I0616 02:17:10.360666 9857 solver.cpp:258] Train net output #1: loss_cls = 0.51732 (* 1 = 0.51732 loss) +I0616 02:17:10.360669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161621 (* 1 = 0.161621 loss) +I0616 02:17:10.360674 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00794932 (* 1 = 0.00794932 loss) +I0616 02:17:10.360678 9857 solver.cpp:571] Iteration 880, lr = 0.001 +I0616 02:17:26.046809 9857 solver.cpp:242] Iteration 900, loss = 1.61712 +I0616 02:17:26.046852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.583751 (* 1 = 0.583751 loss) +I0616 02:17:26.046859 9857 solver.cpp:258] Train net output #1: loss_cls = 0.793247 (* 1 = 0.793247 loss) +I0616 02:17:26.046862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152771 (* 1 = 0.152771 loss) +I0616 02:17:26.046866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0692621 (* 1 = 0.0692621 loss) +I0616 02:17:26.046870 9857 solver.cpp:571] Iteration 900, lr = 0.001 +I0616 02:17:43.589730 9857 solver.cpp:242] Iteration 920, loss = 1.42336 +I0616 02:17:43.589772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347398 (* 1 = 0.347398 loss) +I0616 02:17:43.589778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372151 (* 1 = 0.372151 loss) +I0616 02:17:43.589782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.346246 (* 1 = 0.346246 loss) +I0616 02:17:43.589787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0067923 (* 1 = 0.0067923 loss) +I0616 02:17:43.589790 9857 solver.cpp:571] Iteration 920, lr = 0.001 +I0616 02:17:57.613250 9857 solver.cpp:242] Iteration 940, loss = 1.00885 +I0616 02:17:57.613294 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401577 (* 1 = 0.401577 loss) +I0616 02:17:57.613301 9857 solver.cpp:258] Train net output #1: loss_cls = 0.81336 (* 1 = 0.81336 loss) +I0616 02:17:57.613306 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106018 (* 1 = 0.106018 loss) +I0616 02:17:57.613309 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181932 (* 1 = 0.0181932 loss) +I0616 02:17:57.613313 9857 solver.cpp:571] Iteration 940, lr = 0.001 +I0616 02:18:11.147518 9857 solver.cpp:242] Iteration 960, loss = 0.835377 +I0616 02:18:11.147562 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.335733 (* 1 = 0.335733 loss) +I0616 02:18:11.147568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.383648 (* 1 = 0.383648 loss) +I0616 02:18:11.147572 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125797 (* 1 = 0.125797 loss) +I0616 02:18:11.147577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173958 (* 1 = 0.0173958 loss) +I0616 02:18:11.147580 9857 solver.cpp:571] Iteration 960, lr = 0.001 +I0616 02:18:27.087200 9857 solver.cpp:242] Iteration 980, loss = 1.38562 +I0616 02:18:27.087246 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.642668 (* 1 = 0.642668 loss) +I0616 02:18:27.087252 9857 solver.cpp:258] Train net output #1: loss_cls = 1.31869 (* 1 = 1.31869 loss) +I0616 02:18:27.087256 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.245069 (* 1 = 0.245069 loss) +I0616 02:18:27.087261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144099 (* 1 = 0.144099 loss) +I0616 02:18:27.087266 9857 solver.cpp:571] Iteration 980, lr = 0.001 +speed: 0.771s / iter +I0616 02:18:41.825685 9857 solver.cpp:242] Iteration 1000, loss = 3.02841 +I0616 02:18:41.825736 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.614316 (* 1 = 0.614316 loss) +I0616 02:18:41.825744 9857 solver.cpp:258] Train net output #1: loss_cls = 1.80773 (* 1 = 1.80773 loss) +I0616 02:18:41.825750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.523366 (* 1 = 0.523366 loss) +I0616 02:18:41.825762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.816685 (* 1 = 0.816685 loss) +I0616 02:18:41.825768 9857 solver.cpp:571] Iteration 1000, lr = 0.001 +I0616 02:18:56.649768 9857 solver.cpp:242] Iteration 1020, loss = 1.74117 +I0616 02:18:56.649813 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488187 (* 1 = 0.488187 loss) +I0616 02:18:56.649818 9857 solver.cpp:258] Train net output #1: loss_cls = 0.835292 (* 1 = 0.835292 loss) +I0616 02:18:56.649822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.242822 (* 1 = 0.242822 loss) +I0616 02:18:56.649827 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.153778 (* 1 = 0.153778 loss) +I0616 02:18:56.649830 9857 solver.cpp:571] Iteration 1020, lr = 0.001 +I0616 02:19:13.252563 9857 solver.cpp:242] Iteration 1040, loss = 2.29626 +I0616 02:19:13.252607 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.630172 (* 1 = 0.630172 loss) +I0616 02:19:13.252614 9857 solver.cpp:258] Train net output #1: loss_cls = 1.32558 (* 1 = 1.32558 loss) +I0616 02:19:13.252617 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.503912 (* 1 = 0.503912 loss) +I0616 02:19:13.252621 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.149148 (* 1 = 0.149148 loss) +I0616 02:19:13.252625 9857 solver.cpp:571] Iteration 1040, lr = 0.001 +I0616 02:19:28.018661 9857 solver.cpp:242] Iteration 1060, loss = 1.7815 +I0616 02:19:28.018713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.643652 (* 1 = 0.643652 loss) +I0616 02:19:28.018721 9857 solver.cpp:258] Train net output #1: loss_cls = 1.3407 (* 1 = 1.3407 loss) +I0616 02:19:28.018726 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126354 (* 1 = 0.126354 loss) +I0616 02:19:28.018733 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103235 (* 1 = 0.103235 loss) +I0616 02:19:28.018739 9857 solver.cpp:571] Iteration 1060, lr = 0.001 +I0616 02:19:41.609001 9857 solver.cpp:242] Iteration 1080, loss = 2.51978 +I0616 02:19:41.609045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.568048 (* 1 = 0.568048 loss) +I0616 02:19:41.609050 9857 solver.cpp:258] Train net output #1: loss_cls = 1.75894 (* 1 = 1.75894 loss) +I0616 02:19:41.609053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.432093 (* 1 = 0.432093 loss) +I0616 02:19:41.609057 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.309877 (* 1 = 0.309877 loss) +I0616 02:19:41.609061 9857 solver.cpp:571] Iteration 1080, lr = 0.001 +I0616 02:19:56.320796 9857 solver.cpp:242] Iteration 1100, loss = 1.79158 +I0616 02:19:56.320855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435586 (* 1 = 0.435586 loss) +I0616 02:19:56.320865 9857 solver.cpp:258] Train net output #1: loss_cls = 1.49711 (* 1 = 1.49711 loss) +I0616 02:19:56.320873 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159555 (* 1 = 0.159555 loss) +I0616 02:19:56.320880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011807 (* 1 = 0.011807 loss) +I0616 02:19:56.320895 9857 solver.cpp:571] Iteration 1100, lr = 0.001 +I0616 02:20:12.196121 9857 solver.cpp:242] Iteration 1120, loss = 1.20621 +I0616 02:20:12.196166 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296081 (* 1 = 0.296081 loss) +I0616 02:20:12.196185 9857 solver.cpp:258] Train net output #1: loss_cls = 0.421477 (* 1 = 0.421477 loss) +I0616 02:20:12.196190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11989 (* 1 = 0.11989 loss) +I0616 02:20:12.196194 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00836908 (* 1 = 0.00836908 loss) +I0616 02:20:12.196198 9857 solver.cpp:571] Iteration 1120, lr = 0.001 +I0616 02:20:26.740569 9857 solver.cpp:242] Iteration 1140, loss = 2.21743 +I0616 02:20:26.740619 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.581938 (* 1 = 0.581938 loss) +I0616 02:20:26.740628 9857 solver.cpp:258] Train net output #1: loss_cls = 0.998679 (* 1 = 0.998679 loss) +I0616 02:20:26.740633 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.495947 (* 1 = 0.495947 loss) +I0616 02:20:26.740639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0899781 (* 1 = 0.0899781 loss) +I0616 02:20:26.740646 9857 solver.cpp:571] Iteration 1140, lr = 0.001 +I0616 02:20:41.197497 9857 solver.cpp:242] Iteration 1160, loss = 1.6623 +I0616 02:20:41.197542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.491138 (* 1 = 0.491138 loss) +I0616 02:20:41.197548 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38152 (* 1 = 0.38152 loss) +I0616 02:20:41.197552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0480514 (* 1 = 0.0480514 loss) +I0616 02:20:41.197556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0339345 (* 1 = 0.0339345 loss) +I0616 02:20:41.197561 9857 solver.cpp:571] Iteration 1160, lr = 0.001 +I0616 02:20:57.227843 9857 solver.cpp:242] Iteration 1180, loss = 2.73345 +I0616 02:20:57.227901 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.557391 (* 1 = 0.557391 loss) +I0616 02:20:57.227912 9857 solver.cpp:258] Train net output #1: loss_cls = 1.96342 (* 1 = 1.96342 loss) +I0616 02:20:57.227919 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.457537 (* 1 = 0.457537 loss) +I0616 02:20:57.227931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110572 (* 1 = 0.110572 loss) +I0616 02:20:57.227938 9857 solver.cpp:571] Iteration 1180, lr = 0.001 +speed: 0.768s / iter +I0616 02:21:11.695204 9857 solver.cpp:242] Iteration 1200, loss = 1.47154 +I0616 02:21:11.695245 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0389067 (* 1 = 0.0389067 loss) +I0616 02:21:11.695251 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127858 (* 1 = 0.127858 loss) +I0616 02:21:11.695255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144256 (* 1 = 0.144256 loss) +I0616 02:21:11.695260 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030373 (* 1 = 0.030373 loss) +I0616 02:21:11.695263 9857 solver.cpp:571] Iteration 1200, lr = 0.001 +I0616 02:21:27.289069 9857 solver.cpp:242] Iteration 1220, loss = 1.48721 +I0616 02:21:27.289113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.34448 (* 1 = 0.34448 loss) +I0616 02:21:27.289119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.878613 (* 1 = 0.878613 loss) +I0616 02:21:27.289124 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102436 (* 1 = 0.102436 loss) +I0616 02:21:27.289127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104075 (* 1 = 0.104075 loss) +I0616 02:21:27.289131 9857 solver.cpp:571] Iteration 1220, lr = 0.001 +I0616 02:21:41.456452 9857 solver.cpp:242] Iteration 1240, loss = 0.645753 +I0616 02:21:41.456508 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366579 (* 1 = 0.366579 loss) +I0616 02:21:41.456516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225114 (* 1 = 0.225114 loss) +I0616 02:21:41.456522 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274303 (* 1 = 0.0274303 loss) +I0616 02:21:41.456528 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00681171 (* 1 = 0.00681171 loss) +I0616 02:21:41.456535 9857 solver.cpp:571] Iteration 1240, lr = 0.001 +I0616 02:21:55.152420 9857 solver.cpp:242] Iteration 1260, loss = 2.64821 +I0616 02:21:55.152470 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.597741 (* 1 = 0.597741 loss) +I0616 02:21:55.152477 9857 solver.cpp:258] Train net output #1: loss_cls = 1.7746 (* 1 = 1.7746 loss) +I0616 02:21:55.152483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.239465 (* 1 = 0.239465 loss) +I0616 02:21:55.152488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030117 (* 1 = 0.030117 loss) +I0616 02:21:55.152498 9857 solver.cpp:571] Iteration 1260, lr = 0.001 +I0616 02:22:12.387706 9857 solver.cpp:242] Iteration 1280, loss = 2.69978 +I0616 02:22:12.387749 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.648419 (* 1 = 0.648419 loss) +I0616 02:22:12.387755 9857 solver.cpp:258] Train net output #1: loss_cls = 1.84857 (* 1 = 1.84857 loss) +I0616 02:22:12.387759 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.721246 (* 1 = 0.721246 loss) +I0616 02:22:12.387763 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.377932 (* 1 = 0.377932 loss) +I0616 02:22:12.387768 9857 solver.cpp:571] Iteration 1280, lr = 0.001 +I0616 02:22:25.466887 9857 solver.cpp:242] Iteration 1300, loss = 1.80715 +I0616 02:22:25.466933 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.522314 (* 1 = 0.522314 loss) +I0616 02:22:25.466938 9857 solver.cpp:258] Train net output #1: loss_cls = 1.0751 (* 1 = 1.0751 loss) +I0616 02:22:25.466941 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193474 (* 1 = 0.193474 loss) +I0616 02:22:25.466945 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.155961 (* 1 = 0.155961 loss) +I0616 02:22:25.466949 9857 solver.cpp:571] Iteration 1300, lr = 0.001 +I0616 02:22:41.047595 9857 solver.cpp:242] Iteration 1320, loss = 1.891 +I0616 02:22:41.047636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.659648 (* 1 = 0.659648 loss) +I0616 02:22:41.047641 9857 solver.cpp:258] Train net output #1: loss_cls = 1.27848 (* 1 = 1.27848 loss) +I0616 02:22:41.047646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.307715 (* 1 = 0.307715 loss) +I0616 02:22:41.047649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0918145 (* 1 = 0.0918145 loss) +I0616 02:22:41.047654 9857 solver.cpp:571] Iteration 1320, lr = 0.001 +I0616 02:22:58.719534 9857 solver.cpp:242] Iteration 1340, loss = 1.9004 +I0616 02:22:58.719578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.56272 (* 1 = 0.56272 loss) +I0616 02:22:58.719584 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15366 (* 1 = 1.15366 loss) +I0616 02:22:58.719588 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.386357 (* 1 = 0.386357 loss) +I0616 02:22:58.719591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494201 (* 1 = 0.0494201 loss) +I0616 02:22:58.719596 9857 solver.cpp:571] Iteration 1340, lr = 0.001 +I0616 02:23:15.407213 9857 solver.cpp:242] Iteration 1360, loss = 1.00926 +I0616 02:23:15.407259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18213 (* 1 = 0.18213 loss) +I0616 02:23:15.407264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337477 (* 1 = 0.337477 loss) +I0616 02:23:15.407269 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.245867 (* 1 = 0.245867 loss) +I0616 02:23:15.407274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0445607 (* 1 = 0.0445607 loss) +I0616 02:23:15.407292 9857 solver.cpp:571] Iteration 1360, lr = 0.001 +I0616 02:23:29.597991 9857 solver.cpp:242] Iteration 1380, loss = 1.77158 +I0616 02:23:29.598047 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365232 (* 1 = 0.365232 loss) +I0616 02:23:29.598057 9857 solver.cpp:258] Train net output #1: loss_cls = 0.749806 (* 1 = 0.749806 loss) +I0616 02:23:29.598064 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0474545 (* 1 = 0.0474545 loss) +I0616 02:23:29.598073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00735179 (* 1 = 0.00735179 loss) +I0616 02:23:29.598081 9857 solver.cpp:571] Iteration 1380, lr = 0.001 +speed: 0.768s / iter +I0616 02:23:45.493825 9857 solver.cpp:242] Iteration 1400, loss = 1.01255 +I0616 02:23:45.493868 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371281 (* 1 = 0.371281 loss) +I0616 02:23:45.493875 9857 solver.cpp:258] Train net output #1: loss_cls = 0.583299 (* 1 = 0.583299 loss) +I0616 02:23:45.493878 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154084 (* 1 = 0.154084 loss) +I0616 02:23:45.493882 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132863 (* 1 = 0.0132863 loss) +I0616 02:23:45.493901 9857 solver.cpp:571] Iteration 1400, lr = 0.001 +I0616 02:24:02.753865 9857 solver.cpp:242] Iteration 1420, loss = 0.847808 +I0616 02:24:02.753911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114684 (* 1 = 0.114684 loss) +I0616 02:24:02.753917 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246507 (* 1 = 0.246507 loss) +I0616 02:24:02.753921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101845 (* 1 = 0.101845 loss) +I0616 02:24:02.753926 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00980254 (* 1 = 0.00980254 loss) +I0616 02:24:02.753931 9857 solver.cpp:571] Iteration 1420, lr = 0.001 +I0616 02:24:19.399245 9857 solver.cpp:242] Iteration 1440, loss = 1.69847 +I0616 02:24:19.399308 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.470374 (* 1 = 0.470374 loss) +I0616 02:24:19.399318 9857 solver.cpp:258] Train net output #1: loss_cls = 1.66908 (* 1 = 1.66908 loss) +I0616 02:24:19.399325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.469121 (* 1 = 0.469121 loss) +I0616 02:24:19.399333 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.187167 (* 1 = 0.187167 loss) +I0616 02:24:19.399344 9857 solver.cpp:571] Iteration 1440, lr = 0.001 +I0616 02:24:34.827558 9857 solver.cpp:242] Iteration 1460, loss = 2.39418 +I0616 02:24:34.827605 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.670531 (* 1 = 0.670531 loss) +I0616 02:24:34.827610 9857 solver.cpp:258] Train net output #1: loss_cls = 1.94387 (* 1 = 1.94387 loss) +I0616 02:24:34.827615 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.313701 (* 1 = 0.313701 loss) +I0616 02:24:34.827618 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0724887 (* 1 = 0.0724887 loss) +I0616 02:24:34.827623 9857 solver.cpp:571] Iteration 1460, lr = 0.001 +I0616 02:24:49.613495 9857 solver.cpp:242] Iteration 1480, loss = 0.792429 +I0616 02:24:49.613541 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.00291168 (* 1 = 0.00291168 loss) +I0616 02:24:49.613546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262833 (* 1 = 0.262833 loss) +I0616 02:24:49.613550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.468931 (* 1 = 0.468931 loss) +I0616 02:24:49.613554 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.299506 (* 1 = 0.299506 loss) +I0616 02:24:49.613559 9857 solver.cpp:571] Iteration 1480, lr = 0.001 +I0616 02:25:05.657709 9857 solver.cpp:242] Iteration 1500, loss = 1.35457 +I0616 02:25:05.657755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353232 (* 1 = 0.353232 loss) +I0616 02:25:05.657762 9857 solver.cpp:258] Train net output #1: loss_cls = 0.832644 (* 1 = 0.832644 loss) +I0616 02:25:05.657765 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162911 (* 1 = 0.162911 loss) +I0616 02:25:05.657769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0294813 (* 1 = 0.0294813 loss) +I0616 02:25:05.657773 9857 solver.cpp:571] Iteration 1500, lr = 0.001 +I0616 02:25:21.631392 9857 solver.cpp:242] Iteration 1520, loss = 1.26118 +I0616 02:25:21.631433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.467243 (* 1 = 0.467243 loss) +I0616 02:25:21.631438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.470832 (* 1 = 0.470832 loss) +I0616 02:25:21.631443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0565036 (* 1 = 0.0565036 loss) +I0616 02:25:21.631446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0200897 (* 1 = 0.0200897 loss) +I0616 02:25:21.631450 9857 solver.cpp:571] Iteration 1520, lr = 0.001 +I0616 02:25:38.003101 9857 solver.cpp:242] Iteration 1540, loss = 0.884614 +I0616 02:25:38.003159 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.434547 (* 1 = 0.434547 loss) +I0616 02:25:38.003168 9857 solver.cpp:258] Train net output #1: loss_cls = 0.5156 (* 1 = 0.5156 loss) +I0616 02:25:38.003176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.280979 (* 1 = 0.280979 loss) +I0616 02:25:38.003190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.045896 (* 1 = 0.045896 loss) +I0616 02:25:38.003197 9857 solver.cpp:571] Iteration 1540, lr = 0.001 +I0616 02:25:53.994004 9857 solver.cpp:242] Iteration 1560, loss = 1.27435 +I0616 02:25:53.994061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.514379 (* 1 = 0.514379 loss) +I0616 02:25:53.994071 9857 solver.cpp:258] Train net output #1: loss_cls = 0.7112 (* 1 = 0.7112 loss) +I0616 02:25:53.994079 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166461 (* 1 = 0.166461 loss) +I0616 02:25:53.994086 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0446545 (* 1 = 0.0446545 loss) +I0616 02:25:53.994096 9857 solver.cpp:571] Iteration 1560, lr = 0.001 +I0616 02:26:06.930763 9857 solver.cpp:242] Iteration 1580, loss = 2.20626 +I0616 02:26:06.930811 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.423131 (* 1 = 0.423131 loss) +I0616 02:26:06.930819 9857 solver.cpp:258] Train net output #1: loss_cls = 0.836094 (* 1 = 0.836094 loss) +I0616 02:26:06.930825 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.825478 (* 1 = 0.825478 loss) +I0616 02:26:06.930831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.263445 (* 1 = 0.263445 loss) +I0616 02:26:06.930838 9857 solver.cpp:571] Iteration 1580, lr = 0.001 +speed: 0.770s / iter +I0616 02:26:22.368371 9857 solver.cpp:242] Iteration 1600, loss = 1.64678 +I0616 02:26:22.368419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.592334 (* 1 = 0.592334 loss) +I0616 02:26:22.368427 9857 solver.cpp:258] Train net output #1: loss_cls = 1.43229 (* 1 = 1.43229 loss) +I0616 02:26:22.368432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205694 (* 1 = 0.205694 loss) +I0616 02:26:22.368438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337413 (* 1 = 0.0337413 loss) +I0616 02:26:22.368448 9857 solver.cpp:571] Iteration 1600, lr = 0.001 +I0616 02:26:37.184765 9857 solver.cpp:242] Iteration 1620, loss = 1.60352 +I0616 02:26:37.184808 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310336 (* 1 = 0.310336 loss) +I0616 02:26:37.184814 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314316 (* 1 = 0.314316 loss) +I0616 02:26:37.184818 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0800123 (* 1 = 0.0800123 loss) +I0616 02:26:37.184823 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00279522 (* 1 = 0.00279522 loss) +I0616 02:26:37.184825 9857 solver.cpp:571] Iteration 1620, lr = 0.001 +I0616 02:26:53.284682 9857 solver.cpp:242] Iteration 1640, loss = 1.59067 +I0616 02:26:53.284724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261306 (* 1 = 0.261306 loss) +I0616 02:26:53.284730 9857 solver.cpp:258] Train net output #1: loss_cls = 1.21233 (* 1 = 1.21233 loss) +I0616 02:26:53.284734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.340564 (* 1 = 0.340564 loss) +I0616 02:26:53.284739 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0528103 (* 1 = 0.0528103 loss) +I0616 02:26:53.284742 9857 solver.cpp:571] Iteration 1640, lr = 0.001 +I0616 02:27:09.145236 9857 solver.cpp:242] Iteration 1660, loss = 0.67249 +I0616 02:27:09.145280 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220601 (* 1 = 0.220601 loss) +I0616 02:27:09.145287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278967 (* 1 = 0.278967 loss) +I0616 02:27:09.145290 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.25297 (* 1 = 0.25297 loss) +I0616 02:27:09.145294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.086625 (* 1 = 0.086625 loss) +I0616 02:27:09.145298 9857 solver.cpp:571] Iteration 1660, lr = 0.001 +I0616 02:27:24.653273 9857 solver.cpp:242] Iteration 1680, loss = 2.03262 +I0616 02:27:24.653319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384146 (* 1 = 0.384146 loss) +I0616 02:27:24.653326 9857 solver.cpp:258] Train net output #1: loss_cls = 1.23698 (* 1 = 1.23698 loss) +I0616 02:27:24.653329 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126247 (* 1 = 0.126247 loss) +I0616 02:27:24.653333 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00192088 (* 1 = 0.00192088 loss) +I0616 02:27:24.653337 9857 solver.cpp:571] Iteration 1680, lr = 0.001 +I0616 02:27:38.338428 9857 solver.cpp:242] Iteration 1700, loss = 1.64791 +I0616 02:27:38.338472 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.301065 (* 1 = 0.301065 loss) +I0616 02:27:38.338479 9857 solver.cpp:258] Train net output #1: loss_cls = 0.632023 (* 1 = 0.632023 loss) +I0616 02:27:38.338482 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0648472 (* 1 = 0.0648472 loss) +I0616 02:27:38.338486 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319705 (* 1 = 0.0319705 loss) +I0616 02:27:38.338492 9857 solver.cpp:571] Iteration 1700, lr = 0.001 +I0616 02:27:55.702095 9857 solver.cpp:242] Iteration 1720, loss = 1.69541 +I0616 02:27:55.702142 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.579422 (* 1 = 0.579422 loss) +I0616 02:27:55.702149 9857 solver.cpp:258] Train net output #1: loss_cls = 0.981107 (* 1 = 0.981107 loss) +I0616 02:27:55.702157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.313742 (* 1 = 0.313742 loss) +I0616 02:27:55.702175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128312 (* 1 = 0.128312 loss) +I0616 02:27:55.702181 9857 solver.cpp:571] Iteration 1720, lr = 0.001 +I0616 02:28:11.487854 9857 solver.cpp:242] Iteration 1740, loss = 0.95425 +I0616 02:28:11.487911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3458 (* 1 = 0.3458 loss) +I0616 02:28:11.487917 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292413 (* 1 = 0.292413 loss) +I0616 02:28:11.487921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121898 (* 1 = 0.121898 loss) +I0616 02:28:11.487938 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00744771 (* 1 = 0.00744771 loss) +I0616 02:28:11.487942 9857 solver.cpp:571] Iteration 1740, lr = 0.001 +I0616 02:28:25.204730 9857 solver.cpp:242] Iteration 1760, loss = 2.79055 +I0616 02:28:25.204776 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.588959 (* 1 = 0.588959 loss) +I0616 02:28:25.204782 9857 solver.cpp:258] Train net output #1: loss_cls = 1.32127 (* 1 = 1.32127 loss) +I0616 02:28:25.204785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.572914 (* 1 = 0.572914 loss) +I0616 02:28:25.204789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162117 (* 1 = 0.162117 loss) +I0616 02:28:25.204793 9857 solver.cpp:571] Iteration 1760, lr = 0.001 +I0616 02:28:39.931617 9857 solver.cpp:242] Iteration 1780, loss = 0.803365 +I0616 02:28:39.931659 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268633 (* 1 = 0.268633 loss) +I0616 02:28:39.931665 9857 solver.cpp:258] Train net output #1: loss_cls = 0.714116 (* 1 = 0.714116 loss) +I0616 02:28:39.931669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235178 (* 1 = 0.235178 loss) +I0616 02:28:39.931673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0338616 (* 1 = 0.0338616 loss) +I0616 02:28:39.931676 9857 solver.cpp:571] Iteration 1780, lr = 0.001 +speed: 0.769s / iter +I0616 02:28:54.727360 9857 solver.cpp:242] Iteration 1800, loss = 0.827792 +I0616 02:28:54.727409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.403273 (* 1 = 0.403273 loss) +I0616 02:28:54.727417 9857 solver.cpp:258] Train net output #1: loss_cls = 0.429152 (* 1 = 0.429152 loss) +I0616 02:28:54.727422 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0302375 (* 1 = 0.0302375 loss) +I0616 02:28:54.727428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0124967 (* 1 = 0.0124967 loss) +I0616 02:28:54.727435 9857 solver.cpp:571] Iteration 1800, lr = 0.001 +I0616 02:29:10.110860 9857 solver.cpp:242] Iteration 1820, loss = 1.1024 +I0616 02:29:10.110904 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237471 (* 1 = 0.237471 loss) +I0616 02:29:10.110910 9857 solver.cpp:258] Train net output #1: loss_cls = 0.898656 (* 1 = 0.898656 loss) +I0616 02:29:10.110914 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.228342 (* 1 = 0.228342 loss) +I0616 02:29:10.110918 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00294717 (* 1 = 0.00294717 loss) +I0616 02:29:10.110923 9857 solver.cpp:571] Iteration 1820, lr = 0.001 +I0616 02:29:24.714969 9857 solver.cpp:242] Iteration 1840, loss = 1.46794 +I0616 02:29:24.715014 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.325916 (* 1 = 0.325916 loss) +I0616 02:29:24.715020 9857 solver.cpp:258] Train net output #1: loss_cls = 0.450828 (* 1 = 0.450828 loss) +I0616 02:29:24.715024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.203577 (* 1 = 0.203577 loss) +I0616 02:29:24.715029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0300394 (* 1 = 0.0300394 loss) +I0616 02:29:24.715032 9857 solver.cpp:571] Iteration 1840, lr = 0.001 +I0616 02:29:39.227200 9857 solver.cpp:242] Iteration 1860, loss = 2.4904 +I0616 02:29:39.227236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.577531 (* 1 = 0.577531 loss) +I0616 02:29:39.227244 9857 solver.cpp:258] Train net output #1: loss_cls = 1.18099 (* 1 = 1.18099 loss) +I0616 02:29:39.227253 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0922878 (* 1 = 0.0922878 loss) +I0616 02:29:39.227262 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0403883 (* 1 = 0.0403883 loss) +I0616 02:29:39.227267 9857 solver.cpp:571] Iteration 1860, lr = 0.001 +I0616 02:29:55.026814 9857 solver.cpp:242] Iteration 1880, loss = 1.44477 +I0616 02:29:55.026861 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.433003 (* 1 = 0.433003 loss) +I0616 02:29:55.026870 9857 solver.cpp:258] Train net output #1: loss_cls = 0.433049 (* 1 = 0.433049 loss) +I0616 02:29:55.026875 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128702 (* 1 = 0.128702 loss) +I0616 02:29:55.026880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0342489 (* 1 = 0.0342489 loss) +I0616 02:29:55.026887 9857 solver.cpp:571] Iteration 1880, lr = 0.001 +I0616 02:30:10.543503 9857 solver.cpp:242] Iteration 1900, loss = 1.2746 +I0616 02:30:10.543547 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363603 (* 1 = 0.363603 loss) +I0616 02:30:10.543552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.665514 (* 1 = 0.665514 loss) +I0616 02:30:10.543557 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102299 (* 1 = 0.102299 loss) +I0616 02:30:10.543561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00494926 (* 1 = 0.00494926 loss) +I0616 02:30:10.543565 9857 solver.cpp:571] Iteration 1900, lr = 0.001 +I0616 02:30:24.092211 9857 solver.cpp:242] Iteration 1920, loss = 2.10262 +I0616 02:30:24.092262 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355104 (* 1 = 0.355104 loss) +I0616 02:30:24.092270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.792907 (* 1 = 0.792907 loss) +I0616 02:30:24.092277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106697 (* 1 = 0.106697 loss) +I0616 02:30:24.092283 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0242687 (* 1 = 0.0242687 loss) +I0616 02:30:24.092288 9857 solver.cpp:571] Iteration 1920, lr = 0.001 +I0616 02:30:38.714799 9857 solver.cpp:242] Iteration 1940, loss = 2.63316 +I0616 02:30:38.714854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.573788 (* 1 = 0.573788 loss) +I0616 02:30:38.714865 9857 solver.cpp:258] Train net output #1: loss_cls = 1.32003 (* 1 = 1.32003 loss) +I0616 02:30:38.714874 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.30287 (* 1 = 0.30287 loss) +I0616 02:30:38.714881 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.174245 (* 1 = 0.174245 loss) +I0616 02:30:38.714890 9857 solver.cpp:571] Iteration 1940, lr = 0.001 +I0616 02:30:55.148012 9857 solver.cpp:242] Iteration 1960, loss = 1.13612 +I0616 02:30:55.148056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.551331 (* 1 = 0.551331 loss) +I0616 02:30:55.148062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337733 (* 1 = 0.337733 loss) +I0616 02:30:55.148066 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.37646 (* 1 = 0.37646 loss) +I0616 02:30:55.148069 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.207955 (* 1 = 0.207955 loss) +I0616 02:30:55.148073 9857 solver.cpp:571] Iteration 1960, lr = 0.001 +I0616 02:31:08.323206 9857 solver.cpp:242] Iteration 1980, loss = 1.79252 +I0616 02:31:08.323249 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.639834 (* 1 = 0.639834 loss) +I0616 02:31:08.323256 9857 solver.cpp:258] Train net output #1: loss_cls = 1.41953 (* 1 = 1.41953 loss) +I0616 02:31:08.323259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179255 (* 1 = 0.179255 loss) +I0616 02:31:08.323263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131957 (* 1 = 0.0131957 loss) +I0616 02:31:08.323267 9857 solver.cpp:571] Iteration 1980, lr = 0.001 +speed: 0.767s / iter +I0616 02:31:23.840379 9857 solver.cpp:242] Iteration 2000, loss = 1.53207 +I0616 02:31:23.840427 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.500591 (* 1 = 0.500591 loss) +I0616 02:31:23.840435 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10939 (* 1 = 1.10939 loss) +I0616 02:31:23.840441 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0985612 (* 1 = 0.0985612 loss) +I0616 02:31:23.840446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0955547 (* 1 = 0.0955547 loss) +I0616 02:31:23.840453 9857 solver.cpp:571] Iteration 2000, lr = 0.001 +I0616 02:31:37.649333 9857 solver.cpp:242] Iteration 2020, loss = 3.52833 +I0616 02:31:37.649379 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.584806 (* 1 = 0.584806 loss) +I0616 02:31:37.649384 9857 solver.cpp:258] Train net output #1: loss_cls = 1.49423 (* 1 = 1.49423 loss) +I0616 02:31:37.649387 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 1.07954 (* 1 = 1.07954 loss) +I0616 02:31:37.649391 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 2.23242 (* 1 = 2.23242 loss) +I0616 02:31:37.649395 9857 solver.cpp:571] Iteration 2020, lr = 0.001 +I0616 02:31:52.562801 9857 solver.cpp:242] Iteration 2040, loss = 1.97853 +I0616 02:31:52.562870 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.585753 (* 1 = 0.585753 loss) +I0616 02:31:52.562876 9857 solver.cpp:258] Train net output #1: loss_cls = 1.01664 (* 1 = 1.01664 loss) +I0616 02:31:52.562880 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.366723 (* 1 = 0.366723 loss) +I0616 02:31:52.562885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0498997 (* 1 = 0.0498997 loss) +I0616 02:31:52.562890 9857 solver.cpp:571] Iteration 2040, lr = 0.001 +I0616 02:32:09.601867 9857 solver.cpp:242] Iteration 2060, loss = 0.7148 +I0616 02:32:09.601917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409061 (* 1 = 0.409061 loss) +I0616 02:32:09.601925 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286505 (* 1 = 0.286505 loss) +I0616 02:32:09.601932 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142483 (* 1 = 0.142483 loss) +I0616 02:32:09.601938 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0516354 (* 1 = 0.0516354 loss) +I0616 02:32:09.601945 9857 solver.cpp:571] Iteration 2060, lr = 0.001 +I0616 02:32:25.317100 9857 solver.cpp:242] Iteration 2080, loss = 0.972383 +I0616 02:32:25.317162 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.243153 (* 1 = 0.243153 loss) +I0616 02:32:25.317173 9857 solver.cpp:258] Train net output #1: loss_cls = 0.621121 (* 1 = 0.621121 loss) +I0616 02:32:25.317178 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0708691 (* 1 = 0.0708691 loss) +I0616 02:32:25.317185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0523339 (* 1 = 0.0523339 loss) +I0616 02:32:25.317193 9857 solver.cpp:571] Iteration 2080, lr = 0.001 +I0616 02:32:40.358374 9857 solver.cpp:242] Iteration 2100, loss = 2.03987 +I0616 02:32:40.358425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.503369 (* 1 = 0.503369 loss) +I0616 02:32:40.358435 9857 solver.cpp:258] Train net output #1: loss_cls = 1.45115 (* 1 = 1.45115 loss) +I0616 02:32:40.358441 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.238318 (* 1 = 0.238318 loss) +I0616 02:32:40.358448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.146914 (* 1 = 0.146914 loss) +I0616 02:32:40.358456 9857 solver.cpp:571] Iteration 2100, lr = 0.001 +I0616 02:32:55.023092 9857 solver.cpp:242] Iteration 2120, loss = 1.34364 +I0616 02:32:55.023134 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447629 (* 1 = 0.447629 loss) +I0616 02:32:55.023140 9857 solver.cpp:258] Train net output #1: loss_cls = 0.538928 (* 1 = 0.538928 loss) +I0616 02:32:55.023144 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188297 (* 1 = 0.188297 loss) +I0616 02:32:55.023149 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0396499 (* 1 = 0.0396499 loss) +I0616 02:32:55.023152 9857 solver.cpp:571] Iteration 2120, lr = 0.001 +I0616 02:33:10.944416 9857 solver.cpp:242] Iteration 2140, loss = 0.800757 +I0616 02:33:10.944468 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.289509 (* 1 = 0.289509 loss) +I0616 02:33:10.944475 9857 solver.cpp:258] Train net output #1: loss_cls = 0.382396 (* 1 = 0.382396 loss) +I0616 02:33:10.944483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0558526 (* 1 = 0.0558526 loss) +I0616 02:33:10.944488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163958 (* 1 = 0.0163958 loss) +I0616 02:33:10.944495 9857 solver.cpp:571] Iteration 2140, lr = 0.001 +I0616 02:33:26.641800 9857 solver.cpp:242] Iteration 2160, loss = 1.777 +I0616 02:33:26.641845 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181671 (* 1 = 0.181671 loss) +I0616 02:33:26.641850 9857 solver.cpp:258] Train net output #1: loss_cls = 0.348594 (* 1 = 0.348594 loss) +I0616 02:33:26.641855 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.253057 (* 1 = 0.253057 loss) +I0616 02:33:26.641858 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161477 (* 1 = 0.0161477 loss) +I0616 02:33:26.641862 9857 solver.cpp:571] Iteration 2160, lr = 0.001 +I0616 02:33:41.342752 9857 solver.cpp:242] Iteration 2180, loss = 1.69979 +I0616 02:33:41.342800 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38887 (* 1 = 0.38887 loss) +I0616 02:33:41.342806 9857 solver.cpp:258] Train net output #1: loss_cls = 1.17751 (* 1 = 1.17751 loss) +I0616 02:33:41.342810 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.265793 (* 1 = 0.265793 loss) +I0616 02:33:41.342814 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111445 (* 1 = 0.0111445 loss) +I0616 02:33:41.342819 9857 solver.cpp:571] Iteration 2180, lr = 0.001 +speed: 0.767s / iter +I0616 02:33:57.205605 9857 solver.cpp:242] Iteration 2200, loss = 2.91099 +I0616 02:33:57.205660 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.644046 (* 1 = 0.644046 loss) +I0616 02:33:57.205670 9857 solver.cpp:258] Train net output #1: loss_cls = 2.10561 (* 1 = 2.10561 loss) +I0616 02:33:57.205693 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.203587 (* 1 = 0.203587 loss) +I0616 02:33:57.205699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116882 (* 1 = 0.0116882 loss) +I0616 02:33:57.205708 9857 solver.cpp:571] Iteration 2200, lr = 0.001 +I0616 02:34:10.817311 9857 solver.cpp:242] Iteration 2220, loss = 2.11371 +I0616 02:34:10.817354 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.415796 (* 1 = 0.415796 loss) +I0616 02:34:10.817359 9857 solver.cpp:258] Train net output #1: loss_cls = 1.29224 (* 1 = 1.29224 loss) +I0616 02:34:10.817363 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162258 (* 1 = 0.162258 loss) +I0616 02:34:10.817368 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00484482 (* 1 = 0.00484482 loss) +I0616 02:34:10.817371 9857 solver.cpp:571] Iteration 2220, lr = 0.001 +I0616 02:34:27.396708 9857 solver.cpp:242] Iteration 2240, loss = 1.65788 +I0616 02:34:27.396752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.598349 (* 1 = 0.598349 loss) +I0616 02:34:27.396757 9857 solver.cpp:258] Train net output #1: loss_cls = 1.51943 (* 1 = 1.51943 loss) +I0616 02:34:27.396761 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.491953 (* 1 = 0.491953 loss) +I0616 02:34:27.396765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0804792 (* 1 = 0.0804792 loss) +I0616 02:34:27.396785 9857 solver.cpp:571] Iteration 2240, lr = 0.001 +I0616 02:34:43.622148 9857 solver.cpp:242] Iteration 2260, loss = 1.6112 +I0616 02:34:43.622187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.495828 (* 1 = 0.495828 loss) +I0616 02:34:43.622194 9857 solver.cpp:258] Train net output #1: loss_cls = 1.29297 (* 1 = 1.29297 loss) +I0616 02:34:43.622197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.652971 (* 1 = 0.652971 loss) +I0616 02:34:43.622201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.278138 (* 1 = 0.278138 loss) +I0616 02:34:43.622205 9857 solver.cpp:571] Iteration 2260, lr = 0.001 +I0616 02:34:59.320647 9857 solver.cpp:242] Iteration 2280, loss = 2.033 +I0616 02:34:59.320693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411334 (* 1 = 0.411334 loss) +I0616 02:34:59.320699 9857 solver.cpp:258] Train net output #1: loss_cls = 1.04113 (* 1 = 1.04113 loss) +I0616 02:34:59.320703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.268865 (* 1 = 0.268865 loss) +I0616 02:34:59.320708 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0430156 (* 1 = 0.0430156 loss) +I0616 02:34:59.320714 9857 solver.cpp:571] Iteration 2280, lr = 0.001 +I0616 02:35:14.066692 9857 solver.cpp:242] Iteration 2300, loss = 0.666443 +I0616 02:35:14.066735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374304 (* 1 = 0.374304 loss) +I0616 02:35:14.066740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275629 (* 1 = 0.275629 loss) +I0616 02:35:14.066743 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0860967 (* 1 = 0.0860967 loss) +I0616 02:35:14.066747 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00318953 (* 1 = 0.00318953 loss) +I0616 02:35:14.066752 9857 solver.cpp:571] Iteration 2300, lr = 0.001 +I0616 02:35:30.935292 9857 solver.cpp:242] Iteration 2320, loss = 0.742182 +I0616 02:35:30.935345 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0225139 (* 1 = 0.0225139 loss) +I0616 02:35:30.935356 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426013 (* 1 = 0.426013 loss) +I0616 02:35:30.935364 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.252283 (* 1 = 0.252283 loss) +I0616 02:35:30.935371 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.20458 (* 1 = 0.20458 loss) +I0616 02:35:30.935379 9857 solver.cpp:571] Iteration 2320, lr = 0.001 +I0616 02:35:50.694813 9857 solver.cpp:242] Iteration 2340, loss = 1.85167 +I0616 02:35:50.694880 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179084 (* 1 = 0.179084 loss) +I0616 02:35:50.694890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399106 (* 1 = 0.399106 loss) +I0616 02:35:50.694896 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0578261 (* 1 = 0.0578261 loss) +I0616 02:35:50.694902 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113138 (* 1 = 0.0113138 loss) +I0616 02:35:50.694917 9857 solver.cpp:571] Iteration 2340, lr = 0.001 +I0616 02:36:04.527959 9857 solver.cpp:242] Iteration 2360, loss = 0.61571 +I0616 02:36:04.528019 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297499 (* 1 = 0.297499 loss) +I0616 02:36:04.528030 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315784 (* 1 = 0.315784 loss) +I0616 02:36:04.528053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122711 (* 1 = 0.122711 loss) +I0616 02:36:04.528059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.036041 (* 1 = 0.036041 loss) +I0616 02:36:04.528069 9857 solver.cpp:571] Iteration 2360, lr = 0.001 +I0616 02:36:19.487551 9857 solver.cpp:242] Iteration 2380, loss = 1.08993 +I0616 02:36:19.487598 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.405746 (* 1 = 0.405746 loss) +I0616 02:36:19.487606 9857 solver.cpp:258] Train net output #1: loss_cls = 1.06455 (* 1 = 1.06455 loss) +I0616 02:36:19.487609 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0713317 (* 1 = 0.0713317 loss) +I0616 02:36:19.487613 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.241118 (* 1 = 0.241118 loss) +I0616 02:36:19.487618 9857 solver.cpp:571] Iteration 2380, lr = 0.001 +speed: 0.768s / iter +I0616 02:36:33.015382 9857 solver.cpp:242] Iteration 2400, loss = 1.00102 +I0616 02:36:33.015426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.369713 (* 1 = 0.369713 loss) +I0616 02:36:33.015432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.2612 (* 1 = 0.2612 loss) +I0616 02:36:33.015436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137542 (* 1 = 0.137542 loss) +I0616 02:36:33.015441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0503871 (* 1 = 0.0503871 loss) +I0616 02:36:33.015445 9857 solver.cpp:571] Iteration 2400, lr = 0.001 +I0616 02:36:50.768234 9857 solver.cpp:242] Iteration 2420, loss = 0.863935 +I0616 02:36:50.768285 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17102 (* 1 = 0.17102 loss) +I0616 02:36:50.768293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.423612 (* 1 = 0.423612 loss) +I0616 02:36:50.768301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0611566 (* 1 = 0.0611566 loss) +I0616 02:36:50.768306 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234572 (* 1 = 0.0234572 loss) +I0616 02:36:50.768313 9857 solver.cpp:571] Iteration 2420, lr = 0.001 +I0616 02:37:07.862892 9857 solver.cpp:242] Iteration 2440, loss = 2.44328 +I0616 02:37:07.862941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333485 (* 1 = 0.333485 loss) +I0616 02:37:07.862951 9857 solver.cpp:258] Train net output #1: loss_cls = 1.13265 (* 1 = 1.13265 loss) +I0616 02:37:07.862972 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12657 (* 1 = 0.12657 loss) +I0616 02:37:07.862978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00828181 (* 1 = 0.00828181 loss) +I0616 02:37:07.862984 9857 solver.cpp:571] Iteration 2440, lr = 0.001 +I0616 02:37:23.834465 9857 solver.cpp:242] Iteration 2460, loss = 1.25151 +I0616 02:37:23.834523 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.356283 (* 1 = 0.356283 loss) +I0616 02:37:23.834533 9857 solver.cpp:258] Train net output #1: loss_cls = 0.810923 (* 1 = 0.810923 loss) +I0616 02:37:23.834542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0383314 (* 1 = 0.0383314 loss) +I0616 02:37:23.834552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00605745 (* 1 = 0.00605745 loss) +I0616 02:37:23.834559 9857 solver.cpp:571] Iteration 2460, lr = 0.001 +I0616 02:37:37.684908 9857 solver.cpp:242] Iteration 2480, loss = 2.59779 +I0616 02:37:37.684955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.572283 (* 1 = 0.572283 loss) +I0616 02:37:37.684962 9857 solver.cpp:258] Train net output #1: loss_cls = 1.81037 (* 1 = 1.81037 loss) +I0616 02:37:37.684965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 1.00855 (* 1 = 1.00855 loss) +I0616 02:37:37.684969 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0611568 (* 1 = 0.0611568 loss) +I0616 02:37:37.684974 9857 solver.cpp:571] Iteration 2480, lr = 0.001 +I0616 02:37:53.851670 9857 solver.cpp:242] Iteration 2500, loss = 1.11134 +I0616 02:37:53.851714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.549636 (* 1 = 0.549636 loss) +I0616 02:37:53.851721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.595079 (* 1 = 0.595079 loss) +I0616 02:37:53.851724 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.216231 (* 1 = 0.216231 loss) +I0616 02:37:53.851728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.197767 (* 1 = 0.197767 loss) +I0616 02:37:53.851732 9857 solver.cpp:571] Iteration 2500, lr = 0.001 +I0616 02:38:09.536509 9857 solver.cpp:242] Iteration 2520, loss = 1.7434 +I0616 02:38:09.536551 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264738 (* 1 = 0.264738 loss) +I0616 02:38:09.536558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.801664 (* 1 = 0.801664 loss) +I0616 02:38:09.536562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.202113 (* 1 = 0.202113 loss) +I0616 02:38:09.536566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230037 (* 1 = 0.0230037 loss) +I0616 02:38:09.536571 9857 solver.cpp:571] Iteration 2520, lr = 0.001 +I0616 02:38:26.010896 9857 solver.cpp:242] Iteration 2540, loss = 1.36618 +I0616 02:38:26.010941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285539 (* 1 = 0.285539 loss) +I0616 02:38:26.010946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.564203 (* 1 = 0.564203 loss) +I0616 02:38:26.010951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0450515 (* 1 = 0.0450515 loss) +I0616 02:38:26.010956 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00886215 (* 1 = 0.00886215 loss) +I0616 02:38:26.010959 9857 solver.cpp:571] Iteration 2540, lr = 0.001 +I0616 02:38:42.337318 9857 solver.cpp:242] Iteration 2560, loss = 1.3348 +I0616 02:38:42.337363 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327091 (* 1 = 0.327091 loss) +I0616 02:38:42.337369 9857 solver.cpp:258] Train net output #1: loss_cls = 0.778349 (* 1 = 0.778349 loss) +I0616 02:38:42.337374 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106046 (* 1 = 0.106046 loss) +I0616 02:38:42.337378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.098673 (* 1 = 0.098673 loss) +I0616 02:38:42.337383 9857 solver.cpp:571] Iteration 2560, lr = 0.001 +I0616 02:38:56.310613 9857 solver.cpp:242] Iteration 2580, loss = 1.02425 +I0616 02:38:56.310657 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237505 (* 1 = 0.237505 loss) +I0616 02:38:56.310663 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322914 (* 1 = 0.322914 loss) +I0616 02:38:56.310667 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.41376 (* 1 = 0.41376 loss) +I0616 02:38:56.310672 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.133399 (* 1 = 0.133399 loss) +I0616 02:38:56.310675 9857 solver.cpp:571] Iteration 2580, lr = 0.001 +speed: 0.769s / iter +I0616 02:39:10.830540 9857 solver.cpp:242] Iteration 2600, loss = 1.44426 +I0616 02:39:10.830585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0474327 (* 1 = 0.0474327 loss) +I0616 02:39:10.830591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.277611 (* 1 = 0.277611 loss) +I0616 02:39:10.830595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.21142 (* 1 = 0.21142 loss) +I0616 02:39:10.830600 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0324732 (* 1 = 0.0324732 loss) +I0616 02:39:10.830605 9857 solver.cpp:571] Iteration 2600, lr = 0.001 +I0616 02:39:25.629318 9857 solver.cpp:242] Iteration 2620, loss = 2.02165 +I0616 02:39:25.629369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.557867 (* 1 = 0.557867 loss) +I0616 02:39:25.629376 9857 solver.cpp:258] Train net output #1: loss_cls = 1.71317 (* 1 = 1.71317 loss) +I0616 02:39:25.629384 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.338475 (* 1 = 0.338475 loss) +I0616 02:39:25.629390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.471008 (* 1 = 0.471008 loss) +I0616 02:39:25.629396 9857 solver.cpp:571] Iteration 2620, lr = 0.001 +I0616 02:39:42.920634 9857 solver.cpp:242] Iteration 2640, loss = 1.23327 +I0616 02:39:42.920680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409946 (* 1 = 0.409946 loss) +I0616 02:39:42.920686 9857 solver.cpp:258] Train net output #1: loss_cls = 0.747966 (* 1 = 0.747966 loss) +I0616 02:39:42.920689 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130588 (* 1 = 0.130588 loss) +I0616 02:39:42.920693 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0581855 (* 1 = 0.0581855 loss) +I0616 02:39:42.920698 9857 solver.cpp:571] Iteration 2640, lr = 0.001 +I0616 02:39:57.877439 9857 solver.cpp:242] Iteration 2660, loss = 1.7609 +I0616 02:39:57.877483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.598843 (* 1 = 0.598843 loss) +I0616 02:39:57.877488 9857 solver.cpp:258] Train net output #1: loss_cls = 1.62759 (* 1 = 1.62759 loss) +I0616 02:39:57.877493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184863 (* 1 = 0.184863 loss) +I0616 02:39:57.877497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.021917 (* 1 = 0.021917 loss) +I0616 02:39:57.877501 9857 solver.cpp:571] Iteration 2660, lr = 0.001 +I0616 02:40:12.575286 9857 solver.cpp:242] Iteration 2680, loss = 0.850853 +I0616 02:40:12.575331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.417253 (* 1 = 0.417253 loss) +I0616 02:40:12.575337 9857 solver.cpp:258] Train net output #1: loss_cls = 0.949874 (* 1 = 0.949874 loss) +I0616 02:40:12.575341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0398721 (* 1 = 0.0398721 loss) +I0616 02:40:12.575345 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00801238 (* 1 = 0.00801238 loss) +I0616 02:40:12.575350 9857 solver.cpp:571] Iteration 2680, lr = 0.001 +I0616 02:40:26.781944 9857 solver.cpp:242] Iteration 2700, loss = 1.68126 +I0616 02:40:26.781990 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436742 (* 1 = 0.436742 loss) +I0616 02:40:26.781996 9857 solver.cpp:258] Train net output #1: loss_cls = 1.3541 (* 1 = 1.3541 loss) +I0616 02:40:26.782001 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.333007 (* 1 = 0.333007 loss) +I0616 02:40:26.782003 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0357871 (* 1 = 0.0357871 loss) +I0616 02:40:26.782008 9857 solver.cpp:571] Iteration 2700, lr = 0.001 +I0616 02:40:41.454912 9857 solver.cpp:242] Iteration 2720, loss = 1.23699 +I0616 02:40:41.454972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.456231 (* 1 = 0.456231 loss) +I0616 02:40:41.454982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.738389 (* 1 = 0.738389 loss) +I0616 02:40:41.454989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104485 (* 1 = 0.104485 loss) +I0616 02:40:41.454996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0361908 (* 1 = 0.0361908 loss) +I0616 02:40:41.455005 9857 solver.cpp:571] Iteration 2720, lr = 0.001 +I0616 02:40:56.397266 9857 solver.cpp:242] Iteration 2740, loss = 1.69562 +I0616 02:40:56.397310 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351886 (* 1 = 0.351886 loss) +I0616 02:40:56.397315 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15437 (* 1 = 1.15437 loss) +I0616 02:40:56.397320 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206943 (* 1 = 0.206943 loss) +I0616 02:40:56.397323 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010904 (* 1 = 0.010904 loss) +I0616 02:40:56.397328 9857 solver.cpp:571] Iteration 2740, lr = 0.001 +I0616 02:41:10.919096 9857 solver.cpp:242] Iteration 2760, loss = 1.35143 +I0616 02:41:10.919137 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.601429 (* 1 = 0.601429 loss) +I0616 02:41:10.919143 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00151 (* 1 = 1.00151 loss) +I0616 02:41:10.919147 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166652 (* 1 = 0.166652 loss) +I0616 02:41:10.919152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.148627 (* 1 = 0.148627 loss) +I0616 02:41:10.919157 9857 solver.cpp:571] Iteration 2760, lr = 0.001 +I0616 02:41:26.724654 9857 solver.cpp:242] Iteration 2780, loss = 1.60082 +I0616 02:41:26.724699 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.564406 (* 1 = 0.564406 loss) +I0616 02:41:26.724704 9857 solver.cpp:258] Train net output #1: loss_cls = 1.14676 (* 1 = 1.14676 loss) +I0616 02:41:26.724707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.236789 (* 1 = 0.236789 loss) +I0616 02:41:26.724711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0447454 (* 1 = 0.0447454 loss) +I0616 02:41:26.724715 9857 solver.cpp:571] Iteration 2780, lr = 0.001 +speed: 0.768s / iter +I0616 02:41:41.514204 9857 solver.cpp:242] Iteration 2800, loss = 0.931352 +I0616 02:41:41.514247 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203595 (* 1 = 0.203595 loss) +I0616 02:41:41.514252 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266991 (* 1 = 0.266991 loss) +I0616 02:41:41.514257 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0769062 (* 1 = 0.0769062 loss) +I0616 02:41:41.514261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150714 (* 1 = 0.0150714 loss) +I0616 02:41:41.514266 9857 solver.cpp:571] Iteration 2800, lr = 0.001 +I0616 02:41:57.595302 9857 solver.cpp:242] Iteration 2820, loss = 1.32216 +I0616 02:41:57.595350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.536574 (* 1 = 0.536574 loss) +I0616 02:41:57.595355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.895647 (* 1 = 0.895647 loss) +I0616 02:41:57.595360 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.324688 (* 1 = 0.324688 loss) +I0616 02:41:57.595362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.310882 (* 1 = 0.310882 loss) +I0616 02:41:57.595367 9857 solver.cpp:571] Iteration 2820, lr = 0.001 +I0616 02:42:12.091856 9857 solver.cpp:242] Iteration 2840, loss = 1.83333 +I0616 02:42:12.091905 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135084 (* 1 = 0.135084 loss) +I0616 02:42:12.091913 9857 solver.cpp:258] Train net output #1: loss_cls = 0.583048 (* 1 = 0.583048 loss) +I0616 02:42:12.091919 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.232196 (* 1 = 0.232196 loss) +I0616 02:42:12.091939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.160528 (* 1 = 0.160528 loss) +I0616 02:42:12.091944 9857 solver.cpp:571] Iteration 2840, lr = 0.001 +I0616 02:42:27.470731 9857 solver.cpp:242] Iteration 2860, loss = 1.41472 +I0616 02:42:27.470800 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.604186 (* 1 = 0.604186 loss) +I0616 02:42:27.470823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.779838 (* 1 = 0.779838 loss) +I0616 02:42:27.470829 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.293816 (* 1 = 0.293816 loss) +I0616 02:42:27.470834 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.475524 (* 1 = 0.475524 loss) +I0616 02:42:27.470854 9857 solver.cpp:571] Iteration 2860, lr = 0.001 +I0616 02:42:43.379719 9857 solver.cpp:242] Iteration 2880, loss = 0.621156 +I0616 02:42:43.379762 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202555 (* 1 = 0.202555 loss) +I0616 02:42:43.379767 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322297 (* 1 = 0.322297 loss) +I0616 02:42:43.379772 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0649018 (* 1 = 0.0649018 loss) +I0616 02:42:43.379776 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0639377 (* 1 = 0.0639377 loss) +I0616 02:42:43.379781 9857 solver.cpp:571] Iteration 2880, lr = 0.001 +I0616 02:42:57.135970 9857 solver.cpp:242] Iteration 2900, loss = 0.826548 +I0616 02:42:57.136019 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374157 (* 1 = 0.374157 loss) +I0616 02:42:57.136026 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306948 (* 1 = 0.306948 loss) +I0616 02:42:57.136032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145873 (* 1 = 0.145873 loss) +I0616 02:42:57.136037 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00710825 (* 1 = 0.00710825 loss) +I0616 02:42:57.136044 9857 solver.cpp:571] Iteration 2900, lr = 0.001 +I0616 02:43:11.716979 9857 solver.cpp:242] Iteration 2920, loss = 0.91621 +I0616 02:43:11.717031 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309368 (* 1 = 0.309368 loss) +I0616 02:43:11.717039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.786936 (* 1 = 0.786936 loss) +I0616 02:43:11.717046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0876437 (* 1 = 0.0876437 loss) +I0616 02:43:11.717052 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.043889 (* 1 = 0.043889 loss) +I0616 02:43:11.717061 9857 solver.cpp:571] Iteration 2920, lr = 0.001 +I0616 02:43:27.690559 9857 solver.cpp:242] Iteration 2940, loss = 2.11532 +I0616 02:43:27.690603 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.492012 (* 1 = 0.492012 loss) +I0616 02:43:27.690609 9857 solver.cpp:258] Train net output #1: loss_cls = 0.478652 (* 1 = 0.478652 loss) +I0616 02:43:27.690613 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220656 (* 1 = 0.220656 loss) +I0616 02:43:27.690618 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0510375 (* 1 = 0.0510375 loss) +I0616 02:43:27.690621 9857 solver.cpp:571] Iteration 2940, lr = 0.001 +I0616 02:43:44.784018 9857 solver.cpp:242] Iteration 2960, loss = 1.42121 +I0616 02:43:44.784061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185286 (* 1 = 0.185286 loss) +I0616 02:43:44.784067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.596881 (* 1 = 0.596881 loss) +I0616 02:43:44.784071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.531332 (* 1 = 0.531332 loss) +I0616 02:43:44.784075 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102256 (* 1 = 0.102256 loss) +I0616 02:43:44.784080 9857 solver.cpp:571] Iteration 2960, lr = 0.001 +I0616 02:43:58.564363 9857 solver.cpp:242] Iteration 2980, loss = 1.51966 +I0616 02:43:58.564419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386273 (* 1 = 0.386273 loss) +I0616 02:43:58.564430 9857 solver.cpp:258] Train net output #1: loss_cls = 0.356412 (* 1 = 0.356412 loss) +I0616 02:43:58.564437 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.198957 (* 1 = 0.198957 loss) +I0616 02:43:58.564445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246649 (* 1 = 0.0246649 loss) +I0616 02:43:58.564451 9857 solver.cpp:571] Iteration 2980, lr = 0.001 +speed: 0.768s / iter +I0616 02:44:14.279666 9857 solver.cpp:242] Iteration 3000, loss = 2.16917 +I0616 02:44:14.279711 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39913 (* 1 = 0.39913 loss) +I0616 02:44:14.279719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.958411 (* 1 = 0.958411 loss) +I0616 02:44:14.279726 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201289 (* 1 = 0.201289 loss) +I0616 02:44:14.279731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201666 (* 1 = 0.0201666 loss) +I0616 02:44:14.279741 9857 solver.cpp:571] Iteration 3000, lr = 0.001 +I0616 02:44:29.269798 9857 solver.cpp:242] Iteration 3020, loss = 2.04397 +I0616 02:44:29.269840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.627764 (* 1 = 0.627764 loss) +I0616 02:44:29.269847 9857 solver.cpp:258] Train net output #1: loss_cls = 1.35793 (* 1 = 1.35793 loss) +I0616 02:44:29.269851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.771534 (* 1 = 0.771534 loss) +I0616 02:44:29.269855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.147695 (* 1 = 0.147695 loss) +I0616 02:44:29.269860 9857 solver.cpp:571] Iteration 3020, lr = 0.001 +I0616 02:44:43.950644 9857 solver.cpp:242] Iteration 3040, loss = 1.32667 +I0616 02:44:43.950692 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.500801 (* 1 = 0.500801 loss) +I0616 02:44:43.950700 9857 solver.cpp:258] Train net output #1: loss_cls = 0.93861 (* 1 = 0.93861 loss) +I0616 02:44:43.950706 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0790481 (* 1 = 0.0790481 loss) +I0616 02:44:43.950712 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00160142 (* 1 = 0.00160142 loss) +I0616 02:44:43.950718 9857 solver.cpp:571] Iteration 3040, lr = 0.001 +I0616 02:44:59.842403 9857 solver.cpp:242] Iteration 3060, loss = 1.02589 +I0616 02:44:59.842447 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312927 (* 1 = 0.312927 loss) +I0616 02:44:59.842453 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394353 (* 1 = 0.394353 loss) +I0616 02:44:59.842458 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188285 (* 1 = 0.188285 loss) +I0616 02:44:59.842461 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0324415 (* 1 = 0.0324415 loss) +I0616 02:44:59.842465 9857 solver.cpp:571] Iteration 3060, lr = 0.001 +I0616 02:45:15.001579 9857 solver.cpp:242] Iteration 3080, loss = 1.97523 +I0616 02:45:15.001621 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.460206 (* 1 = 0.460206 loss) +I0616 02:45:15.001626 9857 solver.cpp:258] Train net output #1: loss_cls = 1.31883 (* 1 = 1.31883 loss) +I0616 02:45:15.001631 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.364812 (* 1 = 0.364812 loss) +I0616 02:45:15.001636 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0613095 (* 1 = 0.0613095 loss) +I0616 02:45:15.001639 9857 solver.cpp:571] Iteration 3080, lr = 0.001 +I0616 02:45:28.166075 9857 solver.cpp:242] Iteration 3100, loss = 2.40351 +I0616 02:45:28.166118 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.673045 (* 1 = 0.673045 loss) +I0616 02:45:28.166124 9857 solver.cpp:258] Train net output #1: loss_cls = 1.30835 (* 1 = 1.30835 loss) +I0616 02:45:28.166128 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145366 (* 1 = 0.145366 loss) +I0616 02:45:28.166131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0413973 (* 1 = 0.0413973 loss) +I0616 02:45:28.166136 9857 solver.cpp:571] Iteration 3100, lr = 0.001 +I0616 02:45:42.674314 9857 solver.cpp:242] Iteration 3120, loss = 1.43727 +I0616 02:45:42.674358 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111617 (* 1 = 0.111617 loss) +I0616 02:45:42.674365 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246975 (* 1 = 0.246975 loss) +I0616 02:45:42.674368 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.240637 (* 1 = 0.240637 loss) +I0616 02:45:42.674372 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.165057 (* 1 = 0.165057 loss) +I0616 02:45:42.674376 9857 solver.cpp:571] Iteration 3120, lr = 0.001 +I0616 02:46:01.055208 9857 solver.cpp:242] Iteration 3140, loss = 3.16676 +I0616 02:46:01.055253 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.549969 (* 1 = 0.549969 loss) +I0616 02:46:01.055259 9857 solver.cpp:258] Train net output #1: loss_cls = 1.52441 (* 1 = 1.52441 loss) +I0616 02:46:01.055263 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.609409 (* 1 = 0.609409 loss) +I0616 02:46:01.055268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.225977 (* 1 = 0.225977 loss) +I0616 02:46:01.055271 9857 solver.cpp:571] Iteration 3140, lr = 0.001 +I0616 02:46:16.633222 9857 solver.cpp:242] Iteration 3160, loss = 1.51416 +I0616 02:46:16.633266 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.49374 (* 1 = 0.49374 loss) +I0616 02:46:16.633271 9857 solver.cpp:258] Train net output #1: loss_cls = 0.96764 (* 1 = 0.96764 loss) +I0616 02:46:16.633275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.292385 (* 1 = 0.292385 loss) +I0616 02:46:16.633280 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.517396 (* 1 = 0.517396 loss) +I0616 02:46:16.633283 9857 solver.cpp:571] Iteration 3160, lr = 0.001 +I0616 02:46:32.553706 9857 solver.cpp:242] Iteration 3180, loss = 1.64972 +I0616 02:46:32.553752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.467404 (* 1 = 0.467404 loss) +I0616 02:46:32.553757 9857 solver.cpp:258] Train net output #1: loss_cls = 0.50892 (* 1 = 0.50892 loss) +I0616 02:46:32.553762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058717 (* 1 = 0.058717 loss) +I0616 02:46:32.553766 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407869 (* 1 = 0.0407869 loss) +I0616 02:46:32.553771 9857 solver.cpp:571] Iteration 3180, lr = 0.001 +speed: 0.768s / iter +I0616 02:46:48.490551 9857 solver.cpp:242] Iteration 3200, loss = 1.47307 +I0616 02:46:48.490597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.665303 (* 1 = 0.665303 loss) +I0616 02:46:48.490602 9857 solver.cpp:258] Train net output #1: loss_cls = 0.527447 (* 1 = 0.527447 loss) +I0616 02:46:48.490607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150771 (* 1 = 0.150771 loss) +I0616 02:46:48.490610 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0649879 (* 1 = 0.0649879 loss) +I0616 02:46:48.490615 9857 solver.cpp:571] Iteration 3200, lr = 0.001 +I0616 02:47:06.757473 9857 solver.cpp:242] Iteration 3220, loss = 0.947724 +I0616 02:47:06.757517 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260266 (* 1 = 0.260266 loss) +I0616 02:47:06.757522 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267845 (* 1 = 0.267845 loss) +I0616 02:47:06.757526 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0296022 (* 1 = 0.0296022 loss) +I0616 02:47:06.757530 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00466618 (* 1 = 0.00466618 loss) +I0616 02:47:06.757534 9857 solver.cpp:571] Iteration 3220, lr = 0.001 +I0616 02:47:22.780202 9857 solver.cpp:242] Iteration 3240, loss = 2.26658 +I0616 02:47:22.780247 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.672061 (* 1 = 0.672061 loss) +I0616 02:47:22.780253 9857 solver.cpp:258] Train net output #1: loss_cls = 1.7711 (* 1 = 1.7711 loss) +I0616 02:47:22.780257 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.266097 (* 1 = 0.266097 loss) +I0616 02:47:22.780261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0515598 (* 1 = 0.0515598 loss) +I0616 02:47:22.780267 9857 solver.cpp:571] Iteration 3240, lr = 0.001 +I0616 02:47:37.757166 9857 solver.cpp:242] Iteration 3260, loss = 1.50721 +I0616 02:47:37.757210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300091 (* 1 = 0.300091 loss) +I0616 02:47:37.757215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.607949 (* 1 = 0.607949 loss) +I0616 02:47:37.757220 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12234 (* 1 = 0.12234 loss) +I0616 02:47:37.757223 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189781 (* 1 = 0.0189781 loss) +I0616 02:47:37.757228 9857 solver.cpp:571] Iteration 3260, lr = 0.001 +I0616 02:47:52.420192 9857 solver.cpp:242] Iteration 3280, loss = 1.42173 +I0616 02:47:52.420236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.448124 (* 1 = 0.448124 loss) +I0616 02:47:52.420243 9857 solver.cpp:258] Train net output #1: loss_cls = 0.36279 (* 1 = 0.36279 loss) +I0616 02:47:52.420246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201095 (* 1 = 0.201095 loss) +I0616 02:47:52.420250 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.101797 (* 1 = 0.101797 loss) +I0616 02:47:52.420254 9857 solver.cpp:571] Iteration 3280, lr = 0.001 +I0616 02:48:09.376417 9857 solver.cpp:242] Iteration 3300, loss = 2.60696 +I0616 02:48:09.376466 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.576092 (* 1 = 0.576092 loss) +I0616 02:48:09.376473 9857 solver.cpp:258] Train net output #1: loss_cls = 1.95552 (* 1 = 1.95552 loss) +I0616 02:48:09.376479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168247 (* 1 = 0.168247 loss) +I0616 02:48:09.376484 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.043411 (* 1 = 0.043411 loss) +I0616 02:48:09.376493 9857 solver.cpp:571] Iteration 3300, lr = 0.001 +I0616 02:48:24.098701 9857 solver.cpp:242] Iteration 3320, loss = 1.43416 +I0616 02:48:24.098744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28215 (* 1 = 0.28215 loss) +I0616 02:48:24.098750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.719012 (* 1 = 0.719012 loss) +I0616 02:48:24.098755 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.462802 (* 1 = 0.462802 loss) +I0616 02:48:24.098762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.588283 (* 1 = 0.588283 loss) +I0616 02:48:24.098767 9857 solver.cpp:571] Iteration 3320, lr = 0.001 +I0616 02:48:38.734236 9857 solver.cpp:242] Iteration 3340, loss = 1.20783 +I0616 02:48:38.734302 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209662 (* 1 = 0.209662 loss) +I0616 02:48:38.734313 9857 solver.cpp:258] Train net output #1: loss_cls = 0.737118 (* 1 = 0.737118 loss) +I0616 02:48:38.734319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100706 (* 1 = 0.100706 loss) +I0616 02:48:38.734324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0396427 (* 1 = 0.0396427 loss) +I0616 02:48:38.734331 9857 solver.cpp:571] Iteration 3340, lr = 0.001 +I0616 02:48:53.716720 9857 solver.cpp:242] Iteration 3360, loss = 1.7131 +I0616 02:48:53.716765 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357136 (* 1 = 0.357136 loss) +I0616 02:48:53.716771 9857 solver.cpp:258] Train net output #1: loss_cls = 0.812225 (* 1 = 0.812225 loss) +I0616 02:48:53.716775 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0736437 (* 1 = 0.0736437 loss) +I0616 02:48:53.716779 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223784 (* 1 = 0.0223784 loss) +I0616 02:48:53.716784 9857 solver.cpp:571] Iteration 3360, lr = 0.001 +I0616 02:49:10.438136 9857 solver.cpp:242] Iteration 3380, loss = 1.34493 +I0616 02:49:10.438182 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359032 (* 1 = 0.359032 loss) +I0616 02:49:10.438187 9857 solver.cpp:258] Train net output #1: loss_cls = 0.879518 (* 1 = 0.879518 loss) +I0616 02:49:10.438191 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0556421 (* 1 = 0.0556421 loss) +I0616 02:49:10.438195 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.101098 (* 1 = 0.101098 loss) +I0616 02:49:10.438200 9857 solver.cpp:571] Iteration 3380, lr = 0.001 +speed: 0.769s / iter +I0616 02:49:25.155108 9857 solver.cpp:242] Iteration 3400, loss = 1.39797 +I0616 02:49:25.155153 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231446 (* 1 = 0.231446 loss) +I0616 02:49:25.155158 9857 solver.cpp:258] Train net output #1: loss_cls = 0.740357 (* 1 = 0.740357 loss) +I0616 02:49:25.155161 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146883 (* 1 = 0.146883 loss) +I0616 02:49:25.155165 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0137581 (* 1 = 0.0137581 loss) +I0616 02:49:25.155184 9857 solver.cpp:571] Iteration 3400, lr = 0.001 +I0616 02:49:38.836329 9857 solver.cpp:242] Iteration 3420, loss = 1.35055 +I0616 02:49:38.836361 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.58375 (* 1 = 0.58375 loss) +I0616 02:49:38.836367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.744671 (* 1 = 0.744671 loss) +I0616 02:49:38.836371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.240449 (* 1 = 0.240449 loss) +I0616 02:49:38.836376 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0970745 (* 1 = 0.0970745 loss) +I0616 02:49:38.836381 9857 solver.cpp:571] Iteration 3420, lr = 0.001 +I0616 02:49:55.863247 9857 solver.cpp:242] Iteration 3440, loss = 1.23521 +I0616 02:49:55.863292 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.362886 (* 1 = 0.362886 loss) +I0616 02:49:55.863299 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267216 (* 1 = 0.267216 loss) +I0616 02:49:55.863306 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0845489 (* 1 = 0.0845489 loss) +I0616 02:49:55.863312 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152987 (* 1 = 0.0152987 loss) +I0616 02:49:55.863317 9857 solver.cpp:571] Iteration 3440, lr = 0.001 +I0616 02:50:11.338814 9857 solver.cpp:242] Iteration 3460, loss = 2.04117 +I0616 02:50:11.338856 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.415977 (* 1 = 0.415977 loss) +I0616 02:50:11.338861 9857 solver.cpp:258] Train net output #1: loss_cls = 1.07679 (* 1 = 1.07679 loss) +I0616 02:50:11.338865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.366049 (* 1 = 0.366049 loss) +I0616 02:50:11.338870 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103553 (* 1 = 0.103553 loss) +I0616 02:50:11.338873 9857 solver.cpp:571] Iteration 3460, lr = 0.001 +I0616 02:50:24.768869 9857 solver.cpp:242] Iteration 3480, loss = 1.79392 +I0616 02:50:24.768914 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.378646 (* 1 = 0.378646 loss) +I0616 02:50:24.768919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.349883 (* 1 = 0.349883 loss) +I0616 02:50:24.768923 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102901 (* 1 = 0.102901 loss) +I0616 02:50:24.768930 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00851919 (* 1 = 0.00851919 loss) +I0616 02:50:24.768936 9857 solver.cpp:571] Iteration 3480, lr = 0.001 +I0616 02:50:38.832489 9857 solver.cpp:242] Iteration 3500, loss = 2.12509 +I0616 02:50:38.832536 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.556175 (* 1 = 0.556175 loss) +I0616 02:50:38.832545 9857 solver.cpp:258] Train net output #1: loss_cls = 1.21391 (* 1 = 1.21391 loss) +I0616 02:50:38.832551 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200608 (* 1 = 0.200608 loss) +I0616 02:50:38.832556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0548331 (* 1 = 0.0548331 loss) +I0616 02:50:38.832561 9857 solver.cpp:571] Iteration 3500, lr = 0.001 +I0616 02:50:53.767863 9857 solver.cpp:242] Iteration 3520, loss = 2.80867 +I0616 02:50:53.767911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.437763 (* 1 = 0.437763 loss) +I0616 02:50:53.767920 9857 solver.cpp:258] Train net output #1: loss_cls = 1.11832 (* 1 = 1.11832 loss) +I0616 02:50:53.767925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0329902 (* 1 = 0.0329902 loss) +I0616 02:50:53.767931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126261 (* 1 = 0.0126261 loss) +I0616 02:50:53.767937 9857 solver.cpp:571] Iteration 3520, lr = 0.001 +I0616 02:51:07.278478 9857 solver.cpp:242] Iteration 3540, loss = 1.40107 +I0616 02:51:07.278525 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.451817 (* 1 = 0.451817 loss) +I0616 02:51:07.278530 9857 solver.cpp:258] Train net output #1: loss_cls = 0.955562 (* 1 = 0.955562 loss) +I0616 02:51:07.278534 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.341083 (* 1 = 0.341083 loss) +I0616 02:51:07.278538 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0920491 (* 1 = 0.0920491 loss) +I0616 02:51:07.278542 9857 solver.cpp:571] Iteration 3540, lr = 0.001 +I0616 02:51:23.886678 9857 solver.cpp:242] Iteration 3560, loss = 1.49838 +I0616 02:51:23.886725 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.539361 (* 1 = 0.539361 loss) +I0616 02:51:23.886732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.989908 (* 1 = 0.989908 loss) +I0616 02:51:23.886739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109631 (* 1 = 0.109631 loss) +I0616 02:51:23.886744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0771188 (* 1 = 0.0771188 loss) +I0616 02:51:23.886749 9857 solver.cpp:571] Iteration 3560, lr = 0.001 +I0616 02:51:38.838999 9857 solver.cpp:242] Iteration 3580, loss = 0.664999 +I0616 02:51:38.839062 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.416861 (* 1 = 0.416861 loss) +I0616 02:51:38.839068 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286518 (* 1 = 0.286518 loss) +I0616 02:51:38.839072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0421277 (* 1 = 0.0421277 loss) +I0616 02:51:38.839076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0124822 (* 1 = 0.0124822 loss) +I0616 02:51:38.839082 9857 solver.cpp:571] Iteration 3580, lr = 0.001 +speed: 0.767s / iter +I0616 02:51:52.847757 9857 solver.cpp:242] Iteration 3600, loss = 2.01927 +I0616 02:51:52.847801 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230432 (* 1 = 0.230432 loss) +I0616 02:51:52.847806 9857 solver.cpp:258] Train net output #1: loss_cls = 0.501111 (* 1 = 0.501111 loss) +I0616 02:51:52.847811 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.237339 (* 1 = 0.237339 loss) +I0616 02:51:52.847815 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00279246 (* 1 = 0.00279246 loss) +I0616 02:51:52.847818 9857 solver.cpp:571] Iteration 3600, lr = 0.001 +I0616 02:52:08.286010 9857 solver.cpp:242] Iteration 3620, loss = 1.01051 +I0616 02:52:08.286070 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.500728 (* 1 = 0.500728 loss) +I0616 02:52:08.286080 9857 solver.cpp:258] Train net output #1: loss_cls = 0.649032 (* 1 = 0.649032 loss) +I0616 02:52:08.286087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.195801 (* 1 = 0.195801 loss) +I0616 02:52:08.286093 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0754173 (* 1 = 0.0754173 loss) +I0616 02:52:08.286103 9857 solver.cpp:571] Iteration 3620, lr = 0.001 +I0616 02:52:22.941804 9857 solver.cpp:242] Iteration 3640, loss = 1.77929 +I0616 02:52:22.941866 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.506098 (* 1 = 0.506098 loss) +I0616 02:52:22.941876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.618482 (* 1 = 0.618482 loss) +I0616 02:52:22.941884 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.374684 (* 1 = 0.374684 loss) +I0616 02:52:22.941890 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.37615 (* 1 = 0.37615 loss) +I0616 02:52:22.941900 9857 solver.cpp:571] Iteration 3640, lr = 0.001 +I0616 02:52:38.698700 9857 solver.cpp:242] Iteration 3660, loss = 0.722861 +I0616 02:52:38.698768 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141468 (* 1 = 0.141468 loss) +I0616 02:52:38.698778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.41879 (* 1 = 0.41879 loss) +I0616 02:52:38.698786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0900954 (* 1 = 0.0900954 loss) +I0616 02:52:38.698792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00768065 (* 1 = 0.00768065 loss) +I0616 02:52:38.698803 9857 solver.cpp:571] Iteration 3660, lr = 0.001 +I0616 02:52:53.827911 9857 solver.cpp:242] Iteration 3680, loss = 1.95057 +I0616 02:52:53.827972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392578 (* 1 = 0.392578 loss) +I0616 02:52:53.827982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.974045 (* 1 = 0.974045 loss) +I0616 02:52:53.827989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.170942 (* 1 = 0.170942 loss) +I0616 02:52:53.827996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.183085 (* 1 = 0.183085 loss) +I0616 02:52:53.828009 9857 solver.cpp:571] Iteration 3680, lr = 0.001 +I0616 02:53:08.417098 9857 solver.cpp:242] Iteration 3700, loss = 2.06005 +I0616 02:53:08.417158 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.530403 (* 1 = 0.530403 loss) +I0616 02:53:08.417168 9857 solver.cpp:258] Train net output #1: loss_cls = 1.39479 (* 1 = 1.39479 loss) +I0616 02:53:08.417176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.27164 (* 1 = 0.27164 loss) +I0616 02:53:08.417182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117488 (* 1 = 0.117488 loss) +I0616 02:53:08.417193 9857 solver.cpp:571] Iteration 3700, lr = 0.001 +I0616 02:53:22.020803 9857 solver.cpp:242] Iteration 3720, loss = 1.34765 +I0616 02:53:22.020850 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275517 (* 1 = 0.275517 loss) +I0616 02:53:22.020858 9857 solver.cpp:258] Train net output #1: loss_cls = 1.05512 (* 1 = 1.05512 loss) +I0616 02:53:22.020862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0369745 (* 1 = 0.0369745 loss) +I0616 02:53:22.020867 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0514235 (* 1 = 0.0514235 loss) +I0616 02:53:22.020872 9857 solver.cpp:571] Iteration 3720, lr = 0.001 +I0616 02:53:37.262904 9857 solver.cpp:242] Iteration 3740, loss = 1.33816 +I0616 02:53:37.262948 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.499585 (* 1 = 0.499585 loss) +I0616 02:53:37.262953 9857 solver.cpp:258] Train net output #1: loss_cls = 1.11842 (* 1 = 1.11842 loss) +I0616 02:53:37.262958 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.293627 (* 1 = 0.293627 loss) +I0616 02:53:37.262961 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.072024 (* 1 = 0.072024 loss) +I0616 02:53:37.262965 9857 solver.cpp:571] Iteration 3740, lr = 0.001 +I0616 02:53:50.323189 9857 solver.cpp:242] Iteration 3760, loss = 1.15402 +I0616 02:53:50.323235 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.426779 (* 1 = 0.426779 loss) +I0616 02:53:50.323240 9857 solver.cpp:258] Train net output #1: loss_cls = 0.838787 (* 1 = 0.838787 loss) +I0616 02:53:50.323245 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169828 (* 1 = 0.169828 loss) +I0616 02:53:50.323248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0633791 (* 1 = 0.0633791 loss) +I0616 02:53:50.323252 9857 solver.cpp:571] Iteration 3760, lr = 0.001 +I0616 02:54:05.143970 9857 solver.cpp:242] Iteration 3780, loss = 1.91352 +I0616 02:54:05.144027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22443 (* 1 = 0.22443 loss) +I0616 02:54:05.144037 9857 solver.cpp:258] Train net output #1: loss_cls = 0.273294 (* 1 = 0.273294 loss) +I0616 02:54:05.144044 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10159 (* 1 = 0.10159 loss) +I0616 02:54:05.144050 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00666187 (* 1 = 0.00666187 loss) +I0616 02:54:05.144058 9857 solver.cpp:571] Iteration 3780, lr = 0.001 +speed: 0.765s / iter +I0616 02:54:19.034677 9857 solver.cpp:242] Iteration 3800, loss = 1.09777 +I0616 02:54:19.034736 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.653614 (* 1 = 0.653614 loss) +I0616 02:54:19.034746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.929571 (* 1 = 0.929571 loss) +I0616 02:54:19.034754 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164739 (* 1 = 0.164739 loss) +I0616 02:54:19.034764 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199953 (* 1 = 0.0199953 loss) +I0616 02:54:19.034770 9857 solver.cpp:571] Iteration 3800, lr = 0.001 +I0616 02:54:31.012485 9857 solver.cpp:242] Iteration 3820, loss = 1.59546 +I0616 02:54:31.012547 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214827 (* 1 = 0.214827 loss) +I0616 02:54:31.012557 9857 solver.cpp:258] Train net output #1: loss_cls = 0.465595 (* 1 = 0.465595 loss) +I0616 02:54:31.012564 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.182054 (* 1 = 0.182054 loss) +I0616 02:54:31.012570 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0154091 (* 1 = 0.0154091 loss) +I0616 02:54:31.012578 9857 solver.cpp:571] Iteration 3820, lr = 0.001 +I0616 02:54:47.367713 9857 solver.cpp:242] Iteration 3840, loss = 0.690068 +I0616 02:54:47.367763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.544685 (* 1 = 0.544685 loss) +I0616 02:54:47.367770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23404 (* 1 = 0.23404 loss) +I0616 02:54:47.367776 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0424191 (* 1 = 0.0424191 loss) +I0616 02:54:47.367781 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103828 (* 1 = 0.0103828 loss) +I0616 02:54:47.367792 9857 solver.cpp:571] Iteration 3840, lr = 0.001 +I0616 02:55:00.927711 9857 solver.cpp:242] Iteration 3860, loss = 1.11698 +I0616 02:55:00.927772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.328758 (* 1 = 0.328758 loss) +I0616 02:55:00.927780 9857 solver.cpp:258] Train net output #1: loss_cls = 0.430004 (* 1 = 0.430004 loss) +I0616 02:55:00.927788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129796 (* 1 = 0.129796 loss) +I0616 02:55:00.927793 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0445054 (* 1 = 0.0445054 loss) +I0616 02:55:00.927804 9857 solver.cpp:571] Iteration 3860, lr = 0.001 +I0616 02:55:15.510329 9857 solver.cpp:242] Iteration 3880, loss = 0.611308 +I0616 02:55:15.510393 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182096 (* 1 = 0.182096 loss) +I0616 02:55:15.510402 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147919 (* 1 = 0.147919 loss) +I0616 02:55:15.510409 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0131344 (* 1 = 0.0131344 loss) +I0616 02:55:15.510416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0804248 (* 1 = 0.0804248 loss) +I0616 02:55:15.510424 9857 solver.cpp:571] Iteration 3880, lr = 0.001 +I0616 02:55:30.876278 9857 solver.cpp:242] Iteration 3900, loss = 0.641144 +I0616 02:55:30.876327 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22593 (* 1 = 0.22593 loss) +I0616 02:55:30.876333 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218941 (* 1 = 0.218941 loss) +I0616 02:55:30.876339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0293276 (* 1 = 0.0293276 loss) +I0616 02:55:30.876343 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0383057 (* 1 = 0.0383057 loss) +I0616 02:55:30.876348 9857 solver.cpp:571] Iteration 3900, lr = 0.001 +I0616 02:55:44.433342 9857 solver.cpp:242] Iteration 3920, loss = 2.09345 +I0616 02:55:44.433401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.40074 (* 1 = 0.40074 loss) +I0616 02:55:44.433410 9857 solver.cpp:258] Train net output #1: loss_cls = 0.71945 (* 1 = 0.71945 loss) +I0616 02:55:44.433418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.357906 (* 1 = 0.357906 loss) +I0616 02:55:44.433423 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.380192 (* 1 = 0.380192 loss) +I0616 02:55:44.433431 9857 solver.cpp:571] Iteration 3920, lr = 0.001 +I0616 02:55:59.543434 9857 solver.cpp:242] Iteration 3940, loss = 0.838558 +I0616 02:55:59.543480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.391517 (* 1 = 0.391517 loss) +I0616 02:55:59.543485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321817 (* 1 = 0.321817 loss) +I0616 02:55:59.543490 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0448952 (* 1 = 0.0448952 loss) +I0616 02:55:59.543494 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 6.06219e-05 (* 1 = 6.06219e-05 loss) +I0616 02:55:59.543498 9857 solver.cpp:571] Iteration 3940, lr = 0.001 +I0616 02:56:14.433804 9857 solver.cpp:242] Iteration 3960, loss = 2.08295 +I0616 02:56:14.433863 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.53984 (* 1 = 0.53984 loss) +I0616 02:56:14.433872 9857 solver.cpp:258] Train net output #1: loss_cls = 1.30536 (* 1 = 1.30536 loss) +I0616 02:56:14.433879 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103884 (* 1 = 0.103884 loss) +I0616 02:56:14.433887 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.052598 (* 1 = 0.052598 loss) +I0616 02:56:14.433894 9857 solver.cpp:571] Iteration 3960, lr = 0.001 +I0616 02:56:27.988663 9857 solver.cpp:242] Iteration 3980, loss = 1.69833 +I0616 02:56:27.988721 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.499208 (* 1 = 0.499208 loss) +I0616 02:56:27.988731 9857 solver.cpp:258] Train net output #1: loss_cls = 0.953095 (* 1 = 0.953095 loss) +I0616 02:56:27.988737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134865 (* 1 = 0.134865 loss) +I0616 02:56:27.988744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.19261 (* 1 = 0.19261 loss) +I0616 02:56:27.988754 9857 solver.cpp:571] Iteration 3980, lr = 0.001 +speed: 0.763s / iter +I0616 02:56:42.471760 9857 solver.cpp:242] Iteration 4000, loss = 1.35116 +I0616 02:56:42.471819 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.57924 (* 1 = 0.57924 loss) +I0616 02:56:42.471828 9857 solver.cpp:258] Train net output #1: loss_cls = 1.09444 (* 1 = 1.09444 loss) +I0616 02:56:42.471835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161193 (* 1 = 0.161193 loss) +I0616 02:56:42.471843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269709 (* 1 = 0.0269709 loss) +I0616 02:56:42.471851 9857 solver.cpp:571] Iteration 4000, lr = 0.001 +I0616 02:56:56.978458 9857 solver.cpp:242] Iteration 4020, loss = 1.12858 +I0616 02:56:56.978512 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0398315 (* 1 = 0.0398315 loss) +I0616 02:56:56.978520 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288962 (* 1 = 0.288962 loss) +I0616 02:56:56.978528 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215124 (* 1 = 0.215124 loss) +I0616 02:56:56.978531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033551 (* 1 = 0.033551 loss) +I0616 02:56:56.978536 9857 solver.cpp:571] Iteration 4020, lr = 0.001 +I0616 02:57:10.346112 9857 solver.cpp:242] Iteration 4040, loss = 1.47889 +I0616 02:57:10.346173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278971 (* 1 = 0.278971 loss) +I0616 02:57:10.346182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426484 (* 1 = 0.426484 loss) +I0616 02:57:10.346204 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0553646 (* 1 = 0.0553646 loss) +I0616 02:57:10.346210 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00772323 (* 1 = 0.00772323 loss) +I0616 02:57:10.346216 9857 solver.cpp:571] Iteration 4040, lr = 0.001 +I0616 02:57:24.808151 9857 solver.cpp:242] Iteration 4060, loss = 1.36484 +I0616 02:57:24.808213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421504 (* 1 = 0.421504 loss) +I0616 02:57:24.808223 9857 solver.cpp:258] Train net output #1: loss_cls = 1.04785 (* 1 = 1.04785 loss) +I0616 02:57:24.808230 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133857 (* 1 = 0.133857 loss) +I0616 02:57:24.808238 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.05516 (* 1 = 0.05516 loss) +I0616 02:57:24.808246 9857 solver.cpp:571] Iteration 4060, lr = 0.001 +I0616 02:57:37.837101 9857 solver.cpp:242] Iteration 4080, loss = 0.650951 +I0616 02:57:37.837149 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.344667 (* 1 = 0.344667 loss) +I0616 02:57:37.837157 9857 solver.cpp:258] Train net output #1: loss_cls = 0.390271 (* 1 = 0.390271 loss) +I0616 02:57:37.837162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0840054 (* 1 = 0.0840054 loss) +I0616 02:57:37.837167 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.183772 (* 1 = 0.183772 loss) +I0616 02:57:37.837172 9857 solver.cpp:571] Iteration 4080, lr = 0.001 +I0616 02:57:52.738466 9857 solver.cpp:242] Iteration 4100, loss = 0.87089 +I0616 02:57:52.738507 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.33406 (* 1 = 0.33406 loss) +I0616 02:57:52.738512 9857 solver.cpp:258] Train net output #1: loss_cls = 0.446422 (* 1 = 0.446422 loss) +I0616 02:57:52.738517 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142237 (* 1 = 0.142237 loss) +I0616 02:57:52.738520 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.1004 (* 1 = 0.1004 loss) +I0616 02:57:52.738524 9857 solver.cpp:571] Iteration 4100, lr = 0.001 +I0616 02:58:06.104169 9857 solver.cpp:242] Iteration 4120, loss = 0.730988 +I0616 02:58:06.104213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.219133 (* 1 = 0.219133 loss) +I0616 02:58:06.104218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498733 (* 1 = 0.498733 loss) +I0616 02:58:06.104223 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.207684 (* 1 = 0.207684 loss) +I0616 02:58:06.104226 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0258232 (* 1 = 0.0258232 loss) +I0616 02:58:06.104230 9857 solver.cpp:571] Iteration 4120, lr = 0.001 +I0616 02:58:21.283543 9857 solver.cpp:242] Iteration 4140, loss = 0.872902 +I0616 02:58:21.283586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0974219 (* 1 = 0.0974219 loss) +I0616 02:58:21.283591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163794 (* 1 = 0.163794 loss) +I0616 02:58:21.283596 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.046329 (* 1 = 0.046329 loss) +I0616 02:58:21.283599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152128 (* 1 = 0.0152128 loss) +I0616 02:58:21.283603 9857 solver.cpp:571] Iteration 4140, lr = 0.001 +I0616 02:58:36.944628 9857 solver.cpp:242] Iteration 4160, loss = 1.69763 +I0616 02:58:36.944674 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205829 (* 1 = 0.205829 loss) +I0616 02:58:36.944679 9857 solver.cpp:258] Train net output #1: loss_cls = 0.643401 (* 1 = 0.643401 loss) +I0616 02:58:36.944684 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0375948 (* 1 = 0.0375948 loss) +I0616 02:58:36.944687 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135688 (* 1 = 0.0135688 loss) +I0616 02:58:36.944691 9857 solver.cpp:571] Iteration 4160, lr = 0.001 +I0616 02:58:52.905649 9857 solver.cpp:242] Iteration 4180, loss = 1.46801 +I0616 02:58:52.905699 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45239 (* 1 = 0.45239 loss) +I0616 02:58:52.905707 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413696 (* 1 = 0.413696 loss) +I0616 02:58:52.905714 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0425458 (* 1 = 0.0425458 loss) +I0616 02:58:52.905719 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0040143 (* 1 = 0.0040143 loss) +I0616 02:58:52.905725 9857 solver.cpp:571] Iteration 4180, lr = 0.001 +speed: 0.761s / iter +I0616 02:59:08.291048 9857 solver.cpp:242] Iteration 4200, loss = 1.22436 +I0616 02:59:08.291090 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399746 (* 1 = 0.399746 loss) +I0616 02:59:08.291096 9857 solver.cpp:258] Train net output #1: loss_cls = 0.578591 (* 1 = 0.578591 loss) +I0616 02:59:08.291100 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.291384 (* 1 = 0.291384 loss) +I0616 02:59:08.291103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.362784 (* 1 = 0.362784 loss) +I0616 02:59:08.291107 9857 solver.cpp:571] Iteration 4200, lr = 0.001 +I0616 02:59:25.552744 9857 solver.cpp:242] Iteration 4220, loss = 1.36795 +I0616 02:59:25.552801 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.328962 (* 1 = 0.328962 loss) +I0616 02:59:25.552810 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562231 (* 1 = 0.562231 loss) +I0616 02:59:25.552817 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.04069 (* 1 = 0.04069 loss) +I0616 02:59:25.552824 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010503 (* 1 = 0.010503 loss) +I0616 02:59:25.552830 9857 solver.cpp:571] Iteration 4220, lr = 0.001 +I0616 02:59:40.008667 9857 solver.cpp:242] Iteration 4240, loss = 1.55707 +I0616 02:59:40.008711 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275504 (* 1 = 0.275504 loss) +I0616 02:59:40.008718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.572483 (* 1 = 0.572483 loss) +I0616 02:59:40.008721 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0373391 (* 1 = 0.0373391 loss) +I0616 02:59:40.008725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0349163 (* 1 = 0.0349163 loss) +I0616 02:59:40.008729 9857 solver.cpp:571] Iteration 4240, lr = 0.001 +I0616 02:59:56.115980 9857 solver.cpp:242] Iteration 4260, loss = 2.22419 +I0616 02:59:56.116029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.670034 (* 1 = 0.670034 loss) +I0616 02:59:56.116036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.802402 (* 1 = 0.802402 loss) +I0616 02:59:56.116042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.262425 (* 1 = 0.262425 loss) +I0616 02:59:56.116049 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0567362 (* 1 = 0.0567362 loss) +I0616 02:59:56.116057 9857 solver.cpp:571] Iteration 4260, lr = 0.001 +I0616 03:00:14.078779 9857 solver.cpp:242] Iteration 4280, loss = 1.39971 +I0616 03:00:14.078826 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298294 (* 1 = 0.298294 loss) +I0616 03:00:14.078836 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45163 (* 1 = 0.45163 loss) +I0616 03:00:14.078843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168941 (* 1 = 0.168941 loss) +I0616 03:00:14.078850 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0315089 (* 1 = 0.0315089 loss) +I0616 03:00:14.078856 9857 solver.cpp:571] Iteration 4280, lr = 0.001 +I0616 03:00:27.795591 9857 solver.cpp:242] Iteration 4300, loss = 1.61585 +I0616 03:00:27.795624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.527296 (* 1 = 0.527296 loss) +I0616 03:00:27.795632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.829596 (* 1 = 0.829596 loss) +I0616 03:00:27.795639 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162546 (* 1 = 0.162546 loss) +I0616 03:00:27.795644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0612183 (* 1 = 0.0612183 loss) +I0616 03:00:27.795651 9857 solver.cpp:571] Iteration 4300, lr = 0.001 +I0616 03:00:44.036425 9857 solver.cpp:242] Iteration 4320, loss = 1.20193 +I0616 03:00:44.036458 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.457291 (* 1 = 0.457291 loss) +I0616 03:00:44.036464 9857 solver.cpp:258] Train net output #1: loss_cls = 0.932689 (* 1 = 0.932689 loss) +I0616 03:00:44.036471 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210584 (* 1 = 0.210584 loss) +I0616 03:00:44.036478 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0632383 (* 1 = 0.0632383 loss) +I0616 03:00:44.036484 9857 solver.cpp:571] Iteration 4320, lr = 0.001 +I0616 03:01:01.584082 9857 solver.cpp:242] Iteration 4340, loss = 2.03228 +I0616 03:01:01.584113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.565742 (* 1 = 0.565742 loss) +I0616 03:01:01.584120 9857 solver.cpp:258] Train net output #1: loss_cls = 1.59229 (* 1 = 1.59229 loss) +I0616 03:01:01.584127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.069045 (* 1 = 0.069045 loss) +I0616 03:01:01.584133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162298 (* 1 = 0.162298 loss) +I0616 03:01:01.584141 9857 solver.cpp:571] Iteration 4340, lr = 0.001 +I0616 03:01:16.348178 9857 solver.cpp:242] Iteration 4360, loss = 0.908282 +I0616 03:01:16.348208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.582886 (* 1 = 0.582886 loss) +I0616 03:01:16.348217 9857 solver.cpp:258] Train net output #1: loss_cls = 0.583723 (* 1 = 0.583723 loss) +I0616 03:01:16.348223 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.095421 (* 1 = 0.095421 loss) +I0616 03:01:16.348229 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0263335 (* 1 = 0.0263335 loss) +I0616 03:01:16.348237 9857 solver.cpp:571] Iteration 4360, lr = 0.001 +I0616 03:01:32.590296 9857 solver.cpp:242] Iteration 4380, loss = 1.25037 +I0616 03:01:32.590323 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.433317 (* 1 = 0.433317 loss) +I0616 03:01:32.590332 9857 solver.cpp:258] Train net output #1: loss_cls = 0.778659 (* 1 = 0.778659 loss) +I0616 03:01:32.590337 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.223086 (* 1 = 0.223086 loss) +I0616 03:01:32.590348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.037652 (* 1 = 0.037652 loss) +I0616 03:01:32.590355 9857 solver.cpp:571] Iteration 4380, lr = 0.001 +speed: 0.763s / iter +I0616 03:01:48.776924 9857 solver.cpp:242] Iteration 4400, loss = 1.84566 +I0616 03:01:48.776988 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.425176 (* 1 = 0.425176 loss) +I0616 03:01:48.776998 9857 solver.cpp:258] Train net output #1: loss_cls = 1.2002 (* 1 = 1.2002 loss) +I0616 03:01:48.777004 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142318 (* 1 = 0.142318 loss) +I0616 03:01:48.777010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.051177 (* 1 = 0.051177 loss) +I0616 03:01:48.777019 9857 solver.cpp:571] Iteration 4400, lr = 0.001 +I0616 03:02:04.656819 9857 solver.cpp:242] Iteration 4420, loss = 2.74214 +I0616 03:02:04.656865 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.49392 (* 1 = 0.49392 loss) +I0616 03:02:04.656870 9857 solver.cpp:258] Train net output #1: loss_cls = 1.6551 (* 1 = 1.6551 loss) +I0616 03:02:04.656875 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0883595 (* 1 = 0.0883595 loss) +I0616 03:02:04.656878 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189358 (* 1 = 0.0189358 loss) +I0616 03:02:04.656883 9857 solver.cpp:571] Iteration 4420, lr = 0.001 +I0616 03:02:20.164819 9857 solver.cpp:242] Iteration 4440, loss = 1.10551 +I0616 03:02:20.164862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275077 (* 1 = 0.275077 loss) +I0616 03:02:20.164868 9857 solver.cpp:258] Train net output #1: loss_cls = 0.9335 (* 1 = 0.9335 loss) +I0616 03:02:20.164872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.29793 (* 1 = 0.29793 loss) +I0616 03:02:20.164876 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404164 (* 1 = 0.0404164 loss) +I0616 03:02:20.164880 9857 solver.cpp:571] Iteration 4440, lr = 0.001 +I0616 03:02:38.522860 9857 solver.cpp:242] Iteration 4460, loss = 1.02251 +I0616 03:02:38.522903 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359569 (* 1 = 0.359569 loss) +I0616 03:02:38.522909 9857 solver.cpp:258] Train net output #1: loss_cls = 0.380534 (* 1 = 0.380534 loss) +I0616 03:02:38.522913 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113727 (* 1 = 0.113727 loss) +I0616 03:02:38.522917 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0349013 (* 1 = 0.0349013 loss) +I0616 03:02:38.522922 9857 solver.cpp:571] Iteration 4460, lr = 0.001 +I0616 03:02:56.056809 9857 solver.cpp:242] Iteration 4480, loss = 2.32747 +I0616 03:02:56.056852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.583843 (* 1 = 0.583843 loss) +I0616 03:02:56.056859 9857 solver.cpp:258] Train net output #1: loss_cls = 0.614681 (* 1 = 0.614681 loss) +I0616 03:02:56.056862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138072 (* 1 = 0.138072 loss) +I0616 03:02:56.056866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207185 (* 1 = 0.0207185 loss) +I0616 03:02:56.056870 9857 solver.cpp:571] Iteration 4480, lr = 0.001 +I0616 03:03:08.833011 9857 solver.cpp:242] Iteration 4500, loss = 0.634075 +I0616 03:03:08.833055 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.407241 (* 1 = 0.407241 loss) +I0616 03:03:08.833060 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366264 (* 1 = 0.366264 loss) +I0616 03:03:08.833065 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0177046 (* 1 = 0.0177046 loss) +I0616 03:03:08.833068 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109039 (* 1 = 0.0109039 loss) +I0616 03:03:08.833072 9857 solver.cpp:571] Iteration 4500, lr = 0.001 +I0616 03:03:27.627200 9857 solver.cpp:242] Iteration 4520, loss = 1.89228 +I0616 03:03:27.627244 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.620026 (* 1 = 0.620026 loss) +I0616 03:03:27.627251 9857 solver.cpp:258] Train net output #1: loss_cls = 1.25477 (* 1 = 1.25477 loss) +I0616 03:03:27.627255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.361568 (* 1 = 0.361568 loss) +I0616 03:03:27.627259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.729749 (* 1 = 0.729749 loss) +I0616 03:03:27.627264 9857 solver.cpp:571] Iteration 4520, lr = 0.001 +I0616 03:03:44.739148 9857 solver.cpp:242] Iteration 4540, loss = 1.49124 +I0616 03:03:44.739189 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287173 (* 1 = 0.287173 loss) +I0616 03:03:44.739195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.383918 (* 1 = 0.383918 loss) +I0616 03:03:44.739199 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.234635 (* 1 = 0.234635 loss) +I0616 03:03:44.739203 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.147991 (* 1 = 0.147991 loss) +I0616 03:03:44.739207 9857 solver.cpp:571] Iteration 4540, lr = 0.001 +I0616 03:03:58.326692 9857 solver.cpp:242] Iteration 4560, loss = 1.54299 +I0616 03:03:58.326740 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.461173 (* 1 = 0.461173 loss) +I0616 03:03:58.326750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.396411 (* 1 = 0.396411 loss) +I0616 03:03:58.326774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186623 (* 1 = 0.186623 loss) +I0616 03:03:58.326791 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0265902 (* 1 = 0.0265902 loss) +I0616 03:03:58.326810 9857 solver.cpp:571] Iteration 4560, lr = 0.001 +I0616 03:04:14.426645 9857 solver.cpp:242] Iteration 4580, loss = 1.63389 +I0616 03:04:14.426676 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413151 (* 1 = 0.413151 loss) +I0616 03:04:14.426683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.932953 (* 1 = 0.932953 loss) +I0616 03:04:14.426703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.461221 (* 1 = 0.461221 loss) +I0616 03:04:14.426709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.726818 (* 1 = 0.726818 loss) +I0616 03:04:14.426715 9857 solver.cpp:571] Iteration 4580, lr = 0.001 +speed: 0.766s / iter +I0616 03:04:32.889544 9857 solver.cpp:242] Iteration 4600, loss = 1.63058 +I0616 03:04:32.889588 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401067 (* 1 = 0.401067 loss) +I0616 03:04:32.889595 9857 solver.cpp:258] Train net output #1: loss_cls = 1.01141 (* 1 = 1.01141 loss) +I0616 03:04:32.889598 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130056 (* 1 = 0.130056 loss) +I0616 03:04:32.889602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173648 (* 1 = 0.0173648 loss) +I0616 03:04:32.889607 9857 solver.cpp:571] Iteration 4600, lr = 0.001 +I0616 03:04:50.465529 9857 solver.cpp:242] Iteration 4620, loss = 1.81972 +I0616 03:04:50.465574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.700032 (* 1 = 0.700032 loss) +I0616 03:04:50.465579 9857 solver.cpp:258] Train net output #1: loss_cls = 1.56984 (* 1 = 1.56984 loss) +I0616 03:04:50.465584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104587 (* 1 = 0.104587 loss) +I0616 03:04:50.465587 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00647636 (* 1 = 0.00647636 loss) +I0616 03:04:50.465592 9857 solver.cpp:571] Iteration 4620, lr = 0.001 +I0616 03:05:07.485802 9857 solver.cpp:242] Iteration 4640, loss = 1.94969 +I0616 03:05:07.485848 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394505 (* 1 = 0.394505 loss) +I0616 03:05:07.485853 9857 solver.cpp:258] Train net output #1: loss_cls = 1.20254 (* 1 = 1.20254 loss) +I0616 03:05:07.485857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147732 (* 1 = 0.147732 loss) +I0616 03:05:07.485862 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133302 (* 1 = 0.0133302 loss) +I0616 03:05:07.485865 9857 solver.cpp:571] Iteration 4640, lr = 0.001 +I0616 03:05:22.307729 9857 solver.cpp:242] Iteration 4660, loss = 1.78551 +I0616 03:05:22.307770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.430256 (* 1 = 0.430256 loss) +I0616 03:05:22.307775 9857 solver.cpp:258] Train net output #1: loss_cls = 1.02876 (* 1 = 1.02876 loss) +I0616 03:05:22.307780 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.054758 (* 1 = 0.054758 loss) +I0616 03:05:22.307783 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116257 (* 1 = 0.0116257 loss) +I0616 03:05:22.307787 9857 solver.cpp:571] Iteration 4660, lr = 0.001 +I0616 03:05:39.361399 9857 solver.cpp:242] Iteration 4680, loss = 1.66825 +I0616 03:05:39.361443 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.584248 (* 1 = 0.584248 loss) +I0616 03:05:39.361449 9857 solver.cpp:258] Train net output #1: loss_cls = 1.49998 (* 1 = 1.49998 loss) +I0616 03:05:39.361454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.239772 (* 1 = 0.239772 loss) +I0616 03:05:39.361457 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0456979 (* 1 = 0.0456979 loss) +I0616 03:05:39.361461 9857 solver.cpp:571] Iteration 4680, lr = 0.001 +I0616 03:05:57.985091 9857 solver.cpp:242] Iteration 4700, loss = 1.2164 +I0616 03:05:57.985173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323431 (* 1 = 0.323431 loss) +I0616 03:05:57.985188 9857 solver.cpp:258] Train net output #1: loss_cls = 0.261053 (* 1 = 0.261053 loss) +I0616 03:05:57.985196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0226604 (* 1 = 0.0226604 loss) +I0616 03:05:57.985205 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0396094 (* 1 = 0.0396094 loss) +I0616 03:05:57.985214 9857 solver.cpp:571] Iteration 4700, lr = 0.001 +I0616 03:06:12.975785 9857 solver.cpp:242] Iteration 4720, loss = 0.564093 +I0616 03:06:12.975831 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111172 (* 1 = 0.111172 loss) +I0616 03:06:12.975836 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275069 (* 1 = 0.275069 loss) +I0616 03:06:12.975841 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186726 (* 1 = 0.186726 loss) +I0616 03:06:12.975844 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123472 (* 1 = 0.0123472 loss) +I0616 03:06:12.975849 9857 solver.cpp:571] Iteration 4720, lr = 0.001 +I0616 03:06:29.110599 9857 solver.cpp:242] Iteration 4740, loss = 2.15228 +I0616 03:06:29.110641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.681202 (* 1 = 0.681202 loss) +I0616 03:06:29.110646 9857 solver.cpp:258] Train net output #1: loss_cls = 1.72968 (* 1 = 1.72968 loss) +I0616 03:06:29.110651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114262 (* 1 = 0.114262 loss) +I0616 03:06:29.110656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159649 (* 1 = 0.0159649 loss) +I0616 03:06:29.110659 9857 solver.cpp:571] Iteration 4740, lr = 0.001 +I0616 03:06:48.007488 9857 solver.cpp:242] Iteration 4760, loss = 1.71146 +I0616 03:06:48.007531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.656255 (* 1 = 0.656255 loss) +I0616 03:06:48.007537 9857 solver.cpp:258] Train net output #1: loss_cls = 1.02088 (* 1 = 1.02088 loss) +I0616 03:06:48.007542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.182911 (* 1 = 0.182911 loss) +I0616 03:06:48.007546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.040334 (* 1 = 0.040334 loss) +I0616 03:06:48.007550 9857 solver.cpp:571] Iteration 4760, lr = 0.001 +I0616 03:07:01.563001 9857 solver.cpp:242] Iteration 4780, loss = 1.46481 +I0616 03:07:01.563045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.611742 (* 1 = 0.611742 loss) +I0616 03:07:01.563051 9857 solver.cpp:258] Train net output #1: loss_cls = 1.42749 (* 1 = 1.42749 loss) +I0616 03:07:01.563055 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0411324 (* 1 = 0.0411324 loss) +I0616 03:07:01.563060 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306186 (* 1 = 0.0306186 loss) +I0616 03:07:01.563063 9857 solver.cpp:571] Iteration 4780, lr = 0.001 +speed: 0.768s / iter +I0616 03:07:18.590260 9857 solver.cpp:242] Iteration 4800, loss = 1.63901 +I0616 03:07:18.590303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.076647 (* 1 = 0.076647 loss) +I0616 03:07:18.590309 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302352 (* 1 = 0.302352 loss) +I0616 03:07:18.590313 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.177896 (* 1 = 0.177896 loss) +I0616 03:07:18.590317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00246667 (* 1 = 0.00246667 loss) +I0616 03:07:18.590322 9857 solver.cpp:571] Iteration 4800, lr = 0.001 +I0616 03:07:34.499619 9857 solver.cpp:242] Iteration 4820, loss = 1.14419 +I0616 03:07:34.499663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.452562 (* 1 = 0.452562 loss) +I0616 03:07:34.499668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.686041 (* 1 = 0.686041 loss) +I0616 03:07:34.499672 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.257316 (* 1 = 0.257316 loss) +I0616 03:07:34.499676 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0821619 (* 1 = 0.0821619 loss) +I0616 03:07:34.499680 9857 solver.cpp:571] Iteration 4820, lr = 0.001 +I0616 03:07:50.973384 9857 solver.cpp:242] Iteration 4840, loss = 2.19736 +I0616 03:07:50.973428 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.442265 (* 1 = 0.442265 loss) +I0616 03:07:50.973433 9857 solver.cpp:258] Train net output #1: loss_cls = 0.782963 (* 1 = 0.782963 loss) +I0616 03:07:50.973436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.391527 (* 1 = 0.391527 loss) +I0616 03:07:50.973440 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.532135 (* 1 = 0.532135 loss) +I0616 03:07:50.973444 9857 solver.cpp:571] Iteration 4840, lr = 0.001 +I0616 03:08:09.234145 9857 solver.cpp:242] Iteration 4860, loss = 1.24158 +I0616 03:08:09.234190 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.650679 (* 1 = 0.650679 loss) +I0616 03:08:09.234196 9857 solver.cpp:258] Train net output #1: loss_cls = 0.485503 (* 1 = 0.485503 loss) +I0616 03:08:09.234200 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.29479 (* 1 = 0.29479 loss) +I0616 03:08:09.234205 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0677109 (* 1 = 0.0677109 loss) +I0616 03:08:09.234210 9857 solver.cpp:571] Iteration 4860, lr = 0.001 +I0616 03:08:25.232576 9857 solver.cpp:242] Iteration 4880, loss = 1.32643 +I0616 03:08:25.232620 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.456042 (* 1 = 0.456042 loss) +I0616 03:08:25.232625 9857 solver.cpp:258] Train net output #1: loss_cls = 0.882092 (* 1 = 0.882092 loss) +I0616 03:08:25.232630 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103684 (* 1 = 0.103684 loss) +I0616 03:08:25.232633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0424712 (* 1 = 0.0424712 loss) +I0616 03:08:25.232637 9857 solver.cpp:571] Iteration 4880, lr = 0.001 +I0616 03:08:39.877387 9857 solver.cpp:242] Iteration 4900, loss = 1.26697 +I0616 03:08:39.877432 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168942 (* 1 = 0.168942 loss) +I0616 03:08:39.877439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190779 (* 1 = 0.190779 loss) +I0616 03:08:39.877442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0756374 (* 1 = 0.0756374 loss) +I0616 03:08:39.877446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388262 (* 1 = 0.0388262 loss) +I0616 03:08:39.877450 9857 solver.cpp:571] Iteration 4900, lr = 0.001 +I0616 03:08:56.945327 9857 solver.cpp:242] Iteration 4920, loss = 1.71111 +I0616 03:08:56.945370 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.624739 (* 1 = 0.624739 loss) +I0616 03:08:56.945376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.941795 (* 1 = 0.941795 loss) +I0616 03:08:56.945380 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106387 (* 1 = 0.106387 loss) +I0616 03:08:56.945384 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0391997 (* 1 = 0.0391997 loss) +I0616 03:08:56.945389 9857 solver.cpp:571] Iteration 4920, lr = 0.001 +I0616 03:09:14.233227 9857 solver.cpp:242] Iteration 4940, loss = 2.22791 +I0616 03:09:14.233271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.668991 (* 1 = 0.668991 loss) +I0616 03:09:14.233276 9857 solver.cpp:258] Train net output #1: loss_cls = 2.02519 (* 1 = 2.02519 loss) +I0616 03:09:14.233280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141269 (* 1 = 0.141269 loss) +I0616 03:09:14.233284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0441673 (* 1 = 0.0441673 loss) +I0616 03:09:14.233289 9857 solver.cpp:571] Iteration 4940, lr = 0.001 +I0616 03:09:30.465742 9857 solver.cpp:242] Iteration 4960, loss = 1.16705 +I0616 03:09:30.465787 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.558016 (* 1 = 0.558016 loss) +I0616 03:09:30.465793 9857 solver.cpp:258] Train net output #1: loss_cls = 0.994485 (* 1 = 0.994485 loss) +I0616 03:09:30.465797 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206702 (* 1 = 0.206702 loss) +I0616 03:09:30.465801 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.076536 (* 1 = 0.076536 loss) +I0616 03:09:30.465806 9857 solver.cpp:571] Iteration 4960, lr = 0.001 +I0616 03:09:44.803617 9857 solver.cpp:242] Iteration 4980, loss = 1.77934 +I0616 03:09:44.803675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.758223 (* 1 = 0.758223 loss) +I0616 03:09:44.803685 9857 solver.cpp:258] Train net output #1: loss_cls = 1.88467 (* 1 = 1.88467 loss) +I0616 03:09:44.803694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249173 (* 1 = 0.249173 loss) +I0616 03:09:44.803704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0647345 (* 1 = 0.0647345 loss) +I0616 03:09:44.803712 9857 solver.cpp:571] Iteration 4980, lr = 0.001 +speed: 0.771s / iter +I0616 03:10:03.106614 9857 solver.cpp:242] Iteration 5000, loss = 2.62091 +I0616 03:10:03.106673 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.586969 (* 1 = 0.586969 loss) +I0616 03:10:03.106683 9857 solver.cpp:258] Train net output #1: loss_cls = 1.73618 (* 1 = 1.73618 loss) +I0616 03:10:03.106690 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.377056 (* 1 = 0.377056 loss) +I0616 03:10:03.106698 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0691901 (* 1 = 0.0691901 loss) +I0616 03:10:03.106708 9857 solver.cpp:571] Iteration 5000, lr = 0.001 +I0616 03:10:20.911274 9857 solver.cpp:242] Iteration 5020, loss = 1.77623 +I0616 03:10:20.911319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360133 (* 1 = 0.360133 loss) +I0616 03:10:20.911325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.594397 (* 1 = 0.594397 loss) +I0616 03:10:20.911330 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.497412 (* 1 = 0.497412 loss) +I0616 03:10:20.911334 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.33689 (* 1 = 0.33689 loss) +I0616 03:10:20.911340 9857 solver.cpp:571] Iteration 5020, lr = 0.001 +I0616 03:10:34.049196 9857 solver.cpp:242] Iteration 5040, loss = 1.37258 +I0616 03:10:34.049240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251617 (* 1 = 0.251617 loss) +I0616 03:10:34.049247 9857 solver.cpp:258] Train net output #1: loss_cls = 0.477785 (* 1 = 0.477785 loss) +I0616 03:10:34.049250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166639 (* 1 = 0.166639 loss) +I0616 03:10:34.049254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.237518 (* 1 = 0.237518 loss) +I0616 03:10:34.049258 9857 solver.cpp:571] Iteration 5040, lr = 0.001 +I0616 03:10:51.196332 9857 solver.cpp:242] Iteration 5060, loss = 0.729131 +I0616 03:10:51.196370 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360086 (* 1 = 0.360086 loss) +I0616 03:10:51.196378 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31199 (* 1 = 0.31199 loss) +I0616 03:10:51.196384 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0804771 (* 1 = 0.0804771 loss) +I0616 03:10:51.196390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252521 (* 1 = 0.0252521 loss) +I0616 03:10:51.196395 9857 solver.cpp:571] Iteration 5060, lr = 0.001 +I0616 03:11:06.061167 9857 solver.cpp:242] Iteration 5080, loss = 1.79785 +I0616 03:11:06.061210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.455697 (* 1 = 0.455697 loss) +I0616 03:11:06.061216 9857 solver.cpp:258] Train net output #1: loss_cls = 1.30976 (* 1 = 1.30976 loss) +I0616 03:11:06.061220 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.357447 (* 1 = 0.357447 loss) +I0616 03:11:06.061224 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.274407 (* 1 = 0.274407 loss) +I0616 03:11:06.061229 9857 solver.cpp:571] Iteration 5080, lr = 0.001 +I0616 03:11:22.248117 9857 solver.cpp:242] Iteration 5100, loss = 0.929574 +I0616 03:11:22.248164 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242613 (* 1 = 0.242613 loss) +I0616 03:11:22.248172 9857 solver.cpp:258] Train net output #1: loss_cls = 0.712766 (* 1 = 0.712766 loss) +I0616 03:11:22.248179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0621052 (* 1 = 0.0621052 loss) +I0616 03:11:22.248185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0089698 (* 1 = 0.0089698 loss) +I0616 03:11:22.248191 9857 solver.cpp:571] Iteration 5100, lr = 0.001 +I0616 03:11:38.134794 9857 solver.cpp:242] Iteration 5120, loss = 1.88475 +I0616 03:11:38.134837 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365945 (* 1 = 0.365945 loss) +I0616 03:11:38.134843 9857 solver.cpp:258] Train net output #1: loss_cls = 0.393096 (* 1 = 0.393096 loss) +I0616 03:11:38.134847 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.213609 (* 1 = 0.213609 loss) +I0616 03:11:38.134851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.120191 (* 1 = 0.120191 loss) +I0616 03:11:38.134855 9857 solver.cpp:571] Iteration 5120, lr = 0.001 +I0616 03:11:51.701189 9857 solver.cpp:242] Iteration 5140, loss = 1.68715 +I0616 03:11:51.701232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357295 (* 1 = 0.357295 loss) +I0616 03:11:51.701238 9857 solver.cpp:258] Train net output #1: loss_cls = 0.564682 (* 1 = 0.564682 loss) +I0616 03:11:51.701243 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0858618 (* 1 = 0.0858618 loss) +I0616 03:11:51.701246 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168859 (* 1 = 0.0168859 loss) +I0616 03:11:51.701251 9857 solver.cpp:571] Iteration 5140, lr = 0.001 +I0616 03:12:08.646039 9857 solver.cpp:242] Iteration 5160, loss = 1.10223 +I0616 03:12:08.646081 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386048 (* 1 = 0.386048 loss) +I0616 03:12:08.646087 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402371 (* 1 = 0.402371 loss) +I0616 03:12:08.646091 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110418 (* 1 = 0.110418 loss) +I0616 03:12:08.646095 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292553 (* 1 = 0.0292553 loss) +I0616 03:12:08.646100 9857 solver.cpp:571] Iteration 5160, lr = 0.001 +I0616 03:12:24.201768 9857 solver.cpp:242] Iteration 5180, loss = 2.4315 +I0616 03:12:24.201817 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.635419 (* 1 = 0.635419 loss) +I0616 03:12:24.201824 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00563 (* 1 = 1.00563 loss) +I0616 03:12:24.201830 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.362448 (* 1 = 0.362448 loss) +I0616 03:12:24.201836 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0682617 (* 1 = 0.0682617 loss) +I0616 03:12:24.201844 9857 solver.cpp:571] Iteration 5180, lr = 0.001 +speed: 0.771s / iter +I0616 03:12:39.295507 9857 solver.cpp:242] Iteration 5200, loss = 1.24029 +I0616 03:12:39.295559 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171104 (* 1 = 0.171104 loss) +I0616 03:12:39.295567 9857 solver.cpp:258] Train net output #1: loss_cls = 0.440264 (* 1 = 0.440264 loss) +I0616 03:12:39.295573 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0250114 (* 1 = 0.0250114 loss) +I0616 03:12:39.295593 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133437 (* 1 = 0.0133437 loss) +I0616 03:12:39.295600 9857 solver.cpp:571] Iteration 5200, lr = 0.001 +I0616 03:12:53.150185 9857 solver.cpp:242] Iteration 5220, loss = 0.621187 +I0616 03:12:53.150233 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22438 (* 1 = 0.22438 loss) +I0616 03:12:53.150241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.607794 (* 1 = 0.607794 loss) +I0616 03:12:53.150249 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0444838 (* 1 = 0.0444838 loss) +I0616 03:12:53.150254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00919781 (* 1 = 0.00919781 loss) +I0616 03:12:53.150264 9857 solver.cpp:571] Iteration 5220, lr = 0.001 +I0616 03:13:09.220986 9857 solver.cpp:242] Iteration 5240, loss = 1.60817 +I0616 03:13:09.221034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125353 (* 1 = 0.125353 loss) +I0616 03:13:09.221040 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147487 (* 1 = 0.147487 loss) +I0616 03:13:09.221045 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0539158 (* 1 = 0.0539158 loss) +I0616 03:13:09.221048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.17262 (* 1 = 0.17262 loss) +I0616 03:13:09.221055 9857 solver.cpp:571] Iteration 5240, lr = 0.001 +I0616 03:13:25.580690 9857 solver.cpp:242] Iteration 5260, loss = 1.20078 +I0616 03:13:25.580732 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.585499 (* 1 = 0.585499 loss) +I0616 03:13:25.580739 9857 solver.cpp:258] Train net output #1: loss_cls = 0.731481 (* 1 = 0.731481 loss) +I0616 03:13:25.580742 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157143 (* 1 = 0.157143 loss) +I0616 03:13:25.580746 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388082 (* 1 = 0.0388082 loss) +I0616 03:13:25.580750 9857 solver.cpp:571] Iteration 5260, lr = 0.001 +I0616 03:13:40.766001 9857 solver.cpp:242] Iteration 5280, loss = 2.09213 +I0616 03:13:40.766047 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.555965 (* 1 = 0.555965 loss) +I0616 03:13:40.766052 9857 solver.cpp:258] Train net output #1: loss_cls = 0.570651 (* 1 = 0.570651 loss) +I0616 03:13:40.766057 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.204699 (* 1 = 0.204699 loss) +I0616 03:13:40.766060 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0647414 (* 1 = 0.0647414 loss) +I0616 03:13:40.766064 9857 solver.cpp:571] Iteration 5280, lr = 0.001 +I0616 03:13:56.740270 9857 solver.cpp:242] Iteration 5300, loss = 1.41719 +I0616 03:13:56.740321 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.532035 (* 1 = 0.532035 loss) +I0616 03:13:56.740329 9857 solver.cpp:258] Train net output #1: loss_cls = 0.879288 (* 1 = 0.879288 loss) +I0616 03:13:56.740334 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.251086 (* 1 = 0.251086 loss) +I0616 03:13:56.740340 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.163988 (* 1 = 0.163988 loss) +I0616 03:13:56.740345 9857 solver.cpp:571] Iteration 5300, lr = 0.001 +I0616 03:14:11.155493 9857 solver.cpp:242] Iteration 5320, loss = 0.668716 +I0616 03:14:11.155539 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188626 (* 1 = 0.188626 loss) +I0616 03:14:11.155547 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235325 (* 1 = 0.235325 loss) +I0616 03:14:11.155553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0526259 (* 1 = 0.0526259 loss) +I0616 03:14:11.155560 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0401438 (* 1 = 0.0401438 loss) +I0616 03:14:11.155568 9857 solver.cpp:571] Iteration 5320, lr = 0.001 +I0616 03:14:27.624596 9857 solver.cpp:242] Iteration 5340, loss = 2.85552 +I0616 03:14:27.624656 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.563919 (* 1 = 0.563919 loss) +I0616 03:14:27.624666 9857 solver.cpp:258] Train net output #1: loss_cls = 1.43268 (* 1 = 1.43268 loss) +I0616 03:14:27.624689 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.548983 (* 1 = 0.548983 loss) +I0616 03:14:27.624696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0937944 (* 1 = 0.0937944 loss) +I0616 03:14:27.624706 9857 solver.cpp:571] Iteration 5340, lr = 0.001 +I0616 03:14:43.043267 9857 solver.cpp:242] Iteration 5360, loss = 1.08045 +I0616 03:14:43.043316 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232581 (* 1 = 0.232581 loss) +I0616 03:14:43.043325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.833207 (* 1 = 0.833207 loss) +I0616 03:14:43.043332 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0824393 (* 1 = 0.0824393 loss) +I0616 03:14:43.043339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.143535 (* 1 = 0.143535 loss) +I0616 03:14:43.043347 9857 solver.cpp:571] Iteration 5360, lr = 0.001 +I0616 03:14:56.537791 9857 solver.cpp:242] Iteration 5380, loss = 1.06675 +I0616 03:14:56.537833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447769 (* 1 = 0.447769 loss) +I0616 03:14:56.537839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.602161 (* 1 = 0.602161 loss) +I0616 03:14:56.537843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.063904 (* 1 = 0.063904 loss) +I0616 03:14:56.537848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0963475 (* 1 = 0.0963475 loss) +I0616 03:14:56.537853 9857 solver.cpp:571] Iteration 5380, lr = 0.001 +speed: 0.771s / iter +I0616 03:15:12.199072 9857 solver.cpp:242] Iteration 5400, loss = 2.35584 +I0616 03:15:12.199117 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.673476 (* 1 = 0.673476 loss) +I0616 03:15:12.199122 9857 solver.cpp:258] Train net output #1: loss_cls = 1.33678 (* 1 = 1.33678 loss) +I0616 03:15:12.199127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.598829 (* 1 = 0.598829 loss) +I0616 03:15:12.199131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.150795 (* 1 = 0.150795 loss) +I0616 03:15:12.199136 9857 solver.cpp:571] Iteration 5400, lr = 0.001 +I0616 03:15:27.053557 9857 solver.cpp:242] Iteration 5420, loss = 2.32624 +I0616 03:15:27.053608 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.586898 (* 1 = 0.586898 loss) +I0616 03:15:27.053617 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03512 (* 1 = 1.03512 loss) +I0616 03:15:27.053623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.80713 (* 1 = 0.80713 loss) +I0616 03:15:27.053629 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.692767 (* 1 = 0.692767 loss) +I0616 03:15:27.053635 9857 solver.cpp:571] Iteration 5420, lr = 0.001 +I0616 03:15:43.096958 9857 solver.cpp:242] Iteration 5440, loss = 1.0097 +I0616 03:15:43.097002 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294698 (* 1 = 0.294698 loss) +I0616 03:15:43.097008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.758334 (* 1 = 0.758334 loss) +I0616 03:15:43.097012 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149454 (* 1 = 0.149454 loss) +I0616 03:15:43.097017 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0652051 (* 1 = 0.0652051 loss) +I0616 03:15:43.097020 9857 solver.cpp:571] Iteration 5440, lr = 0.001 +I0616 03:15:57.499905 9857 solver.cpp:242] Iteration 5460, loss = 0.876068 +I0616 03:15:57.499948 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.485096 (* 1 = 0.485096 loss) +I0616 03:15:57.499954 9857 solver.cpp:258] Train net output #1: loss_cls = 0.612369 (* 1 = 0.612369 loss) +I0616 03:15:57.499958 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0767118 (* 1 = 0.0767118 loss) +I0616 03:15:57.499963 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198375 (* 1 = 0.0198375 loss) +I0616 03:15:57.499966 9857 solver.cpp:571] Iteration 5460, lr = 0.001 +I0616 03:16:11.302963 9857 solver.cpp:242] Iteration 5480, loss = 2.16605 +I0616 03:16:11.303014 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.496951 (* 1 = 0.496951 loss) +I0616 03:16:11.303023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.807216 (* 1 = 0.807216 loss) +I0616 03:16:11.303030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.398632 (* 1 = 0.398632 loss) +I0616 03:16:11.303035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.1843 (* 1 = 0.1843 loss) +I0616 03:16:11.303045 9857 solver.cpp:571] Iteration 5480, lr = 0.001 +I0616 03:16:26.155818 9857 solver.cpp:242] Iteration 5500, loss = 1.90418 +I0616 03:16:26.155863 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368585 (* 1 = 0.368585 loss) +I0616 03:16:26.155869 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280668 (* 1 = 0.280668 loss) +I0616 03:16:26.155872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126615 (* 1 = 0.126615 loss) +I0616 03:16:26.155876 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103063 (* 1 = 0.103063 loss) +I0616 03:16:26.155880 9857 solver.cpp:571] Iteration 5500, lr = 0.001 +I0616 03:16:42.503341 9857 solver.cpp:242] Iteration 5520, loss = 1.78429 +I0616 03:16:42.503391 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.494825 (* 1 = 0.494825 loss) +I0616 03:16:42.503397 9857 solver.cpp:258] Train net output #1: loss_cls = 1.40105 (* 1 = 1.40105 loss) +I0616 03:16:42.503401 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.231447 (* 1 = 0.231447 loss) +I0616 03:16:42.503406 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173948 (* 1 = 0.0173948 loss) +I0616 03:16:42.503409 9857 solver.cpp:571] Iteration 5520, lr = 0.001 +I0616 03:16:57.699034 9857 solver.cpp:242] Iteration 5540, loss = 1.60268 +I0616 03:16:57.699085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.328755 (* 1 = 0.328755 loss) +I0616 03:16:57.699093 9857 solver.cpp:258] Train net output #1: loss_cls = 0.518627 (* 1 = 0.518627 loss) +I0616 03:16:57.699100 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0751993 (* 1 = 0.0751993 loss) +I0616 03:16:57.699105 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10307 (* 1 = 0.10307 loss) +I0616 03:16:57.699112 9857 solver.cpp:571] Iteration 5540, lr = 0.001 +I0616 03:17:12.383410 9857 solver.cpp:242] Iteration 5560, loss = 0.829185 +I0616 03:17:12.383455 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358951 (* 1 = 0.358951 loss) +I0616 03:17:12.383460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.387668 (* 1 = 0.387668 loss) +I0616 03:17:12.383465 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114635 (* 1 = 0.114635 loss) +I0616 03:17:12.383468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306549 (* 1 = 0.0306549 loss) +I0616 03:17:12.383472 9857 solver.cpp:571] Iteration 5560, lr = 0.001 +I0616 03:17:27.347842 9857 solver.cpp:242] Iteration 5580, loss = 0.95199 +I0616 03:17:27.347885 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0892008 (* 1 = 0.0892008 loss) +I0616 03:17:27.347890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.330583 (* 1 = 0.330583 loss) +I0616 03:17:27.347895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103532 (* 1 = 0.103532 loss) +I0616 03:17:27.347899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.214006 (* 1 = 0.214006 loss) +I0616 03:17:27.347903 9857 solver.cpp:571] Iteration 5580, lr = 0.001 +speed: 0.770s / iter +I0616 03:17:44.380856 9857 solver.cpp:242] Iteration 5600, loss = 0.729905 +I0616 03:17:44.380913 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311679 (* 1 = 0.311679 loss) +I0616 03:17:44.380923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313525 (* 1 = 0.313525 loss) +I0616 03:17:44.380928 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149486 (* 1 = 0.149486 loss) +I0616 03:17:44.380934 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157589 (* 1 = 0.0157589 loss) +I0616 03:17:44.380941 9857 solver.cpp:571] Iteration 5600, lr = 0.001 +I0616 03:17:59.295099 9857 solver.cpp:242] Iteration 5620, loss = 1.58141 +I0616 03:17:59.295150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316228 (* 1 = 0.316228 loss) +I0616 03:17:59.295156 9857 solver.cpp:258] Train net output #1: loss_cls = 0.5267 (* 1 = 0.5267 loss) +I0616 03:17:59.295162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0777118 (* 1 = 0.0777118 loss) +I0616 03:17:59.295168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0567394 (* 1 = 0.0567394 loss) +I0616 03:17:59.295174 9857 solver.cpp:571] Iteration 5620, lr = 0.001 +I0616 03:18:14.541239 9857 solver.cpp:242] Iteration 5640, loss = 2.55726 +I0616 03:18:14.541285 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.448153 (* 1 = 0.448153 loss) +I0616 03:18:14.541293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.615017 (* 1 = 0.615017 loss) +I0616 03:18:14.541301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.427386 (* 1 = 0.427386 loss) +I0616 03:18:14.541306 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137745 (* 1 = 0.137745 loss) +I0616 03:18:14.541313 9857 solver.cpp:571] Iteration 5640, lr = 0.001 +I0616 03:18:27.739959 9857 solver.cpp:242] Iteration 5660, loss = 1.37318 +I0616 03:18:27.740000 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118945 (* 1 = 0.118945 loss) +I0616 03:18:27.740006 9857 solver.cpp:258] Train net output #1: loss_cls = 0.683997 (* 1 = 0.683997 loss) +I0616 03:18:27.740010 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.300576 (* 1 = 0.300576 loss) +I0616 03:18:27.740015 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0650235 (* 1 = 0.0650235 loss) +I0616 03:18:27.740018 9857 solver.cpp:571] Iteration 5660, lr = 0.001 +I0616 03:18:43.705896 9857 solver.cpp:242] Iteration 5680, loss = 1.85983 +I0616 03:18:43.705941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.570399 (* 1 = 0.570399 loss) +I0616 03:18:43.705948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.924027 (* 1 = 0.924027 loss) +I0616 03:18:43.705953 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.260363 (* 1 = 0.260363 loss) +I0616 03:18:43.705957 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0438337 (* 1 = 0.0438337 loss) +I0616 03:18:43.705961 9857 solver.cpp:571] Iteration 5680, lr = 0.001 +I0616 03:18:59.937299 9857 solver.cpp:242] Iteration 5700, loss = 1.42621 +I0616 03:18:59.937342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.468692 (* 1 = 0.468692 loss) +I0616 03:18:59.937348 9857 solver.cpp:258] Train net output #1: loss_cls = 0.919363 (* 1 = 0.919363 loss) +I0616 03:18:59.937352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120768 (* 1 = 0.120768 loss) +I0616 03:18:59.937356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0431226 (* 1 = 0.0431226 loss) +I0616 03:18:59.937361 9857 solver.cpp:571] Iteration 5700, lr = 0.001 +I0616 03:19:15.116731 9857 solver.cpp:242] Iteration 5720, loss = 1.05299 +I0616 03:19:15.116775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.26558 (* 1 = 0.26558 loss) +I0616 03:19:15.116781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.398504 (* 1 = 0.398504 loss) +I0616 03:19:15.116786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.345862 (* 1 = 0.345862 loss) +I0616 03:19:15.116789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.489013 (* 1 = 0.489013 loss) +I0616 03:19:15.116793 9857 solver.cpp:571] Iteration 5720, lr = 0.001 +I0616 03:19:28.237342 9857 solver.cpp:242] Iteration 5740, loss = 0.868314 +I0616 03:19:28.237392 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.624705 (* 1 = 0.624705 loss) +I0616 03:19:28.237401 9857 solver.cpp:258] Train net output #1: loss_cls = 0.398384 (* 1 = 0.398384 loss) +I0616 03:19:28.237406 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119565 (* 1 = 0.119565 loss) +I0616 03:19:28.237426 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104172 (* 1 = 0.0104172 loss) +I0616 03:19:28.237434 9857 solver.cpp:571] Iteration 5740, lr = 0.001 +I0616 03:19:43.039803 9857 solver.cpp:242] Iteration 5760, loss = 0.936344 +I0616 03:19:43.039854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.431838 (* 1 = 0.431838 loss) +I0616 03:19:43.039861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.361751 (* 1 = 0.361751 loss) +I0616 03:19:43.039867 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129964 (* 1 = 0.129964 loss) +I0616 03:19:43.039873 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251725 (* 1 = 0.0251725 loss) +I0616 03:19:43.039880 9857 solver.cpp:571] Iteration 5760, lr = 0.001 +I0616 03:19:59.387838 9857 solver.cpp:242] Iteration 5780, loss = 1.31513 +I0616 03:19:59.387887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.5002 (* 1 = 0.5002 loss) +I0616 03:19:59.387894 9857 solver.cpp:258] Train net output #1: loss_cls = 1.25391 (* 1 = 1.25391 loss) +I0616 03:19:59.387900 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183961 (* 1 = 0.183961 loss) +I0616 03:19:59.387907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00357646 (* 1 = 0.00357646 loss) +I0616 03:19:59.387912 9857 solver.cpp:571] Iteration 5780, lr = 0.001 +speed: 0.770s / iter +I0616 03:20:15.100071 9857 solver.cpp:242] Iteration 5800, loss = 1.61869 +I0616 03:20:15.100116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.570421 (* 1 = 0.570421 loss) +I0616 03:20:15.100121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.805543 (* 1 = 0.805543 loss) +I0616 03:20:15.100126 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.225843 (* 1 = 0.225843 loss) +I0616 03:20:15.100129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.633349 (* 1 = 0.633349 loss) +I0616 03:20:15.100133 9857 solver.cpp:571] Iteration 5800, lr = 0.001 +I0616 03:20:29.737800 9857 solver.cpp:242] Iteration 5820, loss = 1.51149 +I0616 03:20:29.737841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147498 (* 1 = 0.147498 loss) +I0616 03:20:29.737848 9857 solver.cpp:258] Train net output #1: loss_cls = 0.599428 (* 1 = 0.599428 loss) +I0616 03:20:29.737851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116676 (* 1 = 0.116676 loss) +I0616 03:20:29.737855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.055695 (* 1 = 0.055695 loss) +I0616 03:20:29.737859 9857 solver.cpp:571] Iteration 5820, lr = 0.001 +I0616 03:20:43.989298 9857 solver.cpp:242] Iteration 5840, loss = 1.33415 +I0616 03:20:43.989341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.540463 (* 1 = 0.540463 loss) +I0616 03:20:43.989346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.582629 (* 1 = 0.582629 loss) +I0616 03:20:43.989351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.23923 (* 1 = 0.23923 loss) +I0616 03:20:43.989354 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.638713 (* 1 = 0.638713 loss) +I0616 03:20:43.989359 9857 solver.cpp:571] Iteration 5840, lr = 0.001 +I0616 03:20:59.590034 9857 solver.cpp:242] Iteration 5860, loss = 0.631624 +I0616 03:20:59.590078 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149659 (* 1 = 0.149659 loss) +I0616 03:20:59.590085 9857 solver.cpp:258] Train net output #1: loss_cls = 0.220796 (* 1 = 0.220796 loss) +I0616 03:20:59.590088 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0453128 (* 1 = 0.0453128 loss) +I0616 03:20:59.590092 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00135776 (* 1 = 0.00135776 loss) +I0616 03:20:59.590096 9857 solver.cpp:571] Iteration 5860, lr = 0.001 +I0616 03:21:15.311172 9857 solver.cpp:242] Iteration 5880, loss = 1.22176 +I0616 03:21:15.311218 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.580267 (* 1 = 0.580267 loss) +I0616 03:21:15.311224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.936323 (* 1 = 0.936323 loss) +I0616 03:21:15.311228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.284598 (* 1 = 0.284598 loss) +I0616 03:21:15.311233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0908346 (* 1 = 0.0908346 loss) +I0616 03:21:15.311236 9857 solver.cpp:571] Iteration 5880, lr = 0.001 +I0616 03:21:31.051497 9857 solver.cpp:242] Iteration 5900, loss = 1.58015 +I0616 03:21:31.051542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275764 (* 1 = 0.275764 loss) +I0616 03:21:31.051547 9857 solver.cpp:258] Train net output #1: loss_cls = 0.850593 (* 1 = 0.850593 loss) +I0616 03:21:31.051551 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137985 (* 1 = 0.137985 loss) +I0616 03:21:31.051555 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169109 (* 1 = 0.0169109 loss) +I0616 03:21:31.051559 9857 solver.cpp:571] Iteration 5900, lr = 0.001 +I0616 03:21:46.012056 9857 solver.cpp:242] Iteration 5920, loss = 1.045 +I0616 03:21:46.012096 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367917 (* 1 = 0.367917 loss) +I0616 03:21:46.012101 9857 solver.cpp:258] Train net output #1: loss_cls = 0.619391 (* 1 = 0.619391 loss) +I0616 03:21:46.012106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.174853 (* 1 = 0.174853 loss) +I0616 03:21:46.012110 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.281362 (* 1 = 0.281362 loss) +I0616 03:21:46.012115 9857 solver.cpp:571] Iteration 5920, lr = 0.001 +I0616 03:22:04.726660 9857 solver.cpp:242] Iteration 5940, loss = 1.53829 +I0616 03:22:04.726706 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296151 (* 1 = 0.296151 loss) +I0616 03:22:04.726711 9857 solver.cpp:258] Train net output #1: loss_cls = 0.793883 (* 1 = 0.793883 loss) +I0616 03:22:04.726716 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0669474 (* 1 = 0.0669474 loss) +I0616 03:22:04.726719 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255525 (* 1 = 0.0255525 loss) +I0616 03:22:04.726723 9857 solver.cpp:571] Iteration 5940, lr = 0.001 +I0616 03:22:22.818390 9857 solver.cpp:242] Iteration 5960, loss = 1.44636 +I0616 03:22:22.818440 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135542 (* 1 = 0.135542 loss) +I0616 03:22:22.818449 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310067 (* 1 = 0.310067 loss) +I0616 03:22:22.818454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.301391 (* 1 = 0.301391 loss) +I0616 03:22:22.818459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.290231 (* 1 = 0.290231 loss) +I0616 03:22:22.818467 9857 solver.cpp:571] Iteration 5960, lr = 0.001 +I0616 03:22:36.415021 9857 solver.cpp:242] Iteration 5980, loss = 1.96149 +I0616 03:22:36.415069 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.530021 (* 1 = 0.530021 loss) +I0616 03:22:36.415077 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10158 (* 1 = 1.10158 loss) +I0616 03:22:36.415083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117034 (* 1 = 0.117034 loss) +I0616 03:22:36.415089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283393 (* 1 = 0.0283393 loss) +I0616 03:22:36.415096 9857 solver.cpp:571] Iteration 5980, lr = 0.001 +speed: 0.770s / iter +I0616 03:22:50.752455 9857 solver.cpp:242] Iteration 6000, loss = 1.4634 +I0616 03:22:50.752497 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.468006 (* 1 = 0.468006 loss) +I0616 03:22:50.752503 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03698 (* 1 = 1.03698 loss) +I0616 03:22:50.752507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19634 (* 1 = 0.19634 loss) +I0616 03:22:50.752511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.105831 (* 1 = 0.105831 loss) +I0616 03:22:50.752516 9857 solver.cpp:571] Iteration 6000, lr = 0.001 +I0616 03:23:06.153095 9857 solver.cpp:242] Iteration 6020, loss = 1.84741 +I0616 03:23:06.153146 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153956 (* 1 = 0.153956 loss) +I0616 03:23:06.153152 9857 solver.cpp:258] Train net output #1: loss_cls = 0.561066 (* 1 = 0.561066 loss) +I0616 03:23:06.153156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151916 (* 1 = 0.151916 loss) +I0616 03:23:06.153161 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109982 (* 1 = 0.0109982 loss) +I0616 03:23:06.153164 9857 solver.cpp:571] Iteration 6020, lr = 0.001 +I0616 03:23:22.005944 9857 solver.cpp:242] Iteration 6040, loss = 2.19602 +I0616 03:23:22.005993 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.512948 (* 1 = 0.512948 loss) +I0616 03:23:22.006000 9857 solver.cpp:258] Train net output #1: loss_cls = 1.459 (* 1 = 1.459 loss) +I0616 03:23:22.006006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.721851 (* 1 = 0.721851 loss) +I0616 03:23:22.006012 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.437942 (* 1 = 0.437942 loss) +I0616 03:23:22.006019 9857 solver.cpp:571] Iteration 6040, lr = 0.001 +I0616 03:23:37.602587 9857 solver.cpp:242] Iteration 6060, loss = 0.663694 +I0616 03:23:37.602635 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2671 (* 1 = 0.2671 loss) +I0616 03:23:37.602641 9857 solver.cpp:258] Train net output #1: loss_cls = 0.572564 (* 1 = 0.572564 loss) +I0616 03:23:37.602646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0448834 (* 1 = 0.0448834 loss) +I0616 03:23:37.602649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0315091 (* 1 = 0.0315091 loss) +I0616 03:23:37.602654 9857 solver.cpp:571] Iteration 6060, lr = 0.001 +I0616 03:23:51.544612 9857 solver.cpp:242] Iteration 6080, loss = 1.42991 +I0616 03:23:51.544656 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329719 (* 1 = 0.329719 loss) +I0616 03:23:51.544662 9857 solver.cpp:258] Train net output #1: loss_cls = 0.408779 (* 1 = 0.408779 loss) +I0616 03:23:51.544667 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0827428 (* 1 = 0.0827428 loss) +I0616 03:23:51.544672 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184403 (* 1 = 0.0184403 loss) +I0616 03:23:51.544675 9857 solver.cpp:571] Iteration 6080, lr = 0.001 +I0616 03:24:06.908221 9857 solver.cpp:242] Iteration 6100, loss = 1.35713 +I0616 03:24:06.908269 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.451511 (* 1 = 0.451511 loss) +I0616 03:24:06.908277 9857 solver.cpp:258] Train net output #1: loss_cls = 0.470774 (* 1 = 0.470774 loss) +I0616 03:24:06.908284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.24529 (* 1 = 0.24529 loss) +I0616 03:24:06.908290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0729417 (* 1 = 0.0729417 loss) +I0616 03:24:06.908296 9857 solver.cpp:571] Iteration 6100, lr = 0.001 +I0616 03:24:21.671896 9857 solver.cpp:242] Iteration 6120, loss = 1.35423 +I0616 03:24:21.671939 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30827 (* 1 = 0.30827 loss) +I0616 03:24:21.671946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.393595 (* 1 = 0.393595 loss) +I0616 03:24:21.671949 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.292909 (* 1 = 0.292909 loss) +I0616 03:24:21.671953 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.196223 (* 1 = 0.196223 loss) +I0616 03:24:21.671957 9857 solver.cpp:571] Iteration 6120, lr = 0.001 +I0616 03:24:37.631654 9857 solver.cpp:242] Iteration 6140, loss = 1.3305 +I0616 03:24:37.631697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396368 (* 1 = 0.396368 loss) +I0616 03:24:37.631703 9857 solver.cpp:258] Train net output #1: loss_cls = 0.589719 (* 1 = 0.589719 loss) +I0616 03:24:37.631707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0972543 (* 1 = 0.0972543 loss) +I0616 03:24:37.631711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106571 (* 1 = 0.0106571 loss) +I0616 03:24:37.631716 9857 solver.cpp:571] Iteration 6140, lr = 0.001 +I0616 03:24:53.568418 9857 solver.cpp:242] Iteration 6160, loss = 1.8791 +I0616 03:24:53.568459 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.412274 (* 1 = 0.412274 loss) +I0616 03:24:53.568465 9857 solver.cpp:258] Train net output #1: loss_cls = 0.666251 (* 1 = 0.666251 loss) +I0616 03:24:53.568470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206773 (* 1 = 0.206773 loss) +I0616 03:24:53.568473 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167314 (* 1 = 0.0167314 loss) +I0616 03:24:53.568477 9857 solver.cpp:571] Iteration 6160, lr = 0.001 +I0616 03:25:08.593508 9857 solver.cpp:242] Iteration 6180, loss = 0.512113 +I0616 03:25:08.593554 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222961 (* 1 = 0.222961 loss) +I0616 03:25:08.593562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404519 (* 1 = 0.404519 loss) +I0616 03:25:08.593570 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0600524 (* 1 = 0.0600524 loss) +I0616 03:25:08.593580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194276 (* 1 = 0.0194276 loss) +I0616 03:25:08.593586 9857 solver.cpp:571] Iteration 6180, lr = 0.001 +speed: 0.770s / iter +I0616 03:25:24.756840 9857 solver.cpp:242] Iteration 6200, loss = 0.794806 +I0616 03:25:24.756883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161458 (* 1 = 0.161458 loss) +I0616 03:25:24.756891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.813195 (* 1 = 0.813195 loss) +I0616 03:25:24.756894 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0875788 (* 1 = 0.0875788 loss) +I0616 03:25:24.756898 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00598453 (* 1 = 0.00598453 loss) +I0616 03:25:24.756902 9857 solver.cpp:571] Iteration 6200, lr = 0.001 +I0616 03:25:38.453310 9857 solver.cpp:242] Iteration 6220, loss = 0.918775 +I0616 03:25:38.453354 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232408 (* 1 = 0.232408 loss) +I0616 03:25:38.453361 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38949 (* 1 = 0.38949 loss) +I0616 03:25:38.453366 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108613 (* 1 = 0.108613 loss) +I0616 03:25:38.453369 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00238421 (* 1 = 0.00238421 loss) +I0616 03:25:38.453373 9857 solver.cpp:571] Iteration 6220, lr = 0.001 +I0616 03:25:53.059624 9857 solver.cpp:242] Iteration 6240, loss = 0.853724 +I0616 03:25:53.059675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.245411 (* 1 = 0.245411 loss) +I0616 03:25:53.059684 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370964 (* 1 = 0.370964 loss) +I0616 03:25:53.059689 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0641167 (* 1 = 0.0641167 loss) +I0616 03:25:53.059695 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00533862 (* 1 = 0.00533862 loss) +I0616 03:25:53.059702 9857 solver.cpp:571] Iteration 6240, lr = 0.001 +I0616 03:26:09.388025 9857 solver.cpp:242] Iteration 6260, loss = 1.36613 +I0616 03:26:09.388070 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.5728 (* 1 = 0.5728 loss) +I0616 03:26:09.388075 9857 solver.cpp:258] Train net output #1: loss_cls = 1.19165 (* 1 = 1.19165 loss) +I0616 03:26:09.388080 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14953 (* 1 = 0.14953 loss) +I0616 03:26:09.388084 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030569 (* 1 = 0.030569 loss) +I0616 03:26:09.388088 9857 solver.cpp:571] Iteration 6260, lr = 0.001 +I0616 03:26:26.638697 9857 solver.cpp:242] Iteration 6280, loss = 0.685869 +I0616 03:26:26.638751 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189299 (* 1 = 0.189299 loss) +I0616 03:26:26.638802 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292171 (* 1 = 0.292171 loss) +I0616 03:26:26.638811 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.068915 (* 1 = 0.068915 loss) +I0616 03:26:26.638820 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.138437 (* 1 = 0.138437 loss) +I0616 03:26:26.638840 9857 solver.cpp:571] Iteration 6280, lr = 0.001 +I0616 03:26:40.254922 9857 solver.cpp:242] Iteration 6300, loss = 1.27167 +I0616 03:26:40.254966 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.56785 (* 1 = 0.56785 loss) +I0616 03:26:40.254972 9857 solver.cpp:258] Train net output #1: loss_cls = 1.09351 (* 1 = 1.09351 loss) +I0616 03:26:40.254976 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.099703 (* 1 = 0.099703 loss) +I0616 03:26:40.254981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.022664 (* 1 = 0.022664 loss) +I0616 03:26:40.254984 9857 solver.cpp:571] Iteration 6300, lr = 0.001 +I0616 03:26:55.620082 9857 solver.cpp:242] Iteration 6320, loss = 1.2259 +I0616 03:26:55.620132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379852 (* 1 = 0.379852 loss) +I0616 03:26:55.620141 9857 solver.cpp:258] Train net output #1: loss_cls = 0.523039 (* 1 = 0.523039 loss) +I0616 03:26:55.620146 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115432 (* 1 = 0.115432 loss) +I0616 03:26:55.620152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0656412 (* 1 = 0.0656412 loss) +I0616 03:26:55.620159 9857 solver.cpp:571] Iteration 6320, lr = 0.001 +I0616 03:27:10.623648 9857 solver.cpp:242] Iteration 6340, loss = 1.62905 +I0616 03:27:10.623693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.545359 (* 1 = 0.545359 loss) +I0616 03:27:10.623698 9857 solver.cpp:258] Train net output #1: loss_cls = 1.13304 (* 1 = 1.13304 loss) +I0616 03:27:10.623703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.357383 (* 1 = 0.357383 loss) +I0616 03:27:10.623706 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.257726 (* 1 = 0.257726 loss) +I0616 03:27:10.623711 9857 solver.cpp:571] Iteration 6340, lr = 0.001 +I0616 03:27:26.424026 9857 solver.cpp:242] Iteration 6360, loss = 1.50875 +I0616 03:27:26.424069 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.475802 (* 1 = 0.475802 loss) +I0616 03:27:26.424078 9857 solver.cpp:258] Train net output #1: loss_cls = 0.762967 (* 1 = 0.762967 loss) +I0616 03:27:26.424085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.296583 (* 1 = 0.296583 loss) +I0616 03:27:26.424105 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0749332 (* 1 = 0.0749332 loss) +I0616 03:27:26.424113 9857 solver.cpp:571] Iteration 6360, lr = 0.001 +I0616 03:27:41.176606 9857 solver.cpp:242] Iteration 6380, loss = 0.968842 +I0616 03:27:41.176650 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.464382 (* 1 = 0.464382 loss) +I0616 03:27:41.176654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.537808 (* 1 = 0.537808 loss) +I0616 03:27:41.176658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227051 (* 1 = 0.227051 loss) +I0616 03:27:41.176662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0496076 (* 1 = 0.0496076 loss) +I0616 03:27:41.176666 9857 solver.cpp:571] Iteration 6380, lr = 0.001 +speed: 0.770s / iter +I0616 03:27:56.388451 9857 solver.cpp:242] Iteration 6400, loss = 0.917592 +I0616 03:27:56.388501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267215 (* 1 = 0.267215 loss) +I0616 03:27:56.388509 9857 solver.cpp:258] Train net output #1: loss_cls = 0.349352 (* 1 = 0.349352 loss) +I0616 03:27:56.388515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0392988 (* 1 = 0.0392988 loss) +I0616 03:27:56.388521 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00934605 (* 1 = 0.00934605 loss) +I0616 03:27:56.388530 9857 solver.cpp:571] Iteration 6400, lr = 0.001 +I0616 03:28:14.567714 9857 solver.cpp:242] Iteration 6420, loss = 1.85541 +I0616 03:28:14.567759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.546563 (* 1 = 0.546563 loss) +I0616 03:28:14.567764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.983599 (* 1 = 0.983599 loss) +I0616 03:28:14.567769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.393725 (* 1 = 0.393725 loss) +I0616 03:28:14.567772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.884959 (* 1 = 0.884959 loss) +I0616 03:28:14.567776 9857 solver.cpp:571] Iteration 6420, lr = 0.001 +I0616 03:28:30.754515 9857 solver.cpp:242] Iteration 6440, loss = 1.502 +I0616 03:28:30.754560 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.48881 (* 1 = 0.48881 loss) +I0616 03:28:30.754566 9857 solver.cpp:258] Train net output #1: loss_cls = 0.645744 (* 1 = 0.645744 loss) +I0616 03:28:30.754570 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.506516 (* 1 = 0.506516 loss) +I0616 03:28:30.754575 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.206489 (* 1 = 0.206489 loss) +I0616 03:28:30.754578 9857 solver.cpp:571] Iteration 6440, lr = 0.001 +I0616 03:28:50.129403 9857 solver.cpp:242] Iteration 6460, loss = 1.51083 +I0616 03:28:50.129446 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333176 (* 1 = 0.333176 loss) +I0616 03:28:50.129451 9857 solver.cpp:258] Train net output #1: loss_cls = 1.32697 (* 1 = 1.32697 loss) +I0616 03:28:50.129454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.710505 (* 1 = 0.710505 loss) +I0616 03:28:50.129458 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.120967 (* 1 = 0.120967 loss) +I0616 03:28:50.129462 9857 solver.cpp:571] Iteration 6460, lr = 0.001 +I0616 03:29:02.932835 9857 solver.cpp:242] Iteration 6480, loss = 1.19072 +I0616 03:29:02.932879 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.456985 (* 1 = 0.456985 loss) +I0616 03:29:02.932884 9857 solver.cpp:258] Train net output #1: loss_cls = 0.741023 (* 1 = 0.741023 loss) +I0616 03:29:02.932889 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0488833 (* 1 = 0.0488833 loss) +I0616 03:29:02.932893 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0902035 (* 1 = 0.0902035 loss) +I0616 03:29:02.932898 9857 solver.cpp:571] Iteration 6480, lr = 0.001 +I0616 03:29:16.204013 9857 solver.cpp:242] Iteration 6500, loss = 0.547022 +I0616 03:29:16.204058 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120176 (* 1 = 0.120176 loss) +I0616 03:29:16.204063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268367 (* 1 = 0.268367 loss) +I0616 03:29:16.204068 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0781884 (* 1 = 0.0781884 loss) +I0616 03:29:16.204071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0809982 (* 1 = 0.0809982 loss) +I0616 03:29:16.204076 9857 solver.cpp:571] Iteration 6500, lr = 0.001 +I0616 03:29:29.279129 9857 solver.cpp:242] Iteration 6520, loss = 1.16238 +I0616 03:29:29.279170 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186801 (* 1 = 0.186801 loss) +I0616 03:29:29.279175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211311 (* 1 = 0.211311 loss) +I0616 03:29:29.279180 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0640907 (* 1 = 0.0640907 loss) +I0616 03:29:29.279183 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0299167 (* 1 = 0.0299167 loss) +I0616 03:29:29.279187 9857 solver.cpp:571] Iteration 6520, lr = 0.001 +I0616 03:29:42.445449 9857 solver.cpp:242] Iteration 6540, loss = 1.07953 +I0616 03:29:42.445494 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376188 (* 1 = 0.376188 loss) +I0616 03:29:42.445499 9857 solver.cpp:258] Train net output #1: loss_cls = 0.637297 (* 1 = 0.637297 loss) +I0616 03:29:42.445504 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120437 (* 1 = 0.120437 loss) +I0616 03:29:42.445508 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194575 (* 1 = 0.0194575 loss) +I0616 03:29:42.445513 9857 solver.cpp:571] Iteration 6540, lr = 0.001 +I0616 03:29:56.302187 9857 solver.cpp:242] Iteration 6560, loss = 0.740022 +I0616 03:29:56.302247 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275113 (* 1 = 0.275113 loss) +I0616 03:29:56.302254 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371137 (* 1 = 0.371137 loss) +I0616 03:29:56.302263 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215614 (* 1 = 0.215614 loss) +I0616 03:29:56.302268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.017808 (* 1 = 0.017808 loss) +I0616 03:29:56.302278 9857 solver.cpp:571] Iteration 6560, lr = 0.001 +I0616 03:30:11.680071 9857 solver.cpp:242] Iteration 6580, loss = 0.482 +I0616 03:30:11.680116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182715 (* 1 = 0.182715 loss) +I0616 03:30:11.680124 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165033 (* 1 = 0.165033 loss) +I0616 03:30:11.680127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0121043 (* 1 = 0.0121043 loss) +I0616 03:30:11.680131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00754142 (* 1 = 0.00754142 loss) +I0616 03:30:11.680135 9857 solver.cpp:571] Iteration 6580, lr = 0.001 +speed: 0.769s / iter +I0616 03:30:25.533059 9857 solver.cpp:242] Iteration 6600, loss = 1.63488 +I0616 03:30:25.533104 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.480589 (* 1 = 0.480589 loss) +I0616 03:30:25.533110 9857 solver.cpp:258] Train net output #1: loss_cls = 0.858575 (* 1 = 0.858575 loss) +I0616 03:30:25.533114 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.497555 (* 1 = 0.497555 loss) +I0616 03:30:25.533118 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.402431 (* 1 = 0.402431 loss) +I0616 03:30:25.533123 9857 solver.cpp:571] Iteration 6600, lr = 0.001 +I0616 03:30:39.704352 9857 solver.cpp:242] Iteration 6620, loss = 0.684649 +I0616 03:30:39.704397 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0796808 (* 1 = 0.0796808 loss) +I0616 03:30:39.704402 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137083 (* 1 = 0.137083 loss) +I0616 03:30:39.704407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146013 (* 1 = 0.146013 loss) +I0616 03:30:39.704411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00876343 (* 1 = 0.00876343 loss) +I0616 03:30:39.704414 9857 solver.cpp:571] Iteration 6620, lr = 0.001 +I0616 03:30:54.482045 9857 solver.cpp:242] Iteration 6640, loss = 0.805089 +I0616 03:30:54.482089 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326892 (* 1 = 0.326892 loss) +I0616 03:30:54.482094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.556439 (* 1 = 0.556439 loss) +I0616 03:30:54.482097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193511 (* 1 = 0.193511 loss) +I0616 03:30:54.482101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224981 (* 1 = 0.0224981 loss) +I0616 03:30:54.482105 9857 solver.cpp:571] Iteration 6640, lr = 0.001 +I0616 03:31:06.244525 9857 solver.cpp:242] Iteration 6660, loss = 1.27856 +I0616 03:31:06.244575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224467 (* 1 = 0.224467 loss) +I0616 03:31:06.244583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.760628 (* 1 = 0.760628 loss) +I0616 03:31:06.244590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126896 (* 1 = 0.126896 loss) +I0616 03:31:06.244596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0683447 (* 1 = 0.0683447 loss) +I0616 03:31:06.244601 9857 solver.cpp:571] Iteration 6660, lr = 0.001 +I0616 03:31:19.538370 9857 solver.cpp:242] Iteration 6680, loss = 0.83111 +I0616 03:31:19.538419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148201 (* 1 = 0.148201 loss) +I0616 03:31:19.538425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32719 (* 1 = 0.32719 loss) +I0616 03:31:19.538431 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215862 (* 1 = 0.215862 loss) +I0616 03:31:19.538437 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.123256 (* 1 = 0.123256 loss) +I0616 03:31:19.538445 9857 solver.cpp:571] Iteration 6680, lr = 0.001 +I0616 03:31:33.806028 9857 solver.cpp:242] Iteration 6700, loss = 1.18027 +I0616 03:31:33.806071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247989 (* 1 = 0.247989 loss) +I0616 03:31:33.806077 9857 solver.cpp:258] Train net output #1: loss_cls = 0.37154 (* 1 = 0.37154 loss) +I0616 03:31:33.806082 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153637 (* 1 = 0.153637 loss) +I0616 03:31:33.806087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164801 (* 1 = 0.0164801 loss) +I0616 03:31:33.806090 9857 solver.cpp:571] Iteration 6700, lr = 0.001 +I0616 03:31:47.691017 9857 solver.cpp:242] Iteration 6720, loss = 1.9796 +I0616 03:31:47.691064 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.674195 (* 1 = 0.674195 loss) +I0616 03:31:47.691071 9857 solver.cpp:258] Train net output #1: loss_cls = 1.37285 (* 1 = 1.37285 loss) +I0616 03:31:47.691077 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.467052 (* 1 = 0.467052 loss) +I0616 03:31:47.691082 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.173756 (* 1 = 0.173756 loss) +I0616 03:31:47.691088 9857 solver.cpp:571] Iteration 6720, lr = 0.001 +I0616 03:32:02.300456 9857 solver.cpp:242] Iteration 6740, loss = 1.54378 +I0616 03:32:02.300500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.495135 (* 1 = 0.495135 loss) +I0616 03:32:02.300506 9857 solver.cpp:258] Train net output #1: loss_cls = 1.44981 (* 1 = 1.44981 loss) +I0616 03:32:02.300510 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.380185 (* 1 = 0.380185 loss) +I0616 03:32:02.300514 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.182419 (* 1 = 0.182419 loss) +I0616 03:32:02.300518 9857 solver.cpp:571] Iteration 6740, lr = 0.001 +I0616 03:32:17.874788 9857 solver.cpp:242] Iteration 6760, loss = 1.03547 +I0616 03:32:17.874833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379598 (* 1 = 0.379598 loss) +I0616 03:32:17.874838 9857 solver.cpp:258] Train net output #1: loss_cls = 1.1688 (* 1 = 1.1688 loss) +I0616 03:32:17.874843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11578 (* 1 = 0.11578 loss) +I0616 03:32:17.874847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0570237 (* 1 = 0.0570237 loss) +I0616 03:32:17.874852 9857 solver.cpp:571] Iteration 6760, lr = 0.001 +I0616 03:32:32.169138 9857 solver.cpp:242] Iteration 6780, loss = 1.09624 +I0616 03:32:32.169183 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.194139 (* 1 = 0.194139 loss) +I0616 03:32:32.169188 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149191 (* 1 = 0.149191 loss) +I0616 03:32:32.169193 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0169985 (* 1 = 0.0169985 loss) +I0616 03:32:32.169196 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120376 (* 1 = 0.0120376 loss) +I0616 03:32:32.169200 9857 solver.cpp:571] Iteration 6780, lr = 0.001 +speed: 0.767s / iter +I0616 03:32:45.768326 9857 solver.cpp:242] Iteration 6800, loss = 1.69911 +I0616 03:32:45.768367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163637 (* 1 = 0.163637 loss) +I0616 03:32:45.768373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.224981 (* 1 = 0.224981 loss) +I0616 03:32:45.768376 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0633453 (* 1 = 0.0633453 loss) +I0616 03:32:45.768380 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184213 (* 1 = 0.0184213 loss) +I0616 03:32:45.768383 9857 solver.cpp:571] Iteration 6800, lr = 0.001 +I0616 03:32:59.296850 9857 solver.cpp:242] Iteration 6820, loss = 0.673885 +I0616 03:32:59.296895 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124381 (* 1 = 0.124381 loss) +I0616 03:32:59.296900 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152171 (* 1 = 0.152171 loss) +I0616 03:32:59.296905 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0501178 (* 1 = 0.0501178 loss) +I0616 03:32:59.296908 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0356123 (* 1 = 0.0356123 loss) +I0616 03:32:59.296913 9857 solver.cpp:571] Iteration 6820, lr = 0.001 +I0616 03:33:15.935361 9857 solver.cpp:242] Iteration 6840, loss = 0.677178 +I0616 03:33:15.935407 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.219817 (* 1 = 0.219817 loss) +I0616 03:33:15.935412 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313103 (* 1 = 0.313103 loss) +I0616 03:33:15.935417 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104589 (* 1 = 0.104589 loss) +I0616 03:33:15.935421 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0332873 (* 1 = 0.0332873 loss) +I0616 03:33:15.935425 9857 solver.cpp:571] Iteration 6840, lr = 0.001 +I0616 03:33:29.557173 9857 solver.cpp:242] Iteration 6860, loss = 2.13556 +I0616 03:33:29.557214 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.431546 (* 1 = 0.431546 loss) +I0616 03:33:29.557220 9857 solver.cpp:258] Train net output #1: loss_cls = 0.513652 (* 1 = 0.513652 loss) +I0616 03:33:29.557224 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0581522 (* 1 = 0.0581522 loss) +I0616 03:33:29.557229 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0766597 (* 1 = 0.0766597 loss) +I0616 03:33:29.557232 9857 solver.cpp:571] Iteration 6860, lr = 0.001 +I0616 03:33:44.014982 9857 solver.cpp:242] Iteration 6880, loss = 1.03625 +I0616 03:33:44.015027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.418711 (* 1 = 0.418711 loss) +I0616 03:33:44.015033 9857 solver.cpp:258] Train net output #1: loss_cls = 0.545874 (* 1 = 0.545874 loss) +I0616 03:33:44.015036 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100322 (* 1 = 0.100322 loss) +I0616 03:33:44.015040 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221303 (* 1 = 0.0221303 loss) +I0616 03:33:44.015044 9857 solver.cpp:571] Iteration 6880, lr = 0.001 +I0616 03:33:59.682579 9857 solver.cpp:242] Iteration 6900, loss = 1.34022 +I0616 03:33:59.682624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.494193 (* 1 = 0.494193 loss) +I0616 03:33:59.682631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.690861 (* 1 = 0.690861 loss) +I0616 03:33:59.682634 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0948726 (* 1 = 0.0948726 loss) +I0616 03:33:59.682638 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221092 (* 1 = 0.0221092 loss) +I0616 03:33:59.682642 9857 solver.cpp:571] Iteration 6900, lr = 0.001 +I0616 03:34:13.815467 9857 solver.cpp:242] Iteration 6920, loss = 1.84315 +I0616 03:34:13.815512 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.582942 (* 1 = 0.582942 loss) +I0616 03:34:13.815518 9857 solver.cpp:258] Train net output #1: loss_cls = 1.90834 (* 1 = 1.90834 loss) +I0616 03:34:13.815523 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20553 (* 1 = 0.20553 loss) +I0616 03:34:13.815526 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0628031 (* 1 = 0.0628031 loss) +I0616 03:34:13.815531 9857 solver.cpp:571] Iteration 6920, lr = 0.001 +I0616 03:34:27.407855 9857 solver.cpp:242] Iteration 6940, loss = 1.4921 +I0616 03:34:27.407907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.517102 (* 1 = 0.517102 loss) +I0616 03:34:27.407914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.73566 (* 1 = 0.73566 loss) +I0616 03:34:27.407920 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215428 (* 1 = 0.215428 loss) +I0616 03:34:27.407927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0500503 (* 1 = 0.0500503 loss) +I0616 03:34:27.407934 9857 solver.cpp:571] Iteration 6940, lr = 0.001 +I0616 03:34:42.449661 9857 solver.cpp:242] Iteration 6960, loss = 1.80661 +I0616 03:34:42.449717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180622 (* 1 = 0.180622 loss) +I0616 03:34:42.449726 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399665 (* 1 = 0.399665 loss) +I0616 03:34:42.449733 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.3256 (* 1 = 0.3256 loss) +I0616 03:34:42.449739 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.4424 (* 1 = 1.4424 loss) +I0616 03:34:42.449748 9857 solver.cpp:571] Iteration 6960, lr = 0.001 +I0616 03:34:56.619943 9857 solver.cpp:242] Iteration 6980, loss = 1.13373 +I0616 03:34:56.619988 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.463316 (* 1 = 0.463316 loss) +I0616 03:34:56.619994 9857 solver.cpp:258] Train net output #1: loss_cls = 0.937412 (* 1 = 0.937412 loss) +I0616 03:34:56.619998 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194347 (* 1 = 0.194347 loss) +I0616 03:34:56.620002 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0228282 (* 1 = 0.0228282 loss) +I0616 03:34:56.620007 9857 solver.cpp:571] Iteration 6980, lr = 0.001 +speed: 0.766s / iter +I0616 03:35:10.564637 9857 solver.cpp:242] Iteration 7000, loss = 1.6551 +I0616 03:35:10.564683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.562858 (* 1 = 0.562858 loss) +I0616 03:35:10.564692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.815518 (* 1 = 0.815518 loss) +I0616 03:35:10.564697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.290885 (* 1 = 0.290885 loss) +I0616 03:35:10.564703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0490788 (* 1 = 0.0490788 loss) +I0616 03:35:10.564710 9857 solver.cpp:571] Iteration 7000, lr = 0.001 +I0616 03:35:23.426779 9857 solver.cpp:242] Iteration 7020, loss = 1.41875 +I0616 03:35:23.426826 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0909419 (* 1 = 0.0909419 loss) +I0616 03:35:23.426831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175064 (* 1 = 0.175064 loss) +I0616 03:35:23.426836 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0397942 (* 1 = 0.0397942 loss) +I0616 03:35:23.426839 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00359611 (* 1 = 0.00359611 loss) +I0616 03:35:23.426843 9857 solver.cpp:571] Iteration 7020, lr = 0.001 +I0616 03:35:37.388979 9857 solver.cpp:242] Iteration 7040, loss = 1.20216 +I0616 03:35:37.389024 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192166 (* 1 = 0.192166 loss) +I0616 03:35:37.389029 9857 solver.cpp:258] Train net output #1: loss_cls = 0.47479 (* 1 = 0.47479 loss) +I0616 03:35:37.389034 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121887 (* 1 = 0.121887 loss) +I0616 03:35:37.389037 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291476 (* 1 = 0.0291476 loss) +I0616 03:35:37.389041 9857 solver.cpp:571] Iteration 7040, lr = 0.001 +I0616 03:35:52.717730 9857 solver.cpp:242] Iteration 7060, loss = 1.45563 +I0616 03:35:52.717775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.431239 (* 1 = 0.431239 loss) +I0616 03:35:52.717780 9857 solver.cpp:258] Train net output #1: loss_cls = 1.18246 (* 1 = 1.18246 loss) +I0616 03:35:52.717784 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0446392 (* 1 = 0.0446392 loss) +I0616 03:35:52.717788 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0925201 (* 1 = 0.0925201 loss) +I0616 03:35:52.717792 9857 solver.cpp:571] Iteration 7060, lr = 0.001 +I0616 03:36:07.142369 9857 solver.cpp:242] Iteration 7080, loss = 0.982519 +I0616 03:36:07.142421 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.479711 (* 1 = 0.479711 loss) +I0616 03:36:07.142428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378844 (* 1 = 0.378844 loss) +I0616 03:36:07.142434 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0763496 (* 1 = 0.0763496 loss) +I0616 03:36:07.142441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132916 (* 1 = 0.0132916 loss) +I0616 03:36:07.142446 9857 solver.cpp:571] Iteration 7080, lr = 0.001 +I0616 03:36:21.965054 9857 solver.cpp:242] Iteration 7100, loss = 0.923669 +I0616 03:36:21.965104 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260271 (* 1 = 0.260271 loss) +I0616 03:36:21.965113 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253575 (* 1 = 0.253575 loss) +I0616 03:36:21.965119 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123073 (* 1 = 0.123073 loss) +I0616 03:36:21.965126 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.056762 (* 1 = 0.056762 loss) +I0616 03:36:21.965136 9857 solver.cpp:571] Iteration 7100, lr = 0.001 +I0616 03:36:35.173099 9857 solver.cpp:242] Iteration 7120, loss = 1.13847 +I0616 03:36:35.173142 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.240935 (* 1 = 0.240935 loss) +I0616 03:36:35.173148 9857 solver.cpp:258] Train net output #1: loss_cls = 0.220892 (* 1 = 0.220892 loss) +I0616 03:36:35.173152 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0140803 (* 1 = 0.0140803 loss) +I0616 03:36:35.173156 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00433788 (* 1 = 0.00433788 loss) +I0616 03:36:35.173161 9857 solver.cpp:571] Iteration 7120, lr = 0.001 +I0616 03:36:50.930536 9857 solver.cpp:242] Iteration 7140, loss = 1.1772 +I0616 03:36:50.930580 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315683 (* 1 = 0.315683 loss) +I0616 03:36:50.930585 9857 solver.cpp:258] Train net output #1: loss_cls = 0.893979 (* 1 = 0.893979 loss) +I0616 03:36:50.930590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0651622 (* 1 = 0.0651622 loss) +I0616 03:36:50.930595 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159026 (* 1 = 0.0159026 loss) +I0616 03:36:50.930601 9857 solver.cpp:571] Iteration 7140, lr = 0.001 +I0616 03:37:03.633270 9857 solver.cpp:242] Iteration 7160, loss = 0.629662 +I0616 03:37:03.633314 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121707 (* 1 = 0.121707 loss) +I0616 03:37:03.633321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338891 (* 1 = 0.338891 loss) +I0616 03:37:03.633324 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100998 (* 1 = 0.100998 loss) +I0616 03:37:03.633328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.288896 (* 1 = 0.288896 loss) +I0616 03:37:03.633332 9857 solver.cpp:571] Iteration 7160, lr = 0.001 +I0616 03:37:19.308995 9857 solver.cpp:242] Iteration 7180, loss = 0.834317 +I0616 03:37:19.309041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225843 (* 1 = 0.225843 loss) +I0616 03:37:19.309051 9857 solver.cpp:258] Train net output #1: loss_cls = 0.848224 (* 1 = 0.848224 loss) +I0616 03:37:19.309072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127177 (* 1 = 0.127177 loss) +I0616 03:37:19.309079 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00410608 (* 1 = 0.00410608 loss) +I0616 03:37:19.309087 9857 solver.cpp:571] Iteration 7180, lr = 0.001 +speed: 0.764s / iter +I0616 03:37:33.061811 9857 solver.cpp:242] Iteration 7200, loss = 0.767693 +I0616 03:37:33.061854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0832845 (* 1 = 0.0832845 loss) +I0616 03:37:33.061861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130413 (* 1 = 0.130413 loss) +I0616 03:37:33.061866 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0620251 (* 1 = 0.0620251 loss) +I0616 03:37:33.061869 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0082602 (* 1 = 0.0082602 loss) +I0616 03:37:33.061873 9857 solver.cpp:571] Iteration 7200, lr = 0.001 +I0616 03:37:48.147971 9857 solver.cpp:242] Iteration 7220, loss = 1.20235 +I0616 03:37:48.148020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.514329 (* 1 = 0.514329 loss) +I0616 03:37:48.148027 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416409 (* 1 = 0.416409 loss) +I0616 03:37:48.148033 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118894 (* 1 = 0.118894 loss) +I0616 03:37:48.148039 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0593106 (* 1 = 0.0593106 loss) +I0616 03:37:48.148046 9857 solver.cpp:571] Iteration 7220, lr = 0.001 +I0616 03:38:01.232672 9857 solver.cpp:242] Iteration 7240, loss = 1.14654 +I0616 03:38:01.232714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45356 (* 1 = 0.45356 loss) +I0616 03:38:01.232720 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378798 (* 1 = 0.378798 loss) +I0616 03:38:01.232725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.278958 (* 1 = 0.278958 loss) +I0616 03:38:01.232728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.241598 (* 1 = 0.241598 loss) +I0616 03:38:01.232733 9857 solver.cpp:571] Iteration 7240, lr = 0.001 +I0616 03:38:16.098047 9857 solver.cpp:242] Iteration 7260, loss = 0.866601 +I0616 03:38:16.098088 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184005 (* 1 = 0.184005 loss) +I0616 03:38:16.098094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281929 (* 1 = 0.281929 loss) +I0616 03:38:16.098098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166138 (* 1 = 0.166138 loss) +I0616 03:38:16.098103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0545916 (* 1 = 0.0545916 loss) +I0616 03:38:16.098106 9857 solver.cpp:571] Iteration 7260, lr = 0.001 +I0616 03:38:30.902418 9857 solver.cpp:242] Iteration 7280, loss = 1.14996 +I0616 03:38:30.902463 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242054 (* 1 = 0.242054 loss) +I0616 03:38:30.902469 9857 solver.cpp:258] Train net output #1: loss_cls = 0.642606 (* 1 = 0.642606 loss) +I0616 03:38:30.902473 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125203 (* 1 = 0.125203 loss) +I0616 03:38:30.902477 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180953 (* 1 = 0.0180953 loss) +I0616 03:38:30.902482 9857 solver.cpp:571] Iteration 7280, lr = 0.001 +I0616 03:38:43.942016 9857 solver.cpp:242] Iteration 7300, loss = 2.37527 +I0616 03:38:43.942059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.503661 (* 1 = 0.503661 loss) +I0616 03:38:43.942065 9857 solver.cpp:258] Train net output #1: loss_cls = 0.723094 (* 1 = 0.723094 loss) +I0616 03:38:43.942070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.706682 (* 1 = 0.706682 loss) +I0616 03:38:43.942073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.181179 (* 1 = 0.181179 loss) +I0616 03:38:43.942077 9857 solver.cpp:571] Iteration 7300, lr = 0.001 +I0616 03:38:57.821452 9857 solver.cpp:242] Iteration 7320, loss = 2.50814 +I0616 03:38:57.821498 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.523192 (* 1 = 0.523192 loss) +I0616 03:38:57.821504 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10526 (* 1 = 1.10526 loss) +I0616 03:38:57.821508 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.527695 (* 1 = 0.527695 loss) +I0616 03:38:57.821512 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.18261 (* 1 = 0.18261 loss) +I0616 03:38:57.821516 9857 solver.cpp:571] Iteration 7320, lr = 0.001 +I0616 03:39:12.737695 9857 solver.cpp:242] Iteration 7340, loss = 1.29027 +I0616 03:39:12.737740 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379501 (* 1 = 0.379501 loss) +I0616 03:39:12.737745 9857 solver.cpp:258] Train net output #1: loss_cls = 0.46402 (* 1 = 0.46402 loss) +I0616 03:39:12.737749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.268696 (* 1 = 0.268696 loss) +I0616 03:39:12.737753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0601404 (* 1 = 0.0601404 loss) +I0616 03:39:12.737757 9857 solver.cpp:571] Iteration 7340, lr = 0.001 +I0616 03:39:26.376351 9857 solver.cpp:242] Iteration 7360, loss = 1.97346 +I0616 03:39:26.376394 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.512632 (* 1 = 0.512632 loss) +I0616 03:39:26.376399 9857 solver.cpp:258] Train net output #1: loss_cls = 0.749347 (* 1 = 0.749347 loss) +I0616 03:39:26.376404 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.238708 (* 1 = 0.238708 loss) +I0616 03:39:26.376407 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.297034 (* 1 = 0.297034 loss) +I0616 03:39:26.376411 9857 solver.cpp:571] Iteration 7360, lr = 0.001 +I0616 03:39:41.102264 9857 solver.cpp:242] Iteration 7380, loss = 0.894009 +I0616 03:39:41.102308 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.497159 (* 1 = 0.497159 loss) +I0616 03:39:41.102314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.387834 (* 1 = 0.387834 loss) +I0616 03:39:41.102319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.202523 (* 1 = 0.202523 loss) +I0616 03:39:41.102322 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00470736 (* 1 = 0.00470736 loss) +I0616 03:39:41.102326 9857 solver.cpp:571] Iteration 7380, lr = 0.001 +speed: 0.763s / iter +I0616 03:39:55.298651 9857 solver.cpp:242] Iteration 7400, loss = 1.62744 +I0616 03:39:55.298696 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.531092 (* 1 = 0.531092 loss) +I0616 03:39:55.298702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.828744 (* 1 = 0.828744 loss) +I0616 03:39:55.298707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.254384 (* 1 = 0.254384 loss) +I0616 03:39:55.298710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0279435 (* 1 = 0.0279435 loss) +I0616 03:39:55.298714 9857 solver.cpp:571] Iteration 7400, lr = 0.001 +I0616 03:40:09.195857 9857 solver.cpp:242] Iteration 7420, loss = 1.43616 +I0616 03:40:09.195904 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323621 (* 1 = 0.323621 loss) +I0616 03:40:09.195911 9857 solver.cpp:258] Train net output #1: loss_cls = 0.60503 (* 1 = 0.60503 loss) +I0616 03:40:09.195917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128505 (* 1 = 0.128505 loss) +I0616 03:40:09.195924 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0300634 (* 1 = 0.0300634 loss) +I0616 03:40:09.195930 9857 solver.cpp:571] Iteration 7420, lr = 0.001 +I0616 03:40:23.484833 9857 solver.cpp:242] Iteration 7440, loss = 1.20957 +I0616 03:40:23.484879 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255733 (* 1 = 0.255733 loss) +I0616 03:40:23.484887 9857 solver.cpp:258] Train net output #1: loss_cls = 0.574876 (* 1 = 0.574876 loss) +I0616 03:40:23.484894 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.097389 (* 1 = 0.097389 loss) +I0616 03:40:23.484899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0578212 (* 1 = 0.0578212 loss) +I0616 03:40:23.484908 9857 solver.cpp:571] Iteration 7440, lr = 0.001 +I0616 03:40:38.361233 9857 solver.cpp:242] Iteration 7460, loss = 1.20883 +I0616 03:40:38.361277 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359591 (* 1 = 0.359591 loss) +I0616 03:40:38.361284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.505363 (* 1 = 0.505363 loss) +I0616 03:40:38.361289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0929501 (* 1 = 0.0929501 loss) +I0616 03:40:38.361292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0452535 (* 1 = 0.0452535 loss) +I0616 03:40:38.361296 9857 solver.cpp:571] Iteration 7460, lr = 0.001 +I0616 03:40:53.030433 9857 solver.cpp:242] Iteration 7480, loss = 1.60981 +I0616 03:40:53.030478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.489448 (* 1 = 0.489448 loss) +I0616 03:40:53.030483 9857 solver.cpp:258] Train net output #1: loss_cls = 1.09578 (* 1 = 1.09578 loss) +I0616 03:40:53.030488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166728 (* 1 = 0.166728 loss) +I0616 03:40:53.030491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107485 (* 1 = 0.107485 loss) +I0616 03:40:53.030495 9857 solver.cpp:571] Iteration 7480, lr = 0.001 +I0616 03:41:07.865247 9857 solver.cpp:242] Iteration 7500, loss = 1.71341 +I0616 03:41:07.865290 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332819 (* 1 = 0.332819 loss) +I0616 03:41:07.865296 9857 solver.cpp:258] Train net output #1: loss_cls = 0.88145 (* 1 = 0.88145 loss) +I0616 03:41:07.865300 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.264991 (* 1 = 0.264991 loss) +I0616 03:41:07.865304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0515491 (* 1 = 0.0515491 loss) +I0616 03:41:07.865309 9857 solver.cpp:571] Iteration 7500, lr = 0.001 +I0616 03:41:21.542372 9857 solver.cpp:242] Iteration 7520, loss = 0.527894 +I0616 03:41:21.542419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249324 (* 1 = 0.249324 loss) +I0616 03:41:21.542426 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167632 (* 1 = 0.167632 loss) +I0616 03:41:21.542433 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.225569 (* 1 = 0.225569 loss) +I0616 03:41:21.542438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0327899 (* 1 = 0.0327899 loss) +I0616 03:41:21.542445 9857 solver.cpp:571] Iteration 7520, lr = 0.001 +I0616 03:41:35.681396 9857 solver.cpp:242] Iteration 7540, loss = 0.770968 +I0616 03:41:35.681439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22856 (* 1 = 0.22856 loss) +I0616 03:41:35.681445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.307064 (* 1 = 0.307064 loss) +I0616 03:41:35.681449 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0326678 (* 1 = 0.0326678 loss) +I0616 03:41:35.681453 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134108 (* 1 = 0.0134108 loss) +I0616 03:41:35.681460 9857 solver.cpp:571] Iteration 7540, lr = 0.001 +I0616 03:41:51.891077 9857 solver.cpp:242] Iteration 7560, loss = 1.33988 +I0616 03:41:51.891120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158339 (* 1 = 0.158339 loss) +I0616 03:41:51.891126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301178 (* 1 = 0.301178 loss) +I0616 03:41:51.891130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.254228 (* 1 = 0.254228 loss) +I0616 03:41:51.891134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162931 (* 1 = 0.0162931 loss) +I0616 03:41:51.891139 9857 solver.cpp:571] Iteration 7560, lr = 0.001 +I0616 03:42:06.192447 9857 solver.cpp:242] Iteration 7580, loss = 1.84262 +I0616 03:42:06.192492 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.434416 (* 1 = 0.434416 loss) +I0616 03:42:06.192497 9857 solver.cpp:258] Train net output #1: loss_cls = 0.963206 (* 1 = 0.963206 loss) +I0616 03:42:06.192502 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294845 (* 1 = 0.294845 loss) +I0616 03:42:06.192507 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0542538 (* 1 = 0.0542538 loss) +I0616 03:42:06.192510 9857 solver.cpp:571] Iteration 7580, lr = 0.001 +speed: 0.762s / iter +I0616 03:42:20.600112 9857 solver.cpp:242] Iteration 7600, loss = 0.572057 +I0616 03:42:20.600157 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.223008 (* 1 = 0.223008 loss) +I0616 03:42:20.600162 9857 solver.cpp:258] Train net output #1: loss_cls = 0.284133 (* 1 = 0.284133 loss) +I0616 03:42:20.600167 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127592 (* 1 = 0.127592 loss) +I0616 03:42:20.600170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0419031 (* 1 = 0.0419031 loss) +I0616 03:42:20.600175 9857 solver.cpp:571] Iteration 7600, lr = 0.001 +I0616 03:42:33.457875 9857 solver.cpp:242] Iteration 7620, loss = 1.06378 +I0616 03:42:33.457927 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.52242 (* 1 = 0.52242 loss) +I0616 03:42:33.457936 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00797 (* 1 = 1.00797 loss) +I0616 03:42:33.457942 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129996 (* 1 = 0.129996 loss) +I0616 03:42:33.457947 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104646 (* 1 = 0.0104646 loss) +I0616 03:42:33.457955 9857 solver.cpp:571] Iteration 7620, lr = 0.001 +I0616 03:42:48.859365 9857 solver.cpp:242] Iteration 7640, loss = 1.38407 +I0616 03:42:48.859407 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.317913 (* 1 = 0.317913 loss) +I0616 03:42:48.859416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.58906 (* 1 = 0.58906 loss) +I0616 03:42:48.859422 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.263254 (* 1 = 0.263254 loss) +I0616 03:42:48.859429 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.421804 (* 1 = 0.421804 loss) +I0616 03:42:48.859436 9857 solver.cpp:571] Iteration 7640, lr = 0.001 +I0616 03:43:03.510331 9857 solver.cpp:242] Iteration 7660, loss = 1.6017 +I0616 03:43:03.510375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.487974 (* 1 = 0.487974 loss) +I0616 03:43:03.510380 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10029 (* 1 = 1.10029 loss) +I0616 03:43:03.510383 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.345163 (* 1 = 0.345163 loss) +I0616 03:43:03.510387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126135 (* 1 = 0.126135 loss) +I0616 03:43:03.510391 9857 solver.cpp:571] Iteration 7660, lr = 0.001 +I0616 03:43:18.106750 9857 solver.cpp:242] Iteration 7680, loss = 0.954822 +I0616 03:43:18.106812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331145 (* 1 = 0.331145 loss) +I0616 03:43:18.106822 9857 solver.cpp:258] Train net output #1: loss_cls = 0.495375 (* 1 = 0.495375 loss) +I0616 03:43:18.106828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139821 (* 1 = 0.139821 loss) +I0616 03:43:18.106835 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168495 (* 1 = 0.0168495 loss) +I0616 03:43:18.106844 9857 solver.cpp:571] Iteration 7680, lr = 0.001 +I0616 03:43:33.058106 9857 solver.cpp:242] Iteration 7700, loss = 1.30944 +I0616 03:43:33.058153 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.457469 (* 1 = 0.457469 loss) +I0616 03:43:33.058161 9857 solver.cpp:258] Train net output #1: loss_cls = 1.51507 (* 1 = 1.51507 loss) +I0616 03:43:33.058167 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100003 (* 1 = 0.100003 loss) +I0616 03:43:33.058173 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102556 (* 1 = 0.102556 loss) +I0616 03:43:33.058179 9857 solver.cpp:571] Iteration 7700, lr = 0.001 +I0616 03:43:46.182190 9857 solver.cpp:242] Iteration 7720, loss = 0.860237 +I0616 03:43:46.182234 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212494 (* 1 = 0.212494 loss) +I0616 03:43:46.182240 9857 solver.cpp:258] Train net output #1: loss_cls = 0.554528 (* 1 = 0.554528 loss) +I0616 03:43:46.182245 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0829653 (* 1 = 0.0829653 loss) +I0616 03:43:46.182248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152534 (* 1 = 0.0152534 loss) +I0616 03:43:46.182253 9857 solver.cpp:571] Iteration 7720, lr = 0.001 +I0616 03:44:01.361881 9857 solver.cpp:242] Iteration 7740, loss = 1.50833 +I0616 03:44:01.361927 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.469693 (* 1 = 0.469693 loss) +I0616 03:44:01.361932 9857 solver.cpp:258] Train net output #1: loss_cls = 1.40539 (* 1 = 1.40539 loss) +I0616 03:44:01.361937 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.312676 (* 1 = 0.312676 loss) +I0616 03:44:01.361940 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0701914 (* 1 = 0.0701914 loss) +I0616 03:44:01.361944 9857 solver.cpp:571] Iteration 7740, lr = 0.001 +I0616 03:44:15.407531 9857 solver.cpp:242] Iteration 7760, loss = 0.988804 +I0616 03:44:15.407604 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201447 (* 1 = 0.201447 loss) +I0616 03:44:15.407614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.611129 (* 1 = 0.611129 loss) +I0616 03:44:15.407620 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0872553 (* 1 = 0.0872553 loss) +I0616 03:44:15.407627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000926407 (* 1 = 0.000926407 loss) +I0616 03:44:15.407634 9857 solver.cpp:571] Iteration 7760, lr = 0.001 +I0616 03:44:30.205847 9857 solver.cpp:242] Iteration 7780, loss = 0.992798 +I0616 03:44:30.205891 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221262 (* 1 = 0.221262 loss) +I0616 03:44:30.205898 9857 solver.cpp:258] Train net output #1: loss_cls = 0.733399 (* 1 = 0.733399 loss) +I0616 03:44:30.205901 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0950073 (* 1 = 0.0950073 loss) +I0616 03:44:30.205905 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00672291 (* 1 = 0.00672291 loss) +I0616 03:44:30.205909 9857 solver.cpp:571] Iteration 7780, lr = 0.001 +speed: 0.761s / iter +I0616 03:44:46.690948 9857 solver.cpp:242] Iteration 7800, loss = 0.590435 +I0616 03:44:46.690989 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12066 (* 1 = 0.12066 loss) +I0616 03:44:46.690994 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195198 (* 1 = 0.195198 loss) +I0616 03:44:46.690999 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0772087 (* 1 = 0.0772087 loss) +I0616 03:44:46.691004 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115675 (* 1 = 0.0115675 loss) +I0616 03:44:46.691007 9857 solver.cpp:571] Iteration 7800, lr = 0.001 +I0616 03:45:03.440632 9857 solver.cpp:242] Iteration 7820, loss = 1.80065 +I0616 03:45:03.440691 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111073 (* 1 = 0.111073 loss) +I0616 03:45:03.440701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.325162 (* 1 = 0.325162 loss) +I0616 03:45:03.440709 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.046269 (* 1 = 0.046269 loss) +I0616 03:45:03.440717 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00251644 (* 1 = 0.00251644 loss) +I0616 03:45:03.440726 9857 solver.cpp:571] Iteration 7820, lr = 0.001 +I0616 03:45:16.442641 9857 solver.cpp:242] Iteration 7840, loss = 1.30176 +I0616 03:45:16.442701 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.612782 (* 1 = 0.612782 loss) +I0616 03:45:16.442713 9857 solver.cpp:258] Train net output #1: loss_cls = 0.946671 (* 1 = 0.946671 loss) +I0616 03:45:16.442720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0866388 (* 1 = 0.0866388 loss) +I0616 03:45:16.442726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133896 (* 1 = 0.0133896 loss) +I0616 03:45:16.442734 9857 solver.cpp:571] Iteration 7840, lr = 0.001 +I0616 03:45:31.773258 9857 solver.cpp:242] Iteration 7860, loss = 1.3024 +I0616 03:45:31.773303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.507232 (* 1 = 0.507232 loss) +I0616 03:45:31.773308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.424483 (* 1 = 0.424483 loss) +I0616 03:45:31.773313 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142402 (* 1 = 0.142402 loss) +I0616 03:45:31.773316 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159561 (* 1 = 0.159561 loss) +I0616 03:45:31.773321 9857 solver.cpp:571] Iteration 7860, lr = 0.001 +I0616 03:45:46.645813 9857 solver.cpp:242] Iteration 7880, loss = 1.41089 +I0616 03:45:46.645869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359052 (* 1 = 0.359052 loss) +I0616 03:45:46.645875 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381716 (* 1 = 0.381716 loss) +I0616 03:45:46.645892 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0648013 (* 1 = 0.0648013 loss) +I0616 03:45:46.645897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0187967 (* 1 = 0.0187967 loss) +I0616 03:45:46.645901 9857 solver.cpp:571] Iteration 7880, lr = 0.001 +I0616 03:46:01.301208 9857 solver.cpp:242] Iteration 7900, loss = 2.03441 +I0616 03:46:01.301252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.545506 (* 1 = 0.545506 loss) +I0616 03:46:01.301259 9857 solver.cpp:258] Train net output #1: loss_cls = 1.56836 (* 1 = 1.56836 loss) +I0616 03:46:01.301262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.26651 (* 1 = 0.26651 loss) +I0616 03:46:01.301266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170398 (* 1 = 0.0170398 loss) +I0616 03:46:01.301271 9857 solver.cpp:571] Iteration 7900, lr = 0.001 +I0616 03:46:15.242552 9857 solver.cpp:242] Iteration 7920, loss = 0.835487 +I0616 03:46:15.242596 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.31288 (* 1 = 0.31288 loss) +I0616 03:46:15.242601 9857 solver.cpp:258] Train net output #1: loss_cls = 0.789922 (* 1 = 0.789922 loss) +I0616 03:46:15.242606 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0732346 (* 1 = 0.0732346 loss) +I0616 03:46:15.242610 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00671079 (* 1 = 0.00671079 loss) +I0616 03:46:15.242614 9857 solver.cpp:571] Iteration 7920, lr = 0.001 +I0616 03:46:30.399561 9857 solver.cpp:242] Iteration 7940, loss = 1.66448 +I0616 03:46:30.399606 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.41509 (* 1 = 0.41509 loss) +I0616 03:46:30.399612 9857 solver.cpp:258] Train net output #1: loss_cls = 0.666529 (* 1 = 0.666529 loss) +I0616 03:46:30.399616 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.208783 (* 1 = 0.208783 loss) +I0616 03:46:30.399621 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306595 (* 1 = 0.0306595 loss) +I0616 03:46:30.399624 9857 solver.cpp:571] Iteration 7940, lr = 0.001 +I0616 03:46:44.313618 9857 solver.cpp:242] Iteration 7960, loss = 0.791599 +I0616 03:46:44.313663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386137 (* 1 = 0.386137 loss) +I0616 03:46:44.313668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21831 (* 1 = 0.21831 loss) +I0616 03:46:44.313673 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0584697 (* 1 = 0.0584697 loss) +I0616 03:46:44.313676 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269239 (* 1 = 0.0269239 loss) +I0616 03:46:44.313680 9857 solver.cpp:571] Iteration 7960, lr = 0.001 +I0616 03:46:58.873877 9857 solver.cpp:242] Iteration 7980, loss = 0.599645 +I0616 03:46:58.873925 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0603847 (* 1 = 0.0603847 loss) +I0616 03:46:58.873934 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103178 (* 1 = 0.103178 loss) +I0616 03:46:58.873939 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0642302 (* 1 = 0.0642302 loss) +I0616 03:46:58.873945 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.027172 (* 1 = 0.027172 loss) +I0616 03:46:58.873953 9857 solver.cpp:571] Iteration 7980, lr = 0.001 +speed: 0.760s / iter +I0616 03:47:13.726644 9857 solver.cpp:242] Iteration 8000, loss = 1.04722 +I0616 03:47:13.726686 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199559 (* 1 = 0.199559 loss) +I0616 03:47:13.726693 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24085 (* 1 = 0.24085 loss) +I0616 03:47:13.726697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0363371 (* 1 = 0.0363371 loss) +I0616 03:47:13.726701 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164335 (* 1 = 0.0164335 loss) +I0616 03:47:13.726706 9857 solver.cpp:571] Iteration 8000, lr = 0.001 +I0616 03:47:27.815301 9857 solver.cpp:242] Iteration 8020, loss = 1.28767 +I0616 03:47:27.815346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.633786 (* 1 = 0.633786 loss) +I0616 03:47:27.815353 9857 solver.cpp:258] Train net output #1: loss_cls = 0.975551 (* 1 = 0.975551 loss) +I0616 03:47:27.815357 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.191966 (* 1 = 0.191966 loss) +I0616 03:47:27.815361 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0272922 (* 1 = 0.0272922 loss) +I0616 03:47:27.815366 9857 solver.cpp:571] Iteration 8020, lr = 0.001 +I0616 03:47:42.466538 9857 solver.cpp:242] Iteration 8040, loss = 2.06749 +I0616 03:47:42.466583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.685215 (* 1 = 0.685215 loss) +I0616 03:47:42.466588 9857 solver.cpp:258] Train net output #1: loss_cls = 1.80327 (* 1 = 1.80327 loss) +I0616 03:47:42.466593 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.729769 (* 1 = 0.729769 loss) +I0616 03:47:42.466596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.223134 (* 1 = 0.223134 loss) +I0616 03:47:42.466600 9857 solver.cpp:571] Iteration 8040, lr = 0.001 +I0616 03:47:56.627189 9857 solver.cpp:242] Iteration 8060, loss = 0.959088 +I0616 03:47:56.627233 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.464808 (* 1 = 0.464808 loss) +I0616 03:47:56.627239 9857 solver.cpp:258] Train net output #1: loss_cls = 0.813886 (* 1 = 0.813886 loss) +I0616 03:47:56.627243 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.077525 (* 1 = 0.077525 loss) +I0616 03:47:56.627248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0409365 (* 1 = 0.0409365 loss) +I0616 03:47:56.627251 9857 solver.cpp:571] Iteration 8060, lr = 0.001 +I0616 03:48:11.478544 9857 solver.cpp:242] Iteration 8080, loss = 1.53252 +I0616 03:48:11.478588 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.344714 (* 1 = 0.344714 loss) +I0616 03:48:11.478595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.560699 (* 1 = 0.560699 loss) +I0616 03:48:11.478600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145352 (* 1 = 0.145352 loss) +I0616 03:48:11.478603 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0343462 (* 1 = 0.0343462 loss) +I0616 03:48:11.478607 9857 solver.cpp:571] Iteration 8080, lr = 0.001 +I0616 03:48:25.306463 9857 solver.cpp:242] Iteration 8100, loss = 0.944617 +I0616 03:48:25.306506 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332607 (* 1 = 0.332607 loss) +I0616 03:48:25.306512 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207695 (* 1 = 0.207695 loss) +I0616 03:48:25.306517 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0237234 (* 1 = 0.0237234 loss) +I0616 03:48:25.306521 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00167389 (* 1 = 0.00167389 loss) +I0616 03:48:25.306526 9857 solver.cpp:571] Iteration 8100, lr = 0.001 +I0616 03:48:40.629570 9857 solver.cpp:242] Iteration 8120, loss = 1.20758 +I0616 03:48:40.629616 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.525095 (* 1 = 0.525095 loss) +I0616 03:48:40.629622 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15474 (* 1 = 1.15474 loss) +I0616 03:48:40.629627 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114321 (* 1 = 0.114321 loss) +I0616 03:48:40.629631 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0322202 (* 1 = 0.0322202 loss) +I0616 03:48:40.629636 9857 solver.cpp:571] Iteration 8120, lr = 0.001 +I0616 03:48:54.223003 9857 solver.cpp:242] Iteration 8140, loss = 0.918947 +I0616 03:48:54.223063 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284957 (* 1 = 0.284957 loss) +I0616 03:48:54.223073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.680342 (* 1 = 0.680342 loss) +I0616 03:48:54.223083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0428328 (* 1 = 0.0428328 loss) +I0616 03:48:54.223089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00739166 (* 1 = 0.00739166 loss) +I0616 03:48:54.223099 9857 solver.cpp:571] Iteration 8140, lr = 0.001 +I0616 03:49:07.888188 9857 solver.cpp:242] Iteration 8160, loss = 1.78612 +I0616 03:49:07.888233 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355665 (* 1 = 0.355665 loss) +I0616 03:49:07.888239 9857 solver.cpp:258] Train net output #1: loss_cls = 1.24145 (* 1 = 1.24145 loss) +I0616 03:49:07.888243 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227839 (* 1 = 0.227839 loss) +I0616 03:49:07.888247 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.182233 (* 1 = 0.182233 loss) +I0616 03:49:07.888252 9857 solver.cpp:571] Iteration 8160, lr = 0.001 +I0616 03:49:22.171886 9857 solver.cpp:242] Iteration 8180, loss = 0.912382 +I0616 03:49:22.171931 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152264 (* 1 = 0.152264 loss) +I0616 03:49:22.171937 9857 solver.cpp:258] Train net output #1: loss_cls = 0.407976 (* 1 = 0.407976 loss) +I0616 03:49:22.171941 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100716 (* 1 = 0.100716 loss) +I0616 03:49:22.171946 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175975 (* 1 = 0.0175975 loss) +I0616 03:49:22.171949 9857 solver.cpp:571] Iteration 8180, lr = 0.001 +speed: 0.759s / iter +I0616 03:49:37.715495 9857 solver.cpp:242] Iteration 8200, loss = 0.53824 +I0616 03:49:37.715540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205947 (* 1 = 0.205947 loss) +I0616 03:49:37.715546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316869 (* 1 = 0.316869 loss) +I0616 03:49:37.715550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137049 (* 1 = 0.137049 loss) +I0616 03:49:37.715554 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00510139 (* 1 = 0.00510139 loss) +I0616 03:49:37.715559 9857 solver.cpp:571] Iteration 8200, lr = 0.001 +I0616 03:49:50.527434 9857 solver.cpp:242] Iteration 8220, loss = 0.669064 +I0616 03:49:50.527483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313406 (* 1 = 0.313406 loss) +I0616 03:49:50.527492 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233901 (* 1 = 0.233901 loss) +I0616 03:49:50.527498 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183681 (* 1 = 0.183681 loss) +I0616 03:49:50.527503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.065386 (* 1 = 0.065386 loss) +I0616 03:49:50.527509 9857 solver.cpp:571] Iteration 8220, lr = 0.001 +I0616 03:50:06.456676 9857 solver.cpp:242] Iteration 8240, loss = 1.03498 +I0616 03:50:06.456717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209017 (* 1 = 0.209017 loss) +I0616 03:50:06.456723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.494953 (* 1 = 0.494953 loss) +I0616 03:50:06.456728 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.024744 (* 1 = 0.024744 loss) +I0616 03:50:06.456732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182747 (* 1 = 0.0182747 loss) +I0616 03:50:06.456737 9857 solver.cpp:571] Iteration 8240, lr = 0.001 +I0616 03:50:18.536947 9857 solver.cpp:242] Iteration 8260, loss = 0.651708 +I0616 03:50:18.536993 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0434834 (* 1 = 0.0434834 loss) +I0616 03:50:18.536998 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225587 (* 1 = 0.225587 loss) +I0616 03:50:18.537003 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112188 (* 1 = 0.112188 loss) +I0616 03:50:18.537008 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.353975 (* 1 = 0.353975 loss) +I0616 03:50:18.537011 9857 solver.cpp:571] Iteration 8260, lr = 0.001 +I0616 03:50:32.963356 9857 solver.cpp:242] Iteration 8280, loss = 0.880655 +I0616 03:50:32.963407 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190778 (* 1 = 0.190778 loss) +I0616 03:50:32.963415 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14769 (* 1 = 0.14769 loss) +I0616 03:50:32.963423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0560626 (* 1 = 0.0560626 loss) +I0616 03:50:32.963428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00671182 (* 1 = 0.00671182 loss) +I0616 03:50:32.963434 9857 solver.cpp:571] Iteration 8280, lr = 0.001 +I0616 03:50:46.943143 9857 solver.cpp:242] Iteration 8300, loss = 1.21986 +I0616 03:50:46.943197 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.402846 (* 1 = 0.402846 loss) +I0616 03:50:46.943204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.617004 (* 1 = 0.617004 loss) +I0616 03:50:46.943210 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149203 (* 1 = 0.149203 loss) +I0616 03:50:46.943217 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0406856 (* 1 = 0.0406856 loss) +I0616 03:50:46.943225 9857 solver.cpp:571] Iteration 8300, lr = 0.001 +I0616 03:51:03.125134 9857 solver.cpp:242] Iteration 8320, loss = 1.20644 +I0616 03:51:03.125177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221585 (* 1 = 0.221585 loss) +I0616 03:51:03.125183 9857 solver.cpp:258] Train net output #1: loss_cls = 1.11766 (* 1 = 1.11766 loss) +I0616 03:51:03.125187 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0170414 (* 1 = 0.0170414 loss) +I0616 03:51:03.125191 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000587206 (* 1 = 0.000587206 loss) +I0616 03:51:03.125196 9857 solver.cpp:571] Iteration 8320, lr = 0.001 +I0616 03:51:17.127517 9857 solver.cpp:242] Iteration 8340, loss = 1.48951 +I0616 03:51:17.127563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279099 (* 1 = 0.279099 loss) +I0616 03:51:17.127568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.982888 (* 1 = 0.982888 loss) +I0616 03:51:17.127573 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.40231 (* 1 = 0.40231 loss) +I0616 03:51:17.127578 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0873549 (* 1 = 0.0873549 loss) +I0616 03:51:17.127581 9857 solver.cpp:571] Iteration 8340, lr = 0.001 +I0616 03:51:30.364295 9857 solver.cpp:242] Iteration 8360, loss = 0.502305 +I0616 03:51:30.364343 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193597 (* 1 = 0.193597 loss) +I0616 03:51:30.364352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.341176 (* 1 = 0.341176 loss) +I0616 03:51:30.364359 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0469836 (* 1 = 0.0469836 loss) +I0616 03:51:30.364367 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109769 (* 1 = 0.0109769 loss) +I0616 03:51:30.364374 9857 solver.cpp:571] Iteration 8360, lr = 0.001 +I0616 03:51:44.327507 9857 solver.cpp:242] Iteration 8380, loss = 0.901281 +I0616 03:51:44.327553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.552633 (* 1 = 0.552633 loss) +I0616 03:51:44.327558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.541653 (* 1 = 0.541653 loss) +I0616 03:51:44.327563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.318741 (* 1 = 0.318741 loss) +I0616 03:51:44.327566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0367889 (* 1 = 0.0367889 loss) +I0616 03:51:44.327570 9857 solver.cpp:571] Iteration 8380, lr = 0.001 +speed: 0.758s / iter +I0616 03:51:59.230278 9857 solver.cpp:242] Iteration 8400, loss = 1.2623 +I0616 03:51:59.230326 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324143 (* 1 = 0.324143 loss) +I0616 03:51:59.230334 9857 solver.cpp:258] Train net output #1: loss_cls = 0.684093 (* 1 = 0.684093 loss) +I0616 03:51:59.230340 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175753 (* 1 = 0.175753 loss) +I0616 03:51:59.230346 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0372818 (* 1 = 0.0372818 loss) +I0616 03:51:59.230352 9857 solver.cpp:571] Iteration 8400, lr = 0.001 +I0616 03:52:15.313293 9857 solver.cpp:242] Iteration 8420, loss = 1.74491 +I0616 03:52:15.313338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.441878 (* 1 = 0.441878 loss) +I0616 03:52:15.313344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.706292 (* 1 = 0.706292 loss) +I0616 03:52:15.313349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0950003 (* 1 = 0.0950003 loss) +I0616 03:52:15.313352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0689656 (* 1 = 0.0689656 loss) +I0616 03:52:15.313356 9857 solver.cpp:571] Iteration 8420, lr = 0.001 +I0616 03:52:28.645962 9857 solver.cpp:242] Iteration 8440, loss = 1.69021 +I0616 03:52:28.646013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305531 (* 1 = 0.305531 loss) +I0616 03:52:28.646018 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404844 (* 1 = 0.404844 loss) +I0616 03:52:28.646023 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108392 (* 1 = 0.108392 loss) +I0616 03:52:28.646026 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0411299 (* 1 = 0.0411299 loss) +I0616 03:52:28.646030 9857 solver.cpp:571] Iteration 8440, lr = 0.001 +I0616 03:52:43.837211 9857 solver.cpp:242] Iteration 8460, loss = 1.61806 +I0616 03:52:43.837255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408071 (* 1 = 0.408071 loss) +I0616 03:52:43.837262 9857 solver.cpp:258] Train net output #1: loss_cls = 1.39141 (* 1 = 1.39141 loss) +I0616 03:52:43.837266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15384 (* 1 = 0.15384 loss) +I0616 03:52:43.837270 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0381376 (* 1 = 0.0381376 loss) +I0616 03:52:43.837275 9857 solver.cpp:571] Iteration 8460, lr = 0.001 +I0616 03:52:57.688585 9857 solver.cpp:242] Iteration 8480, loss = 0.659149 +I0616 03:52:57.688632 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280033 (* 1 = 0.280033 loss) +I0616 03:52:57.688638 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370095 (* 1 = 0.370095 loss) +I0616 03:52:57.688642 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0788742 (* 1 = 0.0788742 loss) +I0616 03:52:57.688647 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0214072 (* 1 = 0.0214072 loss) +I0616 03:52:57.688652 9857 solver.cpp:571] Iteration 8480, lr = 0.001 +I0616 03:53:14.111410 9857 solver.cpp:242] Iteration 8500, loss = 1.06313 +I0616 03:53:14.111461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342936 (* 1 = 0.342936 loss) +I0616 03:53:14.111470 9857 solver.cpp:258] Train net output #1: loss_cls = 0.538543 (* 1 = 0.538543 loss) +I0616 03:53:14.111476 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0177804 (* 1 = 0.0177804 loss) +I0616 03:53:14.111488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286731 (* 1 = 0.0286731 loss) +I0616 03:53:14.111495 9857 solver.cpp:571] Iteration 8500, lr = 0.001 +I0616 03:53:27.926700 9857 solver.cpp:242] Iteration 8520, loss = 0.928233 +I0616 03:53:27.926746 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.081444 (* 1 = 0.081444 loss) +I0616 03:53:27.926754 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158964 (* 1 = 0.158964 loss) +I0616 03:53:27.926764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.054968 (* 1 = 0.054968 loss) +I0616 03:53:27.926769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113948 (* 1 = 0.0113948 loss) +I0616 03:53:27.926775 9857 solver.cpp:571] Iteration 8520, lr = 0.001 +I0616 03:53:41.540165 9857 solver.cpp:242] Iteration 8540, loss = 1.82823 +I0616 03:53:41.540213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453585 (* 1 = 0.453585 loss) +I0616 03:53:41.540221 9857 solver.cpp:258] Train net output #1: loss_cls = 1.60472 (* 1 = 1.60472 loss) +I0616 03:53:41.540228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.598272 (* 1 = 0.598272 loss) +I0616 03:53:41.540233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.234383 (* 1 = 0.234383 loss) +I0616 03:53:41.540240 9857 solver.cpp:571] Iteration 8540, lr = 0.001 +I0616 03:53:55.075672 9857 solver.cpp:242] Iteration 8560, loss = 0.555897 +I0616 03:53:55.075717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266986 (* 1 = 0.266986 loss) +I0616 03:53:55.075723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232989 (* 1 = 0.232989 loss) +I0616 03:53:55.075728 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124219 (* 1 = 0.124219 loss) +I0616 03:53:55.075732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255135 (* 1 = 0.0255135 loss) +I0616 03:53:55.075736 9857 solver.cpp:571] Iteration 8560, lr = 0.001 +I0616 03:54:11.161242 9857 solver.cpp:242] Iteration 8580, loss = 0.774087 +I0616 03:54:11.161289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297118 (* 1 = 0.297118 loss) +I0616 03:54:11.161298 9857 solver.cpp:258] Train net output #1: loss_cls = 0.768333 (* 1 = 0.768333 loss) +I0616 03:54:11.161303 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.17361 (* 1 = 0.17361 loss) +I0616 03:54:11.161309 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189333 (* 1 = 0.0189333 loss) +I0616 03:54:11.161315 9857 solver.cpp:571] Iteration 8580, lr = 0.001 +speed: 0.757s / iter +I0616 03:54:24.655987 9857 solver.cpp:242] Iteration 8600, loss = 1.94574 +I0616 03:54:24.656028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12899 (* 1 = 0.12899 loss) +I0616 03:54:24.656035 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354586 (* 1 = 0.354586 loss) +I0616 03:54:24.656040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.511601 (* 1 = 0.511601 loss) +I0616 03:54:24.656044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.373047 (* 1 = 0.373047 loss) +I0616 03:54:24.656047 9857 solver.cpp:571] Iteration 8600, lr = 0.001 +I0616 03:54:39.611335 9857 solver.cpp:242] Iteration 8620, loss = 1.58925 +I0616 03:54:39.611388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.540116 (* 1 = 0.540116 loss) +I0616 03:54:39.611397 9857 solver.cpp:258] Train net output #1: loss_cls = 0.91423 (* 1 = 0.91423 loss) +I0616 03:54:39.611400 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200224 (* 1 = 0.200224 loss) +I0616 03:54:39.611404 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.025324 (* 1 = 0.025324 loss) +I0616 03:54:39.611408 9857 solver.cpp:571] Iteration 8620, lr = 0.001 +I0616 03:54:52.788849 9857 solver.cpp:242] Iteration 8640, loss = 2.00569 +I0616 03:54:52.788897 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167551 (* 1 = 0.167551 loss) +I0616 03:54:52.788902 9857 solver.cpp:258] Train net output #1: loss_cls = 0.655768 (* 1 = 0.655768 loss) +I0616 03:54:52.788907 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109583 (* 1 = 0.109583 loss) +I0616 03:54:52.788910 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103373 (* 1 = 0.0103373 loss) +I0616 03:54:52.788915 9857 solver.cpp:571] Iteration 8640, lr = 0.001 +I0616 03:55:06.665654 9857 solver.cpp:242] Iteration 8660, loss = 0.623353 +I0616 03:55:06.665709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163495 (* 1 = 0.163495 loss) +I0616 03:55:06.665715 9857 solver.cpp:258] Train net output #1: loss_cls = 0.486501 (* 1 = 0.486501 loss) +I0616 03:55:06.665734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0964673 (* 1 = 0.0964673 loss) +I0616 03:55:06.665737 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205204 (* 1 = 0.0205204 loss) +I0616 03:55:06.665741 9857 solver.cpp:571] Iteration 8660, lr = 0.001 +I0616 03:55:24.851254 9857 solver.cpp:242] Iteration 8680, loss = 0.656165 +I0616 03:55:24.851299 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112497 (* 1 = 0.112497 loss) +I0616 03:55:24.851305 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208694 (* 1 = 0.208694 loss) +I0616 03:55:24.851308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0261965 (* 1 = 0.0261965 loss) +I0616 03:55:24.851312 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0110609 (* 1 = 0.0110609 loss) +I0616 03:55:24.851316 9857 solver.cpp:571] Iteration 8680, lr = 0.001 +I0616 03:55:40.601719 9857 solver.cpp:242] Iteration 8700, loss = 1.30227 +I0616 03:55:40.601778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271624 (* 1 = 0.271624 loss) +I0616 03:55:40.601788 9857 solver.cpp:258] Train net output #1: loss_cls = 0.732047 (* 1 = 0.732047 loss) +I0616 03:55:40.601795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.506093 (* 1 = 0.506093 loss) +I0616 03:55:40.601802 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.160907 (* 1 = 0.160907 loss) +I0616 03:55:40.601811 9857 solver.cpp:571] Iteration 8700, lr = 0.001 +I0616 03:55:54.119114 9857 solver.cpp:242] Iteration 8720, loss = 1.57528 +I0616 03:55:54.119158 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.506911 (* 1 = 0.506911 loss) +I0616 03:55:54.119163 9857 solver.cpp:258] Train net output #1: loss_cls = 1.86448 (* 1 = 1.86448 loss) +I0616 03:55:54.119168 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166285 (* 1 = 0.166285 loss) +I0616 03:55:54.119171 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.040626 (* 1 = 0.040626 loss) +I0616 03:55:54.119175 9857 solver.cpp:571] Iteration 8720, lr = 0.001 +I0616 03:56:08.554071 9857 solver.cpp:242] Iteration 8740, loss = 0.70799 +I0616 03:56:08.554116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139534 (* 1 = 0.139534 loss) +I0616 03:56:08.554123 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119303 (* 1 = 0.119303 loss) +I0616 03:56:08.554127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0451808 (* 1 = 0.0451808 loss) +I0616 03:56:08.554131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168127 (* 1 = 0.0168127 loss) +I0616 03:56:08.554137 9857 solver.cpp:571] Iteration 8740, lr = 0.001 +I0616 03:56:21.628557 9857 solver.cpp:242] Iteration 8760, loss = 0.683054 +I0616 03:56:21.628607 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256579 (* 1 = 0.256579 loss) +I0616 03:56:21.628614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.3052 (* 1 = 0.3052 loss) +I0616 03:56:21.628620 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129588 (* 1 = 0.129588 loss) +I0616 03:56:21.628626 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0270548 (* 1 = 0.0270548 loss) +I0616 03:56:21.628634 9857 solver.cpp:571] Iteration 8760, lr = 0.001 +I0616 03:56:37.334544 9857 solver.cpp:242] Iteration 8780, loss = 0.956874 +I0616 03:56:37.334590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311557 (* 1 = 0.311557 loss) +I0616 03:56:37.334596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.532143 (* 1 = 0.532143 loss) +I0616 03:56:37.334600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210917 (* 1 = 0.210917 loss) +I0616 03:56:37.334604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113865 (* 1 = 0.0113865 loss) +I0616 03:56:37.334609 9857 solver.cpp:571] Iteration 8780, lr = 0.001 +speed: 0.757s / iter +I0616 03:56:51.453825 9857 solver.cpp:242] Iteration 8800, loss = 1.0069 +I0616 03:56:51.453869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367432 (* 1 = 0.367432 loss) +I0616 03:56:51.453874 9857 solver.cpp:258] Train net output #1: loss_cls = 0.529819 (* 1 = 0.529819 loss) +I0616 03:56:51.453879 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0381857 (* 1 = 0.0381857 loss) +I0616 03:56:51.453883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00834183 (* 1 = 0.00834183 loss) +I0616 03:56:51.453886 9857 solver.cpp:571] Iteration 8800, lr = 0.001 +I0616 03:57:04.978183 9857 solver.cpp:242] Iteration 8820, loss = 1.39429 +I0616 03:57:04.978229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.479047 (* 1 = 0.479047 loss) +I0616 03:57:04.978235 9857 solver.cpp:258] Train net output #1: loss_cls = 0.821988 (* 1 = 0.821988 loss) +I0616 03:57:04.978238 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0741744 (* 1 = 0.0741744 loss) +I0616 03:57:04.978242 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0572757 (* 1 = 0.0572757 loss) +I0616 03:57:04.978246 9857 solver.cpp:571] Iteration 8820, lr = 0.001 +I0616 03:57:18.722369 9857 solver.cpp:242] Iteration 8840, loss = 0.861351 +I0616 03:57:18.722416 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.349001 (* 1 = 0.349001 loss) +I0616 03:57:18.722424 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294505 (* 1 = 0.294505 loss) +I0616 03:57:18.722429 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124201 (* 1 = 0.124201 loss) +I0616 03:57:18.722431 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120547 (* 1 = 0.0120547 loss) +I0616 03:57:18.722435 9857 solver.cpp:571] Iteration 8840, lr = 0.001 +I0616 03:57:33.403275 9857 solver.cpp:242] Iteration 8860, loss = 1.18311 +I0616 03:57:33.403323 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.438086 (* 1 = 0.438086 loss) +I0616 03:57:33.403331 9857 solver.cpp:258] Train net output #1: loss_cls = 0.464498 (* 1 = 0.464498 loss) +I0616 03:57:33.403337 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.208309 (* 1 = 0.208309 loss) +I0616 03:57:33.403343 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0999009 (* 1 = 0.0999009 loss) +I0616 03:57:33.403350 9857 solver.cpp:571] Iteration 8860, lr = 0.001 +I0616 03:57:47.387614 9857 solver.cpp:242] Iteration 8880, loss = 0.829747 +I0616 03:57:47.387660 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.43067 (* 1 = 0.43067 loss) +I0616 03:57:47.387665 9857 solver.cpp:258] Train net output #1: loss_cls = 0.525007 (* 1 = 0.525007 loss) +I0616 03:57:47.387670 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0956134 (* 1 = 0.0956134 loss) +I0616 03:57:47.387675 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0310503 (* 1 = 0.0310503 loss) +I0616 03:57:47.387678 9857 solver.cpp:571] Iteration 8880, lr = 0.001 +I0616 03:58:01.470041 9857 solver.cpp:242] Iteration 8900, loss = 0.902224 +I0616 03:58:01.470091 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358279 (* 1 = 0.358279 loss) +I0616 03:58:01.470099 9857 solver.cpp:258] Train net output #1: loss_cls = 0.503204 (* 1 = 0.503204 loss) +I0616 03:58:01.470105 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.428412 (* 1 = 0.428412 loss) +I0616 03:58:01.470111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.11068 (* 1 = 0.11068 loss) +I0616 03:58:01.470118 9857 solver.cpp:571] Iteration 8900, lr = 0.001 +I0616 03:58:16.697681 9857 solver.cpp:242] Iteration 8920, loss = 0.98163 +I0616 03:58:16.697726 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274133 (* 1 = 0.274133 loss) +I0616 03:58:16.697732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233337 (* 1 = 0.233337 loss) +I0616 03:58:16.697736 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0477559 (* 1 = 0.0477559 loss) +I0616 03:58:16.697741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182222 (* 1 = 0.0182222 loss) +I0616 03:58:16.697746 9857 solver.cpp:571] Iteration 8920, lr = 0.001 +I0616 03:58:31.649000 9857 solver.cpp:242] Iteration 8940, loss = 0.742822 +I0616 03:58:31.649049 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390087 (* 1 = 0.390087 loss) +I0616 03:58:31.649057 9857 solver.cpp:258] Train net output #1: loss_cls = 0.365392 (* 1 = 0.365392 loss) +I0616 03:58:31.649063 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0847087 (* 1 = 0.0847087 loss) +I0616 03:58:31.649070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273428 (* 1 = 0.0273428 loss) +I0616 03:58:31.649075 9857 solver.cpp:571] Iteration 8940, lr = 0.001 +I0616 03:58:45.535223 9857 solver.cpp:242] Iteration 8960, loss = 0.916253 +I0616 03:58:45.535271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389373 (* 1 = 0.389373 loss) +I0616 03:58:45.535279 9857 solver.cpp:258] Train net output #1: loss_cls = 0.481709 (* 1 = 0.481709 loss) +I0616 03:58:45.535285 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249405 (* 1 = 0.249405 loss) +I0616 03:58:45.535290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288192 (* 1 = 0.0288192 loss) +I0616 03:58:45.535296 9857 solver.cpp:571] Iteration 8960, lr = 0.001 +I0616 03:58:59.104606 9857 solver.cpp:242] Iteration 8980, loss = 1.5078 +I0616 03:58:59.104660 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.539634 (* 1 = 0.539634 loss) +I0616 03:58:59.104671 9857 solver.cpp:258] Train net output #1: loss_cls = 1.1691 (* 1 = 1.1691 loss) +I0616 03:58:59.104678 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11241 (* 1 = 0.11241 loss) +I0616 03:58:59.104691 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164647 (* 1 = 0.0164647 loss) +I0616 03:58:59.104698 9857 solver.cpp:571] Iteration 8980, lr = 0.001 +speed: 0.756s / iter +I0616 03:59:12.962961 9857 solver.cpp:242] Iteration 9000, loss = 1.28007 +I0616 03:59:12.963006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.437737 (* 1 = 0.437737 loss) +I0616 03:59:12.963012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.922251 (* 1 = 0.922251 loss) +I0616 03:59:12.963016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141888 (* 1 = 0.141888 loss) +I0616 03:59:12.963021 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0600581 (* 1 = 0.0600581 loss) +I0616 03:59:12.963026 9857 solver.cpp:571] Iteration 9000, lr = 0.001 +I0616 03:59:27.320940 9857 solver.cpp:242] Iteration 9020, loss = 2.05917 +I0616 03:59:27.321009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308311 (* 1 = 0.308311 loss) +I0616 03:59:27.321019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.737033 (* 1 = 0.737033 loss) +I0616 03:59:27.321027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0962601 (* 1 = 0.0962601 loss) +I0616 03:59:27.321036 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0442442 (* 1 = 0.0442442 loss) +I0616 03:59:27.321044 9857 solver.cpp:571] Iteration 9020, lr = 0.001 +I0616 03:59:41.758911 9857 solver.cpp:242] Iteration 9040, loss = 0.56696 +I0616 03:59:41.758955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153639 (* 1 = 0.153639 loss) +I0616 03:59:41.758961 9857 solver.cpp:258] Train net output #1: loss_cls = 0.35205 (* 1 = 0.35205 loss) +I0616 03:59:41.758965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121063 (* 1 = 0.121063 loss) +I0616 03:59:41.758970 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00644096 (* 1 = 0.00644096 loss) +I0616 03:59:41.758973 9857 solver.cpp:571] Iteration 9040, lr = 0.001 +I0616 03:59:57.854081 9857 solver.cpp:242] Iteration 9060, loss = 0.51548 +I0616 03:59:57.854111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124375 (* 1 = 0.124375 loss) +I0616 03:59:57.854117 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278273 (* 1 = 0.278273 loss) +I0616 03:59:57.854121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0648309 (* 1 = 0.0648309 loss) +I0616 03:59:57.854125 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0298225 (* 1 = 0.0298225 loss) +I0616 03:59:57.854130 9857 solver.cpp:571] Iteration 9060, lr = 0.001 +I0616 04:00:12.135367 9857 solver.cpp:242] Iteration 9080, loss = 1.25533 +I0616 04:00:12.135426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195383 (* 1 = 0.195383 loss) +I0616 04:00:12.135435 9857 solver.cpp:258] Train net output #1: loss_cls = 0.75378 (* 1 = 0.75378 loss) +I0616 04:00:12.135442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126869 (* 1 = 0.126869 loss) +I0616 04:00:12.135449 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.145098 (* 1 = 0.145098 loss) +I0616 04:00:12.135459 9857 solver.cpp:571] Iteration 9080, lr = 0.001 +I0616 04:00:26.844157 9857 solver.cpp:242] Iteration 9100, loss = 0.760954 +I0616 04:00:26.844202 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379847 (* 1 = 0.379847 loss) +I0616 04:00:26.844208 9857 solver.cpp:258] Train net output #1: loss_cls = 0.324712 (* 1 = 0.324712 loss) +I0616 04:00:26.844213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.026358 (* 1 = 0.026358 loss) +I0616 04:00:26.844216 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231103 (* 1 = 0.0231103 loss) +I0616 04:00:26.844221 9857 solver.cpp:571] Iteration 9100, lr = 0.001 +I0616 04:00:40.150534 9857 solver.cpp:242] Iteration 9120, loss = 1.15921 +I0616 04:00:40.150581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323545 (* 1 = 0.323545 loss) +I0616 04:00:40.150586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.965157 (* 1 = 0.965157 loss) +I0616 04:00:40.150591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205191 (* 1 = 0.205191 loss) +I0616 04:00:40.150595 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494261 (* 1 = 0.0494261 loss) +I0616 04:00:40.150599 9857 solver.cpp:571] Iteration 9120, lr = 0.001 +I0616 04:00:55.982357 9857 solver.cpp:242] Iteration 9140, loss = 1.70487 +I0616 04:00:55.982403 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.51244 (* 1 = 0.51244 loss) +I0616 04:00:55.982409 9857 solver.cpp:258] Train net output #1: loss_cls = 0.486292 (* 1 = 0.486292 loss) +I0616 04:00:55.982414 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.23386 (* 1 = 0.23386 loss) +I0616 04:00:55.982416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0321234 (* 1 = 0.0321234 loss) +I0616 04:00:55.982421 9857 solver.cpp:571] Iteration 9140, lr = 0.001 +I0616 04:01:10.404294 9857 solver.cpp:242] Iteration 9160, loss = 1.57693 +I0616 04:01:10.404352 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214405 (* 1 = 0.214405 loss) +I0616 04:01:10.404362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.516593 (* 1 = 0.516593 loss) +I0616 04:01:10.404371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.214136 (* 1 = 0.214136 loss) +I0616 04:01:10.404379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116372 (* 1 = 0.0116372 loss) +I0616 04:01:10.404387 9857 solver.cpp:571] Iteration 9160, lr = 0.001 +I0616 04:01:27.854079 9857 solver.cpp:242] Iteration 9180, loss = 0.4675 +I0616 04:01:27.854121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181756 (* 1 = 0.181756 loss) +I0616 04:01:27.854127 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263749 (* 1 = 0.263749 loss) +I0616 04:01:27.854131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.042929 (* 1 = 0.042929 loss) +I0616 04:01:27.854136 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404878 (* 1 = 0.0404878 loss) +I0616 04:01:27.854140 9857 solver.cpp:571] Iteration 9180, lr = 0.001 +speed: 0.756s / iter +I0616 04:01:42.450485 9857 solver.cpp:242] Iteration 9200, loss = 0.911396 +I0616 04:01:42.450531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159122 (* 1 = 0.159122 loss) +I0616 04:01:42.450537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22784 (* 1 = 0.22784 loss) +I0616 04:01:42.450541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0897026 (* 1 = 0.0897026 loss) +I0616 04:01:42.450546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215875 (* 1 = 0.0215875 loss) +I0616 04:01:42.450551 9857 solver.cpp:571] Iteration 9200, lr = 0.001 +I0616 04:01:57.502529 9857 solver.cpp:242] Iteration 9220, loss = 0.670809 +I0616 04:01:57.502574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213796 (* 1 = 0.213796 loss) +I0616 04:01:57.502580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459743 (* 1 = 0.459743 loss) +I0616 04:01:57.502584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0961755 (* 1 = 0.0961755 loss) +I0616 04:01:57.502588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404055 (* 1 = 0.0404055 loss) +I0616 04:01:57.502593 9857 solver.cpp:571] Iteration 9220, lr = 0.001 +I0616 04:02:12.716949 9857 solver.cpp:242] Iteration 9240, loss = 0.690264 +I0616 04:02:12.717010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18325 (* 1 = 0.18325 loss) +I0616 04:02:12.717020 9857 solver.cpp:258] Train net output #1: loss_cls = 0.479404 (* 1 = 0.479404 loss) +I0616 04:02:12.717027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126106 (* 1 = 0.126106 loss) +I0616 04:02:12.717033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.053754 (* 1 = 0.053754 loss) +I0616 04:02:12.717041 9857 solver.cpp:571] Iteration 9240, lr = 0.001 +I0616 04:02:27.615175 9857 solver.cpp:242] Iteration 9260, loss = 0.886174 +I0616 04:02:27.615229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318799 (* 1 = 0.318799 loss) +I0616 04:02:27.615236 9857 solver.cpp:258] Train net output #1: loss_cls = 0.277678 (* 1 = 0.277678 loss) +I0616 04:02:27.615241 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110672 (* 1 = 0.110672 loss) +I0616 04:02:27.615244 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202033 (* 1 = 0.0202033 loss) +I0616 04:02:27.615248 9857 solver.cpp:571] Iteration 9260, lr = 0.001 +I0616 04:02:44.441838 9857 solver.cpp:242] Iteration 9280, loss = 1.94242 +I0616 04:02:44.441887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424511 (* 1 = 0.424511 loss) +I0616 04:02:44.441897 9857 solver.cpp:258] Train net output #1: loss_cls = 0.921145 (* 1 = 0.921145 loss) +I0616 04:02:44.441905 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.212756 (* 1 = 0.212756 loss) +I0616 04:02:44.441912 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131647 (* 1 = 0.0131647 loss) +I0616 04:02:44.441917 9857 solver.cpp:571] Iteration 9280, lr = 0.001 +I0616 04:02:57.271406 9857 solver.cpp:242] Iteration 9300, loss = 1.23512 +I0616 04:02:57.271448 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224058 (* 1 = 0.224058 loss) +I0616 04:02:57.271455 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302508 (* 1 = 0.302508 loss) +I0616 04:02:57.271458 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138919 (* 1 = 0.138919 loss) +I0616 04:02:57.271462 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152329 (* 1 = 0.0152329 loss) +I0616 04:02:57.271466 9857 solver.cpp:571] Iteration 9300, lr = 0.001 +I0616 04:03:14.461233 9857 solver.cpp:242] Iteration 9320, loss = 1.24069 +I0616 04:03:14.461279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327985 (* 1 = 0.327985 loss) +I0616 04:03:14.461285 9857 solver.cpp:258] Train net output #1: loss_cls = 0.320168 (* 1 = 0.320168 loss) +I0616 04:03:14.461289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147233 (* 1 = 0.147233 loss) +I0616 04:03:14.461293 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0416345 (* 1 = 0.0416345 loss) +I0616 04:03:14.461298 9857 solver.cpp:571] Iteration 9320, lr = 0.001 +I0616 04:03:29.734297 9857 solver.cpp:242] Iteration 9340, loss = 0.644064 +I0616 04:03:29.734339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276295 (* 1 = 0.276295 loss) +I0616 04:03:29.734345 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271164 (* 1 = 0.271164 loss) +I0616 04:03:29.734349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0818939 (* 1 = 0.0818939 loss) +I0616 04:03:29.734354 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00200092 (* 1 = 0.00200092 loss) +I0616 04:03:29.734357 9857 solver.cpp:571] Iteration 9340, lr = 0.001 +I0616 04:03:45.366271 9857 solver.cpp:242] Iteration 9360, loss = 2.13336 +I0616 04:03:45.366334 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.691344 (* 1 = 0.691344 loss) +I0616 04:03:45.366343 9857 solver.cpp:258] Train net output #1: loss_cls = 1.82119 (* 1 = 1.82119 loss) +I0616 04:03:45.366349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.649494 (* 1 = 0.649494 loss) +I0616 04:03:45.366356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0916783 (* 1 = 0.0916783 loss) +I0616 04:03:45.366364 9857 solver.cpp:571] Iteration 9360, lr = 0.001 +I0616 04:04:01.260929 9857 solver.cpp:242] Iteration 9380, loss = 1.59765 +I0616 04:04:01.260972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290145 (* 1 = 0.290145 loss) +I0616 04:04:01.260977 9857 solver.cpp:258] Train net output #1: loss_cls = 1.01474 (* 1 = 1.01474 loss) +I0616 04:04:01.260982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.026901 (* 1 = 0.026901 loss) +I0616 04:04:01.260987 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0562128 (* 1 = 0.0562128 loss) +I0616 04:04:01.260994 9857 solver.cpp:571] Iteration 9380, lr = 0.001 +speed: 0.756s / iter +I0616 04:04:19.349213 9857 solver.cpp:242] Iteration 9400, loss = 0.647241 +I0616 04:04:19.349257 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406233 (* 1 = 0.406233 loss) +I0616 04:04:19.349263 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32561 (* 1 = 0.32561 loss) +I0616 04:04:19.349267 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0539585 (* 1 = 0.0539585 loss) +I0616 04:04:19.349272 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140464 (* 1 = 0.0140464 loss) +I0616 04:04:19.349275 9857 solver.cpp:571] Iteration 9400, lr = 0.001 +I0616 04:04:33.193614 9857 solver.cpp:242] Iteration 9420, loss = 1.24909 +I0616 04:04:33.193656 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176531 (* 1 = 0.176531 loss) +I0616 04:04:33.193661 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164433 (* 1 = 0.164433 loss) +I0616 04:04:33.193665 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0271958 (* 1 = 0.0271958 loss) +I0616 04:04:33.193670 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106752 (* 1 = 0.0106752 loss) +I0616 04:04:33.193673 9857 solver.cpp:571] Iteration 9420, lr = 0.001 +I0616 04:04:48.799492 9857 solver.cpp:242] Iteration 9440, loss = 1.52963 +I0616 04:04:48.799543 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.597282 (* 1 = 0.597282 loss) +I0616 04:04:48.799551 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16388 (* 1 = 1.16388 loss) +I0616 04:04:48.799556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14318 (* 1 = 0.14318 loss) +I0616 04:04:48.799559 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0995689 (* 1 = 0.0995689 loss) +I0616 04:04:48.799563 9857 solver.cpp:571] Iteration 9440, lr = 0.001 +I0616 04:05:04.842288 9857 solver.cpp:242] Iteration 9460, loss = 1.26673 +I0616 04:05:04.842330 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.70221 (* 1 = 0.70221 loss) +I0616 04:05:04.842336 9857 solver.cpp:258] Train net output #1: loss_cls = 1.05763 (* 1 = 1.05763 loss) +I0616 04:05:04.842340 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201298 (* 1 = 0.201298 loss) +I0616 04:05:04.842344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245164 (* 1 = 0.0245164 loss) +I0616 04:05:04.842349 9857 solver.cpp:571] Iteration 9460, lr = 0.001 +I0616 04:05:20.223644 9857 solver.cpp:242] Iteration 9480, loss = 0.970103 +I0616 04:05:20.223701 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343223 (* 1 = 0.343223 loss) +I0616 04:05:20.223711 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375147 (* 1 = 0.375147 loss) +I0616 04:05:20.223719 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107402 (* 1 = 0.107402 loss) +I0616 04:05:20.223728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0248118 (* 1 = 0.0248118 loss) +I0616 04:05:20.223736 9857 solver.cpp:571] Iteration 9480, lr = 0.001 +I0616 04:05:35.930011 9857 solver.cpp:242] Iteration 9500, loss = 1.36498 +I0616 04:05:35.930061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229521 (* 1 = 0.229521 loss) +I0616 04:05:35.930068 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186467 (* 1 = 0.186467 loss) +I0616 04:05:35.930073 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.055112 (* 1 = 0.055112 loss) +I0616 04:05:35.930076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0389515 (* 1 = 0.0389515 loss) +I0616 04:05:35.930081 9857 solver.cpp:571] Iteration 9500, lr = 0.001 +I0616 04:05:50.122315 9857 solver.cpp:242] Iteration 9520, loss = 1.18152 +I0616 04:05:50.122375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229313 (* 1 = 0.229313 loss) +I0616 04:05:50.122386 9857 solver.cpp:258] Train net output #1: loss_cls = 0.571828 (* 1 = 0.571828 loss) +I0616 04:05:50.122408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0662814 (* 1 = 0.0662814 loss) +I0616 04:05:50.122416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126278 (* 1 = 0.0126278 loss) +I0616 04:05:50.122424 9857 solver.cpp:571] Iteration 9520, lr = 0.001 +I0616 04:06:05.746096 9857 solver.cpp:242] Iteration 9540, loss = 0.874603 +I0616 04:06:05.746142 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27065 (* 1 = 0.27065 loss) +I0616 04:06:05.746147 9857 solver.cpp:258] Train net output #1: loss_cls = 0.891897 (* 1 = 0.891897 loss) +I0616 04:06:05.746152 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0540634 (* 1 = 0.0540634 loss) +I0616 04:06:05.746155 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209494 (* 1 = 0.0209494 loss) +I0616 04:06:05.746160 9857 solver.cpp:571] Iteration 9540, lr = 0.001 +I0616 04:06:21.794591 9857 solver.cpp:242] Iteration 9560, loss = 0.671065 +I0616 04:06:21.794634 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247098 (* 1 = 0.247098 loss) +I0616 04:06:21.794641 9857 solver.cpp:258] Train net output #1: loss_cls = 0.284397 (* 1 = 0.284397 loss) +I0616 04:06:21.794644 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.269458 (* 1 = 0.269458 loss) +I0616 04:06:21.794648 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.170925 (* 1 = 0.170925 loss) +I0616 04:06:21.794652 9857 solver.cpp:571] Iteration 9560, lr = 0.001 +I0616 04:06:38.441871 9857 solver.cpp:242] Iteration 9580, loss = 1.22293 +I0616 04:06:38.441916 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45026 (* 1 = 0.45026 loss) +I0616 04:06:38.441922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.596921 (* 1 = 0.596921 loss) +I0616 04:06:38.441926 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.296608 (* 1 = 0.296608 loss) +I0616 04:06:38.441931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0657102 (* 1 = 0.0657102 loss) +I0616 04:06:38.441934 9857 solver.cpp:571] Iteration 9580, lr = 0.001 +speed: 0.757s / iter +I0616 04:06:52.920776 9857 solver.cpp:242] Iteration 9600, loss = 0.867194 +I0616 04:06:52.920821 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163732 (* 1 = 0.163732 loss) +I0616 04:06:52.920827 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16972 (* 1 = 0.16972 loss) +I0616 04:06:52.920831 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0223626 (* 1 = 0.0223626 loss) +I0616 04:06:52.920835 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0178937 (* 1 = 0.0178937 loss) +I0616 04:06:52.920840 9857 solver.cpp:571] Iteration 9600, lr = 0.001 +I0616 04:07:08.959449 9857 solver.cpp:242] Iteration 9620, loss = 1.63623 +I0616 04:07:08.959491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206359 (* 1 = 0.206359 loss) +I0616 04:07:08.959497 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4095 (* 1 = 0.4095 loss) +I0616 04:07:08.959502 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0253478 (* 1 = 0.0253478 loss) +I0616 04:07:08.959506 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0023324 (* 1 = 0.0023324 loss) +I0616 04:07:08.959511 9857 solver.cpp:571] Iteration 9620, lr = 0.001 +I0616 04:07:22.002203 9857 solver.cpp:242] Iteration 9640, loss = 1.14018 +I0616 04:07:22.002245 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235649 (* 1 = 0.235649 loss) +I0616 04:07:22.002251 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49953 (* 1 = 0.49953 loss) +I0616 04:07:22.002255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0956823 (* 1 = 0.0956823 loss) +I0616 04:07:22.002259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197402 (* 1 = 0.0197402 loss) +I0616 04:07:22.002264 9857 solver.cpp:571] Iteration 9640, lr = 0.001 +I0616 04:07:38.760421 9857 solver.cpp:242] Iteration 9660, loss = 1.00134 +I0616 04:07:38.760467 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.429127 (* 1 = 0.429127 loss) +I0616 04:07:38.760473 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394931 (* 1 = 0.394931 loss) +I0616 04:07:38.760476 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161108 (* 1 = 0.161108 loss) +I0616 04:07:38.760480 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0526449 (* 1 = 0.0526449 loss) +I0616 04:07:38.760485 9857 solver.cpp:571] Iteration 9660, lr = 0.001 +I0616 04:07:55.752364 9857 solver.cpp:242] Iteration 9680, loss = 1.27134 +I0616 04:07:55.752410 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30117 (* 1 = 0.30117 loss) +I0616 04:07:55.752416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209312 (* 1 = 0.209312 loss) +I0616 04:07:55.752420 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0633847 (* 1 = 0.0633847 loss) +I0616 04:07:55.752424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0482471 (* 1 = 0.0482471 loss) +I0616 04:07:55.752429 9857 solver.cpp:571] Iteration 9680, lr = 0.001 +I0616 04:08:10.636016 9857 solver.cpp:242] Iteration 9700, loss = 1.92603 +I0616 04:08:10.636062 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.654475 (* 1 = 0.654475 loss) +I0616 04:08:10.636068 9857 solver.cpp:258] Train net output #1: loss_cls = 1.5187 (* 1 = 1.5187 loss) +I0616 04:08:10.636072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.345172 (* 1 = 0.345172 loss) +I0616 04:08:10.636077 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.065857 (* 1 = 0.065857 loss) +I0616 04:08:10.636081 9857 solver.cpp:571] Iteration 9700, lr = 0.001 +I0616 04:08:26.083480 9857 solver.cpp:242] Iteration 9720, loss = 1.63054 +I0616 04:08:26.083560 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.484136 (* 1 = 0.484136 loss) +I0616 04:08:26.083572 9857 solver.cpp:258] Train net output #1: loss_cls = 0.561452 (* 1 = 0.561452 loss) +I0616 04:08:26.083580 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179937 (* 1 = 0.179937 loss) +I0616 04:08:26.083588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0651504 (* 1 = 0.0651504 loss) +I0616 04:08:26.083602 9857 solver.cpp:571] Iteration 9720, lr = 0.001 +I0616 04:08:43.452236 9857 solver.cpp:242] Iteration 9740, loss = 1.58629 +I0616 04:08:43.452281 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.372902 (* 1 = 0.372902 loss) +I0616 04:08:43.452286 9857 solver.cpp:258] Train net output #1: loss_cls = 0.572166 (* 1 = 0.572166 loss) +I0616 04:08:43.452291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.359589 (* 1 = 0.359589 loss) +I0616 04:08:43.452294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.42844 (* 1 = 0.42844 loss) +I0616 04:08:43.452299 9857 solver.cpp:571] Iteration 9740, lr = 0.001 +I0616 04:08:58.739405 9857 solver.cpp:242] Iteration 9760, loss = 0.673328 +I0616 04:08:58.739473 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288463 (* 1 = 0.288463 loss) +I0616 04:08:58.739485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.455784 (* 1 = 0.455784 loss) +I0616 04:08:58.739493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.070147 (* 1 = 0.070147 loss) +I0616 04:08:58.739500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158308 (* 1 = 0.0158308 loss) +I0616 04:08:58.739514 9857 solver.cpp:571] Iteration 9760, lr = 0.001 +I0616 04:09:15.730867 9857 solver.cpp:242] Iteration 9780, loss = 1.06453 +I0616 04:09:15.730914 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230328 (* 1 = 0.230328 loss) +I0616 04:09:15.730919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32326 (* 1 = 0.32326 loss) +I0616 04:09:15.730924 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.247273 (* 1 = 0.247273 loss) +I0616 04:09:15.730928 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0961741 (* 1 = 0.0961741 loss) +I0616 04:09:15.730932 9857 solver.cpp:571] Iteration 9780, lr = 0.001 +speed: 0.757s / iter +I0616 04:09:30.171967 9857 solver.cpp:242] Iteration 9800, loss = 1.21374 +I0616 04:09:30.172006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392933 (* 1 = 0.392933 loss) +I0616 04:09:30.172013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.688338 (* 1 = 0.688338 loss) +I0616 04:09:30.172016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.338351 (* 1 = 0.338351 loss) +I0616 04:09:30.172020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.476713 (* 1 = 0.476713 loss) +I0616 04:09:30.172024 9857 solver.cpp:571] Iteration 9800, lr = 0.001 +I0616 04:09:44.000489 9857 solver.cpp:242] Iteration 9820, loss = 1.04214 +I0616 04:09:44.000530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.501464 (* 1 = 0.501464 loss) +I0616 04:09:44.000536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.63384 (* 1 = 0.63384 loss) +I0616 04:09:44.000540 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.098663 (* 1 = 0.098663 loss) +I0616 04:09:44.000545 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0891576 (* 1 = 0.0891576 loss) +I0616 04:09:44.000548 9857 solver.cpp:571] Iteration 9820, lr = 0.001 +I0616 04:09:58.390408 9857 solver.cpp:242] Iteration 9840, loss = 1.00131 +I0616 04:09:58.390451 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241156 (* 1 = 0.241156 loss) +I0616 04:09:58.390457 9857 solver.cpp:258] Train net output #1: loss_cls = 0.618913 (* 1 = 0.618913 loss) +I0616 04:09:58.390461 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.09061 (* 1 = 0.09061 loss) +I0616 04:09:58.390465 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114711 (* 1 = 0.0114711 loss) +I0616 04:09:58.390470 9857 solver.cpp:571] Iteration 9840, lr = 0.001 +I0616 04:10:09.724009 9857 solver.cpp:242] Iteration 9860, loss = 1.07435 +I0616 04:10:09.724052 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3056 (* 1 = 0.3056 loss) +I0616 04:10:09.724058 9857 solver.cpp:258] Train net output #1: loss_cls = 0.707792 (* 1 = 0.707792 loss) +I0616 04:10:09.724063 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.048742 (* 1 = 0.048742 loss) +I0616 04:10:09.724067 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273071 (* 1 = 0.0273071 loss) +I0616 04:10:09.724071 9857 solver.cpp:571] Iteration 9860, lr = 0.001 +I0616 04:10:21.278056 9857 solver.cpp:242] Iteration 9880, loss = 0.970301 +I0616 04:10:21.278100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188401 (* 1 = 0.188401 loss) +I0616 04:10:21.278105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.639182 (* 1 = 0.639182 loss) +I0616 04:10:21.278110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0364629 (* 1 = 0.0364629 loss) +I0616 04:10:21.278113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0422036 (* 1 = 0.0422036 loss) +I0616 04:10:21.278117 9857 solver.cpp:571] Iteration 9880, lr = 0.001 +I0616 04:10:32.879329 9857 solver.cpp:242] Iteration 9900, loss = 0.873369 +I0616 04:10:32.879371 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213262 (* 1 = 0.213262 loss) +I0616 04:10:32.879377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399079 (* 1 = 0.399079 loss) +I0616 04:10:32.879381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374019 (* 1 = 0.0374019 loss) +I0616 04:10:32.879385 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013246 (* 1 = 0.013246 loss) +I0616 04:10:32.879390 9857 solver.cpp:571] Iteration 9900, lr = 0.001 +I0616 04:10:44.640494 9857 solver.cpp:242] Iteration 9920, loss = 0.552127 +I0616 04:10:44.640537 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.15795 (* 1 = 0.15795 loss) +I0616 04:10:44.640543 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202837 (* 1 = 0.202837 loss) +I0616 04:10:44.640547 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0965204 (* 1 = 0.0965204 loss) +I0616 04:10:44.640552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00822898 (* 1 = 0.00822898 loss) +I0616 04:10:44.640555 9857 solver.cpp:571] Iteration 9920, lr = 0.001 +I0616 04:10:56.254750 9857 solver.cpp:242] Iteration 9940, loss = 1.21937 +I0616 04:10:56.254797 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209569 (* 1 = 0.209569 loss) +I0616 04:10:56.254803 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269229 (* 1 = 0.269229 loss) +I0616 04:10:56.254807 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0645201 (* 1 = 0.0645201 loss) +I0616 04:10:56.254812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00226262 (* 1 = 0.00226262 loss) +I0616 04:10:56.254814 9857 solver.cpp:571] Iteration 9940, lr = 0.001 +I0616 04:11:07.797767 9857 solver.cpp:242] Iteration 9960, loss = 0.752595 +I0616 04:11:07.797809 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315266 (* 1 = 0.315266 loss) +I0616 04:11:07.797814 9857 solver.cpp:258] Train net output #1: loss_cls = 0.30731 (* 1 = 0.30731 loss) +I0616 04:11:07.797819 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106744 (* 1 = 0.106744 loss) +I0616 04:11:07.797823 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0832054 (* 1 = 0.0832054 loss) +I0616 04:11:07.797827 9857 solver.cpp:571] Iteration 9960, lr = 0.001 +I0616 04:11:19.322896 9857 solver.cpp:242] Iteration 9980, loss = 0.606473 +I0616 04:11:19.322938 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.095199 (* 1 = 0.095199 loss) +I0616 04:11:19.322944 9857 solver.cpp:258] Train net output #1: loss_cls = 0.133437 (* 1 = 0.133437 loss) +I0616 04:11:19.322948 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0527068 (* 1 = 0.0527068 loss) +I0616 04:11:19.322952 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00156297 (* 1 = 0.00156297 loss) +I0616 04:11:19.322958 9857 solver.cpp:571] Iteration 9980, lr = 0.001 +speed: 0.754s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_10000.caffemodel +I0616 04:11:33.838579 9857 solver.cpp:242] Iteration 10000, loss = 1.38888 +I0616 04:11:33.838623 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394316 (* 1 = 0.394316 loss) +I0616 04:11:33.838642 9857 solver.cpp:258] Train net output #1: loss_cls = 0.522359 (* 1 = 0.522359 loss) +I0616 04:11:33.838646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193295 (* 1 = 0.193295 loss) +I0616 04:11:33.838650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111871 (* 1 = 0.111871 loss) +I0616 04:11:33.838654 9857 solver.cpp:571] Iteration 10000, lr = 0.001 +I0616 04:11:45.485358 9857 solver.cpp:242] Iteration 10020, loss = 0.383228 +I0616 04:11:45.485400 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211236 (* 1 = 0.211236 loss) +I0616 04:11:45.485406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.224165 (* 1 = 0.224165 loss) +I0616 04:11:45.485410 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0187268 (* 1 = 0.0187268 loss) +I0616 04:11:45.485414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.026139 (* 1 = 0.026139 loss) +I0616 04:11:45.485432 9857 solver.cpp:571] Iteration 10020, lr = 0.001 +I0616 04:11:57.218806 9857 solver.cpp:242] Iteration 10040, loss = 0.919304 +I0616 04:11:57.218847 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.487458 (* 1 = 0.487458 loss) +I0616 04:11:57.218852 9857 solver.cpp:258] Train net output #1: loss_cls = 0.651255 (* 1 = 0.651255 loss) +I0616 04:11:57.218857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.324933 (* 1 = 0.324933 loss) +I0616 04:11:57.218860 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0517836 (* 1 = 0.0517836 loss) +I0616 04:11:57.218864 9857 solver.cpp:571] Iteration 10040, lr = 0.001 +I0616 04:12:08.721323 9857 solver.cpp:242] Iteration 10060, loss = 0.763853 +I0616 04:12:08.721365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352091 (* 1 = 0.352091 loss) +I0616 04:12:08.721371 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265296 (* 1 = 0.265296 loss) +I0616 04:12:08.721375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0378197 (* 1 = 0.0378197 loss) +I0616 04:12:08.721379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00873937 (* 1 = 0.00873937 loss) +I0616 04:12:08.721384 9857 solver.cpp:571] Iteration 10060, lr = 0.001 +I0616 04:12:20.101768 9857 solver.cpp:242] Iteration 10080, loss = 1.47116 +I0616 04:12:20.101811 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45352 (* 1 = 0.45352 loss) +I0616 04:12:20.101817 9857 solver.cpp:258] Train net output #1: loss_cls = 1.27468 (* 1 = 1.27468 loss) +I0616 04:12:20.101821 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138375 (* 1 = 0.138375 loss) +I0616 04:12:20.101825 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.246372 (* 1 = 0.246372 loss) +I0616 04:12:20.101830 9857 solver.cpp:571] Iteration 10080, lr = 0.001 +I0616 04:12:31.856230 9857 solver.cpp:242] Iteration 10100, loss = 1.20954 +I0616 04:12:31.856273 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.522554 (* 1 = 0.522554 loss) +I0616 04:12:31.856278 9857 solver.cpp:258] Train net output #1: loss_cls = 0.824729 (* 1 = 0.824729 loss) +I0616 04:12:31.856282 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201735 (* 1 = 0.201735 loss) +I0616 04:12:31.856287 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0861203 (* 1 = 0.0861203 loss) +I0616 04:12:31.856290 9857 solver.cpp:571] Iteration 10100, lr = 0.001 +I0616 04:12:43.412194 9857 solver.cpp:242] Iteration 10120, loss = 0.939115 +I0616 04:12:43.412237 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100799 (* 1 = 0.100799 loss) +I0616 04:12:43.412243 9857 solver.cpp:258] Train net output #1: loss_cls = 0.408518 (* 1 = 0.408518 loss) +I0616 04:12:43.412247 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.083141 (* 1 = 0.083141 loss) +I0616 04:12:43.412251 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0347785 (* 1 = 0.0347785 loss) +I0616 04:12:43.412256 9857 solver.cpp:571] Iteration 10120, lr = 0.001 +I0616 04:12:54.916070 9857 solver.cpp:242] Iteration 10140, loss = 1.2531 +I0616 04:12:54.916111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.475063 (* 1 = 0.475063 loss) +I0616 04:12:54.916116 9857 solver.cpp:258] Train net output #1: loss_cls = 0.577682 (* 1 = 0.577682 loss) +I0616 04:12:54.916121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175238 (* 1 = 0.175238 loss) +I0616 04:12:54.916124 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0742531 (* 1 = 0.0742531 loss) +I0616 04:12:54.916128 9857 solver.cpp:571] Iteration 10140, lr = 0.001 +I0616 04:13:06.429692 9857 solver.cpp:242] Iteration 10160, loss = 1.16496 +I0616 04:13:06.429731 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161108 (* 1 = 0.161108 loss) +I0616 04:13:06.429738 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169472 (* 1 = 0.169472 loss) +I0616 04:13:06.429741 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0256122 (* 1 = 0.0256122 loss) +I0616 04:13:06.429745 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164693 (* 1 = 0.0164693 loss) +I0616 04:13:06.429749 9857 solver.cpp:571] Iteration 10160, lr = 0.001 +I0616 04:13:17.859941 9857 solver.cpp:242] Iteration 10180, loss = 1.36735 +I0616 04:13:17.859982 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161252 (* 1 = 0.161252 loss) +I0616 04:13:17.859988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225583 (* 1 = 0.225583 loss) +I0616 04:13:17.859993 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165052 (* 1 = 0.165052 loss) +I0616 04:13:17.859997 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197454 (* 1 = 0.0197454 loss) +I0616 04:13:17.860000 9857 solver.cpp:571] Iteration 10180, lr = 0.001 +speed: 0.751s / iter +I0616 04:13:29.245455 9857 solver.cpp:242] Iteration 10200, loss = 0.744018 +I0616 04:13:29.245496 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199987 (* 1 = 0.199987 loss) +I0616 04:13:29.245502 9857 solver.cpp:258] Train net output #1: loss_cls = 0.532089 (* 1 = 0.532089 loss) +I0616 04:13:29.245507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104558 (* 1 = 0.104558 loss) +I0616 04:13:29.245510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169792 (* 1 = 0.0169792 loss) +I0616 04:13:29.245513 9857 solver.cpp:571] Iteration 10200, lr = 0.001 +I0616 04:13:40.646586 9857 solver.cpp:242] Iteration 10220, loss = 2.17088 +I0616 04:13:40.646630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131793 (* 1 = 0.131793 loss) +I0616 04:13:40.646636 9857 solver.cpp:258] Train net output #1: loss_cls = 0.807613 (* 1 = 0.807613 loss) +I0616 04:13:40.646639 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 1.56224 (* 1 = 1.56224 loss) +I0616 04:13:40.646643 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.16249 (* 1 = 1.16249 loss) +I0616 04:13:40.646647 9857 solver.cpp:571] Iteration 10220, lr = 0.001 +I0616 04:13:52.307634 9857 solver.cpp:242] Iteration 10240, loss = 1.18725 +I0616 04:13:52.307677 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187442 (* 1 = 0.187442 loss) +I0616 04:13:52.307683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.397586 (* 1 = 0.397586 loss) +I0616 04:13:52.307687 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0656105 (* 1 = 0.0656105 loss) +I0616 04:13:52.307692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0678302 (* 1 = 0.0678302 loss) +I0616 04:13:52.307694 9857 solver.cpp:571] Iteration 10240, lr = 0.001 +I0616 04:14:03.670480 9857 solver.cpp:242] Iteration 10260, loss = 2.29196 +I0616 04:14:03.670521 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.393939 (* 1 = 0.393939 loss) +I0616 04:14:03.670526 9857 solver.cpp:258] Train net output #1: loss_cls = 1.20338 (* 1 = 1.20338 loss) +I0616 04:14:03.670531 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105981 (* 1 = 0.105981 loss) +I0616 04:14:03.670534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0480854 (* 1 = 0.0480854 loss) +I0616 04:14:03.670538 9857 solver.cpp:571] Iteration 10260, lr = 0.001 +I0616 04:14:15.385432 9857 solver.cpp:242] Iteration 10280, loss = 0.31846 +I0616 04:14:15.385473 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0774039 (* 1 = 0.0774039 loss) +I0616 04:14:15.385478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178683 (* 1 = 0.178683 loss) +I0616 04:14:15.385483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0220273 (* 1 = 0.0220273 loss) +I0616 04:14:15.385486 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00167824 (* 1 = 0.00167824 loss) +I0616 04:14:15.385490 9857 solver.cpp:571] Iteration 10280, lr = 0.001 +I0616 04:14:26.687824 9857 solver.cpp:242] Iteration 10300, loss = 1.6932 +I0616 04:14:26.687866 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.591869 (* 1 = 0.591869 loss) +I0616 04:14:26.687872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.962129 (* 1 = 0.962129 loss) +I0616 04:14:26.687876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.278011 (* 1 = 0.278011 loss) +I0616 04:14:26.687880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0356281 (* 1 = 0.0356281 loss) +I0616 04:14:26.687885 9857 solver.cpp:571] Iteration 10300, lr = 0.001 +I0616 04:14:38.044306 9857 solver.cpp:242] Iteration 10320, loss = 0.754396 +I0616 04:14:38.044347 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32394 (* 1 = 0.32394 loss) +I0616 04:14:38.044353 9857 solver.cpp:258] Train net output #1: loss_cls = 0.382076 (* 1 = 0.382076 loss) +I0616 04:14:38.044356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122617 (* 1 = 0.122617 loss) +I0616 04:14:38.044360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285273 (* 1 = 0.0285273 loss) +I0616 04:14:38.044364 9857 solver.cpp:571] Iteration 10320, lr = 0.001 +I0616 04:14:49.317757 9857 solver.cpp:242] Iteration 10340, loss = 0.880199 +I0616 04:14:49.317798 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205992 (* 1 = 0.205992 loss) +I0616 04:14:49.317803 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358094 (* 1 = 0.358094 loss) +I0616 04:14:49.317808 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0459788 (* 1 = 0.0459788 loss) +I0616 04:14:49.317811 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.043553 (* 1 = 0.043553 loss) +I0616 04:14:49.317816 9857 solver.cpp:571] Iteration 10340, lr = 0.001 +I0616 04:15:00.869771 9857 solver.cpp:242] Iteration 10360, loss = 0.75484 +I0616 04:15:00.869813 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0854557 (* 1 = 0.0854557 loss) +I0616 04:15:00.869818 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236152 (* 1 = 0.236152 loss) +I0616 04:15:00.869823 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.229962 (* 1 = 0.229962 loss) +I0616 04:15:00.869827 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00585207 (* 1 = 0.00585207 loss) +I0616 04:15:00.869830 9857 solver.cpp:571] Iteration 10360, lr = 0.001 +I0616 04:15:12.405367 9857 solver.cpp:242] Iteration 10380, loss = 1.02512 +I0616 04:15:12.405412 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.335402 (* 1 = 0.335402 loss) +I0616 04:15:12.405417 9857 solver.cpp:258] Train net output #1: loss_cls = 0.617113 (* 1 = 0.617113 loss) +I0616 04:15:12.405422 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0441311 (* 1 = 0.0441311 loss) +I0616 04:15:12.405426 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0432682 (* 1 = 0.0432682 loss) +I0616 04:15:12.405432 9857 solver.cpp:571] Iteration 10380, lr = 0.001 +speed: 0.747s / iter +I0616 04:15:23.887537 9857 solver.cpp:242] Iteration 10400, loss = 1.01905 +I0616 04:15:23.887580 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453632 (* 1 = 0.453632 loss) +I0616 04:15:23.887586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360756 (* 1 = 0.360756 loss) +I0616 04:15:23.887590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111896 (* 1 = 0.111896 loss) +I0616 04:15:23.887594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.224409 (* 1 = 0.224409 loss) +I0616 04:15:23.887598 9857 solver.cpp:571] Iteration 10400, lr = 0.001 +I0616 04:15:35.478940 9857 solver.cpp:242] Iteration 10420, loss = 0.683968 +I0616 04:15:35.478982 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304914 (* 1 = 0.304914 loss) +I0616 04:15:35.478987 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263697 (* 1 = 0.263697 loss) +I0616 04:15:35.478991 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10223 (* 1 = 0.10223 loss) +I0616 04:15:35.478996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00927927 (* 1 = 0.00927927 loss) +I0616 04:15:35.478999 9857 solver.cpp:571] Iteration 10420, lr = 0.001 +I0616 04:15:47.013730 9857 solver.cpp:242] Iteration 10440, loss = 1.13379 +I0616 04:15:47.013770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331761 (* 1 = 0.331761 loss) +I0616 04:15:47.013777 9857 solver.cpp:258] Train net output #1: loss_cls = 0.647662 (* 1 = 0.647662 loss) +I0616 04:15:47.013780 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18753 (* 1 = 0.18753 loss) +I0616 04:15:47.013784 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160186 (* 1 = 0.0160186 loss) +I0616 04:15:47.013788 9857 solver.cpp:571] Iteration 10440, lr = 0.001 +I0616 04:15:58.588217 9857 solver.cpp:242] Iteration 10460, loss = 1.02737 +I0616 04:15:58.588258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227311 (* 1 = 0.227311 loss) +I0616 04:15:58.588264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370257 (* 1 = 0.370257 loss) +I0616 04:15:58.588268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0685585 (* 1 = 0.0685585 loss) +I0616 04:15:58.588273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013873 (* 1 = 0.013873 loss) +I0616 04:15:58.588276 9857 solver.cpp:571] Iteration 10460, lr = 0.001 +I0616 04:16:10.254050 9857 solver.cpp:242] Iteration 10480, loss = 1.45815 +I0616 04:16:10.254093 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.621361 (* 1 = 0.621361 loss) +I0616 04:16:10.254098 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03625 (* 1 = 1.03625 loss) +I0616 04:16:10.254102 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.202184 (* 1 = 0.202184 loss) +I0616 04:16:10.254107 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109117 (* 1 = 0.0109117 loss) +I0616 04:16:10.254113 9857 solver.cpp:571] Iteration 10480, lr = 0.001 +I0616 04:16:21.700875 9857 solver.cpp:242] Iteration 10500, loss = 1.98307 +I0616 04:16:21.700917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.478485 (* 1 = 0.478485 loss) +I0616 04:16:21.700923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.861768 (* 1 = 0.861768 loss) +I0616 04:16:21.700927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.824437 (* 1 = 0.824437 loss) +I0616 04:16:21.700932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.627161 (* 1 = 0.627161 loss) +I0616 04:16:21.700934 9857 solver.cpp:571] Iteration 10500, lr = 0.001 +I0616 04:16:33.047693 9857 solver.cpp:242] Iteration 10520, loss = 1.02628 +I0616 04:16:33.047735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124644 (* 1 = 0.124644 loss) +I0616 04:16:33.047740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257316 (* 1 = 0.257316 loss) +I0616 04:16:33.047744 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.276798 (* 1 = 0.276798 loss) +I0616 04:16:33.047749 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162834 (* 1 = 0.162834 loss) +I0616 04:16:33.047752 9857 solver.cpp:571] Iteration 10520, lr = 0.001 +I0616 04:16:44.657155 9857 solver.cpp:242] Iteration 10540, loss = 0.995575 +I0616 04:16:44.657197 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320765 (* 1 = 0.320765 loss) +I0616 04:16:44.657202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.820239 (* 1 = 0.820239 loss) +I0616 04:16:44.657207 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134479 (* 1 = 0.134479 loss) +I0616 04:16:44.657210 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112681 (* 1 = 0.0112681 loss) +I0616 04:16:44.657214 9857 solver.cpp:571] Iteration 10540, lr = 0.001 +I0616 04:16:56.042007 9857 solver.cpp:242] Iteration 10560, loss = 1.18037 +I0616 04:16:56.042050 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199522 (* 1 = 0.199522 loss) +I0616 04:16:56.042057 9857 solver.cpp:258] Train net output #1: loss_cls = 0.403187 (* 1 = 0.403187 loss) +I0616 04:16:56.042062 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0776857 (* 1 = 0.0776857 loss) +I0616 04:16:56.042064 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00780422 (* 1 = 0.00780422 loss) +I0616 04:16:56.042068 9857 solver.cpp:571] Iteration 10560, lr = 0.001 +I0616 04:17:07.512753 9857 solver.cpp:242] Iteration 10580, loss = 0.884276 +I0616 04:17:07.512796 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244935 (* 1 = 0.244935 loss) +I0616 04:17:07.512801 9857 solver.cpp:258] Train net output #1: loss_cls = 0.530357 (* 1 = 0.530357 loss) +I0616 04:17:07.512806 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.238009 (* 1 = 0.238009 loss) +I0616 04:17:07.512810 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00575673 (* 1 = 0.00575673 loss) +I0616 04:17:07.512814 9857 solver.cpp:571] Iteration 10580, lr = 0.001 +speed: 0.744s / iter +I0616 04:17:18.919495 9857 solver.cpp:242] Iteration 10600, loss = 1.02344 +I0616 04:17:18.919538 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163099 (* 1 = 0.163099 loss) +I0616 04:17:18.919543 9857 solver.cpp:258] Train net output #1: loss_cls = 0.106419 (* 1 = 0.106419 loss) +I0616 04:17:18.919548 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0725845 (* 1 = 0.0725845 loss) +I0616 04:17:18.919551 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00946184 (* 1 = 0.00946184 loss) +I0616 04:17:18.919555 9857 solver.cpp:571] Iteration 10600, lr = 0.001 +I0616 04:17:30.730988 9857 solver.cpp:242] Iteration 10620, loss = 0.684389 +I0616 04:17:30.731030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190078 (* 1 = 0.190078 loss) +I0616 04:17:30.731035 9857 solver.cpp:258] Train net output #1: loss_cls = 0.734658 (* 1 = 0.734658 loss) +I0616 04:17:30.731040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0723978 (* 1 = 0.0723978 loss) +I0616 04:17:30.731043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.058126 (* 1 = 0.058126 loss) +I0616 04:17:30.731047 9857 solver.cpp:571] Iteration 10620, lr = 0.001 +I0616 04:17:42.181047 9857 solver.cpp:242] Iteration 10640, loss = 1.79593 +I0616 04:17:42.181089 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.555667 (* 1 = 0.555667 loss) +I0616 04:17:42.181095 9857 solver.cpp:258] Train net output #1: loss_cls = 1.36573 (* 1 = 1.36573 loss) +I0616 04:17:42.181099 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.189764 (* 1 = 0.189764 loss) +I0616 04:17:42.181102 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0552133 (* 1 = 0.0552133 loss) +I0616 04:17:42.181107 9857 solver.cpp:571] Iteration 10640, lr = 0.001 +I0616 04:17:53.780827 9857 solver.cpp:242] Iteration 10660, loss = 1.63233 +I0616 04:17:53.780869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.579051 (* 1 = 0.579051 loss) +I0616 04:17:53.780874 9857 solver.cpp:258] Train net output #1: loss_cls = 1.4486 (* 1 = 1.4486 loss) +I0616 04:17:53.780879 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1648 (* 1 = 0.1648 loss) +I0616 04:17:53.780882 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0509899 (* 1 = 0.0509899 loss) +I0616 04:17:53.780886 9857 solver.cpp:571] Iteration 10660, lr = 0.001 +I0616 04:18:05.245534 9857 solver.cpp:242] Iteration 10680, loss = 1.36461 +I0616 04:18:05.245574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.58319 (* 1 = 0.58319 loss) +I0616 04:18:05.245581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.661225 (* 1 = 0.661225 loss) +I0616 04:18:05.245585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.376738 (* 1 = 0.376738 loss) +I0616 04:18:05.245589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.165975 (* 1 = 0.165975 loss) +I0616 04:18:05.245592 9857 solver.cpp:571] Iteration 10680, lr = 0.001 +I0616 04:18:16.540169 9857 solver.cpp:242] Iteration 10700, loss = 1.74265 +I0616 04:18:16.540210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363556 (* 1 = 0.363556 loss) +I0616 04:18:16.540216 9857 solver.cpp:258] Train net output #1: loss_cls = 0.716097 (* 1 = 0.716097 loss) +I0616 04:18:16.540220 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.623222 (* 1 = 0.623222 loss) +I0616 04:18:16.540225 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.215782 (* 1 = 0.215782 loss) +I0616 04:18:16.540228 9857 solver.cpp:571] Iteration 10700, lr = 0.001 +I0616 04:18:27.892271 9857 solver.cpp:242] Iteration 10720, loss = 1.40372 +I0616 04:18:27.892313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413642 (* 1 = 0.413642 loss) +I0616 04:18:27.892319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.896335 (* 1 = 0.896335 loss) +I0616 04:18:27.892323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122305 (* 1 = 0.122305 loss) +I0616 04:18:27.892328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494612 (* 1 = 0.0494612 loss) +I0616 04:18:27.892331 9857 solver.cpp:571] Iteration 10720, lr = 0.001 +I0616 04:18:39.322942 9857 solver.cpp:242] Iteration 10740, loss = 1.60475 +I0616 04:18:39.322983 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.319003 (* 1 = 0.319003 loss) +I0616 04:18:39.322988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.737573 (* 1 = 0.737573 loss) +I0616 04:18:39.322993 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.994446 (* 1 = 0.994446 loss) +I0616 04:18:39.322996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.595255 (* 1 = 0.595255 loss) +I0616 04:18:39.322999 9857 solver.cpp:571] Iteration 10740, lr = 0.001 +I0616 04:18:50.479614 9857 solver.cpp:242] Iteration 10760, loss = 0.494288 +I0616 04:18:50.479655 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19607 (* 1 = 0.19607 loss) +I0616 04:18:50.479660 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27908 (* 1 = 0.27908 loss) +I0616 04:18:50.479665 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175484 (* 1 = 0.175484 loss) +I0616 04:18:50.479668 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00952471 (* 1 = 0.00952471 loss) +I0616 04:18:50.479672 9857 solver.cpp:571] Iteration 10760, lr = 0.001 +I0616 04:19:02.279187 9857 solver.cpp:242] Iteration 10780, loss = 1.11234 +I0616 04:19:02.279232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.665133 (* 1 = 0.665133 loss) +I0616 04:19:02.279237 9857 solver.cpp:258] Train net output #1: loss_cls = 0.43153 (* 1 = 0.43153 loss) +I0616 04:19:02.279242 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0836958 (* 1 = 0.0836958 loss) +I0616 04:19:02.279245 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152248 (* 1 = 0.0152248 loss) +I0616 04:19:02.279248 9857 solver.cpp:571] Iteration 10780, lr = 0.001 +speed: 0.741s / iter +I0616 04:19:13.463213 9857 solver.cpp:242] Iteration 10800, loss = 1.89642 +I0616 04:19:13.463258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.494621 (* 1 = 0.494621 loss) +I0616 04:19:13.463263 9857 solver.cpp:258] Train net output #1: loss_cls = 1.54872 (* 1 = 1.54872 loss) +I0616 04:19:13.463266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0818747 (* 1 = 0.0818747 loss) +I0616 04:19:13.463270 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125017 (* 1 = 0.0125017 loss) +I0616 04:19:13.463274 9857 solver.cpp:571] Iteration 10800, lr = 0.001 +I0616 04:19:25.231830 9857 solver.cpp:242] Iteration 10820, loss = 0.983127 +I0616 04:19:25.231871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.492388 (* 1 = 0.492388 loss) +I0616 04:19:25.231876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.881221 (* 1 = 0.881221 loss) +I0616 04:19:25.231881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0994911 (* 1 = 0.0994911 loss) +I0616 04:19:25.231884 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0526084 (* 1 = 0.0526084 loss) +I0616 04:19:25.231890 9857 solver.cpp:571] Iteration 10820, lr = 0.001 +I0616 04:19:36.913110 9857 solver.cpp:242] Iteration 10840, loss = 0.569178 +I0616 04:19:36.913151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258685 (* 1 = 0.258685 loss) +I0616 04:19:36.913172 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381755 (* 1 = 0.381755 loss) +I0616 04:19:36.913177 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0761163 (* 1 = 0.0761163 loss) +I0616 04:19:36.913180 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123984 (* 1 = 0.0123984 loss) +I0616 04:19:36.913184 9857 solver.cpp:571] Iteration 10840, lr = 0.001 +I0616 04:19:48.279434 9857 solver.cpp:242] Iteration 10860, loss = 0.89952 +I0616 04:19:48.279476 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.497458 (* 1 = 0.497458 loss) +I0616 04:19:48.279481 9857 solver.cpp:258] Train net output #1: loss_cls = 0.558711 (* 1 = 0.558711 loss) +I0616 04:19:48.279486 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.347596 (* 1 = 0.347596 loss) +I0616 04:19:48.279489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0491375 (* 1 = 0.0491375 loss) +I0616 04:19:48.279494 9857 solver.cpp:571] Iteration 10860, lr = 0.001 +I0616 04:19:59.744132 9857 solver.cpp:242] Iteration 10880, loss = 1.08452 +I0616 04:19:59.744175 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350932 (* 1 = 0.350932 loss) +I0616 04:19:59.744180 9857 solver.cpp:258] Train net output #1: loss_cls = 0.955492 (* 1 = 0.955492 loss) +I0616 04:19:59.744184 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.185652 (* 1 = 0.185652 loss) +I0616 04:19:59.744189 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0428284 (* 1 = 0.0428284 loss) +I0616 04:19:59.744192 9857 solver.cpp:571] Iteration 10880, lr = 0.001 +I0616 04:20:11.204123 9857 solver.cpp:242] Iteration 10900, loss = 1.6162 +I0616 04:20:11.204166 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284889 (* 1 = 0.284889 loss) +I0616 04:20:11.204171 9857 solver.cpp:258] Train net output #1: loss_cls = 0.744048 (* 1 = 0.744048 loss) +I0616 04:20:11.204176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147425 (* 1 = 0.147425 loss) +I0616 04:20:11.204180 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0412359 (* 1 = 0.0412359 loss) +I0616 04:20:11.204185 9857 solver.cpp:571] Iteration 10900, lr = 0.001 +I0616 04:20:22.881659 9857 solver.cpp:242] Iteration 10920, loss = 0.773102 +I0616 04:20:22.881700 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354303 (* 1 = 0.354303 loss) +I0616 04:20:22.881706 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15651 (* 1 = 0.15651 loss) +I0616 04:20:22.881711 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0420436 (* 1 = 0.0420436 loss) +I0616 04:20:22.881714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227006 (* 1 = 0.0227006 loss) +I0616 04:20:22.881718 9857 solver.cpp:571] Iteration 10920, lr = 0.001 +I0616 04:20:34.363224 9857 solver.cpp:242] Iteration 10940, loss = 1.45316 +I0616 04:20:34.363265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.383553 (* 1 = 0.383553 loss) +I0616 04:20:34.363270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.840974 (* 1 = 0.840974 loss) +I0616 04:20:34.363275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.226422 (* 1 = 0.226422 loss) +I0616 04:20:34.363278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.090799 (* 1 = 0.090799 loss) +I0616 04:20:34.363282 9857 solver.cpp:571] Iteration 10940, lr = 0.001 +I0616 04:20:45.962149 9857 solver.cpp:242] Iteration 10960, loss = 1.19393 +I0616 04:20:45.962191 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256519 (* 1 = 0.256519 loss) +I0616 04:20:45.962198 9857 solver.cpp:258] Train net output #1: loss_cls = 0.842242 (* 1 = 0.842242 loss) +I0616 04:20:45.962201 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110903 (* 1 = 0.110903 loss) +I0616 04:20:45.962205 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.062814 (* 1 = 0.062814 loss) +I0616 04:20:45.962209 9857 solver.cpp:571] Iteration 10960, lr = 0.001 +I0616 04:20:57.784849 9857 solver.cpp:242] Iteration 10980, loss = 0.654773 +I0616 04:20:57.784891 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324879 (* 1 = 0.324879 loss) +I0616 04:20:57.784896 9857 solver.cpp:258] Train net output #1: loss_cls = 0.346259 (* 1 = 0.346259 loss) +I0616 04:20:57.784901 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130761 (* 1 = 0.130761 loss) +I0616 04:20:57.784904 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0229978 (* 1 = 0.0229978 loss) +I0616 04:20:57.784909 9857 solver.cpp:571] Iteration 10980, lr = 0.001 +speed: 0.738s / iter +I0616 04:21:09.344342 9857 solver.cpp:242] Iteration 11000, loss = 0.704549 +I0616 04:21:09.344383 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218042 (* 1 = 0.218042 loss) +I0616 04:21:09.344388 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23341 (* 1 = 0.23341 loss) +I0616 04:21:09.344393 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.213539 (* 1 = 0.213539 loss) +I0616 04:21:09.344396 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0425055 (* 1 = 0.0425055 loss) +I0616 04:21:09.344400 9857 solver.cpp:571] Iteration 11000, lr = 0.001 +I0616 04:21:20.615844 9857 solver.cpp:242] Iteration 11020, loss = 0.574244 +I0616 04:21:20.615886 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220239 (* 1 = 0.220239 loss) +I0616 04:21:20.615892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335607 (* 1 = 0.335607 loss) +I0616 04:21:20.615896 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0523055 (* 1 = 0.0523055 loss) +I0616 04:21:20.615900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129663 (* 1 = 0.0129663 loss) +I0616 04:21:20.615905 9857 solver.cpp:571] Iteration 11020, lr = 0.001 +I0616 04:21:32.312630 9857 solver.cpp:242] Iteration 11040, loss = 0.978246 +I0616 04:21:32.312672 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229161 (* 1 = 0.229161 loss) +I0616 04:21:32.312677 9857 solver.cpp:258] Train net output #1: loss_cls = 0.638946 (* 1 = 0.638946 loss) +I0616 04:21:32.312682 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0724052 (* 1 = 0.0724052 loss) +I0616 04:21:32.312686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283822 (* 1 = 0.0283822 loss) +I0616 04:21:32.312690 9857 solver.cpp:571] Iteration 11040, lr = 0.001 +I0616 04:21:43.967552 9857 solver.cpp:242] Iteration 11060, loss = 0.664618 +I0616 04:21:43.967594 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110758 (* 1 = 0.110758 loss) +I0616 04:21:43.967600 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185785 (* 1 = 0.185785 loss) +I0616 04:21:43.967604 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0240142 (* 1 = 0.0240142 loss) +I0616 04:21:43.967608 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0236032 (* 1 = 0.0236032 loss) +I0616 04:21:43.967612 9857 solver.cpp:571] Iteration 11060, lr = 0.001 +I0616 04:21:55.598872 9857 solver.cpp:242] Iteration 11080, loss = 1.1279 +I0616 04:21:55.598915 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.450364 (* 1 = 0.450364 loss) +I0616 04:21:55.598920 9857 solver.cpp:258] Train net output #1: loss_cls = 0.423098 (* 1 = 0.423098 loss) +I0616 04:21:55.598925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246409 (* 1 = 0.246409 loss) +I0616 04:21:55.598929 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0909683 (* 1 = 0.0909683 loss) +I0616 04:21:55.598935 9857 solver.cpp:571] Iteration 11080, lr = 0.001 +I0616 04:22:07.320883 9857 solver.cpp:242] Iteration 11100, loss = 0.552078 +I0616 04:22:07.320924 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239003 (* 1 = 0.239003 loss) +I0616 04:22:07.320930 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205063 (* 1 = 0.205063 loss) +I0616 04:22:07.320935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0498064 (* 1 = 0.0498064 loss) +I0616 04:22:07.320937 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168393 (* 1 = 0.0168393 loss) +I0616 04:22:07.320941 9857 solver.cpp:571] Iteration 11100, lr = 0.001 +I0616 04:22:18.628201 9857 solver.cpp:242] Iteration 11120, loss = 0.976431 +I0616 04:22:18.628242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384408 (* 1 = 0.384408 loss) +I0616 04:22:18.628247 9857 solver.cpp:258] Train net output #1: loss_cls = 0.470421 (* 1 = 0.470421 loss) +I0616 04:22:18.628252 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0584409 (* 1 = 0.0584409 loss) +I0616 04:22:18.628255 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227476 (* 1 = 0.0227476 loss) +I0616 04:22:18.628259 9857 solver.cpp:571] Iteration 11120, lr = 0.001 +I0616 04:22:30.179513 9857 solver.cpp:242] Iteration 11140, loss = 1.86635 +I0616 04:22:30.179556 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.37829 (* 1 = 0.37829 loss) +I0616 04:22:30.179561 9857 solver.cpp:258] Train net output #1: loss_cls = 1.29177 (* 1 = 1.29177 loss) +I0616 04:22:30.179566 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.211873 (* 1 = 0.211873 loss) +I0616 04:22:30.179569 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0549966 (* 1 = 0.0549966 loss) +I0616 04:22:30.179574 9857 solver.cpp:571] Iteration 11140, lr = 0.001 +I0616 04:22:41.764395 9857 solver.cpp:242] Iteration 11160, loss = 0.582484 +I0616 04:22:41.764437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256887 (* 1 = 0.256887 loss) +I0616 04:22:41.764442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344525 (* 1 = 0.344525 loss) +I0616 04:22:41.764447 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0372517 (* 1 = 0.0372517 loss) +I0616 04:22:41.764451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.007496 (* 1 = 0.007496 loss) +I0616 04:22:41.764454 9857 solver.cpp:571] Iteration 11160, lr = 0.001 +I0616 04:22:53.409955 9857 solver.cpp:242] Iteration 11180, loss = 0.867656 +I0616 04:22:53.409996 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.559787 (* 1 = 0.559787 loss) +I0616 04:22:53.410001 9857 solver.cpp:258] Train net output #1: loss_cls = 0.71874 (* 1 = 0.71874 loss) +I0616 04:22:53.410006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166729 (* 1 = 0.166729 loss) +I0616 04:22:53.410009 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.073107 (* 1 = 0.073107 loss) +I0616 04:22:53.410013 9857 solver.cpp:571] Iteration 11180, lr = 0.001 +speed: 0.735s / iter +I0616 04:23:04.728124 9857 solver.cpp:242] Iteration 11200, loss = 0.538326 +I0616 04:23:04.728165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244418 (* 1 = 0.244418 loss) +I0616 04:23:04.728171 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239456 (* 1 = 0.239456 loss) +I0616 04:23:04.728175 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0736039 (* 1 = 0.0736039 loss) +I0616 04:23:04.728179 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164349 (* 1 = 0.0164349 loss) +I0616 04:23:04.728183 9857 solver.cpp:571] Iteration 11200, lr = 0.001 +I0616 04:23:16.283474 9857 solver.cpp:242] Iteration 11220, loss = 0.811444 +I0616 04:23:16.283516 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25134 (* 1 = 0.25134 loss) +I0616 04:23:16.283521 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15962 (* 1 = 0.15962 loss) +I0616 04:23:16.283525 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133931 (* 1 = 0.133931 loss) +I0616 04:23:16.283529 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028385 (* 1 = 0.028385 loss) +I0616 04:23:16.283534 9857 solver.cpp:571] Iteration 11220, lr = 0.001 +I0616 04:23:27.540119 9857 solver.cpp:242] Iteration 11240, loss = 1.3984 +I0616 04:23:27.540161 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.703635 (* 1 = 0.703635 loss) +I0616 04:23:27.540168 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03652 (* 1 = 1.03652 loss) +I0616 04:23:27.540171 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137491 (* 1 = 0.137491 loss) +I0616 04:23:27.540175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0532837 (* 1 = 0.0532837 loss) +I0616 04:23:27.540179 9857 solver.cpp:571] Iteration 11240, lr = 0.001 +I0616 04:23:39.006726 9857 solver.cpp:242] Iteration 11260, loss = 1.69363 +I0616 04:23:39.006769 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.606743 (* 1 = 0.606743 loss) +I0616 04:23:39.006775 9857 solver.cpp:258] Train net output #1: loss_cls = 1.24419 (* 1 = 1.24419 loss) +I0616 04:23:39.006779 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.388544 (* 1 = 0.388544 loss) +I0616 04:23:39.006783 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.157999 (* 1 = 0.157999 loss) +I0616 04:23:39.006788 9857 solver.cpp:571] Iteration 11260, lr = 0.001 +I0616 04:23:50.445566 9857 solver.cpp:242] Iteration 11280, loss = 0.922396 +I0616 04:23:50.445607 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.216295 (* 1 = 0.216295 loss) +I0616 04:23:50.445613 9857 solver.cpp:258] Train net output #1: loss_cls = 0.641058 (* 1 = 0.641058 loss) +I0616 04:23:50.445617 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.04321 (* 1 = 0.04321 loss) +I0616 04:23:50.445621 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00341716 (* 1 = 0.00341716 loss) +I0616 04:23:50.445626 9857 solver.cpp:571] Iteration 11280, lr = 0.001 +I0616 04:24:01.931785 9857 solver.cpp:242] Iteration 11300, loss = 1.42721 +I0616 04:24:01.931825 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379098 (* 1 = 0.379098 loss) +I0616 04:24:01.931831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344662 (* 1 = 0.344662 loss) +I0616 04:24:01.931835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.09611 (* 1 = 0.09611 loss) +I0616 04:24:01.931839 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0265252 (* 1 = 0.0265252 loss) +I0616 04:24:01.931843 9857 solver.cpp:571] Iteration 11300, lr = 0.001 +I0616 04:24:13.939368 9857 solver.cpp:242] Iteration 11320, loss = 1.39864 +I0616 04:24:13.939409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229437 (* 1 = 0.229437 loss) +I0616 04:24:13.939415 9857 solver.cpp:258] Train net output #1: loss_cls = 0.746995 (* 1 = 0.746995 loss) +I0616 04:24:13.939419 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0490674 (* 1 = 0.0490674 loss) +I0616 04:24:13.939424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00480199 (* 1 = 0.00480199 loss) +I0616 04:24:13.939427 9857 solver.cpp:571] Iteration 11320, lr = 0.001 +I0616 04:24:25.424382 9857 solver.cpp:242] Iteration 11340, loss = 1.01001 +I0616 04:24:25.424425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106427 (* 1 = 0.106427 loss) +I0616 04:24:25.424432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157465 (* 1 = 0.157465 loss) +I0616 04:24:25.424435 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.057998 (* 1 = 0.057998 loss) +I0616 04:24:25.424439 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0241899 (* 1 = 0.0241899 loss) +I0616 04:24:25.424443 9857 solver.cpp:571] Iteration 11340, lr = 0.001 +I0616 04:24:36.913043 9857 solver.cpp:242] Iteration 11360, loss = 1.38896 +I0616 04:24:36.913085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.485011 (* 1 = 0.485011 loss) +I0616 04:24:36.913091 9857 solver.cpp:258] Train net output #1: loss_cls = 0.445247 (* 1 = 0.445247 loss) +I0616 04:24:36.913095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.174328 (* 1 = 0.174328 loss) +I0616 04:24:36.913100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0324912 (* 1 = 0.0324912 loss) +I0616 04:24:36.913103 9857 solver.cpp:571] Iteration 11360, lr = 0.001 +I0616 04:24:48.289180 9857 solver.cpp:242] Iteration 11380, loss = 1.86763 +I0616 04:24:48.289222 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.571216 (* 1 = 0.571216 loss) +I0616 04:24:48.289227 9857 solver.cpp:258] Train net output #1: loss_cls = 0.54945 (* 1 = 0.54945 loss) +I0616 04:24:48.289232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.315 (* 1 = 0.315 loss) +I0616 04:24:48.289235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0657714 (* 1 = 0.0657714 loss) +I0616 04:24:48.289239 9857 solver.cpp:571] Iteration 11380, lr = 0.001 +speed: 0.732s / iter +I0616 04:25:00.141036 9857 solver.cpp:242] Iteration 11400, loss = 1.38647 +I0616 04:25:00.141075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291921 (* 1 = 0.291921 loss) +I0616 04:25:00.141082 9857 solver.cpp:258] Train net output #1: loss_cls = 0.578761 (* 1 = 0.578761 loss) +I0616 04:25:00.141086 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107802 (* 1 = 0.107802 loss) +I0616 04:25:00.141089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0568972 (* 1 = 0.0568972 loss) +I0616 04:25:00.141094 9857 solver.cpp:571] Iteration 11400, lr = 0.001 +I0616 04:25:11.631866 9857 solver.cpp:242] Iteration 11420, loss = 1.26162 +I0616 04:25:11.631907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401012 (* 1 = 0.401012 loss) +I0616 04:25:11.631914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264611 (* 1 = 0.264611 loss) +I0616 04:25:11.631918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155557 (* 1 = 0.155557 loss) +I0616 04:25:11.631922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.025937 (* 1 = 0.025937 loss) +I0616 04:25:11.631927 9857 solver.cpp:571] Iteration 11420, lr = 0.001 +I0616 04:25:23.425026 9857 solver.cpp:242] Iteration 11440, loss = 1.56201 +I0616 04:25:23.425070 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.464794 (* 1 = 0.464794 loss) +I0616 04:25:23.425074 9857 solver.cpp:258] Train net output #1: loss_cls = 1.18585 (* 1 = 1.18585 loss) +I0616 04:25:23.425079 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.528236 (* 1 = 0.528236 loss) +I0616 04:25:23.425082 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.470186 (* 1 = 0.470186 loss) +I0616 04:25:23.425086 9857 solver.cpp:571] Iteration 11440, lr = 0.001 +I0616 04:25:35.007192 9857 solver.cpp:242] Iteration 11460, loss = 1.64196 +I0616 04:25:35.007232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221688 (* 1 = 0.221688 loss) +I0616 04:25:35.007237 9857 solver.cpp:258] Train net output #1: loss_cls = 0.439181 (* 1 = 0.439181 loss) +I0616 04:25:35.007241 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.318024 (* 1 = 0.318024 loss) +I0616 04:25:35.007246 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.724245 (* 1 = 0.724245 loss) +I0616 04:25:35.007249 9857 solver.cpp:571] Iteration 11460, lr = 0.001 +I0616 04:25:46.686621 9857 solver.cpp:242] Iteration 11480, loss = 0.735456 +I0616 04:25:46.686661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227989 (* 1 = 0.227989 loss) +I0616 04:25:46.686666 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155658 (* 1 = 0.155658 loss) +I0616 04:25:46.686671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0982254 (* 1 = 0.0982254 loss) +I0616 04:25:46.686673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00783263 (* 1 = 0.00783263 loss) +I0616 04:25:46.686677 9857 solver.cpp:571] Iteration 11480, lr = 0.001 +I0616 04:25:58.112073 9857 solver.cpp:242] Iteration 11500, loss = 0.621268 +I0616 04:25:58.112115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0489249 (* 1 = 0.0489249 loss) +I0616 04:25:58.112120 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264311 (* 1 = 0.264311 loss) +I0616 04:25:58.112124 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.231582 (* 1 = 0.231582 loss) +I0616 04:25:58.112128 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.167892 (* 1 = 0.167892 loss) +I0616 04:25:58.112133 9857 solver.cpp:571] Iteration 11500, lr = 0.001 +I0616 04:26:09.703936 9857 solver.cpp:242] Iteration 11520, loss = 1.76699 +I0616 04:26:09.703977 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.52186 (* 1 = 0.52186 loss) +I0616 04:26:09.703984 9857 solver.cpp:258] Train net output #1: loss_cls = 1.35002 (* 1 = 1.35002 loss) +I0616 04:26:09.703987 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127127 (* 1 = 0.127127 loss) +I0616 04:26:09.703991 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0287698 (* 1 = 0.0287698 loss) +I0616 04:26:09.703995 9857 solver.cpp:571] Iteration 11520, lr = 0.001 +I0616 04:26:21.216621 9857 solver.cpp:242] Iteration 11540, loss = 0.865489 +I0616 04:26:21.216667 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266097 (* 1 = 0.266097 loss) +I0616 04:26:21.216675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.740167 (* 1 = 0.740167 loss) +I0616 04:26:21.216680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0960613 (* 1 = 0.0960613 loss) +I0616 04:26:21.216684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0473024 (* 1 = 0.0473024 loss) +I0616 04:26:21.216689 9857 solver.cpp:571] Iteration 11540, lr = 0.001 +I0616 04:26:32.599968 9857 solver.cpp:242] Iteration 11560, loss = 2.82902 +I0616 04:26:32.600010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.573458 (* 1 = 0.573458 loss) +I0616 04:26:32.600016 9857 solver.cpp:258] Train net output #1: loss_cls = 1.88304 (* 1 = 1.88304 loss) +I0616 04:26:32.600020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.330637 (* 1 = 0.330637 loss) +I0616 04:26:32.600024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0485977 (* 1 = 0.0485977 loss) +I0616 04:26:32.600028 9857 solver.cpp:571] Iteration 11560, lr = 0.001 +I0616 04:26:44.309234 9857 solver.cpp:242] Iteration 11580, loss = 0.589348 +I0616 04:26:44.309276 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100611 (* 1 = 0.100611 loss) +I0616 04:26:44.309281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.461866 (* 1 = 0.461866 loss) +I0616 04:26:44.309286 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206552 (* 1 = 0.206552 loss) +I0616 04:26:44.309289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00888773 (* 1 = 0.00888773 loss) +I0616 04:26:44.309293 9857 solver.cpp:571] Iteration 11580, lr = 0.001 +speed: 0.730s / iter +I0616 04:26:55.888522 9857 solver.cpp:242] Iteration 11600, loss = 1.53996 +I0616 04:26:55.888563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.426022 (* 1 = 0.426022 loss) +I0616 04:26:55.888569 9857 solver.cpp:258] Train net output #1: loss_cls = 0.336369 (* 1 = 0.336369 loss) +I0616 04:26:55.888573 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112407 (* 1 = 0.112407 loss) +I0616 04:26:55.888577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122249 (* 1 = 0.0122249 loss) +I0616 04:26:55.888581 9857 solver.cpp:571] Iteration 11600, lr = 0.001 +I0616 04:27:07.272899 9857 solver.cpp:242] Iteration 11620, loss = 0.830224 +I0616 04:27:07.272941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.375121 (* 1 = 0.375121 loss) +I0616 04:27:07.272946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.329497 (* 1 = 0.329497 loss) +I0616 04:27:07.272951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0406397 (* 1 = 0.0406397 loss) +I0616 04:27:07.272954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0420741 (* 1 = 0.0420741 loss) +I0616 04:27:07.272958 9857 solver.cpp:571] Iteration 11620, lr = 0.001 +I0616 04:27:18.816921 9857 solver.cpp:242] Iteration 11640, loss = 0.586128 +I0616 04:27:18.816963 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.335886 (* 1 = 0.335886 loss) +I0616 04:27:18.816968 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302258 (* 1 = 0.302258 loss) +I0616 04:27:18.816972 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0832246 (* 1 = 0.0832246 loss) +I0616 04:27:18.816977 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0284306 (* 1 = 0.0284306 loss) +I0616 04:27:18.816980 9857 solver.cpp:571] Iteration 11640, lr = 0.001 +I0616 04:27:30.469205 9857 solver.cpp:242] Iteration 11660, loss = 0.935812 +I0616 04:27:30.469247 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261735 (* 1 = 0.261735 loss) +I0616 04:27:30.469252 9857 solver.cpp:258] Train net output #1: loss_cls = 0.88462 (* 1 = 0.88462 loss) +I0616 04:27:30.469257 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0965588 (* 1 = 0.0965588 loss) +I0616 04:27:30.469261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0671161 (* 1 = 0.0671161 loss) +I0616 04:27:30.469264 9857 solver.cpp:571] Iteration 11660, lr = 0.001 +I0616 04:27:41.977217 9857 solver.cpp:242] Iteration 11680, loss = 1.22739 +I0616 04:27:41.977258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.506259 (* 1 = 0.506259 loss) +I0616 04:27:41.977264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.512967 (* 1 = 0.512967 loss) +I0616 04:27:41.977268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.321795 (* 1 = 0.321795 loss) +I0616 04:27:41.977272 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128157 (* 1 = 0.128157 loss) +I0616 04:27:41.977275 9857 solver.cpp:571] Iteration 11680, lr = 0.001 +I0616 04:27:53.301965 9857 solver.cpp:242] Iteration 11700, loss = 0.597477 +I0616 04:27:53.302007 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307459 (* 1 = 0.307459 loss) +I0616 04:27:53.302012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323416 (* 1 = 0.323416 loss) +I0616 04:27:53.302017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104718 (* 1 = 0.104718 loss) +I0616 04:27:53.302021 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0767315 (* 1 = 0.0767315 loss) +I0616 04:27:53.302026 9857 solver.cpp:571] Iteration 11700, lr = 0.001 +I0616 04:28:04.771559 9857 solver.cpp:242] Iteration 11720, loss = 1.23979 +I0616 04:28:04.771602 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.407216 (* 1 = 0.407216 loss) +I0616 04:28:04.771608 9857 solver.cpp:258] Train net output #1: loss_cls = 0.458689 (* 1 = 0.458689 loss) +I0616 04:28:04.771612 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.285893 (* 1 = 0.285893 loss) +I0616 04:28:04.771616 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0333386 (* 1 = 0.0333386 loss) +I0616 04:28:04.771620 9857 solver.cpp:571] Iteration 11720, lr = 0.001 +I0616 04:28:16.459098 9857 solver.cpp:242] Iteration 11740, loss = 0.788172 +I0616 04:28:16.459139 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.087452 (* 1 = 0.087452 loss) +I0616 04:28:16.459144 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182063 (* 1 = 0.182063 loss) +I0616 04:28:16.459148 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0442395 (* 1 = 0.0442395 loss) +I0616 04:28:16.459152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0625929 (* 1 = 0.0625929 loss) +I0616 04:28:16.459156 9857 solver.cpp:571] Iteration 11740, lr = 0.001 +I0616 04:28:28.088862 9857 solver.cpp:242] Iteration 11760, loss = 0.70712 +I0616 04:28:28.088906 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221884 (* 1 = 0.221884 loss) +I0616 04:28:28.088910 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197838 (* 1 = 0.197838 loss) +I0616 04:28:28.088915 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0492224 (* 1 = 0.0492224 loss) +I0616 04:28:28.088918 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104367 (* 1 = 0.0104367 loss) +I0616 04:28:28.088922 9857 solver.cpp:571] Iteration 11760, lr = 0.001 +I0616 04:28:39.750986 9857 solver.cpp:242] Iteration 11780, loss = 1.75465 +I0616 04:28:39.751030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.26326 (* 1 = 0.26326 loss) +I0616 04:28:39.751035 9857 solver.cpp:258] Train net output #1: loss_cls = 1.31073 (* 1 = 1.31073 loss) +I0616 04:28:39.751040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.916036 (* 1 = 0.916036 loss) +I0616 04:28:39.751044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.350839 (* 1 = 0.350839 loss) +I0616 04:28:39.751047 9857 solver.cpp:571] Iteration 11780, lr = 0.001 +speed: 0.727s / iter +I0616 04:28:51.422261 9857 solver.cpp:242] Iteration 11800, loss = 1.3187 +I0616 04:28:51.422304 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.482148 (* 1 = 0.482148 loss) +I0616 04:28:51.422310 9857 solver.cpp:258] Train net output #1: loss_cls = 0.542934 (* 1 = 0.542934 loss) +I0616 04:28:51.422314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235881 (* 1 = 0.235881 loss) +I0616 04:28:51.422317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.260289 (* 1 = 0.260289 loss) +I0616 04:28:51.422335 9857 solver.cpp:571] Iteration 11800, lr = 0.001 +I0616 04:29:03.109366 9857 solver.cpp:242] Iteration 11820, loss = 1.1373 +I0616 04:29:03.109408 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32178 (* 1 = 0.32178 loss) +I0616 04:29:03.109414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.664834 (* 1 = 0.664834 loss) +I0616 04:29:03.109418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0867648 (* 1 = 0.0867648 loss) +I0616 04:29:03.109422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0203914 (* 1 = 0.0203914 loss) +I0616 04:29:03.109426 9857 solver.cpp:571] Iteration 11820, lr = 0.001 +I0616 04:29:14.466126 9857 solver.cpp:242] Iteration 11840, loss = 0.864513 +I0616 04:29:14.466169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411707 (* 1 = 0.411707 loss) +I0616 04:29:14.466176 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401334 (* 1 = 0.401334 loss) +I0616 04:29:14.466179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201966 (* 1 = 0.201966 loss) +I0616 04:29:14.466183 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159518 (* 1 = 0.159518 loss) +I0616 04:29:14.466187 9857 solver.cpp:571] Iteration 11840, lr = 0.001 +I0616 04:29:26.117180 9857 solver.cpp:242] Iteration 11860, loss = 1.35244 +I0616 04:29:26.117223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.426484 (* 1 = 0.426484 loss) +I0616 04:29:26.117228 9857 solver.cpp:258] Train net output #1: loss_cls = 1.21148 (* 1 = 1.21148 loss) +I0616 04:29:26.117233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124867 (* 1 = 0.124867 loss) +I0616 04:29:26.117235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00570733 (* 1 = 0.00570733 loss) +I0616 04:29:26.117239 9857 solver.cpp:571] Iteration 11860, lr = 0.001 +I0616 04:29:37.640584 9857 solver.cpp:242] Iteration 11880, loss = 0.730771 +I0616 04:29:37.640626 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281908 (* 1 = 0.281908 loss) +I0616 04:29:37.640632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317819 (* 1 = 0.317819 loss) +I0616 04:29:37.640636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0666726 (* 1 = 0.0666726 loss) +I0616 04:29:37.640640 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0401772 (* 1 = 0.0401772 loss) +I0616 04:29:37.640645 9857 solver.cpp:571] Iteration 11880, lr = 0.001 +I0616 04:29:49.115454 9857 solver.cpp:242] Iteration 11900, loss = 0.898554 +I0616 04:29:49.115497 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192074 (* 1 = 0.192074 loss) +I0616 04:29:49.115504 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208366 (* 1 = 0.208366 loss) +I0616 04:29:49.115507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0830259 (* 1 = 0.0830259 loss) +I0616 04:29:49.115511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108864 (* 1 = 0.0108864 loss) +I0616 04:29:49.115514 9857 solver.cpp:571] Iteration 11900, lr = 0.001 +I0616 04:30:00.911167 9857 solver.cpp:242] Iteration 11920, loss = 1.53115 +I0616 04:30:00.911207 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413542 (* 1 = 0.413542 loss) +I0616 04:30:00.911213 9857 solver.cpp:258] Train net output #1: loss_cls = 0.693673 (* 1 = 0.693673 loss) +I0616 04:30:00.911217 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0870429 (* 1 = 0.0870429 loss) +I0616 04:30:00.911221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0377096 (* 1 = 0.0377096 loss) +I0616 04:30:00.911226 9857 solver.cpp:571] Iteration 11920, lr = 0.001 +I0616 04:30:12.442728 9857 solver.cpp:242] Iteration 11940, loss = 0.911131 +I0616 04:30:12.442775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149903 (* 1 = 0.149903 loss) +I0616 04:30:12.442781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226615 (* 1 = 0.226615 loss) +I0616 04:30:12.442785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0457805 (* 1 = 0.0457805 loss) +I0616 04:30:12.442790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0653553 (* 1 = 0.0653553 loss) +I0616 04:30:12.442793 9857 solver.cpp:571] Iteration 11940, lr = 0.001 +I0616 04:30:23.568253 9857 solver.cpp:242] Iteration 11960, loss = 1.03972 +I0616 04:30:23.568295 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347838 (* 1 = 0.347838 loss) +I0616 04:30:23.568301 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302042 (* 1 = 0.302042 loss) +I0616 04:30:23.568305 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0972614 (* 1 = 0.0972614 loss) +I0616 04:30:23.568310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207464 (* 1 = 0.0207464 loss) +I0616 04:30:23.568312 9857 solver.cpp:571] Iteration 11960, lr = 0.001 +I0616 04:30:35.044615 9857 solver.cpp:242] Iteration 11980, loss = 1.20527 +I0616 04:30:35.044654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215622 (* 1 = 0.215622 loss) +I0616 04:30:35.044661 9857 solver.cpp:258] Train net output #1: loss_cls = 0.37792 (* 1 = 0.37792 loss) +I0616 04:30:35.044666 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0626157 (* 1 = 0.0626157 loss) +I0616 04:30:35.044669 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00693812 (* 1 = 0.00693812 loss) +I0616 04:30:35.044672 9857 solver.cpp:571] Iteration 11980, lr = 0.001 +speed: 0.724s / iter +I0616 04:30:46.454677 9857 solver.cpp:242] Iteration 12000, loss = 0.755652 +I0616 04:30:46.454716 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.450139 (* 1 = 0.450139 loss) +I0616 04:30:46.454721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.449935 (* 1 = 0.449935 loss) +I0616 04:30:46.454726 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155918 (* 1 = 0.155918 loss) +I0616 04:30:46.454730 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0527121 (* 1 = 0.0527121 loss) +I0616 04:30:46.454733 9857 solver.cpp:571] Iteration 12000, lr = 0.001 +I0616 04:30:57.860420 9857 solver.cpp:242] Iteration 12020, loss = 1.09165 +I0616 04:30:57.860461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.450811 (* 1 = 0.450811 loss) +I0616 04:30:57.860466 9857 solver.cpp:258] Train net output #1: loss_cls = 0.643774 (* 1 = 0.643774 loss) +I0616 04:30:57.860471 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178055 (* 1 = 0.178055 loss) +I0616 04:30:57.860476 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0538731 (* 1 = 0.0538731 loss) +I0616 04:30:57.860478 9857 solver.cpp:571] Iteration 12020, lr = 0.001 +I0616 04:31:09.524102 9857 solver.cpp:242] Iteration 12040, loss = 0.524407 +I0616 04:31:09.524143 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312772 (* 1 = 0.312772 loss) +I0616 04:31:09.524149 9857 solver.cpp:258] Train net output #1: loss_cls = 0.308246 (* 1 = 0.308246 loss) +I0616 04:31:09.524153 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605073 (* 1 = 0.0605073 loss) +I0616 04:31:09.524158 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239056 (* 1 = 0.0239056 loss) +I0616 04:31:09.524160 9857 solver.cpp:571] Iteration 12040, lr = 0.001 +I0616 04:31:21.178771 9857 solver.cpp:242] Iteration 12060, loss = 1.4446 +I0616 04:31:21.178813 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366798 (* 1 = 0.366798 loss) +I0616 04:31:21.178819 9857 solver.cpp:258] Train net output #1: loss_cls = 0.894703 (* 1 = 0.894703 loss) +I0616 04:31:21.178823 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219055 (* 1 = 0.219055 loss) +I0616 04:31:21.178828 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0479219 (* 1 = 0.0479219 loss) +I0616 04:31:21.178831 9857 solver.cpp:571] Iteration 12060, lr = 0.001 +I0616 04:31:32.786875 9857 solver.cpp:242] Iteration 12080, loss = 0.63151 +I0616 04:31:32.786917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.15899 (* 1 = 0.15899 loss) +I0616 04:31:32.786922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179781 (* 1 = 0.179781 loss) +I0616 04:31:32.786927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222815 (* 1 = 0.0222815 loss) +I0616 04:31:32.786931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0141299 (* 1 = 0.0141299 loss) +I0616 04:31:32.786934 9857 solver.cpp:571] Iteration 12080, lr = 0.001 +I0616 04:31:44.459651 9857 solver.cpp:242] Iteration 12100, loss = 1.17276 +I0616 04:31:44.459693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291998 (* 1 = 0.291998 loss) +I0616 04:31:44.459699 9857 solver.cpp:258] Train net output #1: loss_cls = 0.325434 (* 1 = 0.325434 loss) +I0616 04:31:44.459703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0505028 (* 1 = 0.0505028 loss) +I0616 04:31:44.459707 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115861 (* 1 = 0.0115861 loss) +I0616 04:31:44.459712 9857 solver.cpp:571] Iteration 12100, lr = 0.001 +I0616 04:31:56.073714 9857 solver.cpp:242] Iteration 12120, loss = 1.3841 +I0616 04:31:56.073755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.585921 (* 1 = 0.585921 loss) +I0616 04:31:56.073761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.952338 (* 1 = 0.952338 loss) +I0616 04:31:56.073765 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.506785 (* 1 = 0.506785 loss) +I0616 04:31:56.073770 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.232792 (* 1 = 0.232792 loss) +I0616 04:31:56.073773 9857 solver.cpp:571] Iteration 12120, lr = 0.001 +I0616 04:32:07.452239 9857 solver.cpp:242] Iteration 12140, loss = 0.803146 +I0616 04:32:07.452282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203604 (* 1 = 0.203604 loss) +I0616 04:32:07.452287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155862 (* 1 = 0.155862 loss) +I0616 04:32:07.452292 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0449549 (* 1 = 0.0449549 loss) +I0616 04:32:07.452296 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221571 (* 1 = 0.0221571 loss) +I0616 04:32:07.452299 9857 solver.cpp:571] Iteration 12140, lr = 0.001 +I0616 04:32:19.049921 9857 solver.cpp:242] Iteration 12160, loss = 0.927186 +I0616 04:32:19.049962 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367483 (* 1 = 0.367483 loss) +I0616 04:32:19.049968 9857 solver.cpp:258] Train net output #1: loss_cls = 0.719749 (* 1 = 0.719749 loss) +I0616 04:32:19.049971 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0364148 (* 1 = 0.0364148 loss) +I0616 04:32:19.049975 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.106291 (* 1 = 0.106291 loss) +I0616 04:32:19.049979 9857 solver.cpp:571] Iteration 12160, lr = 0.001 +I0616 04:32:30.930379 9857 solver.cpp:242] Iteration 12180, loss = 1.70872 +I0616 04:32:30.930420 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172037 (* 1 = 0.172037 loss) +I0616 04:32:30.930426 9857 solver.cpp:258] Train net output #1: loss_cls = 0.836946 (* 1 = 0.836946 loss) +I0616 04:32:30.930430 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194489 (* 1 = 0.194489 loss) +I0616 04:32:30.930434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00875117 (* 1 = 0.00875117 loss) +I0616 04:32:30.930438 9857 solver.cpp:571] Iteration 12180, lr = 0.001 +speed: 0.722s / iter +I0616 04:32:42.601732 9857 solver.cpp:242] Iteration 12200, loss = 0.683568 +I0616 04:32:42.601773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159649 (* 1 = 0.159649 loss) +I0616 04:32:42.601779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219901 (* 1 = 0.219901 loss) +I0616 04:32:42.601783 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0876601 (* 1 = 0.0876601 loss) +I0616 04:32:42.601788 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168278 (* 1 = 0.0168278 loss) +I0616 04:32:42.601791 9857 solver.cpp:571] Iteration 12200, lr = 0.001 +I0616 04:32:54.202256 9857 solver.cpp:242] Iteration 12220, loss = 1.8941 +I0616 04:32:54.202297 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.516915 (* 1 = 0.516915 loss) +I0616 04:32:54.202302 9857 solver.cpp:258] Train net output #1: loss_cls = 1.17604 (* 1 = 1.17604 loss) +I0616 04:32:54.202307 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18269 (* 1 = 0.18269 loss) +I0616 04:32:54.202311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170258 (* 1 = 0.0170258 loss) +I0616 04:32:54.202316 9857 solver.cpp:571] Iteration 12220, lr = 0.001 +I0616 04:33:05.615058 9857 solver.cpp:242] Iteration 12240, loss = 0.984854 +I0616 04:33:05.615100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324011 (* 1 = 0.324011 loss) +I0616 04:33:05.615105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.512574 (* 1 = 0.512574 loss) +I0616 04:33:05.615110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129983 (* 1 = 0.129983 loss) +I0616 04:33:05.615114 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0687364 (* 1 = 0.0687364 loss) +I0616 04:33:05.615118 9857 solver.cpp:571] Iteration 12240, lr = 0.001 +I0616 04:33:17.236793 9857 solver.cpp:242] Iteration 12260, loss = 0.559332 +I0616 04:33:17.236835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229388 (* 1 = 0.229388 loss) +I0616 04:33:17.236840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.405465 (* 1 = 0.405465 loss) +I0616 04:33:17.236845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0904715 (* 1 = 0.0904715 loss) +I0616 04:33:17.236848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132984 (* 1 = 0.0132984 loss) +I0616 04:33:17.236852 9857 solver.cpp:571] Iteration 12260, lr = 0.001 +I0616 04:33:28.713229 9857 solver.cpp:242] Iteration 12280, loss = 1.52073 +I0616 04:33:28.713270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.417385 (* 1 = 0.417385 loss) +I0616 04:33:28.713276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.867238 (* 1 = 0.867238 loss) +I0616 04:33:28.713280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.202325 (* 1 = 0.202325 loss) +I0616 04:33:28.713284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.067522 (* 1 = 0.067522 loss) +I0616 04:33:28.713287 9857 solver.cpp:571] Iteration 12280, lr = 0.001 +I0616 04:33:40.551712 9857 solver.cpp:242] Iteration 12300, loss = 1.20784 +I0616 04:33:40.551753 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421106 (* 1 = 0.421106 loss) +I0616 04:33:40.551759 9857 solver.cpp:258] Train net output #1: loss_cls = 0.830705 (* 1 = 0.830705 loss) +I0616 04:33:40.551762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.378518 (* 1 = 0.378518 loss) +I0616 04:33:40.551766 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0287769 (* 1 = 0.0287769 loss) +I0616 04:33:40.551770 9857 solver.cpp:571] Iteration 12300, lr = 0.001 +I0616 04:33:52.282546 9857 solver.cpp:242] Iteration 12320, loss = 0.766836 +I0616 04:33:52.282585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.444805 (* 1 = 0.444805 loss) +I0616 04:33:52.282591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.470942 (* 1 = 0.470942 loss) +I0616 04:33:52.282595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0316771 (* 1 = 0.0316771 loss) +I0616 04:33:52.282599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173175 (* 1 = 0.0173175 loss) +I0616 04:33:52.282603 9857 solver.cpp:571] Iteration 12320, lr = 0.001 +I0616 04:34:03.845583 9857 solver.cpp:242] Iteration 12340, loss = 0.888821 +I0616 04:34:03.845625 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0429793 (* 1 = 0.0429793 loss) +I0616 04:34:03.845631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.506366 (* 1 = 0.506366 loss) +I0616 04:34:03.845635 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119319 (* 1 = 0.119319 loss) +I0616 04:34:03.845639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269497 (* 1 = 0.0269497 loss) +I0616 04:34:03.845643 9857 solver.cpp:571] Iteration 12340, lr = 0.001 +I0616 04:34:15.422431 9857 solver.cpp:242] Iteration 12360, loss = 0.591424 +I0616 04:34:15.422474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179544 (* 1 = 0.179544 loss) +I0616 04:34:15.422479 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196756 (* 1 = 0.196756 loss) +I0616 04:34:15.422483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274542 (* 1 = 0.0274542 loss) +I0616 04:34:15.422487 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00634847 (* 1 = 0.00634847 loss) +I0616 04:34:15.422492 9857 solver.cpp:571] Iteration 12360, lr = 0.001 +I0616 04:34:27.063639 9857 solver.cpp:242] Iteration 12380, loss = 1.04611 +I0616 04:34:27.063681 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207202 (* 1 = 0.207202 loss) +I0616 04:34:27.063688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4793 (* 1 = 0.4793 loss) +I0616 04:34:27.063691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0811801 (* 1 = 0.0811801 loss) +I0616 04:34:27.063694 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0312929 (* 1 = 0.0312929 loss) +I0616 04:34:27.063699 9857 solver.cpp:571] Iteration 12380, lr = 0.001 +speed: 0.720s / iter +I0616 04:34:38.543303 9857 solver.cpp:242] Iteration 12400, loss = 1.63088 +I0616 04:34:38.543344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271964 (* 1 = 0.271964 loss) +I0616 04:34:38.543349 9857 solver.cpp:258] Train net output #1: loss_cls = 1.07374 (* 1 = 1.07374 loss) +I0616 04:34:38.543354 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0490249 (* 1 = 0.0490249 loss) +I0616 04:34:38.543357 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00441928 (* 1 = 0.00441928 loss) +I0616 04:34:38.543361 9857 solver.cpp:571] Iteration 12400, lr = 0.001 +I0616 04:34:50.160560 9857 solver.cpp:242] Iteration 12420, loss = 0.632973 +I0616 04:34:50.160603 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345907 (* 1 = 0.345907 loss) +I0616 04:34:50.160607 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20068 (* 1 = 0.20068 loss) +I0616 04:34:50.160612 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0505234 (* 1 = 0.0505234 loss) +I0616 04:34:50.160616 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0579807 (* 1 = 0.0579807 loss) +I0616 04:34:50.160620 9857 solver.cpp:571] Iteration 12420, lr = 0.001 +I0616 04:35:01.604781 9857 solver.cpp:242] Iteration 12440, loss = 1.21662 +I0616 04:35:01.604823 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39548 (* 1 = 0.39548 loss) +I0616 04:35:01.604830 9857 solver.cpp:258] Train net output #1: loss_cls = 1.22941 (* 1 = 1.22941 loss) +I0616 04:35:01.604833 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163586 (* 1 = 0.163586 loss) +I0616 04:35:01.604837 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112615 (* 1 = 0.0112615 loss) +I0616 04:35:01.604841 9857 solver.cpp:571] Iteration 12440, lr = 0.001 +I0616 04:35:13.087589 9857 solver.cpp:242] Iteration 12460, loss = 0.654805 +I0616 04:35:13.087631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0891398 (* 1 = 0.0891398 loss) +I0616 04:35:13.087636 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147457 (* 1 = 0.147457 loss) +I0616 04:35:13.087641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111904 (* 1 = 0.111904 loss) +I0616 04:35:13.087644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0331563 (* 1 = 0.0331563 loss) +I0616 04:35:13.087648 9857 solver.cpp:571] Iteration 12460, lr = 0.001 +I0616 04:35:24.797065 9857 solver.cpp:242] Iteration 12480, loss = 1.42182 +I0616 04:35:24.797106 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.301343 (* 1 = 0.301343 loss) +I0616 04:35:24.797112 9857 solver.cpp:258] Train net output #1: loss_cls = 0.419647 (* 1 = 0.419647 loss) +I0616 04:35:24.797116 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140529 (* 1 = 0.140529 loss) +I0616 04:35:24.797121 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.122658 (* 1 = 0.122658 loss) +I0616 04:35:24.797124 9857 solver.cpp:571] Iteration 12480, lr = 0.001 +I0616 04:35:36.443265 9857 solver.cpp:242] Iteration 12500, loss = 1.38386 +I0616 04:35:36.443307 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.592708 (* 1 = 0.592708 loss) +I0616 04:35:36.443313 9857 solver.cpp:258] Train net output #1: loss_cls = 1.08756 (* 1 = 1.08756 loss) +I0616 04:35:36.443317 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.287836 (* 1 = 0.287836 loss) +I0616 04:35:36.443321 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0560306 (* 1 = 0.0560306 loss) +I0616 04:35:36.443325 9857 solver.cpp:571] Iteration 12500, lr = 0.001 +I0616 04:35:47.721989 9857 solver.cpp:242] Iteration 12520, loss = 2.07461 +I0616 04:35:47.722030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.700447 (* 1 = 0.700447 loss) +I0616 04:35:47.722036 9857 solver.cpp:258] Train net output #1: loss_cls = 1.61895 (* 1 = 1.61895 loss) +I0616 04:35:47.722040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.579498 (* 1 = 0.579498 loss) +I0616 04:35:47.722044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.057827 (* 1 = 0.057827 loss) +I0616 04:35:47.722048 9857 solver.cpp:571] Iteration 12520, lr = 0.001 +I0616 04:35:59.270084 9857 solver.cpp:242] Iteration 12540, loss = 0.728894 +I0616 04:35:59.270125 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347722 (* 1 = 0.347722 loss) +I0616 04:35:59.270130 9857 solver.cpp:258] Train net output #1: loss_cls = 0.33712 (* 1 = 0.33712 loss) +I0616 04:35:59.270134 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.065095 (* 1 = 0.065095 loss) +I0616 04:35:59.270138 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0382693 (* 1 = 0.0382693 loss) +I0616 04:35:59.270143 9857 solver.cpp:571] Iteration 12540, lr = 0.001 +I0616 04:36:11.019568 9857 solver.cpp:242] Iteration 12560, loss = 0.782055 +I0616 04:36:11.019611 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244892 (* 1 = 0.244892 loss) +I0616 04:36:11.019618 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195254 (* 1 = 0.195254 loss) +I0616 04:36:11.019621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0929048 (* 1 = 0.0929048 loss) +I0616 04:36:11.019625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275363 (* 1 = 0.0275363 loss) +I0616 04:36:11.019629 9857 solver.cpp:571] Iteration 12560, lr = 0.001 +I0616 04:36:22.588627 9857 solver.cpp:242] Iteration 12580, loss = 1.36413 +I0616 04:36:22.588670 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307686 (* 1 = 0.307686 loss) +I0616 04:36:22.588675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.492978 (* 1 = 0.492978 loss) +I0616 04:36:22.588680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.16127 (* 1 = 0.16127 loss) +I0616 04:36:22.588682 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.131474 (* 1 = 0.131474 loss) +I0616 04:36:22.588686 9857 solver.cpp:571] Iteration 12580, lr = 0.001 +speed: 0.718s / iter +I0616 04:36:34.221935 9857 solver.cpp:242] Iteration 12600, loss = 1.50793 +I0616 04:36:34.221974 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.519329 (* 1 = 0.519329 loss) +I0616 04:36:34.221981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.724012 (* 1 = 0.724012 loss) +I0616 04:36:34.221984 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.261782 (* 1 = 0.261782 loss) +I0616 04:36:34.221987 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.279206 (* 1 = 0.279206 loss) +I0616 04:36:34.221992 9857 solver.cpp:571] Iteration 12600, lr = 0.001 +I0616 04:36:45.749979 9857 solver.cpp:242] Iteration 12620, loss = 1.52665 +I0616 04:36:45.750021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396109 (* 1 = 0.396109 loss) +I0616 04:36:45.750027 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432509 (* 1 = 0.432509 loss) +I0616 04:36:45.750032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0958342 (* 1 = 0.0958342 loss) +I0616 04:36:45.750036 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119298 (* 1 = 0.0119298 loss) +I0616 04:36:45.750039 9857 solver.cpp:571] Iteration 12620, lr = 0.001 +I0616 04:36:57.431355 9857 solver.cpp:242] Iteration 12640, loss = 0.938313 +I0616 04:36:57.431398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209685 (* 1 = 0.209685 loss) +I0616 04:36:57.431403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215332 (* 1 = 0.215332 loss) +I0616 04:36:57.431408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230751 (* 1 = 0.0230751 loss) +I0616 04:36:57.431412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014314 (* 1 = 0.014314 loss) +I0616 04:36:57.431416 9857 solver.cpp:571] Iteration 12640, lr = 0.001 +I0616 04:37:08.779317 9857 solver.cpp:242] Iteration 12660, loss = 1.05073 +I0616 04:37:08.779359 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157753 (* 1 = 0.157753 loss) +I0616 04:37:08.779366 9857 solver.cpp:258] Train net output #1: loss_cls = 0.758001 (* 1 = 0.758001 loss) +I0616 04:37:08.779369 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0982248 (* 1 = 0.0982248 loss) +I0616 04:37:08.779373 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0521827 (* 1 = 0.0521827 loss) +I0616 04:37:08.779376 9857 solver.cpp:571] Iteration 12660, lr = 0.001 +I0616 04:37:20.428705 9857 solver.cpp:242] Iteration 12680, loss = 1.58733 +I0616 04:37:20.428748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.481902 (* 1 = 0.481902 loss) +I0616 04:37:20.428753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.712416 (* 1 = 0.712416 loss) +I0616 04:37:20.428758 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0757093 (* 1 = 0.0757093 loss) +I0616 04:37:20.428763 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.143897 (* 1 = 0.143897 loss) +I0616 04:37:20.428769 9857 solver.cpp:571] Iteration 12680, lr = 0.001 +I0616 04:37:31.818372 9857 solver.cpp:242] Iteration 12700, loss = 0.977122 +I0616 04:37:31.818413 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392672 (* 1 = 0.392672 loss) +I0616 04:37:31.818419 9857 solver.cpp:258] Train net output #1: loss_cls = 0.944055 (* 1 = 0.944055 loss) +I0616 04:37:31.818423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0637074 (* 1 = 0.0637074 loss) +I0616 04:37:31.818428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01082 (* 1 = 0.01082 loss) +I0616 04:37:31.818431 9857 solver.cpp:571] Iteration 12700, lr = 0.001 +I0616 04:37:43.303516 9857 solver.cpp:242] Iteration 12720, loss = 1.24281 +I0616 04:37:43.303557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390365 (* 1 = 0.390365 loss) +I0616 04:37:43.303562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.339575 (* 1 = 0.339575 loss) +I0616 04:37:43.303567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135911 (* 1 = 0.135911 loss) +I0616 04:37:43.303570 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0394159 (* 1 = 0.0394159 loss) +I0616 04:37:43.303575 9857 solver.cpp:571] Iteration 12720, lr = 0.001 +I0616 04:37:54.741897 9857 solver.cpp:242] Iteration 12740, loss = 0.720711 +I0616 04:37:54.741940 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221692 (* 1 = 0.221692 loss) +I0616 04:37:54.741945 9857 solver.cpp:258] Train net output #1: loss_cls = 0.355569 (* 1 = 0.355569 loss) +I0616 04:37:54.741950 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0581124 (* 1 = 0.0581124 loss) +I0616 04:37:54.741953 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483759 (* 1 = 0.0483759 loss) +I0616 04:37:54.741957 9857 solver.cpp:571] Iteration 12740, lr = 0.001 +I0616 04:38:06.445387 9857 solver.cpp:242] Iteration 12760, loss = 1.33663 +I0616 04:38:06.445427 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320518 (* 1 = 0.320518 loss) +I0616 04:38:06.445433 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232087 (* 1 = 0.232087 loss) +I0616 04:38:06.445438 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0105062 (* 1 = 0.0105062 loss) +I0616 04:38:06.445441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00429672 (* 1 = 0.00429672 loss) +I0616 04:38:06.445446 9857 solver.cpp:571] Iteration 12760, lr = 0.001 +I0616 04:38:18.109719 9857 solver.cpp:242] Iteration 12780, loss = 1.23598 +I0616 04:38:18.109762 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.36863 (* 1 = 0.36863 loss) +I0616 04:38:18.109769 9857 solver.cpp:258] Train net output #1: loss_cls = 0.461884 (* 1 = 0.461884 loss) +I0616 04:38:18.109773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110453 (* 1 = 0.110453 loss) +I0616 04:38:18.109777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0289311 (* 1 = 0.0289311 loss) +I0616 04:38:18.109781 9857 solver.cpp:571] Iteration 12780, lr = 0.001 +speed: 0.715s / iter +I0616 04:38:29.652938 9857 solver.cpp:242] Iteration 12800, loss = 0.612495 +I0616 04:38:29.652981 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127039 (* 1 = 0.127039 loss) +I0616 04:38:29.652987 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294191 (* 1 = 0.294191 loss) +I0616 04:38:29.652990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.040759 (* 1 = 0.040759 loss) +I0616 04:38:29.652994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00330545 (* 1 = 0.00330545 loss) +I0616 04:38:29.652998 9857 solver.cpp:571] Iteration 12800, lr = 0.001 +I0616 04:38:41.555326 9857 solver.cpp:242] Iteration 12820, loss = 1.14099 +I0616 04:38:41.555369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.388971 (* 1 = 0.388971 loss) +I0616 04:38:41.555376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.726008 (* 1 = 0.726008 loss) +I0616 04:38:41.555379 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108531 (* 1 = 0.108531 loss) +I0616 04:38:41.555383 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116634 (* 1 = 0.116634 loss) +I0616 04:38:41.555387 9857 solver.cpp:571] Iteration 12820, lr = 0.001 +I0616 04:38:53.134237 9857 solver.cpp:242] Iteration 12840, loss = 0.931108 +I0616 04:38:53.134279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106038 (* 1 = 0.106038 loss) +I0616 04:38:53.134284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360109 (* 1 = 0.360109 loss) +I0616 04:38:53.134289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157201 (* 1 = 0.157201 loss) +I0616 04:38:53.134294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00340681 (* 1 = 0.00340681 loss) +I0616 04:38:53.134297 9857 solver.cpp:571] Iteration 12840, lr = 0.001 +I0616 04:39:04.564184 9857 solver.cpp:242] Iteration 12860, loss = 1.18144 +I0616 04:39:04.564229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13558 (* 1 = 0.13558 loss) +I0616 04:39:04.564234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27362 (* 1 = 0.27362 loss) +I0616 04:39:04.564239 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0572511 (* 1 = 0.0572511 loss) +I0616 04:39:04.564242 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225362 (* 1 = 0.0225362 loss) +I0616 04:39:04.564245 9857 solver.cpp:571] Iteration 12860, lr = 0.001 +I0616 04:39:16.395968 9857 solver.cpp:242] Iteration 12880, loss = 0.686809 +I0616 04:39:16.396010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332029 (* 1 = 0.332029 loss) +I0616 04:39:16.396016 9857 solver.cpp:258] Train net output #1: loss_cls = 0.33615 (* 1 = 0.33615 loss) +I0616 04:39:16.396020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0476204 (* 1 = 0.0476204 loss) +I0616 04:39:16.396024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0428949 (* 1 = 0.0428949 loss) +I0616 04:39:16.396028 9857 solver.cpp:571] Iteration 12880, lr = 0.001 +I0616 04:39:27.883134 9857 solver.cpp:242] Iteration 12900, loss = 1.12088 +I0616 04:39:27.883177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.490324 (* 1 = 0.490324 loss) +I0616 04:39:27.883183 9857 solver.cpp:258] Train net output #1: loss_cls = 0.791633 (* 1 = 0.791633 loss) +I0616 04:39:27.883186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11584 (* 1 = 0.11584 loss) +I0616 04:39:27.883190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0410878 (* 1 = 0.0410878 loss) +I0616 04:39:27.883194 9857 solver.cpp:571] Iteration 12900, lr = 0.001 +I0616 04:39:39.636247 9857 solver.cpp:242] Iteration 12920, loss = 0.989065 +I0616 04:39:39.636291 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399122 (* 1 = 0.399122 loss) +I0616 04:39:39.636310 9857 solver.cpp:258] Train net output #1: loss_cls = 0.494294 (* 1 = 0.494294 loss) +I0616 04:39:39.636314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.266496 (* 1 = 0.266496 loss) +I0616 04:39:39.636318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028764 (* 1 = 0.028764 loss) +I0616 04:39:39.636322 9857 solver.cpp:571] Iteration 12920, lr = 0.001 +I0616 04:39:50.936568 9857 solver.cpp:242] Iteration 12940, loss = 0.906274 +I0616 04:39:50.936611 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.428686 (* 1 = 0.428686 loss) +I0616 04:39:50.936617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.376791 (* 1 = 0.376791 loss) +I0616 04:39:50.936621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0636287 (* 1 = 0.0636287 loss) +I0616 04:39:50.936625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0608159 (* 1 = 0.0608159 loss) +I0616 04:39:50.936630 9857 solver.cpp:571] Iteration 12940, lr = 0.001 +I0616 04:40:02.553664 9857 solver.cpp:242] Iteration 12960, loss = 1.09382 +I0616 04:40:02.553705 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389081 (* 1 = 0.389081 loss) +I0616 04:40:02.553711 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292027 (* 1 = 0.292027 loss) +I0616 04:40:02.553715 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0703173 (* 1 = 0.0703173 loss) +I0616 04:40:02.553719 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156488 (* 1 = 0.156488 loss) +I0616 04:40:02.553724 9857 solver.cpp:571] Iteration 12960, lr = 0.001 +I0616 04:40:14.069306 9857 solver.cpp:242] Iteration 12980, loss = 1.5173 +I0616 04:40:14.069349 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355258 (* 1 = 0.355258 loss) +I0616 04:40:14.069355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.761768 (* 1 = 0.761768 loss) +I0616 04:40:14.069358 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0704575 (* 1 = 0.0704575 loss) +I0616 04:40:14.069362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00302971 (* 1 = 0.00302971 loss) +I0616 04:40:14.069366 9857 solver.cpp:571] Iteration 12980, lr = 0.001 +speed: 0.713s / iter +I0616 04:40:25.291993 9857 solver.cpp:242] Iteration 13000, loss = 0.790449 +I0616 04:40:25.292037 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283553 (* 1 = 0.283553 loss) +I0616 04:40:25.292042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.405707 (* 1 = 0.405707 loss) +I0616 04:40:25.292047 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115375 (* 1 = 0.115375 loss) +I0616 04:40:25.292049 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.299521 (* 1 = 0.299521 loss) +I0616 04:40:25.292053 9857 solver.cpp:571] Iteration 13000, lr = 0.001 +I0616 04:40:36.909227 9857 solver.cpp:242] Iteration 13020, loss = 0.935894 +I0616 04:40:36.909271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.467792 (* 1 = 0.467792 loss) +I0616 04:40:36.909276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.605641 (* 1 = 0.605641 loss) +I0616 04:40:36.909281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109123 (* 1 = 0.109123 loss) +I0616 04:40:36.909284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0307671 (* 1 = 0.0307671 loss) +I0616 04:40:36.909288 9857 solver.cpp:571] Iteration 13020, lr = 0.001 +I0616 04:40:48.479595 9857 solver.cpp:242] Iteration 13040, loss = 1.02382 +I0616 04:40:48.479638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143731 (* 1 = 0.143731 loss) +I0616 04:40:48.479645 9857 solver.cpp:258] Train net output #1: loss_cls = 0.297956 (* 1 = 0.297956 loss) +I0616 04:40:48.479650 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0456093 (* 1 = 0.0456093 loss) +I0616 04:40:48.479652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00986083 (* 1 = 0.00986083 loss) +I0616 04:40:48.479656 9857 solver.cpp:571] Iteration 13040, lr = 0.001 +I0616 04:41:00.128021 9857 solver.cpp:242] Iteration 13060, loss = 0.808083 +I0616 04:41:00.128063 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221806 (* 1 = 0.221806 loss) +I0616 04:41:00.128069 9857 solver.cpp:258] Train net output #1: loss_cls = 0.567384 (* 1 = 0.567384 loss) +I0616 04:41:00.128073 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0753096 (* 1 = 0.0753096 loss) +I0616 04:41:00.128077 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247059 (* 1 = 0.0247059 loss) +I0616 04:41:00.128080 9857 solver.cpp:571] Iteration 13060, lr = 0.001 +I0616 04:41:11.912830 9857 solver.cpp:242] Iteration 13080, loss = 0.31149 +I0616 04:41:11.912871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100398 (* 1 = 0.100398 loss) +I0616 04:41:11.912876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216485 (* 1 = 0.216485 loss) +I0616 04:41:11.912880 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0318648 (* 1 = 0.0318648 loss) +I0616 04:41:11.912884 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00475229 (* 1 = 0.00475229 loss) +I0616 04:41:11.912889 9857 solver.cpp:571] Iteration 13080, lr = 0.001 +I0616 04:41:23.413954 9857 solver.cpp:242] Iteration 13100, loss = 1.4555 +I0616 04:41:23.413995 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363736 (* 1 = 0.363736 loss) +I0616 04:41:23.414001 9857 solver.cpp:258] Train net output #1: loss_cls = 1.19564 (* 1 = 1.19564 loss) +I0616 04:41:23.414005 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.187487 (* 1 = 0.187487 loss) +I0616 04:41:23.414010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020731 (* 1 = 0.020731 loss) +I0616 04:41:23.414013 9857 solver.cpp:571] Iteration 13100, lr = 0.001 +I0616 04:41:34.937160 9857 solver.cpp:242] Iteration 13120, loss = 1.79806 +I0616 04:41:34.937203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.385326 (* 1 = 0.385326 loss) +I0616 04:41:34.937208 9857 solver.cpp:258] Train net output #1: loss_cls = 0.657647 (* 1 = 0.657647 loss) +I0616 04:41:34.937213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.382396 (* 1 = 0.382396 loss) +I0616 04:41:34.937216 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.150463 (* 1 = 0.150463 loss) +I0616 04:41:34.937221 9857 solver.cpp:571] Iteration 13120, lr = 0.001 +I0616 04:41:46.624276 9857 solver.cpp:242] Iteration 13140, loss = 1.02554 +I0616 04:41:46.624317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296646 (* 1 = 0.296646 loss) +I0616 04:41:46.624323 9857 solver.cpp:258] Train net output #1: loss_cls = 0.599357 (* 1 = 0.599357 loss) +I0616 04:41:46.624327 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0908067 (* 1 = 0.0908067 loss) +I0616 04:41:46.624331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0915066 (* 1 = 0.0915066 loss) +I0616 04:41:46.624336 9857 solver.cpp:571] Iteration 13140, lr = 0.001 +I0616 04:41:58.394798 9857 solver.cpp:242] Iteration 13160, loss = 1.12103 +I0616 04:41:58.394840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.439207 (* 1 = 0.439207 loss) +I0616 04:41:58.394845 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16983 (* 1 = 1.16983 loss) +I0616 04:41:58.394850 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0452268 (* 1 = 0.0452268 loss) +I0616 04:41:58.394852 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00624347 (* 1 = 0.00624347 loss) +I0616 04:41:58.394856 9857 solver.cpp:571] Iteration 13160, lr = 0.001 +I0616 04:42:09.992718 9857 solver.cpp:242] Iteration 13180, loss = 1.36879 +I0616 04:42:09.992761 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.498706 (* 1 = 0.498706 loss) +I0616 04:42:09.992766 9857 solver.cpp:258] Train net output #1: loss_cls = 1.04727 (* 1 = 1.04727 loss) +I0616 04:42:09.992770 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.414834 (* 1 = 0.414834 loss) +I0616 04:42:09.992774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.251517 (* 1 = 0.251517 loss) +I0616 04:42:09.992779 9857 solver.cpp:571] Iteration 13180, lr = 0.001 +speed: 0.711s / iter +I0616 04:42:21.251624 9857 solver.cpp:242] Iteration 13200, loss = 0.793955 +I0616 04:42:21.251667 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247153 (* 1 = 0.247153 loss) +I0616 04:42:21.251672 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246672 (* 1 = 0.246672 loss) +I0616 04:42:21.251677 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125084 (* 1 = 0.125084 loss) +I0616 04:42:21.251680 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.164329 (* 1 = 0.164329 loss) +I0616 04:42:21.251684 9857 solver.cpp:571] Iteration 13200, lr = 0.001 +I0616 04:42:32.561668 9857 solver.cpp:242] Iteration 13220, loss = 0.889338 +I0616 04:42:32.561710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14931 (* 1 = 0.14931 loss) +I0616 04:42:32.561717 9857 solver.cpp:258] Train net output #1: loss_cls = 0.521682 (* 1 = 0.521682 loss) +I0616 04:42:32.561722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0420788 (* 1 = 0.0420788 loss) +I0616 04:42:32.561724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0615016 (* 1 = 0.0615016 loss) +I0616 04:42:32.561728 9857 solver.cpp:571] Iteration 13220, lr = 0.001 +I0616 04:42:44.194185 9857 solver.cpp:242] Iteration 13240, loss = 0.831824 +I0616 04:42:44.194226 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2947 (* 1 = 0.2947 loss) +I0616 04:42:44.194232 9857 solver.cpp:258] Train net output #1: loss_cls = 0.654921 (* 1 = 0.654921 loss) +I0616 04:42:44.194236 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0924418 (* 1 = 0.0924418 loss) +I0616 04:42:44.194241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00645386 (* 1 = 0.00645386 loss) +I0616 04:42:44.194244 9857 solver.cpp:571] Iteration 13240, lr = 0.001 +I0616 04:42:55.789510 9857 solver.cpp:242] Iteration 13260, loss = 1.31125 +I0616 04:42:55.789552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.626949 (* 1 = 0.626949 loss) +I0616 04:42:55.789558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.714507 (* 1 = 0.714507 loss) +I0616 04:42:55.789562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.430898 (* 1 = 0.430898 loss) +I0616 04:42:55.789566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0972104 (* 1 = 0.0972104 loss) +I0616 04:42:55.789571 9857 solver.cpp:571] Iteration 13260, lr = 0.001 +I0616 04:43:07.164583 9857 solver.cpp:242] Iteration 13280, loss = 1.71208 +I0616 04:43:07.164625 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.623969 (* 1 = 0.623969 loss) +I0616 04:43:07.164631 9857 solver.cpp:258] Train net output #1: loss_cls = 1.34315 (* 1 = 1.34315 loss) +I0616 04:43:07.164635 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.340022 (* 1 = 0.340022 loss) +I0616 04:43:07.164639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.226794 (* 1 = 0.226794 loss) +I0616 04:43:07.164644 9857 solver.cpp:571] Iteration 13280, lr = 0.001 +I0616 04:43:18.630930 9857 solver.cpp:242] Iteration 13300, loss = 0.589904 +I0616 04:43:18.630971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278171 (* 1 = 0.278171 loss) +I0616 04:43:18.630977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371017 (* 1 = 0.371017 loss) +I0616 04:43:18.630982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0659913 (* 1 = 0.0659913 loss) +I0616 04:43:18.630985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00958359 (* 1 = 0.00958359 loss) +I0616 04:43:18.630990 9857 solver.cpp:571] Iteration 13300, lr = 0.001 +I0616 04:43:30.095466 9857 solver.cpp:242] Iteration 13320, loss = 0.991718 +I0616 04:43:30.095510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249803 (* 1 = 0.249803 loss) +I0616 04:43:30.095515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498026 (* 1 = 0.498026 loss) +I0616 04:43:30.095520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110348 (* 1 = 0.110348 loss) +I0616 04:43:30.095523 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.201194 (* 1 = 0.201194 loss) +I0616 04:43:30.095527 9857 solver.cpp:571] Iteration 13320, lr = 0.001 +I0616 04:43:41.642653 9857 solver.cpp:242] Iteration 13340, loss = 1.40584 +I0616 04:43:41.642698 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.568818 (* 1 = 0.568818 loss) +I0616 04:43:41.642702 9857 solver.cpp:258] Train net output #1: loss_cls = 1.55511 (* 1 = 1.55511 loss) +I0616 04:43:41.642707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0719774 (* 1 = 0.0719774 loss) +I0616 04:43:41.642710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113164 (* 1 = 0.0113164 loss) +I0616 04:43:41.642714 9857 solver.cpp:571] Iteration 13340, lr = 0.001 +I0616 04:43:53.264524 9857 solver.cpp:242] Iteration 13360, loss = 1.11598 +I0616 04:43:53.264567 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130977 (* 1 = 0.130977 loss) +I0616 04:43:53.264574 9857 solver.cpp:258] Train net output #1: loss_cls = 0.336951 (* 1 = 0.336951 loss) +I0616 04:43:53.264578 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124174 (* 1 = 0.124174 loss) +I0616 04:43:53.264581 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0956556 (* 1 = 0.0956556 loss) +I0616 04:43:53.264585 9857 solver.cpp:571] Iteration 13360, lr = 0.001 +I0616 04:44:04.840826 9857 solver.cpp:242] Iteration 13380, loss = 0.755892 +I0616 04:44:04.840869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169263 (* 1 = 0.169263 loss) +I0616 04:44:04.840874 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368718 (* 1 = 0.368718 loss) +I0616 04:44:04.840878 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22791 (* 1 = 0.22791 loss) +I0616 04:44:04.840883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239719 (* 1 = 0.0239719 loss) +I0616 04:44:04.840886 9857 solver.cpp:571] Iteration 13380, lr = 0.001 +speed: 0.709s / iter +I0616 04:44:16.500993 9857 solver.cpp:242] Iteration 13400, loss = 1.84831 +I0616 04:44:16.501036 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453625 (* 1 = 0.453625 loss) +I0616 04:44:16.501041 9857 solver.cpp:258] Train net output #1: loss_cls = 0.612784 (* 1 = 0.612784 loss) +I0616 04:44:16.501045 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.381918 (* 1 = 0.381918 loss) +I0616 04:44:16.501049 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.76185 (* 1 = 0.76185 loss) +I0616 04:44:16.501054 9857 solver.cpp:571] Iteration 13400, lr = 0.001 +I0616 04:44:28.170042 9857 solver.cpp:242] Iteration 13420, loss = 1.3083 +I0616 04:44:28.170083 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.526747 (* 1 = 0.526747 loss) +I0616 04:44:28.170089 9857 solver.cpp:258] Train net output #1: loss_cls = 0.496146 (* 1 = 0.496146 loss) +I0616 04:44:28.170094 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164667 (* 1 = 0.164667 loss) +I0616 04:44:28.170097 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0332095 (* 1 = 0.0332095 loss) +I0616 04:44:28.170101 9857 solver.cpp:571] Iteration 13420, lr = 0.001 +I0616 04:44:39.603337 9857 solver.cpp:242] Iteration 13440, loss = 1.21698 +I0616 04:44:39.603379 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.53228 (* 1 = 0.53228 loss) +I0616 04:44:39.603384 9857 solver.cpp:258] Train net output #1: loss_cls = 0.661694 (* 1 = 0.661694 loss) +I0616 04:44:39.603389 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0445861 (* 1 = 0.0445861 loss) +I0616 04:44:39.603392 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0567851 (* 1 = 0.0567851 loss) +I0616 04:44:39.603399 9857 solver.cpp:571] Iteration 13440, lr = 0.001 +I0616 04:44:51.068641 9857 solver.cpp:242] Iteration 13460, loss = 1.35601 +I0616 04:44:51.068683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.441147 (* 1 = 0.441147 loss) +I0616 04:44:51.068689 9857 solver.cpp:258] Train net output #1: loss_cls = 0.700835 (* 1 = 0.700835 loss) +I0616 04:44:51.068693 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.468706 (* 1 = 0.468706 loss) +I0616 04:44:51.068697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0862994 (* 1 = 0.0862994 loss) +I0616 04:44:51.068701 9857 solver.cpp:571] Iteration 13460, lr = 0.001 +I0616 04:45:02.703357 9857 solver.cpp:242] Iteration 13480, loss = 1.9296 +I0616 04:45:02.703398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.418706 (* 1 = 0.418706 loss) +I0616 04:45:02.703404 9857 solver.cpp:258] Train net output #1: loss_cls = 1.05467 (* 1 = 1.05467 loss) +I0616 04:45:02.703408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.284144 (* 1 = 0.284144 loss) +I0616 04:45:02.703413 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210496 (* 1 = 0.0210496 loss) +I0616 04:45:02.703416 9857 solver.cpp:571] Iteration 13480, lr = 0.001 +I0616 04:45:14.238543 9857 solver.cpp:242] Iteration 13500, loss = 0.728402 +I0616 04:45:14.238585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3655 (* 1 = 0.3655 loss) +I0616 04:45:14.238591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.545916 (* 1 = 0.545916 loss) +I0616 04:45:14.238596 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102773 (* 1 = 0.102773 loss) +I0616 04:45:14.238600 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103767 (* 1 = 0.103767 loss) +I0616 04:45:14.238603 9857 solver.cpp:571] Iteration 13500, lr = 0.001 +I0616 04:45:25.738234 9857 solver.cpp:242] Iteration 13520, loss = 1.16591 +I0616 04:45:25.738276 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115023 (* 1 = 0.115023 loss) +I0616 04:45:25.738281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315525 (* 1 = 0.315525 loss) +I0616 04:45:25.738286 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386193 (* 1 = 0.0386193 loss) +I0616 04:45:25.738289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113217 (* 1 = 0.0113217 loss) +I0616 04:45:25.738293 9857 solver.cpp:571] Iteration 13520, lr = 0.001 +I0616 04:45:37.032358 9857 solver.cpp:242] Iteration 13540, loss = 0.988612 +I0616 04:45:37.032400 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160046 (* 1 = 0.160046 loss) +I0616 04:45:37.032407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225305 (* 1 = 0.225305 loss) +I0616 04:45:37.032410 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122095 (* 1 = 0.122095 loss) +I0616 04:45:37.032414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.199046 (* 1 = 0.199046 loss) +I0616 04:45:37.032418 9857 solver.cpp:571] Iteration 13540, lr = 0.001 +I0616 04:45:48.543702 9857 solver.cpp:242] Iteration 13560, loss = 0.662417 +I0616 04:45:48.543743 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112286 (* 1 = 0.112286 loss) +I0616 04:45:48.543750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287898 (* 1 = 0.287898 loss) +I0616 04:45:48.543753 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0513229 (* 1 = 0.0513229 loss) +I0616 04:45:48.543757 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0391801 (* 1 = 0.0391801 loss) +I0616 04:45:48.543761 9857 solver.cpp:571] Iteration 13560, lr = 0.001 +I0616 04:46:00.139369 9857 solver.cpp:242] Iteration 13580, loss = 1.24031 +I0616 04:46:00.139407 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322861 (* 1 = 0.322861 loss) +I0616 04:46:00.139415 9857 solver.cpp:258] Train net output #1: loss_cls = 0.537894 (* 1 = 0.537894 loss) +I0616 04:46:00.139418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135353 (* 1 = 0.135353 loss) +I0616 04:46:00.139422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0192258 (* 1 = 0.0192258 loss) +I0616 04:46:00.139425 9857 solver.cpp:571] Iteration 13580, lr = 0.001 +speed: 0.707s / iter +I0616 04:46:11.617557 9857 solver.cpp:242] Iteration 13600, loss = 1.01794 +I0616 04:46:11.617597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126236 (* 1 = 0.126236 loss) +I0616 04:46:11.617604 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18295 (* 1 = 0.18295 loss) +I0616 04:46:11.617607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0298376 (* 1 = 0.0298376 loss) +I0616 04:46:11.617611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483625 (* 1 = 0.0483625 loss) +I0616 04:46:11.617615 9857 solver.cpp:571] Iteration 13600, lr = 0.001 +I0616 04:46:23.031206 9857 solver.cpp:242] Iteration 13620, loss = 1.99718 +I0616 04:46:23.031250 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.490324 (* 1 = 0.490324 loss) +I0616 04:46:23.031255 9857 solver.cpp:258] Train net output #1: loss_cls = 1.17928 (* 1 = 1.17928 loss) +I0616 04:46:23.031260 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.49066 (* 1 = 0.49066 loss) +I0616 04:46:23.031263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.085658 (* 1 = 0.085658 loss) +I0616 04:46:23.031266 9857 solver.cpp:571] Iteration 13620, lr = 0.001 +I0616 04:46:34.447280 9857 solver.cpp:242] Iteration 13640, loss = 1.44592 +I0616 04:46:34.447322 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0412146 (* 1 = 0.0412146 loss) +I0616 04:46:34.447329 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0801342 (* 1 = 0.0801342 loss) +I0616 04:46:34.447332 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0715461 (* 1 = 0.0715461 loss) +I0616 04:46:34.447336 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.024571 (* 1 = 0.024571 loss) +I0616 04:46:34.447340 9857 solver.cpp:571] Iteration 13640, lr = 0.001 +I0616 04:46:45.898068 9857 solver.cpp:242] Iteration 13660, loss = 1.16798 +I0616 04:46:45.898111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.407338 (* 1 = 0.407338 loss) +I0616 04:46:45.898118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.605945 (* 1 = 0.605945 loss) +I0616 04:46:45.898123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0568109 (* 1 = 0.0568109 loss) +I0616 04:46:45.898125 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194981 (* 1 = 0.0194981 loss) +I0616 04:46:45.898129 9857 solver.cpp:571] Iteration 13660, lr = 0.001 +I0616 04:46:57.516182 9857 solver.cpp:242] Iteration 13680, loss = 0.627274 +I0616 04:46:57.516227 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172409 (* 1 = 0.172409 loss) +I0616 04:46:57.516232 9857 solver.cpp:258] Train net output #1: loss_cls = 0.398593 (* 1 = 0.398593 loss) +I0616 04:46:57.516235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11105 (* 1 = 0.11105 loss) +I0616 04:46:57.516239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252048 (* 1 = 0.0252048 loss) +I0616 04:46:57.516242 9857 solver.cpp:571] Iteration 13680, lr = 0.001 +I0616 04:47:08.948420 9857 solver.cpp:242] Iteration 13700, loss = 1.13858 +I0616 04:47:08.948462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260536 (* 1 = 0.260536 loss) +I0616 04:47:08.948467 9857 solver.cpp:258] Train net output #1: loss_cls = 0.548869 (* 1 = 0.548869 loss) +I0616 04:47:08.948472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.327634 (* 1 = 0.327634 loss) +I0616 04:47:08.948477 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.257973 (* 1 = 0.257973 loss) +I0616 04:47:08.948480 9857 solver.cpp:571] Iteration 13700, lr = 0.001 +I0616 04:47:20.637266 9857 solver.cpp:242] Iteration 13720, loss = 0.969599 +I0616 04:47:20.637310 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326771 (* 1 = 0.326771 loss) +I0616 04:47:20.637315 9857 solver.cpp:258] Train net output #1: loss_cls = 0.913407 (* 1 = 0.913407 loss) +I0616 04:47:20.637320 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224426 (* 1 = 0.224426 loss) +I0616 04:47:20.637323 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143081 (* 1 = 0.0143081 loss) +I0616 04:47:20.637327 9857 solver.cpp:571] Iteration 13720, lr = 0.001 +I0616 04:47:32.365001 9857 solver.cpp:242] Iteration 13740, loss = 0.987973 +I0616 04:47:32.365043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296812 (* 1 = 0.296812 loss) +I0616 04:47:32.365049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.646806 (* 1 = 0.646806 loss) +I0616 04:47:32.365053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.269063 (* 1 = 0.269063 loss) +I0616 04:47:32.365057 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.239503 (* 1 = 0.239503 loss) +I0616 04:47:32.365061 9857 solver.cpp:571] Iteration 13740, lr = 0.001 +I0616 04:47:43.655609 9857 solver.cpp:242] Iteration 13760, loss = 0.895923 +I0616 04:47:43.655650 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287849 (* 1 = 0.287849 loss) +I0616 04:47:43.655656 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219605 (* 1 = 0.219605 loss) +I0616 04:47:43.655661 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125659 (* 1 = 0.125659 loss) +I0616 04:47:43.655664 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108286 (* 1 = 0.108286 loss) +I0616 04:47:43.655668 9857 solver.cpp:571] Iteration 13760, lr = 0.001 +I0616 04:47:55.134341 9857 solver.cpp:242] Iteration 13780, loss = 1.0813 +I0616 04:47:55.134382 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316161 (* 1 = 0.316161 loss) +I0616 04:47:55.134387 9857 solver.cpp:258] Train net output #1: loss_cls = 0.445034 (* 1 = 0.445034 loss) +I0616 04:47:55.134392 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.409336 (* 1 = 0.409336 loss) +I0616 04:47:55.134395 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.418688 (* 1 = 0.418688 loss) +I0616 04:47:55.134399 9857 solver.cpp:571] Iteration 13780, lr = 0.001 +speed: 0.705s / iter +I0616 04:48:06.846436 9857 solver.cpp:242] Iteration 13800, loss = 0.952543 +I0616 04:48:06.846477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154136 (* 1 = 0.154136 loss) +I0616 04:48:06.846482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.240482 (* 1 = 0.240482 loss) +I0616 04:48:06.846487 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0156559 (* 1 = 0.0156559 loss) +I0616 04:48:06.846490 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00936319 (* 1 = 0.00936319 loss) +I0616 04:48:06.846494 9857 solver.cpp:571] Iteration 13800, lr = 0.001 +I0616 04:48:18.436183 9857 solver.cpp:242] Iteration 13820, loss = 1.95871 +I0616 04:48:18.436224 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.543853 (* 1 = 0.543853 loss) +I0616 04:48:18.436230 9857 solver.cpp:258] Train net output #1: loss_cls = 1.50368 (* 1 = 1.50368 loss) +I0616 04:48:18.436235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.654816 (* 1 = 0.654816 loss) +I0616 04:48:18.436239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.115905 (* 1 = 0.115905 loss) +I0616 04:48:18.436242 9857 solver.cpp:571] Iteration 13820, lr = 0.001 +I0616 04:48:30.188360 9857 solver.cpp:242] Iteration 13840, loss = 0.885336 +I0616 04:48:30.188402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286644 (* 1 = 0.286644 loss) +I0616 04:48:30.188407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254042 (* 1 = 0.254042 loss) +I0616 04:48:30.188412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1555 (* 1 = 0.1555 loss) +I0616 04:48:30.188416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260151 (* 1 = 0.0260151 loss) +I0616 04:48:30.188421 9857 solver.cpp:571] Iteration 13840, lr = 0.001 +I0616 04:48:41.749704 9857 solver.cpp:242] Iteration 13860, loss = 0.993152 +I0616 04:48:41.749747 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39966 (* 1 = 0.39966 loss) +I0616 04:48:41.749753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.753577 (* 1 = 0.753577 loss) +I0616 04:48:41.749758 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154818 (* 1 = 0.154818 loss) +I0616 04:48:41.749761 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0510486 (* 1 = 0.0510486 loss) +I0616 04:48:41.749765 9857 solver.cpp:571] Iteration 13860, lr = 0.001 +I0616 04:48:53.319072 9857 solver.cpp:242] Iteration 13880, loss = 0.914061 +I0616 04:48:53.319113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21349 (* 1 = 0.21349 loss) +I0616 04:48:53.319118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.238267 (* 1 = 0.238267 loss) +I0616 04:48:53.319123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19412 (* 1 = 0.19412 loss) +I0616 04:48:53.319128 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0267218 (* 1 = 0.0267218 loss) +I0616 04:48:53.319131 9857 solver.cpp:571] Iteration 13880, lr = 0.001 +I0616 04:49:04.799407 9857 solver.cpp:242] Iteration 13900, loss = 1.12123 +I0616 04:49:04.799449 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.49413 (* 1 = 0.49413 loss) +I0616 04:49:04.799455 9857 solver.cpp:258] Train net output #1: loss_cls = 0.556225 (* 1 = 0.556225 loss) +I0616 04:49:04.799459 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168956 (* 1 = 0.168956 loss) +I0616 04:49:04.799463 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.041734 (* 1 = 0.041734 loss) +I0616 04:49:04.799468 9857 solver.cpp:571] Iteration 13900, lr = 0.001 +I0616 04:49:16.285605 9857 solver.cpp:242] Iteration 13920, loss = 0.478472 +I0616 04:49:16.285647 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0893646 (* 1 = 0.0893646 loss) +I0616 04:49:16.285653 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205149 (* 1 = 0.205149 loss) +I0616 04:49:16.285657 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0497022 (* 1 = 0.0497022 loss) +I0616 04:49:16.285661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115073 (* 1 = 0.0115073 loss) +I0616 04:49:16.285666 9857 solver.cpp:571] Iteration 13920, lr = 0.001 +I0616 04:49:27.875288 9857 solver.cpp:242] Iteration 13940, loss = 0.988898 +I0616 04:49:27.875329 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214502 (* 1 = 0.214502 loss) +I0616 04:49:27.875334 9857 solver.cpp:258] Train net output #1: loss_cls = 0.755837 (* 1 = 0.755837 loss) +I0616 04:49:27.875339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0589671 (* 1 = 0.0589671 loss) +I0616 04:49:27.875344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00830489 (* 1 = 0.00830489 loss) +I0616 04:49:27.875347 9857 solver.cpp:571] Iteration 13940, lr = 0.001 +I0616 04:49:39.399137 9857 solver.cpp:242] Iteration 13960, loss = 0.723335 +I0616 04:49:39.399180 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0887987 (* 1 = 0.0887987 loss) +I0616 04:49:39.399186 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185908 (* 1 = 0.185908 loss) +I0616 04:49:39.399190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0434984 (* 1 = 0.0434984 loss) +I0616 04:49:39.399194 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239297 (* 1 = 0.0239297 loss) +I0616 04:49:39.399197 9857 solver.cpp:571] Iteration 13960, lr = 0.001 +I0616 04:49:51.018244 9857 solver.cpp:242] Iteration 13980, loss = 0.607024 +I0616 04:49:51.018285 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261724 (* 1 = 0.261724 loss) +I0616 04:49:51.018290 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0977547 (* 1 = 0.0977547 loss) +I0616 04:49:51.018295 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0168941 (* 1 = 0.0168941 loss) +I0616 04:49:51.018298 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00591286 (* 1 = 0.00591286 loss) +I0616 04:49:51.018302 9857 solver.cpp:571] Iteration 13980, lr = 0.001 +speed: 0.704s / iter +I0616 04:50:02.707159 9857 solver.cpp:242] Iteration 14000, loss = 1.53335 +I0616 04:50:02.707201 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39344 (* 1 = 0.39344 loss) +I0616 04:50:02.707207 9857 solver.cpp:258] Train net output #1: loss_cls = 0.391245 (* 1 = 0.391245 loss) +I0616 04:50:02.707211 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.248117 (* 1 = 0.248117 loss) +I0616 04:50:02.707216 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136252 (* 1 = 0.136252 loss) +I0616 04:50:02.707218 9857 solver.cpp:571] Iteration 14000, lr = 0.001 +I0616 04:50:14.399009 9857 solver.cpp:242] Iteration 14020, loss = 1.59566 +I0616 04:50:14.399051 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.535381 (* 1 = 0.535381 loss) +I0616 04:50:14.399057 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00048 (* 1 = 1.00048 loss) +I0616 04:50:14.399061 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.326655 (* 1 = 0.326655 loss) +I0616 04:50:14.399065 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0912938 (* 1 = 0.0912938 loss) +I0616 04:50:14.399070 9857 solver.cpp:571] Iteration 14020, lr = 0.001 +I0616 04:50:26.165205 9857 solver.cpp:242] Iteration 14040, loss = 1.13382 +I0616 04:50:26.165243 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318842 (* 1 = 0.318842 loss) +I0616 04:50:26.165249 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31683 (* 1 = 0.31683 loss) +I0616 04:50:26.165254 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117957 (* 1 = 0.117957 loss) +I0616 04:50:26.165257 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.024245 (* 1 = 0.024245 loss) +I0616 04:50:26.165261 9857 solver.cpp:571] Iteration 14040, lr = 0.001 +I0616 04:50:38.236634 9857 solver.cpp:242] Iteration 14060, loss = 0.940858 +I0616 04:50:38.236675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.418971 (* 1 = 0.418971 loss) +I0616 04:50:38.236681 9857 solver.cpp:258] Train net output #1: loss_cls = 0.772886 (* 1 = 0.772886 loss) +I0616 04:50:38.236685 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149132 (* 1 = 0.149132 loss) +I0616 04:50:38.236690 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0513905 (* 1 = 0.0513905 loss) +I0616 04:50:38.236693 9857 solver.cpp:571] Iteration 14060, lr = 0.001 +I0616 04:50:49.686178 9857 solver.cpp:242] Iteration 14080, loss = 0.966633 +I0616 04:50:49.686220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302992 (* 1 = 0.302992 loss) +I0616 04:50:49.686226 9857 solver.cpp:258] Train net output #1: loss_cls = 0.584235 (* 1 = 0.584235 loss) +I0616 04:50:49.686230 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.3101 (* 1 = 0.3101 loss) +I0616 04:50:49.686234 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0557597 (* 1 = 0.0557597 loss) +I0616 04:50:49.686239 9857 solver.cpp:571] Iteration 14080, lr = 0.001 +I0616 04:51:01.182278 9857 solver.cpp:242] Iteration 14100, loss = 0.350728 +I0616 04:51:01.182322 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0827256 (* 1 = 0.0827256 loss) +I0616 04:51:01.182327 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18821 (* 1 = 0.18821 loss) +I0616 04:51:01.182332 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0540999 (* 1 = 0.0540999 loss) +I0616 04:51:01.182335 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130513 (* 1 = 0.0130513 loss) +I0616 04:51:01.182338 9857 solver.cpp:571] Iteration 14100, lr = 0.001 +I0616 04:51:12.608325 9857 solver.cpp:242] Iteration 14120, loss = 1.67163 +I0616 04:51:12.608367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152906 (* 1 = 0.152906 loss) +I0616 04:51:12.608373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342846 (* 1 = 0.342846 loss) +I0616 04:51:12.608377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140358 (* 1 = 0.140358 loss) +I0616 04:51:12.608381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.851357 (* 1 = 0.851357 loss) +I0616 04:51:12.608384 9857 solver.cpp:571] Iteration 14120, lr = 0.001 +I0616 04:51:24.095410 9857 solver.cpp:242] Iteration 14140, loss = 0.684195 +I0616 04:51:24.095453 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182548 (* 1 = 0.182548 loss) +I0616 04:51:24.095458 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222388 (* 1 = 0.222388 loss) +I0616 04:51:24.095463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0239204 (* 1 = 0.0239204 loss) +I0616 04:51:24.095465 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123047 (* 1 = 0.0123047 loss) +I0616 04:51:24.095469 9857 solver.cpp:571] Iteration 14140, lr = 0.001 +I0616 04:51:35.645452 9857 solver.cpp:242] Iteration 14160, loss = 0.515812 +I0616 04:51:35.645494 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127951 (* 1 = 0.127951 loss) +I0616 04:51:35.645500 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274629 (* 1 = 0.274629 loss) +I0616 04:51:35.645504 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0469894 (* 1 = 0.0469894 loss) +I0616 04:51:35.645508 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0320564 (* 1 = 0.0320564 loss) +I0616 04:51:35.645511 9857 solver.cpp:571] Iteration 14160, lr = 0.001 +I0616 04:51:46.991817 9857 solver.cpp:242] Iteration 14180, loss = 1.51469 +I0616 04:51:46.991858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421532 (* 1 = 0.421532 loss) +I0616 04:51:46.991863 9857 solver.cpp:258] Train net output #1: loss_cls = 0.604286 (* 1 = 0.604286 loss) +I0616 04:51:46.991868 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.353956 (* 1 = 0.353956 loss) +I0616 04:51:46.991871 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.642281 (* 1 = 0.642281 loss) +I0616 04:51:46.991875 9857 solver.cpp:571] Iteration 14180, lr = 0.001 +speed: 0.702s / iter +I0616 04:51:58.693344 9857 solver.cpp:242] Iteration 14200, loss = 0.641044 +I0616 04:51:58.693387 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302676 (* 1 = 0.302676 loss) +I0616 04:51:58.693393 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201924 (* 1 = 0.201924 loss) +I0616 04:51:58.693398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.050491 (* 1 = 0.050491 loss) +I0616 04:51:58.693403 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220403 (* 1 = 0.0220403 loss) +I0616 04:51:58.693405 9857 solver.cpp:571] Iteration 14200, lr = 0.001 +I0616 04:52:10.203040 9857 solver.cpp:242] Iteration 14220, loss = 0.723418 +I0616 04:52:10.203079 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222662 (* 1 = 0.222662 loss) +I0616 04:52:10.203085 9857 solver.cpp:258] Train net output #1: loss_cls = 0.565708 (* 1 = 0.565708 loss) +I0616 04:52:10.203089 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109504 (* 1 = 0.109504 loss) +I0616 04:52:10.203094 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107336 (* 1 = 0.0107336 loss) +I0616 04:52:10.203097 9857 solver.cpp:571] Iteration 14220, lr = 0.001 +I0616 04:52:21.832017 9857 solver.cpp:242] Iteration 14240, loss = 1.15836 +I0616 04:52:21.832059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384244 (* 1 = 0.384244 loss) +I0616 04:52:21.832067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.491184 (* 1 = 0.491184 loss) +I0616 04:52:21.832070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126491 (* 1 = 0.126491 loss) +I0616 04:52:21.832074 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220666 (* 1 = 0.0220666 loss) +I0616 04:52:21.832078 9857 solver.cpp:571] Iteration 14240, lr = 0.001 +I0616 04:52:33.387567 9857 solver.cpp:242] Iteration 14260, loss = 1.31131 +I0616 04:52:33.387609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.565448 (* 1 = 0.565448 loss) +I0616 04:52:33.387615 9857 solver.cpp:258] Train net output #1: loss_cls = 0.984939 (* 1 = 0.984939 loss) +I0616 04:52:33.387619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151545 (* 1 = 0.151545 loss) +I0616 04:52:33.387624 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0625765 (* 1 = 0.0625765 loss) +I0616 04:52:33.387627 9857 solver.cpp:571] Iteration 14260, lr = 0.001 +I0616 04:52:45.024514 9857 solver.cpp:242] Iteration 14280, loss = 1.43393 +I0616 04:52:45.024555 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.554995 (* 1 = 0.554995 loss) +I0616 04:52:45.024561 9857 solver.cpp:258] Train net output #1: loss_cls = 1.24296 (* 1 = 1.24296 loss) +I0616 04:52:45.024565 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205002 (* 1 = 0.205002 loss) +I0616 04:52:45.024569 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0046772 (* 1 = 0.0046772 loss) +I0616 04:52:45.024574 9857 solver.cpp:571] Iteration 14280, lr = 0.001 +I0616 04:52:56.405418 9857 solver.cpp:242] Iteration 14300, loss = 0.839927 +I0616 04:52:56.405460 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436211 (* 1 = 0.436211 loss) +I0616 04:52:56.405467 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252572 (* 1 = 0.252572 loss) +I0616 04:52:56.405470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0821254 (* 1 = 0.0821254 loss) +I0616 04:52:56.405474 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00243236 (* 1 = 0.00243236 loss) +I0616 04:52:56.405478 9857 solver.cpp:571] Iteration 14300, lr = 0.001 +I0616 04:53:07.735044 9857 solver.cpp:242] Iteration 14320, loss = 1.72155 +I0616 04:53:07.735086 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.520766 (* 1 = 0.520766 loss) +I0616 04:53:07.735091 9857 solver.cpp:258] Train net output #1: loss_cls = 1.08224 (* 1 = 1.08224 loss) +I0616 04:53:07.735096 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.385315 (* 1 = 0.385315 loss) +I0616 04:53:07.735100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0895658 (* 1 = 0.0895658 loss) +I0616 04:53:07.735103 9857 solver.cpp:571] Iteration 14320, lr = 0.001 +I0616 04:53:19.272584 9857 solver.cpp:242] Iteration 14340, loss = 0.628316 +I0616 04:53:19.272625 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421874 (* 1 = 0.421874 loss) +I0616 04:53:19.272631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368333 (* 1 = 0.368333 loss) +I0616 04:53:19.272635 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0442956 (* 1 = 0.0442956 loss) +I0616 04:53:19.272639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0195057 (* 1 = 0.0195057 loss) +I0616 04:53:19.272644 9857 solver.cpp:571] Iteration 14340, lr = 0.001 +I0616 04:53:30.593338 9857 solver.cpp:242] Iteration 14360, loss = 1.02268 +I0616 04:53:30.593379 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.540235 (* 1 = 0.540235 loss) +I0616 04:53:30.593385 9857 solver.cpp:258] Train net output #1: loss_cls = 0.831628 (* 1 = 0.831628 loss) +I0616 04:53:30.593389 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.265945 (* 1 = 0.265945 loss) +I0616 04:53:30.593394 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0546634 (* 1 = 0.0546634 loss) +I0616 04:53:30.593397 9857 solver.cpp:571] Iteration 14360, lr = 0.001 +I0616 04:53:42.044883 9857 solver.cpp:242] Iteration 14380, loss = 0.591864 +I0616 04:53:42.044926 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161102 (* 1 = 0.161102 loss) +I0616 04:53:42.044931 9857 solver.cpp:258] Train net output #1: loss_cls = 0.228132 (* 1 = 0.228132 loss) +I0616 04:53:42.044935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102185 (* 1 = 0.102185 loss) +I0616 04:53:42.044939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0652348 (* 1 = 0.0652348 loss) +I0616 04:53:42.044944 9857 solver.cpp:571] Iteration 14380, lr = 0.001 +speed: 0.700s / iter +I0616 04:53:53.600090 9857 solver.cpp:242] Iteration 14400, loss = 0.881385 +I0616 04:53:53.600132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.340022 (* 1 = 0.340022 loss) +I0616 04:53:53.600137 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337408 (* 1 = 0.337408 loss) +I0616 04:53:53.600142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.070912 (* 1 = 0.070912 loss) +I0616 04:53:53.600147 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127612 (* 1 = 0.0127612 loss) +I0616 04:53:53.600149 9857 solver.cpp:571] Iteration 14400, lr = 0.001 +I0616 04:54:05.041766 9857 solver.cpp:242] Iteration 14420, loss = 1.45088 +I0616 04:54:05.041806 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.722969 (* 1 = 0.722969 loss) +I0616 04:54:05.041812 9857 solver.cpp:258] Train net output #1: loss_cls = 1.29343 (* 1 = 1.29343 loss) +I0616 04:54:05.041816 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144236 (* 1 = 0.144236 loss) +I0616 04:54:05.041821 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108966 (* 1 = 0.108966 loss) +I0616 04:54:05.041824 9857 solver.cpp:571] Iteration 14420, lr = 0.001 +I0616 04:54:16.585633 9857 solver.cpp:242] Iteration 14440, loss = 0.535957 +I0616 04:54:16.585674 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206621 (* 1 = 0.206621 loss) +I0616 04:54:16.585678 9857 solver.cpp:258] Train net output #1: loss_cls = 0.293113 (* 1 = 0.293113 loss) +I0616 04:54:16.585682 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0844465 (* 1 = 0.0844465 loss) +I0616 04:54:16.585686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0250875 (* 1 = 0.0250875 loss) +I0616 04:54:16.585690 9857 solver.cpp:571] Iteration 14440, lr = 0.001 +I0616 04:54:28.007104 9857 solver.cpp:242] Iteration 14460, loss = 1.54683 +I0616 04:54:28.007146 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.532221 (* 1 = 0.532221 loss) +I0616 04:54:28.007153 9857 solver.cpp:258] Train net output #1: loss_cls = 0.72934 (* 1 = 0.72934 loss) +I0616 04:54:28.007156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294105 (* 1 = 0.294105 loss) +I0616 04:54:28.007159 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.181746 (* 1 = 0.181746 loss) +I0616 04:54:28.007164 9857 solver.cpp:571] Iteration 14460, lr = 0.001 +I0616 04:54:39.466084 9857 solver.cpp:242] Iteration 14480, loss = 0.655925 +I0616 04:54:39.466126 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184968 (* 1 = 0.184968 loss) +I0616 04:54:39.466132 9857 solver.cpp:258] Train net output #1: loss_cls = 0.454705 (* 1 = 0.454705 loss) +I0616 04:54:39.466136 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0751014 (* 1 = 0.0751014 loss) +I0616 04:54:39.466140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0203461 (* 1 = 0.0203461 loss) +I0616 04:54:39.466146 9857 solver.cpp:571] Iteration 14480, lr = 0.001 +I0616 04:54:51.269639 9857 solver.cpp:242] Iteration 14500, loss = 1.05212 +I0616 04:54:51.269680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309717 (* 1 = 0.309717 loss) +I0616 04:54:51.269685 9857 solver.cpp:258] Train net output #1: loss_cls = 0.560565 (* 1 = 0.560565 loss) +I0616 04:54:51.269690 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0923146 (* 1 = 0.0923146 loss) +I0616 04:54:51.269693 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164743 (* 1 = 0.0164743 loss) +I0616 04:54:51.269697 9857 solver.cpp:571] Iteration 14500, lr = 0.001 +I0616 04:55:02.774101 9857 solver.cpp:242] Iteration 14520, loss = 1.84587 +I0616 04:55:02.774142 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.682461 (* 1 = 0.682461 loss) +I0616 04:55:02.774148 9857 solver.cpp:258] Train net output #1: loss_cls = 1.33811 (* 1 = 1.33811 loss) +I0616 04:55:02.774152 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.285448 (* 1 = 0.285448 loss) +I0616 04:55:02.774157 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.373001 (* 1 = 0.373001 loss) +I0616 04:55:02.774160 9857 solver.cpp:571] Iteration 14520, lr = 0.001 +I0616 04:55:14.429405 9857 solver.cpp:242] Iteration 14540, loss = 1.19462 +I0616 04:55:14.429448 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.540067 (* 1 = 0.540067 loss) +I0616 04:55:14.429453 9857 solver.cpp:258] Train net output #1: loss_cls = 0.915479 (* 1 = 0.915479 loss) +I0616 04:55:14.429458 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.241575 (* 1 = 0.241575 loss) +I0616 04:55:14.429461 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.112778 (* 1 = 0.112778 loss) +I0616 04:55:14.429466 9857 solver.cpp:571] Iteration 14540, lr = 0.001 +I0616 04:55:26.009322 9857 solver.cpp:242] Iteration 14560, loss = 0.848504 +I0616 04:55:26.009366 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297667 (* 1 = 0.297667 loss) +I0616 04:55:26.009371 9857 solver.cpp:258] Train net output #1: loss_cls = 0.44052 (* 1 = 0.44052 loss) +I0616 04:55:26.009376 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119281 (* 1 = 0.119281 loss) +I0616 04:55:26.009379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205807 (* 1 = 0.0205807 loss) +I0616 04:55:26.009383 9857 solver.cpp:571] Iteration 14560, lr = 0.001 +I0616 04:55:37.602741 9857 solver.cpp:242] Iteration 14580, loss = 1.65335 +I0616 04:55:37.602784 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0667342 (* 1 = 0.0667342 loss) +I0616 04:55:37.602790 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107977 (* 1 = 0.107977 loss) +I0616 04:55:37.602794 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0743785 (* 1 = 0.0743785 loss) +I0616 04:55:37.602797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0229824 (* 1 = 0.0229824 loss) +I0616 04:55:37.602802 9857 solver.cpp:571] Iteration 14580, lr = 0.001 +speed: 0.698s / iter +I0616 04:55:49.212326 9857 solver.cpp:242] Iteration 14600, loss = 1.61109 +I0616 04:55:49.212368 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.54193 (* 1 = 0.54193 loss) +I0616 04:55:49.212374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.512941 (* 1 = 0.512941 loss) +I0616 04:55:49.212378 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.244149 (* 1 = 0.244149 loss) +I0616 04:55:49.212383 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.489847 (* 1 = 0.489847 loss) +I0616 04:55:49.212385 9857 solver.cpp:571] Iteration 14600, lr = 0.001 +I0616 04:56:00.712105 9857 solver.cpp:242] Iteration 14620, loss = 0.909849 +I0616 04:56:00.712147 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153379 (* 1 = 0.153379 loss) +I0616 04:56:00.712153 9857 solver.cpp:258] Train net output #1: loss_cls = 0.379105 (* 1 = 0.379105 loss) +I0616 04:56:00.712157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136965 (* 1 = 0.136965 loss) +I0616 04:56:00.712162 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205798 (* 1 = 0.0205798 loss) +I0616 04:56:00.712165 9857 solver.cpp:571] Iteration 14620, lr = 0.001 +I0616 04:56:12.256038 9857 solver.cpp:242] Iteration 14640, loss = 1.97954 +I0616 04:56:12.256079 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288609 (* 1 = 0.288609 loss) +I0616 04:56:12.256085 9857 solver.cpp:258] Train net output #1: loss_cls = 0.524978 (* 1 = 0.524978 loss) +I0616 04:56:12.256089 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.284372 (* 1 = 0.284372 loss) +I0616 04:56:12.256093 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.379251 (* 1 = 0.379251 loss) +I0616 04:56:12.256096 9857 solver.cpp:571] Iteration 14640, lr = 0.001 +I0616 04:56:23.951042 9857 solver.cpp:242] Iteration 14660, loss = 0.962784 +I0616 04:56:23.951084 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.295357 (* 1 = 0.295357 loss) +I0616 04:56:23.951089 9857 solver.cpp:258] Train net output #1: loss_cls = 0.319427 (* 1 = 0.319427 loss) +I0616 04:56:23.951093 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121341 (* 1 = 0.121341 loss) +I0616 04:56:23.951097 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.071569 (* 1 = 0.071569 loss) +I0616 04:56:23.951102 9857 solver.cpp:571] Iteration 14660, lr = 0.001 +I0616 04:56:35.477926 9857 solver.cpp:242] Iteration 14680, loss = 0.919396 +I0616 04:56:35.477967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0970967 (* 1 = 0.0970967 loss) +I0616 04:56:35.477973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242258 (* 1 = 0.242258 loss) +I0616 04:56:35.477977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386525 (* 1 = 0.0386525 loss) +I0616 04:56:35.477982 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128335 (* 1 = 0.0128335 loss) +I0616 04:56:35.477985 9857 solver.cpp:571] Iteration 14680, lr = 0.001 +I0616 04:56:47.129999 9857 solver.cpp:242] Iteration 14700, loss = 1.05648 +I0616 04:56:47.130043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232265 (* 1 = 0.232265 loss) +I0616 04:56:47.130051 9857 solver.cpp:258] Train net output #1: loss_cls = 0.48517 (* 1 = 0.48517 loss) +I0616 04:56:47.130059 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0412151 (* 1 = 0.0412151 loss) +I0616 04:56:47.130066 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184901 (* 1 = 0.0184901 loss) +I0616 04:56:47.130074 9857 solver.cpp:571] Iteration 14700, lr = 0.001 +I0616 04:56:58.670871 9857 solver.cpp:242] Iteration 14720, loss = 1.07046 +I0616 04:56:58.670914 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.53407 (* 1 = 0.53407 loss) +I0616 04:56:58.670919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.845532 (* 1 = 0.845532 loss) +I0616 04:56:58.670923 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.204551 (* 1 = 0.204551 loss) +I0616 04:56:58.670928 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0414629 (* 1 = 0.0414629 loss) +I0616 04:56:58.670931 9857 solver.cpp:571] Iteration 14720, lr = 0.001 +I0616 04:57:10.177654 9857 solver.cpp:242] Iteration 14740, loss = 0.64887 +I0616 04:57:10.177697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276831 (* 1 = 0.276831 loss) +I0616 04:57:10.177702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303428 (* 1 = 0.303428 loss) +I0616 04:57:10.177707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.233808 (* 1 = 0.233808 loss) +I0616 04:57:10.177711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.048958 (* 1 = 0.048958 loss) +I0616 04:57:10.177716 9857 solver.cpp:571] Iteration 14740, lr = 0.001 +I0616 04:57:21.633323 9857 solver.cpp:242] Iteration 14760, loss = 0.805966 +I0616 04:57:21.633365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139963 (* 1 = 0.139963 loss) +I0616 04:57:21.633370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24482 (* 1 = 0.24482 loss) +I0616 04:57:21.633375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0780597 (* 1 = 0.0780597 loss) +I0616 04:57:21.633379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201268 (* 1 = 0.0201268 loss) +I0616 04:57:21.633383 9857 solver.cpp:571] Iteration 14760, lr = 0.001 +I0616 04:57:32.899588 9857 solver.cpp:242] Iteration 14780, loss = 1.18449 +I0616 04:57:32.899631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0860536 (* 1 = 0.0860536 loss) +I0616 04:57:32.899636 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215873 (* 1 = 0.215873 loss) +I0616 04:57:32.899641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0867222 (* 1 = 0.0867222 loss) +I0616 04:57:32.899644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102002 (* 1 = 0.0102002 loss) +I0616 04:57:32.899648 9857 solver.cpp:571] Iteration 14780, lr = 0.001 +speed: 0.697s / iter +I0616 04:57:44.406483 9857 solver.cpp:242] Iteration 14800, loss = 1.02954 +I0616 04:57:44.406527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.464346 (* 1 = 0.464346 loss) +I0616 04:57:44.406532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.904393 (* 1 = 0.904393 loss) +I0616 04:57:44.406536 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0813195 (* 1 = 0.0813195 loss) +I0616 04:57:44.406540 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0287559 (* 1 = 0.0287559 loss) +I0616 04:57:44.406543 9857 solver.cpp:571] Iteration 14800, lr = 0.001 +I0616 04:57:55.792475 9857 solver.cpp:242] Iteration 14820, loss = 0.847841 +I0616 04:57:55.792518 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25888 (* 1 = 0.25888 loss) +I0616 04:57:55.792523 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514088 (* 1 = 0.514088 loss) +I0616 04:57:55.792527 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.043803 (* 1 = 0.043803 loss) +I0616 04:57:55.792531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0627934 (* 1 = 0.0627934 loss) +I0616 04:57:55.792536 9857 solver.cpp:571] Iteration 14820, lr = 0.001 +I0616 04:58:07.162467 9857 solver.cpp:242] Iteration 14840, loss = 0.400546 +I0616 04:58:07.162510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176775 (* 1 = 0.176775 loss) +I0616 04:58:07.162515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208185 (* 1 = 0.208185 loss) +I0616 04:58:07.162520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0145858 (* 1 = 0.0145858 loss) +I0616 04:58:07.162523 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291491 (* 1 = 0.0291491 loss) +I0616 04:58:07.162528 9857 solver.cpp:571] Iteration 14840, lr = 0.001 +I0616 04:58:18.715570 9857 solver.cpp:242] Iteration 14860, loss = 1.62288 +I0616 04:58:18.715613 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.414247 (* 1 = 0.414247 loss) +I0616 04:58:18.715620 9857 solver.cpp:258] Train net output #1: loss_cls = 1.24684 (* 1 = 1.24684 loss) +I0616 04:58:18.715623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235299 (* 1 = 0.235299 loss) +I0616 04:58:18.715627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0793182 (* 1 = 0.0793182 loss) +I0616 04:58:18.715631 9857 solver.cpp:571] Iteration 14860, lr = 0.001 +I0616 04:58:30.487103 9857 solver.cpp:242] Iteration 14880, loss = 0.510443 +I0616 04:58:30.487145 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149582 (* 1 = 0.149582 loss) +I0616 04:58:30.487150 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223664 (* 1 = 0.223664 loss) +I0616 04:58:30.487155 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0387569 (* 1 = 0.0387569 loss) +I0616 04:58:30.487159 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136962 (* 1 = 0.0136962 loss) +I0616 04:58:30.487162 9857 solver.cpp:571] Iteration 14880, lr = 0.001 +I0616 04:58:42.365821 9857 solver.cpp:242] Iteration 14900, loss = 0.724199 +I0616 04:58:42.365864 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176846 (* 1 = 0.176846 loss) +I0616 04:58:42.365869 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276473 (* 1 = 0.276473 loss) +I0616 04:58:42.365874 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.071898 (* 1 = 0.071898 loss) +I0616 04:58:42.365876 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0706704 (* 1 = 0.0706704 loss) +I0616 04:58:42.365880 9857 solver.cpp:571] Iteration 14900, lr = 0.001 +I0616 04:58:54.024760 9857 solver.cpp:242] Iteration 14920, loss = 1.56227 +I0616 04:58:54.024803 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.339944 (* 1 = 0.339944 loss) +I0616 04:58:54.024809 9857 solver.cpp:258] Train net output #1: loss_cls = 0.473504 (* 1 = 0.473504 loss) +I0616 04:58:54.024813 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154906 (* 1 = 0.154906 loss) +I0616 04:58:54.024817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.41734 (* 1 = 0.41734 loss) +I0616 04:58:54.024821 9857 solver.cpp:571] Iteration 14920, lr = 0.001 +I0616 04:59:05.592072 9857 solver.cpp:242] Iteration 14940, loss = 0.5289 +I0616 04:59:05.592116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195375 (* 1 = 0.195375 loss) +I0616 04:59:05.592123 9857 solver.cpp:258] Train net output #1: loss_cls = 0.212311 (* 1 = 0.212311 loss) +I0616 04:59:05.592128 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.187467 (* 1 = 0.187467 loss) +I0616 04:59:05.592131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117966 (* 1 = 0.0117966 loss) +I0616 04:59:05.592138 9857 solver.cpp:571] Iteration 14940, lr = 0.001 +I0616 04:59:17.071589 9857 solver.cpp:242] Iteration 14960, loss = 1.34345 +I0616 04:59:17.071630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176273 (* 1 = 0.176273 loss) +I0616 04:59:17.071635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.656489 (* 1 = 0.656489 loss) +I0616 04:59:17.071640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0693367 (* 1 = 0.0693367 loss) +I0616 04:59:17.071643 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0659713 (* 1 = 0.0659713 loss) +I0616 04:59:17.071647 9857 solver.cpp:571] Iteration 14960, lr = 0.001 +I0616 04:59:28.572607 9857 solver.cpp:242] Iteration 14980, loss = 1.13995 +I0616 04:59:28.572649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214598 (* 1 = 0.214598 loss) +I0616 04:59:28.572654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.839919 (* 1 = 0.839919 loss) +I0616 04:59:28.572659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.191579 (* 1 = 0.191579 loss) +I0616 04:59:28.572661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0058889 (* 1 = 0.0058889 loss) +I0616 04:59:28.572665 9857 solver.cpp:571] Iteration 14980, lr = 0.001 +speed: 0.695s / iter +I0616 04:59:39.958667 9857 solver.cpp:242] Iteration 15000, loss = 1.53615 +I0616 04:59:39.958709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292448 (* 1 = 0.292448 loss) +I0616 04:59:39.958715 9857 solver.cpp:258] Train net output #1: loss_cls = 1.06231 (* 1 = 1.06231 loss) +I0616 04:59:39.958719 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.066422 (* 1 = 0.066422 loss) +I0616 04:59:39.958724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0315002 (* 1 = 0.0315002 loss) +I0616 04:59:39.958727 9857 solver.cpp:571] Iteration 15000, lr = 0.001 +I0616 04:59:51.338714 9857 solver.cpp:242] Iteration 15020, loss = 0.626582 +I0616 04:59:51.338758 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.15242 (* 1 = 0.15242 loss) +I0616 04:59:51.338764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326891 (* 1 = 0.326891 loss) +I0616 04:59:51.338769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113928 (* 1 = 0.113928 loss) +I0616 04:59:51.338773 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130645 (* 1 = 0.0130645 loss) +I0616 04:59:51.338778 9857 solver.cpp:571] Iteration 15020, lr = 0.001 +I0616 05:00:02.638339 9857 solver.cpp:242] Iteration 15040, loss = 1.7157 +I0616 05:00:02.638389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.5001 (* 1 = 0.5001 loss) +I0616 05:00:02.638398 9857 solver.cpp:258] Train net output #1: loss_cls = 1.40244 (* 1 = 1.40244 loss) +I0616 05:00:02.638406 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.32073 (* 1 = 0.32073 loss) +I0616 05:00:02.638412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.157256 (* 1 = 0.157256 loss) +I0616 05:00:02.638418 9857 solver.cpp:571] Iteration 15040, lr = 0.001 +I0616 05:00:14.246532 9857 solver.cpp:242] Iteration 15060, loss = 1.50825 +I0616 05:00:14.246574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360389 (* 1 = 0.360389 loss) +I0616 05:00:14.246580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.671329 (* 1 = 0.671329 loss) +I0616 05:00:14.246584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0886626 (* 1 = 0.0886626 loss) +I0616 05:00:14.246588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170754 (* 1 = 0.0170754 loss) +I0616 05:00:14.246592 9857 solver.cpp:571] Iteration 15060, lr = 0.001 +I0616 05:00:25.892050 9857 solver.cpp:242] Iteration 15080, loss = 0.870996 +I0616 05:00:25.892091 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186206 (* 1 = 0.186206 loss) +I0616 05:00:25.892096 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323728 (* 1 = 0.323728 loss) +I0616 05:00:25.892101 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.035052 (* 1 = 0.035052 loss) +I0616 05:00:25.892104 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00522468 (* 1 = 0.00522468 loss) +I0616 05:00:25.892108 9857 solver.cpp:571] Iteration 15080, lr = 0.001 +I0616 05:00:37.803877 9857 solver.cpp:242] Iteration 15100, loss = 1.35412 +I0616 05:00:37.803918 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.451408 (* 1 = 0.451408 loss) +I0616 05:00:37.803923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.750414 (* 1 = 0.750414 loss) +I0616 05:00:37.803928 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249164 (* 1 = 0.249164 loss) +I0616 05:00:37.803931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162876 (* 1 = 0.162876 loss) +I0616 05:00:37.803936 9857 solver.cpp:571] Iteration 15100, lr = 0.001 +I0616 05:00:49.392173 9857 solver.cpp:242] Iteration 15120, loss = 1.15615 +I0616 05:00:49.392215 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.602543 (* 1 = 0.602543 loss) +I0616 05:00:49.392220 9857 solver.cpp:258] Train net output #1: loss_cls = 0.47947 (* 1 = 0.47947 loss) +I0616 05:00:49.392225 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107306 (* 1 = 0.107306 loss) +I0616 05:00:49.392228 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360305 (* 1 = 0.0360305 loss) +I0616 05:00:49.392232 9857 solver.cpp:571] Iteration 15120, lr = 0.001 +I0616 05:01:01.026289 9857 solver.cpp:242] Iteration 15140, loss = 0.773829 +I0616 05:01:01.026330 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172635 (* 1 = 0.172635 loss) +I0616 05:01:01.026336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.677654 (* 1 = 0.677654 loss) +I0616 05:01:01.026340 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103445 (* 1 = 0.103445 loss) +I0616 05:01:01.026345 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0391435 (* 1 = 0.0391435 loss) +I0616 05:01:01.026347 9857 solver.cpp:571] Iteration 15140, lr = 0.001 +I0616 05:01:12.619961 9857 solver.cpp:242] Iteration 15160, loss = 1.09736 +I0616 05:01:12.620003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.219725 (* 1 = 0.219725 loss) +I0616 05:01:12.620010 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562879 (* 1 = 0.562879 loss) +I0616 05:01:12.620014 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10973 (* 1 = 0.10973 loss) +I0616 05:01:12.620018 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407847 (* 1 = 0.0407847 loss) +I0616 05:01:12.620023 9857 solver.cpp:571] Iteration 15160, lr = 0.001 +I0616 05:01:24.049404 9857 solver.cpp:242] Iteration 15180, loss = 0.698485 +I0616 05:01:24.049446 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0693936 (* 1 = 0.0693936 loss) +I0616 05:01:24.049453 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226406 (* 1 = 0.226406 loss) +I0616 05:01:24.049456 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0905675 (* 1 = 0.0905675 loss) +I0616 05:01:24.049460 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117479 (* 1 = 0.117479 loss) +I0616 05:01:24.049463 9857 solver.cpp:571] Iteration 15180, lr = 0.001 +speed: 0.694s / iter +I0616 05:01:35.710939 9857 solver.cpp:242] Iteration 15200, loss = 0.962948 +I0616 05:01:35.710980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.381159 (* 1 = 0.381159 loss) +I0616 05:01:35.710985 9857 solver.cpp:258] Train net output #1: loss_cls = 0.51796 (* 1 = 0.51796 loss) +I0616 05:01:35.710989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141662 (* 1 = 0.141662 loss) +I0616 05:01:35.710994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0579756 (* 1 = 0.0579756 loss) +I0616 05:01:35.710997 9857 solver.cpp:571] Iteration 15200, lr = 0.001 +I0616 05:01:47.234060 9857 solver.cpp:242] Iteration 15220, loss = 0.79603 +I0616 05:01:47.234098 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272732 (* 1 = 0.272732 loss) +I0616 05:01:47.234104 9857 solver.cpp:258] Train net output #1: loss_cls = 0.495418 (* 1 = 0.495418 loss) +I0616 05:01:47.234108 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161887 (* 1 = 0.161887 loss) +I0616 05:01:47.234112 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.229028 (* 1 = 0.229028 loss) +I0616 05:01:47.234117 9857 solver.cpp:571] Iteration 15220, lr = 0.001 +I0616 05:01:59.006690 9857 solver.cpp:242] Iteration 15240, loss = 1.43548 +I0616 05:01:59.006732 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.485423 (* 1 = 0.485423 loss) +I0616 05:01:59.006739 9857 solver.cpp:258] Train net output #1: loss_cls = 0.510828 (* 1 = 0.510828 loss) +I0616 05:01:59.006743 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201389 (* 1 = 0.201389 loss) +I0616 05:01:59.006747 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.046448 (* 1 = 0.046448 loss) +I0616 05:01:59.006750 9857 solver.cpp:571] Iteration 15240, lr = 0.001 +I0616 05:02:10.632961 9857 solver.cpp:242] Iteration 15260, loss = 1.31516 +I0616 05:02:10.633004 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.427682 (* 1 = 0.427682 loss) +I0616 05:02:10.633009 9857 solver.cpp:258] Train net output #1: loss_cls = 0.540391 (* 1 = 0.540391 loss) +I0616 05:02:10.633014 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130433 (* 1 = 0.130433 loss) +I0616 05:02:10.633018 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00807382 (* 1 = 0.00807382 loss) +I0616 05:02:10.633021 9857 solver.cpp:571] Iteration 15260, lr = 0.001 +I0616 05:02:21.848701 9857 solver.cpp:242] Iteration 15280, loss = 0.396018 +I0616 05:02:21.848744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102862 (* 1 = 0.102862 loss) +I0616 05:02:21.848750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236603 (* 1 = 0.236603 loss) +I0616 05:02:21.848755 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0711583 (* 1 = 0.0711583 loss) +I0616 05:02:21.848758 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00617884 (* 1 = 0.00617884 loss) +I0616 05:02:21.848762 9857 solver.cpp:571] Iteration 15280, lr = 0.001 +I0616 05:02:33.470496 9857 solver.cpp:242] Iteration 15300, loss = 0.588033 +I0616 05:02:33.470540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171853 (* 1 = 0.171853 loss) +I0616 05:02:33.470544 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292976 (* 1 = 0.292976 loss) +I0616 05:02:33.470549 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.196168 (* 1 = 0.196168 loss) +I0616 05:02:33.470552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193059 (* 1 = 0.0193059 loss) +I0616 05:02:33.470556 9857 solver.cpp:571] Iteration 15300, lr = 0.001 +I0616 05:02:45.268765 9857 solver.cpp:242] Iteration 15320, loss = 1.41247 +I0616 05:02:45.268807 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284434 (* 1 = 0.284434 loss) +I0616 05:02:45.268813 9857 solver.cpp:258] Train net output #1: loss_cls = 0.728127 (* 1 = 0.728127 loss) +I0616 05:02:45.268818 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0840029 (* 1 = 0.0840029 loss) +I0616 05:02:45.268821 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113235 (* 1 = 0.0113235 loss) +I0616 05:02:45.268826 9857 solver.cpp:571] Iteration 15320, lr = 0.001 +I0616 05:02:57.001597 9857 solver.cpp:242] Iteration 15340, loss = 0.629375 +I0616 05:02:57.001639 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145026 (* 1 = 0.145026 loss) +I0616 05:02:57.001646 9857 solver.cpp:258] Train net output #1: loss_cls = 0.220832 (* 1 = 0.220832 loss) +I0616 05:02:57.001649 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0372037 (* 1 = 0.0372037 loss) +I0616 05:02:57.001653 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00748186 (* 1 = 0.00748186 loss) +I0616 05:02:57.001657 9857 solver.cpp:571] Iteration 15340, lr = 0.001 +I0616 05:03:08.415731 9857 solver.cpp:242] Iteration 15360, loss = 0.624745 +I0616 05:03:08.415773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268571 (* 1 = 0.268571 loss) +I0616 05:03:08.415779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.261325 (* 1 = 0.261325 loss) +I0616 05:03:08.415783 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0959476 (* 1 = 0.0959476 loss) +I0616 05:03:08.415787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223527 (* 1 = 0.0223527 loss) +I0616 05:03:08.415791 9857 solver.cpp:571] Iteration 15360, lr = 0.001 +I0616 05:03:19.886237 9857 solver.cpp:242] Iteration 15380, loss = 0.625166 +I0616 05:03:19.886279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318871 (* 1 = 0.318871 loss) +I0616 05:03:19.886286 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363673 (* 1 = 0.363673 loss) +I0616 05:03:19.886289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0705412 (* 1 = 0.0705412 loss) +I0616 05:03:19.886293 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0657394 (* 1 = 0.0657394 loss) +I0616 05:03:19.886296 9857 solver.cpp:571] Iteration 15380, lr = 0.001 +speed: 0.692s / iter +I0616 05:03:31.540874 9857 solver.cpp:242] Iteration 15400, loss = 1.48687 +I0616 05:03:31.540915 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.423145 (* 1 = 0.423145 loss) +I0616 05:03:31.540920 9857 solver.cpp:258] Train net output #1: loss_cls = 1.2216 (* 1 = 1.2216 loss) +I0616 05:03:31.540925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.252885 (* 1 = 0.252885 loss) +I0616 05:03:31.540928 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149689 (* 1 = 0.0149689 loss) +I0616 05:03:31.540932 9857 solver.cpp:571] Iteration 15400, lr = 0.001 +I0616 05:03:43.186014 9857 solver.cpp:242] Iteration 15420, loss = 0.840456 +I0616 05:03:43.186056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258969 (* 1 = 0.258969 loss) +I0616 05:03:43.186063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.952053 (* 1 = 0.952053 loss) +I0616 05:03:43.186066 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0342675 (* 1 = 0.0342675 loss) +I0616 05:03:43.186070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0365098 (* 1 = 0.0365098 loss) +I0616 05:03:43.186074 9857 solver.cpp:571] Iteration 15420, lr = 0.001 +I0616 05:03:54.732697 9857 solver.cpp:242] Iteration 15440, loss = 1.21719 +I0616 05:03:54.732739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.531859 (* 1 = 0.531859 loss) +I0616 05:03:54.732745 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432437 (* 1 = 0.432437 loss) +I0616 05:03:54.732749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.361316 (* 1 = 0.361316 loss) +I0616 05:03:54.732753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.29771 (* 1 = 0.29771 loss) +I0616 05:03:54.732758 9857 solver.cpp:571] Iteration 15440, lr = 0.001 +I0616 05:04:06.366580 9857 solver.cpp:242] Iteration 15460, loss = 0.933629 +I0616 05:04:06.366621 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252126 (* 1 = 0.252126 loss) +I0616 05:04:06.366627 9857 solver.cpp:258] Train net output #1: loss_cls = 0.406534 (* 1 = 0.406534 loss) +I0616 05:04:06.366632 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0782148 (* 1 = 0.0782148 loss) +I0616 05:04:06.366636 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0148613 (* 1 = 0.0148613 loss) +I0616 05:04:06.366639 9857 solver.cpp:571] Iteration 15460, lr = 0.001 +I0616 05:04:18.174724 9857 solver.cpp:242] Iteration 15480, loss = 1.91696 +I0616 05:04:18.174770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366102 (* 1 = 0.366102 loss) +I0616 05:04:18.174777 9857 solver.cpp:258] Train net output #1: loss_cls = 0.607307 (* 1 = 0.607307 loss) +I0616 05:04:18.174780 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.452697 (* 1 = 0.452697 loss) +I0616 05:04:18.174784 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.313977 (* 1 = 0.313977 loss) +I0616 05:04:18.174787 9857 solver.cpp:571] Iteration 15480, lr = 0.001 +I0616 05:04:29.663169 9857 solver.cpp:242] Iteration 15500, loss = 1.57959 +I0616 05:04:29.663213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.678718 (* 1 = 0.678718 loss) +I0616 05:04:29.663218 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00999 (* 1 = 1.00999 loss) +I0616 05:04:29.663223 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147995 (* 1 = 0.147995 loss) +I0616 05:04:29.663226 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020036 (* 1 = 0.020036 loss) +I0616 05:04:29.663230 9857 solver.cpp:571] Iteration 15500, lr = 0.001 +I0616 05:04:41.291718 9857 solver.cpp:242] Iteration 15520, loss = 1.47844 +I0616 05:04:41.291759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.479606 (* 1 = 0.479606 loss) +I0616 05:04:41.291764 9857 solver.cpp:258] Train net output #1: loss_cls = 1.11966 (* 1 = 1.11966 loss) +I0616 05:04:41.291769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.303008 (* 1 = 0.303008 loss) +I0616 05:04:41.291772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119652 (* 1 = 0.119652 loss) +I0616 05:04:41.291776 9857 solver.cpp:571] Iteration 15520, lr = 0.001 +I0616 05:04:52.857785 9857 solver.cpp:242] Iteration 15540, loss = 1.37197 +I0616 05:04:52.857830 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392679 (* 1 = 0.392679 loss) +I0616 05:04:52.857834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.759933 (* 1 = 0.759933 loss) +I0616 05:04:52.857839 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179821 (* 1 = 0.179821 loss) +I0616 05:04:52.857843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128393 (* 1 = 0.128393 loss) +I0616 05:04:52.857846 9857 solver.cpp:571] Iteration 15540, lr = 0.001 +I0616 05:05:04.457423 9857 solver.cpp:242] Iteration 15560, loss = 1.29487 +I0616 05:05:04.457466 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.375275 (* 1 = 0.375275 loss) +I0616 05:05:04.457471 9857 solver.cpp:258] Train net output #1: loss_cls = 0.484576 (* 1 = 0.484576 loss) +I0616 05:05:04.457476 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0735323 (* 1 = 0.0735323 loss) +I0616 05:05:04.457479 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0195382 (* 1 = 0.0195382 loss) +I0616 05:05:04.457484 9857 solver.cpp:571] Iteration 15560, lr = 0.001 +I0616 05:05:16.141949 9857 solver.cpp:242] Iteration 15580, loss = 0.906975 +I0616 05:05:16.141991 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368842 (* 1 = 0.368842 loss) +I0616 05:05:16.141999 9857 solver.cpp:258] Train net output #1: loss_cls = 0.511101 (* 1 = 0.511101 loss) +I0616 05:05:16.142002 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.189789 (* 1 = 0.189789 loss) +I0616 05:05:16.142006 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0702294 (* 1 = 0.0702294 loss) +I0616 05:05:16.142010 9857 solver.cpp:571] Iteration 15580, lr = 0.001 +speed: 0.691s / iter +I0616 05:05:27.641752 9857 solver.cpp:242] Iteration 15600, loss = 0.745044 +I0616 05:05:27.641790 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167552 (* 1 = 0.167552 loss) +I0616 05:05:27.641796 9857 solver.cpp:258] Train net output #1: loss_cls = 0.511793 (* 1 = 0.511793 loss) +I0616 05:05:27.641800 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0663059 (* 1 = 0.0663059 loss) +I0616 05:05:27.641804 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231227 (* 1 = 0.0231227 loss) +I0616 05:05:27.641808 9857 solver.cpp:571] Iteration 15600, lr = 0.001 +I0616 05:05:39.111490 9857 solver.cpp:242] Iteration 15620, loss = 1.44121 +I0616 05:05:39.111533 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318497 (* 1 = 0.318497 loss) +I0616 05:05:39.111538 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289155 (* 1 = 0.289155 loss) +I0616 05:05:39.111543 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0899972 (* 1 = 0.0899972 loss) +I0616 05:05:39.111547 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0582447 (* 1 = 0.0582447 loss) +I0616 05:05:39.111552 9857 solver.cpp:571] Iteration 15620, lr = 0.001 +I0616 05:05:50.645301 9857 solver.cpp:242] Iteration 15640, loss = 1.01137 +I0616 05:05:50.645344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163875 (* 1 = 0.163875 loss) +I0616 05:05:50.645350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.504151 (* 1 = 0.504151 loss) +I0616 05:05:50.645354 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0306505 (* 1 = 0.0306505 loss) +I0616 05:05:50.645359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133118 (* 1 = 0.0133118 loss) +I0616 05:05:50.645362 9857 solver.cpp:571] Iteration 15640, lr = 0.001 +I0616 05:06:02.002157 9857 solver.cpp:242] Iteration 15660, loss = 1.31468 +I0616 05:06:02.002198 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.425667 (* 1 = 0.425667 loss) +I0616 05:06:02.002204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.73808 (* 1 = 0.73808 loss) +I0616 05:06:02.002208 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.318764 (* 1 = 0.318764 loss) +I0616 05:06:02.002213 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0436186 (* 1 = 0.0436186 loss) +I0616 05:06:02.002216 9857 solver.cpp:571] Iteration 15660, lr = 0.001 +I0616 05:06:13.429502 9857 solver.cpp:242] Iteration 15680, loss = 1.27587 +I0616 05:06:13.429544 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488486 (* 1 = 0.488486 loss) +I0616 05:06:13.429549 9857 solver.cpp:258] Train net output #1: loss_cls = 0.830783 (* 1 = 0.830783 loss) +I0616 05:06:13.429553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142652 (* 1 = 0.142652 loss) +I0616 05:06:13.429558 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0219106 (* 1 = 0.0219106 loss) +I0616 05:06:13.429561 9857 solver.cpp:571] Iteration 15680, lr = 0.001 +I0616 05:06:24.853147 9857 solver.cpp:242] Iteration 15700, loss = 0.764899 +I0616 05:06:24.853189 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.398869 (* 1 = 0.398869 loss) +I0616 05:06:24.853195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38444 (* 1 = 0.38444 loss) +I0616 05:06:24.853199 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.199926 (* 1 = 0.199926 loss) +I0616 05:06:24.853204 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162411 (* 1 = 0.162411 loss) +I0616 05:06:24.853207 9857 solver.cpp:571] Iteration 15700, lr = 0.001 +I0616 05:06:36.506201 9857 solver.cpp:242] Iteration 15720, loss = 0.90858 +I0616 05:06:36.506242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.521705 (* 1 = 0.521705 loss) +I0616 05:06:36.506248 9857 solver.cpp:258] Train net output #1: loss_cls = 0.568647 (* 1 = 0.568647 loss) +I0616 05:06:36.506253 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150113 (* 1 = 0.150113 loss) +I0616 05:06:36.506258 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.051534 (* 1 = 0.051534 loss) +I0616 05:06:36.506260 9857 solver.cpp:571] Iteration 15720, lr = 0.001 +I0616 05:06:47.952381 9857 solver.cpp:242] Iteration 15740, loss = 1.01796 +I0616 05:06:47.952422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177516 (* 1 = 0.177516 loss) +I0616 05:06:47.952428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247896 (* 1 = 0.247896 loss) +I0616 05:06:47.952433 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0625208 (* 1 = 0.0625208 loss) +I0616 05:06:47.952436 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180117 (* 1 = 0.0180117 loss) +I0616 05:06:47.952440 9857 solver.cpp:571] Iteration 15740, lr = 0.001 +I0616 05:06:59.640985 9857 solver.cpp:242] Iteration 15760, loss = 1.40154 +I0616 05:06:59.641027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.400251 (* 1 = 0.400251 loss) +I0616 05:06:59.641033 9857 solver.cpp:258] Train net output #1: loss_cls = 0.86435 (* 1 = 0.86435 loss) +I0616 05:06:59.641037 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246058 (* 1 = 0.246058 loss) +I0616 05:06:59.641041 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.168782 (* 1 = 0.168782 loss) +I0616 05:06:59.641046 9857 solver.cpp:571] Iteration 15760, lr = 0.001 +I0616 05:07:11.169813 9857 solver.cpp:242] Iteration 15780, loss = 1.41973 +I0616 05:07:11.169855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195228 (* 1 = 0.195228 loss) +I0616 05:07:11.169862 9857 solver.cpp:258] Train net output #1: loss_cls = 0.658659 (* 1 = 0.658659 loss) +I0616 05:07:11.169865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120298 (* 1 = 0.120298 loss) +I0616 05:07:11.169869 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0342421 (* 1 = 0.0342421 loss) +I0616 05:07:11.169872 9857 solver.cpp:571] Iteration 15780, lr = 0.001 +speed: 0.689s / iter +I0616 05:07:22.855279 9857 solver.cpp:242] Iteration 15800, loss = 1.63024 +I0616 05:07:22.855321 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.549705 (* 1 = 0.549705 loss) +I0616 05:07:22.855326 9857 solver.cpp:258] Train net output #1: loss_cls = 0.773308 (* 1 = 0.773308 loss) +I0616 05:07:22.855330 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.440575 (* 1 = 0.440575 loss) +I0616 05:07:22.855334 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10159 (* 1 = 0.10159 loss) +I0616 05:07:22.855339 9857 solver.cpp:571] Iteration 15800, lr = 0.001 +I0616 05:07:34.224637 9857 solver.cpp:242] Iteration 15820, loss = 1.09169 +I0616 05:07:34.224680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288153 (* 1 = 0.288153 loss) +I0616 05:07:34.224686 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298393 (* 1 = 0.298393 loss) +I0616 05:07:34.224690 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112633 (* 1 = 0.112633 loss) +I0616 05:07:34.224694 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00630673 (* 1 = 0.00630673 loss) +I0616 05:07:34.224699 9857 solver.cpp:571] Iteration 15820, lr = 0.001 +I0616 05:07:45.734225 9857 solver.cpp:242] Iteration 15840, loss = 0.347353 +I0616 05:07:45.734267 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0947472 (* 1 = 0.0947472 loss) +I0616 05:07:45.734273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126501 (* 1 = 0.126501 loss) +I0616 05:07:45.734277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109864 (* 1 = 0.109864 loss) +I0616 05:07:45.734282 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215948 (* 1 = 0.0215948 loss) +I0616 05:07:45.734285 9857 solver.cpp:571] Iteration 15840, lr = 0.001 +I0616 05:07:57.407269 9857 solver.cpp:242] Iteration 15860, loss = 0.82718 +I0616 05:07:57.407312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357167 (* 1 = 0.357167 loss) +I0616 05:07:57.407318 9857 solver.cpp:258] Train net output #1: loss_cls = 0.593189 (* 1 = 0.593189 loss) +I0616 05:07:57.407323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.077884 (* 1 = 0.077884 loss) +I0616 05:07:57.407326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0663617 (* 1 = 0.0663617 loss) +I0616 05:07:57.407331 9857 solver.cpp:571] Iteration 15860, lr = 0.001 +I0616 05:08:08.728345 9857 solver.cpp:242] Iteration 15880, loss = 0.992481 +I0616 05:08:08.728389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488655 (* 1 = 0.488655 loss) +I0616 05:08:08.728394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.848076 (* 1 = 0.848076 loss) +I0616 05:08:08.728397 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0852686 (* 1 = 0.0852686 loss) +I0616 05:08:08.728401 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126384 (* 1 = 0.126384 loss) +I0616 05:08:08.728405 9857 solver.cpp:571] Iteration 15880, lr = 0.001 +I0616 05:08:20.049799 9857 solver.cpp:242] Iteration 15900, loss = 1.03747 +I0616 05:08:20.049841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0390884 (* 1 = 0.0390884 loss) +I0616 05:08:20.049847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174951 (* 1 = 0.174951 loss) +I0616 05:08:20.049851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.310744 (* 1 = 0.310744 loss) +I0616 05:08:20.049855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0359038 (* 1 = 0.0359038 loss) +I0616 05:08:20.049861 9857 solver.cpp:571] Iteration 15900, lr = 0.001 +I0616 05:08:31.606446 9857 solver.cpp:242] Iteration 15920, loss = 1.29923 +I0616 05:08:31.606487 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266652 (* 1 = 0.266652 loss) +I0616 05:08:31.606493 9857 solver.cpp:258] Train net output #1: loss_cls = 0.455363 (* 1 = 0.455363 loss) +I0616 05:08:31.606498 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.430912 (* 1 = 0.430912 loss) +I0616 05:08:31.606500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.541611 (* 1 = 0.541611 loss) +I0616 05:08:31.606504 9857 solver.cpp:571] Iteration 15920, lr = 0.001 +I0616 05:08:43.409672 9857 solver.cpp:242] Iteration 15940, loss = 0.556947 +I0616 05:08:43.409714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158152 (* 1 = 0.158152 loss) +I0616 05:08:43.409719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266813 (* 1 = 0.266813 loss) +I0616 05:08:43.409724 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111761 (* 1 = 0.111761 loss) +I0616 05:08:43.409729 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10422 (* 1 = 0.10422 loss) +I0616 05:08:43.409731 9857 solver.cpp:571] Iteration 15940, lr = 0.001 +I0616 05:08:55.253111 9857 solver.cpp:242] Iteration 15960, loss = 1.63108 +I0616 05:08:55.253152 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.529324 (* 1 = 0.529324 loss) +I0616 05:08:55.253159 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16119 (* 1 = 1.16119 loss) +I0616 05:08:55.253162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.378107 (* 1 = 0.378107 loss) +I0616 05:08:55.253166 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334396 (* 1 = 0.0334396 loss) +I0616 05:08:55.253170 9857 solver.cpp:571] Iteration 15960, lr = 0.001 +I0616 05:09:06.903920 9857 solver.cpp:242] Iteration 15980, loss = 1.7711 +I0616 05:09:06.903964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.457911 (* 1 = 0.457911 loss) +I0616 05:09:06.903969 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15004 (* 1 = 1.15004 loss) +I0616 05:09:06.903973 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.292462 (* 1 = 0.292462 loss) +I0616 05:09:06.903976 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.152696 (* 1 = 0.152696 loss) +I0616 05:09:06.903980 9857 solver.cpp:571] Iteration 15980, lr = 0.001 +speed: 0.688s / iter +I0616 05:09:18.400230 9857 solver.cpp:242] Iteration 16000, loss = 1.91515 +I0616 05:09:18.400271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355283 (* 1 = 0.355283 loss) +I0616 05:09:18.400277 9857 solver.cpp:258] Train net output #1: loss_cls = 0.749725 (* 1 = 0.749725 loss) +I0616 05:09:18.400281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154164 (* 1 = 0.154164 loss) +I0616 05:09:18.400285 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.145839 (* 1 = 0.145839 loss) +I0616 05:09:18.400290 9857 solver.cpp:571] Iteration 16000, lr = 0.001 +I0616 05:09:30.026528 9857 solver.cpp:242] Iteration 16020, loss = 1.04991 +I0616 05:09:30.026558 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.438967 (* 1 = 0.438967 loss) +I0616 05:09:30.026566 9857 solver.cpp:258] Train net output #1: loss_cls = 1.01435 (* 1 = 1.01435 loss) +I0616 05:09:30.026572 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.2124 (* 1 = 0.2124 loss) +I0616 05:09:30.026578 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0477383 (* 1 = 0.0477383 loss) +I0616 05:09:30.026585 9857 solver.cpp:571] Iteration 16020, lr = 0.001 +I0616 05:09:41.634696 9857 solver.cpp:242] Iteration 16040, loss = 0.826028 +I0616 05:09:41.634726 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360318 (* 1 = 0.360318 loss) +I0616 05:09:41.634732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304178 (* 1 = 0.304178 loss) +I0616 05:09:41.634739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0665667 (* 1 = 0.0665667 loss) +I0616 05:09:41.634745 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276593 (* 1 = 0.0276593 loss) +I0616 05:09:41.634752 9857 solver.cpp:571] Iteration 16040, lr = 0.001 +I0616 05:09:53.151965 9857 solver.cpp:242] Iteration 16060, loss = 0.809561 +I0616 05:09:53.151993 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264659 (* 1 = 0.264659 loss) +I0616 05:09:53.152000 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225287 (* 1 = 0.225287 loss) +I0616 05:09:53.152006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0538175 (* 1 = 0.0538175 loss) +I0616 05:09:53.152014 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015405 (* 1 = 0.015405 loss) +I0616 05:09:53.152022 9857 solver.cpp:571] Iteration 16060, lr = 0.001 +I0616 05:10:04.631965 9857 solver.cpp:242] Iteration 16080, loss = 1.37959 +I0616 05:10:04.631996 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297971 (* 1 = 0.297971 loss) +I0616 05:10:04.632004 9857 solver.cpp:258] Train net output #1: loss_cls = 0.991448 (* 1 = 0.991448 loss) +I0616 05:10:04.632010 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0532549 (* 1 = 0.0532549 loss) +I0616 05:10:04.632016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00474171 (* 1 = 0.00474171 loss) +I0616 05:10:04.632026 9857 solver.cpp:571] Iteration 16080, lr = 0.001 +I0616 05:10:15.986312 9857 solver.cpp:242] Iteration 16100, loss = 1.07424 +I0616 05:10:15.986341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262485 (* 1 = 0.262485 loss) +I0616 05:10:15.986348 9857 solver.cpp:258] Train net output #1: loss_cls = 0.695753 (* 1 = 0.695753 loss) +I0616 05:10:15.986354 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0861112 (* 1 = 0.0861112 loss) +I0616 05:10:15.986361 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0413608 (* 1 = 0.0413608 loss) +I0616 05:10:15.986368 9857 solver.cpp:571] Iteration 16100, lr = 0.001 +I0616 05:10:27.483464 9857 solver.cpp:242] Iteration 16120, loss = 0.821613 +I0616 05:10:27.483494 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148381 (* 1 = 0.148381 loss) +I0616 05:10:27.483501 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263858 (* 1 = 0.263858 loss) +I0616 05:10:27.483522 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0431033 (* 1 = 0.0431033 loss) +I0616 05:10:27.483528 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00813043 (* 1 = 0.00813043 loss) +I0616 05:10:27.483536 9857 solver.cpp:571] Iteration 16120, lr = 0.001 +I0616 05:10:39.182013 9857 solver.cpp:242] Iteration 16140, loss = 0.9184 +I0616 05:10:39.182042 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326264 (* 1 = 0.326264 loss) +I0616 05:10:39.182049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.595722 (* 1 = 0.595722 loss) +I0616 05:10:39.182070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.176218 (* 1 = 0.176218 loss) +I0616 05:10:39.182076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135386 (* 1 = 0.0135386 loss) +I0616 05:10:39.182082 9857 solver.cpp:571] Iteration 16140, lr = 0.001 +I0616 05:10:50.593772 9857 solver.cpp:242] Iteration 16160, loss = 0.403807 +I0616 05:10:50.593801 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146246 (* 1 = 0.146246 loss) +I0616 05:10:50.593809 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222583 (* 1 = 0.222583 loss) +I0616 05:10:50.593816 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0517401 (* 1 = 0.0517401 loss) +I0616 05:10:50.593825 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013734 (* 1 = 0.013734 loss) +I0616 05:10:50.593832 9857 solver.cpp:571] Iteration 16160, lr = 0.001 +I0616 05:11:01.943578 9857 solver.cpp:242] Iteration 16180, loss = 0.717951 +I0616 05:11:01.943608 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19837 (* 1 = 0.19837 loss) +I0616 05:11:01.943615 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287117 (* 1 = 0.287117 loss) +I0616 05:11:01.943621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0677999 (* 1 = 0.0677999 loss) +I0616 05:11:01.943627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158822 (* 1 = 0.0158822 loss) +I0616 05:11:01.943636 9857 solver.cpp:571] Iteration 16180, lr = 0.001 +speed: 0.686s / iter +I0616 05:11:13.597736 9857 solver.cpp:242] Iteration 16200, loss = 1.31129 +I0616 05:11:13.597779 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.58096 (* 1 = 0.58096 loss) +I0616 05:11:13.597785 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232285 (* 1 = 0.232285 loss) +I0616 05:11:13.597790 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.173169 (* 1 = 0.173169 loss) +I0616 05:11:13.597793 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0464233 (* 1 = 0.0464233 loss) +I0616 05:11:13.597796 9857 solver.cpp:571] Iteration 16200, lr = 0.001 +I0616 05:11:25.130298 9857 solver.cpp:242] Iteration 16220, loss = 1.25887 +I0616 05:11:25.130338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227309 (* 1 = 0.227309 loss) +I0616 05:11:25.130344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.494516 (* 1 = 0.494516 loss) +I0616 05:11:25.130348 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0122934 (* 1 = 0.0122934 loss) +I0616 05:11:25.130352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00234317 (* 1 = 0.00234317 loss) +I0616 05:11:25.130357 9857 solver.cpp:571] Iteration 16220, lr = 0.001 +I0616 05:11:36.713083 9857 solver.cpp:242] Iteration 16240, loss = 1.17093 +I0616 05:11:36.713124 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23295 (* 1 = 0.23295 loss) +I0616 05:11:36.713130 9857 solver.cpp:258] Train net output #1: loss_cls = 0.840098 (* 1 = 0.840098 loss) +I0616 05:11:36.713135 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184217 (* 1 = 0.184217 loss) +I0616 05:11:36.713138 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.191321 (* 1 = 0.191321 loss) +I0616 05:11:36.713142 9857 solver.cpp:571] Iteration 16240, lr = 0.001 +I0616 05:11:48.370698 9857 solver.cpp:242] Iteration 16260, loss = 1.38245 +I0616 05:11:48.370738 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.432369 (* 1 = 0.432369 loss) +I0616 05:11:48.370744 9857 solver.cpp:258] Train net output #1: loss_cls = 0.526746 (* 1 = 0.526746 loss) +I0616 05:11:48.370748 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.230265 (* 1 = 0.230265 loss) +I0616 05:11:48.370753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0368999 (* 1 = 0.0368999 loss) +I0616 05:11:48.370761 9857 solver.cpp:571] Iteration 16260, lr = 0.001 +I0616 05:11:59.951741 9857 solver.cpp:242] Iteration 16280, loss = 0.801451 +I0616 05:11:59.951781 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105819 (* 1 = 0.105819 loss) +I0616 05:11:59.951787 9857 solver.cpp:258] Train net output #1: loss_cls = 0.436325 (* 1 = 0.436325 loss) +I0616 05:11:59.951792 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.234015 (* 1 = 0.234015 loss) +I0616 05:11:59.951795 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0303533 (* 1 = 0.0303533 loss) +I0616 05:11:59.951799 9857 solver.cpp:571] Iteration 16280, lr = 0.001 +I0616 05:12:11.670809 9857 solver.cpp:242] Iteration 16300, loss = 0.938389 +I0616 05:12:11.670851 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.000728208 (* 1 = 0.000728208 loss) +I0616 05:12:11.670857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0805954 (* 1 = 0.0805954 loss) +I0616 05:12:11.670861 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20296 (* 1 = 0.20296 loss) +I0616 05:12:11.670866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0370506 (* 1 = 0.0370506 loss) +I0616 05:12:11.670868 9857 solver.cpp:571] Iteration 16300, lr = 0.001 +I0616 05:12:23.001175 9857 solver.cpp:242] Iteration 16320, loss = 0.769234 +I0616 05:12:23.001219 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232802 (* 1 = 0.232802 loss) +I0616 05:12:23.001224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371332 (* 1 = 0.371332 loss) +I0616 05:12:23.001229 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154752 (* 1 = 0.154752 loss) +I0616 05:12:23.001232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00742873 (* 1 = 0.00742873 loss) +I0616 05:12:23.001235 9857 solver.cpp:571] Iteration 16320, lr = 0.001 +I0616 05:12:34.222890 9857 solver.cpp:242] Iteration 16340, loss = 1.14697 +I0616 05:12:34.222930 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355991 (* 1 = 0.355991 loss) +I0616 05:12:34.222935 9857 solver.cpp:258] Train net output #1: loss_cls = 0.62712 (* 1 = 0.62712 loss) +I0616 05:12:34.222940 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161002 (* 1 = 0.161002 loss) +I0616 05:12:34.222944 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.017657 (* 1 = 0.017657 loss) +I0616 05:12:34.222949 9857 solver.cpp:571] Iteration 16340, lr = 0.001 +I0616 05:12:45.818027 9857 solver.cpp:242] Iteration 16360, loss = 2.02799 +I0616 05:12:45.818069 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.489168 (* 1 = 0.489168 loss) +I0616 05:12:45.818075 9857 solver.cpp:258] Train net output #1: loss_cls = 0.845001 (* 1 = 0.845001 loss) +I0616 05:12:45.818080 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.399708 (* 1 = 0.399708 loss) +I0616 05:12:45.818084 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.176642 (* 1 = 0.176642 loss) +I0616 05:12:45.818087 9857 solver.cpp:571] Iteration 16360, lr = 0.001 +I0616 05:12:57.322453 9857 solver.cpp:242] Iteration 16380, loss = 1.80256 +I0616 05:12:57.322496 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.51457 (* 1 = 0.51457 loss) +I0616 05:12:57.322501 9857 solver.cpp:258] Train net output #1: loss_cls = 1.39345 (* 1 = 1.39345 loss) +I0616 05:12:57.322505 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139361 (* 1 = 0.139361 loss) +I0616 05:12:57.322510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0427629 (* 1 = 0.0427629 loss) +I0616 05:12:57.322513 9857 solver.cpp:571] Iteration 16380, lr = 0.001 +speed: 0.685s / iter +I0616 05:13:08.801357 9857 solver.cpp:242] Iteration 16400, loss = 0.682015 +I0616 05:13:08.801399 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204285 (* 1 = 0.204285 loss) +I0616 05:13:08.801404 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401512 (* 1 = 0.401512 loss) +I0616 05:13:08.801409 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0963706 (* 1 = 0.0963706 loss) +I0616 05:13:08.801412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0382084 (* 1 = 0.0382084 loss) +I0616 05:13:08.801416 9857 solver.cpp:571] Iteration 16400, lr = 0.001 +I0616 05:13:20.294034 9857 solver.cpp:242] Iteration 16420, loss = 0.863678 +I0616 05:13:20.294075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.15081 (* 1 = 0.15081 loss) +I0616 05:13:20.294080 9857 solver.cpp:258] Train net output #1: loss_cls = 0.542894 (* 1 = 0.542894 loss) +I0616 05:13:20.294085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.380235 (* 1 = 0.380235 loss) +I0616 05:13:20.294090 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0154569 (* 1 = 0.0154569 loss) +I0616 05:13:20.294092 9857 solver.cpp:571] Iteration 16420, lr = 0.001 +I0616 05:13:31.727128 9857 solver.cpp:242] Iteration 16440, loss = 1.33607 +I0616 05:13:31.727171 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.486725 (* 1 = 0.486725 loss) +I0616 05:13:31.727177 9857 solver.cpp:258] Train net output #1: loss_cls = 0.626911 (* 1 = 0.626911 loss) +I0616 05:13:31.727181 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184463 (* 1 = 0.184463 loss) +I0616 05:13:31.727185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.049563 (* 1 = 0.049563 loss) +I0616 05:13:31.727190 9857 solver.cpp:571] Iteration 16440, lr = 0.001 +I0616 05:13:43.260288 9857 solver.cpp:242] Iteration 16460, loss = 0.851119 +I0616 05:13:43.260329 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292701 (* 1 = 0.292701 loss) +I0616 05:13:43.260335 9857 solver.cpp:258] Train net output #1: loss_cls = 0.238137 (* 1 = 0.238137 loss) +I0616 05:13:43.260339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0798608 (* 1 = 0.0798608 loss) +I0616 05:13:43.260344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0206157 (* 1 = 0.0206157 loss) +I0616 05:13:43.260349 9857 solver.cpp:571] Iteration 16460, lr = 0.001 +I0616 05:13:54.820698 9857 solver.cpp:242] Iteration 16480, loss = 1.26664 +I0616 05:13:54.820740 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322734 (* 1 = 0.322734 loss) +I0616 05:13:54.820746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.790655 (* 1 = 0.790655 loss) +I0616 05:13:54.820750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.286367 (* 1 = 0.286367 loss) +I0616 05:13:54.820755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0717333 (* 1 = 0.0717333 loss) +I0616 05:13:54.820758 9857 solver.cpp:571] Iteration 16480, lr = 0.001 +I0616 05:14:06.493918 9857 solver.cpp:242] Iteration 16500, loss = 0.766482 +I0616 05:14:06.493962 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27229 (* 1 = 0.27229 loss) +I0616 05:14:06.493966 9857 solver.cpp:258] Train net output #1: loss_cls = 0.579795 (* 1 = 0.579795 loss) +I0616 05:14:06.493971 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118538 (* 1 = 0.118538 loss) +I0616 05:14:06.493975 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0892904 (* 1 = 0.0892904 loss) +I0616 05:14:06.493979 9857 solver.cpp:571] Iteration 16500, lr = 0.001 +I0616 05:14:18.045265 9857 solver.cpp:242] Iteration 16520, loss = 1.43003 +I0616 05:14:18.045307 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.289156 (* 1 = 0.289156 loss) +I0616 05:14:18.045313 9857 solver.cpp:258] Train net output #1: loss_cls = 0.618945 (* 1 = 0.618945 loss) +I0616 05:14:18.045317 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.209446 (* 1 = 0.209446 loss) +I0616 05:14:18.045320 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.167918 (* 1 = 0.167918 loss) +I0616 05:14:18.045325 9857 solver.cpp:571] Iteration 16520, lr = 0.001 +I0616 05:14:29.268102 9857 solver.cpp:242] Iteration 16540, loss = 0.738283 +I0616 05:14:29.268144 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153781 (* 1 = 0.153781 loss) +I0616 05:14:29.268149 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296932 (* 1 = 0.296932 loss) +I0616 05:14:29.268154 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0361536 (* 1 = 0.0361536 loss) +I0616 05:14:29.268157 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111936 (* 1 = 0.0111936 loss) +I0616 05:14:29.268162 9857 solver.cpp:571] Iteration 16540, lr = 0.001 +I0616 05:14:40.824585 9857 solver.cpp:242] Iteration 16560, loss = 0.592121 +I0616 05:14:40.824628 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0706046 (* 1 = 0.0706046 loss) +I0616 05:14:40.824635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103093 (* 1 = 0.103093 loss) +I0616 05:14:40.824640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0476985 (* 1 = 0.0476985 loss) +I0616 05:14:40.824642 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0212152 (* 1 = 0.0212152 loss) +I0616 05:14:40.824646 9857 solver.cpp:571] Iteration 16560, lr = 0.001 +I0616 05:14:52.253029 9857 solver.cpp:242] Iteration 16580, loss = 0.562628 +I0616 05:14:52.253072 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16935 (* 1 = 0.16935 loss) +I0616 05:14:52.253077 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143055 (* 1 = 0.143055 loss) +I0616 05:14:52.253082 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0654048 (* 1 = 0.0654048 loss) +I0616 05:14:52.253085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00583995 (* 1 = 0.00583995 loss) +I0616 05:14:52.253092 9857 solver.cpp:571] Iteration 16580, lr = 0.001 +speed: 0.684s / iter +I0616 05:15:03.590869 9857 solver.cpp:242] Iteration 16600, loss = 1.38354 +I0616 05:15:03.590911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.404242 (* 1 = 0.404242 loss) +I0616 05:15:03.590917 9857 solver.cpp:258] Train net output #1: loss_cls = 0.794003 (* 1 = 0.794003 loss) +I0616 05:15:03.590921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0946337 (* 1 = 0.0946337 loss) +I0616 05:15:03.590925 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0393338 (* 1 = 0.0393338 loss) +I0616 05:15:03.590929 9857 solver.cpp:571] Iteration 16600, lr = 0.001 +I0616 05:15:14.710698 9857 solver.cpp:242] Iteration 16620, loss = 1.85054 +I0616 05:15:14.710741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.479986 (* 1 = 0.479986 loss) +I0616 05:15:14.710747 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03251 (* 1 = 1.03251 loss) +I0616 05:15:14.710750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.339633 (* 1 = 0.339633 loss) +I0616 05:15:14.710754 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156166 (* 1 = 0.156166 loss) +I0616 05:15:14.710763 9857 solver.cpp:571] Iteration 16620, lr = 0.001 +I0616 05:15:26.229365 9857 solver.cpp:242] Iteration 16640, loss = 0.538192 +I0616 05:15:26.229406 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230129 (* 1 = 0.230129 loss) +I0616 05:15:26.229413 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271991 (* 1 = 0.271991 loss) +I0616 05:15:26.229416 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0713859 (* 1 = 0.0713859 loss) +I0616 05:15:26.229420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133413 (* 1 = 0.0133413 loss) +I0616 05:15:26.229424 9857 solver.cpp:571] Iteration 16640, lr = 0.001 +I0616 05:15:37.827229 9857 solver.cpp:242] Iteration 16660, loss = 0.710564 +I0616 05:15:37.827271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27412 (* 1 = 0.27412 loss) +I0616 05:15:37.827277 9857 solver.cpp:258] Train net output #1: loss_cls = 0.409996 (* 1 = 0.409996 loss) +I0616 05:15:37.827281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138821 (* 1 = 0.138821 loss) +I0616 05:15:37.827286 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029647 (* 1 = 0.029647 loss) +I0616 05:15:37.827289 9857 solver.cpp:571] Iteration 16660, lr = 0.001 +I0616 05:15:49.488116 9857 solver.cpp:242] Iteration 16680, loss = 1.00609 +I0616 05:15:49.488158 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148094 (* 1 = 0.148094 loss) +I0616 05:15:49.488163 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170401 (* 1 = 0.170401 loss) +I0616 05:15:49.488168 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.011152 (* 1 = 0.011152 loss) +I0616 05:15:49.488173 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00712747 (* 1 = 0.00712747 loss) +I0616 05:15:49.488175 9857 solver.cpp:571] Iteration 16680, lr = 0.001 +I0616 05:16:00.939085 9857 solver.cpp:242] Iteration 16700, loss = 2.05489 +I0616 05:16:00.939127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.533233 (* 1 = 0.533233 loss) +I0616 05:16:00.939133 9857 solver.cpp:258] Train net output #1: loss_cls = 1.24691 (* 1 = 1.24691 loss) +I0616 05:16:00.939137 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.454758 (* 1 = 0.454758 loss) +I0616 05:16:00.939141 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.400522 (* 1 = 0.400522 loss) +I0616 05:16:00.939144 9857 solver.cpp:571] Iteration 16700, lr = 0.001 +I0616 05:16:12.417182 9857 solver.cpp:242] Iteration 16720, loss = 1.12046 +I0616 05:16:12.417225 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272063 (* 1 = 0.272063 loss) +I0616 05:16:12.417230 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17604 (* 1 = 0.17604 loss) +I0616 05:16:12.417235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161012 (* 1 = 0.161012 loss) +I0616 05:16:12.417238 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156588 (* 1 = 0.0156588 loss) +I0616 05:16:12.417243 9857 solver.cpp:571] Iteration 16720, lr = 0.001 +I0616 05:16:23.656424 9857 solver.cpp:242] Iteration 16740, loss = 1.42119 +I0616 05:16:23.656467 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191142 (* 1 = 0.191142 loss) +I0616 05:16:23.656473 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275356 (* 1 = 0.275356 loss) +I0616 05:16:23.656477 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0790025 (* 1 = 0.0790025 loss) +I0616 05:16:23.656481 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00457655 (* 1 = 0.00457655 loss) +I0616 05:16:23.656486 9857 solver.cpp:571] Iteration 16740, lr = 0.001 +I0616 05:16:35.187825 9857 solver.cpp:242] Iteration 16760, loss = 1.98751 +I0616 05:16:35.187866 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.483758 (* 1 = 0.483758 loss) +I0616 05:16:35.187872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.760103 (* 1 = 0.760103 loss) +I0616 05:16:35.187876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.255486 (* 1 = 0.255486 loss) +I0616 05:16:35.187880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.293024 (* 1 = 0.293024 loss) +I0616 05:16:35.187885 9857 solver.cpp:571] Iteration 16760, lr = 0.001 +I0616 05:16:46.490813 9857 solver.cpp:242] Iteration 16780, loss = 0.987076 +I0616 05:16:46.490854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374904 (* 1 = 0.374904 loss) +I0616 05:16:46.490859 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16502 (* 1 = 1.16502 loss) +I0616 05:16:46.490864 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0676668 (* 1 = 0.0676668 loss) +I0616 05:16:46.490867 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230787 (* 1 = 0.0230787 loss) +I0616 05:16:46.490871 9857 solver.cpp:571] Iteration 16780, lr = 0.001 +speed: 0.682s / iter +I0616 05:16:58.131325 9857 solver.cpp:242] Iteration 16800, loss = 0.462677 +I0616 05:16:58.131369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114698 (* 1 = 0.114698 loss) +I0616 05:16:58.131374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0929646 (* 1 = 0.0929646 loss) +I0616 05:16:58.131378 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0225989 (* 1 = 0.0225989 loss) +I0616 05:16:58.131382 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221065 (* 1 = 0.0221065 loss) +I0616 05:16:58.131386 9857 solver.cpp:571] Iteration 16800, lr = 0.001 +I0616 05:17:09.780648 9857 solver.cpp:242] Iteration 16820, loss = 0.725858 +I0616 05:17:09.780689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.26211 (* 1 = 0.26211 loss) +I0616 05:17:09.780695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278978 (* 1 = 0.278978 loss) +I0616 05:17:09.780699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0670018 (* 1 = 0.0670018 loss) +I0616 05:17:09.780704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0190473 (* 1 = 0.0190473 loss) +I0616 05:17:09.780707 9857 solver.cpp:571] Iteration 16820, lr = 0.001 +I0616 05:17:21.413395 9857 solver.cpp:242] Iteration 16840, loss = 1.25008 +I0616 05:17:21.413439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.444509 (* 1 = 0.444509 loss) +I0616 05:17:21.413444 9857 solver.cpp:258] Train net output #1: loss_cls = 0.588747 (* 1 = 0.588747 loss) +I0616 05:17:21.413449 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166159 (* 1 = 0.166159 loss) +I0616 05:17:21.413452 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183564 (* 1 = 0.0183564 loss) +I0616 05:17:21.413456 9857 solver.cpp:571] Iteration 16840, lr = 0.001 +I0616 05:17:33.154712 9857 solver.cpp:242] Iteration 16860, loss = 1.34176 +I0616 05:17:33.154755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.52662 (* 1 = 0.52662 loss) +I0616 05:17:33.154767 9857 solver.cpp:258] Train net output #1: loss_cls = 0.635605 (* 1 = 0.635605 loss) +I0616 05:17:33.154772 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.281965 (* 1 = 0.281965 loss) +I0616 05:17:33.154775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223962 (* 1 = 0.0223962 loss) +I0616 05:17:33.154779 9857 solver.cpp:571] Iteration 16860, lr = 0.001 +I0616 05:17:44.534761 9857 solver.cpp:242] Iteration 16880, loss = 1.33825 +I0616 05:17:44.534804 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.557599 (* 1 = 0.557599 loss) +I0616 05:17:44.534809 9857 solver.cpp:258] Train net output #1: loss_cls = 1.34413 (* 1 = 1.34413 loss) +I0616 05:17:44.534814 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0579659 (* 1 = 0.0579659 loss) +I0616 05:17:44.534817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0394877 (* 1 = 0.0394877 loss) +I0616 05:17:44.534821 9857 solver.cpp:571] Iteration 16880, lr = 0.001 +I0616 05:17:56.176686 9857 solver.cpp:242] Iteration 16900, loss = 1.33055 +I0616 05:17:56.176728 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.459783 (* 1 = 0.459783 loss) +I0616 05:17:56.176733 9857 solver.cpp:258] Train net output #1: loss_cls = 1.43361 (* 1 = 1.43361 loss) +I0616 05:17:56.176738 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136609 (* 1 = 0.136609 loss) +I0616 05:17:56.176741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103406 (* 1 = 0.0103406 loss) +I0616 05:17:56.176745 9857 solver.cpp:571] Iteration 16900, lr = 0.001 +I0616 05:18:07.783604 9857 solver.cpp:242] Iteration 16920, loss = 0.848517 +I0616 05:18:07.783648 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345505 (* 1 = 0.345505 loss) +I0616 05:18:07.783654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.60287 (* 1 = 0.60287 loss) +I0616 05:18:07.783658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134926 (* 1 = 0.134926 loss) +I0616 05:18:07.783663 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0512702 (* 1 = 0.0512702 loss) +I0616 05:18:07.783666 9857 solver.cpp:571] Iteration 16920, lr = 0.001 +I0616 05:18:19.328145 9857 solver.cpp:242] Iteration 16940, loss = 1.01881 +I0616 05:18:19.328186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.375633 (* 1 = 0.375633 loss) +I0616 05:18:19.328191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264074 (* 1 = 0.264074 loss) +I0616 05:18:19.328194 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0242213 (* 1 = 0.0242213 loss) +I0616 05:18:19.328198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103395 (* 1 = 0.0103395 loss) +I0616 05:18:19.328202 9857 solver.cpp:571] Iteration 16940, lr = 0.001 +I0616 05:18:30.767462 9857 solver.cpp:242] Iteration 16960, loss = 0.976229 +I0616 05:18:30.767503 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323352 (* 1 = 0.323352 loss) +I0616 05:18:30.767508 9857 solver.cpp:258] Train net output #1: loss_cls = 1.13153 (* 1 = 1.13153 loss) +I0616 05:18:30.767513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0614333 (* 1 = 0.0614333 loss) +I0616 05:18:30.767516 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00386946 (* 1 = 0.00386946 loss) +I0616 05:18:30.767520 9857 solver.cpp:571] Iteration 16960, lr = 0.001 +I0616 05:18:42.261165 9857 solver.cpp:242] Iteration 16980, loss = 1.23205 +I0616 05:18:42.261206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238088 (* 1 = 0.238088 loss) +I0616 05:18:42.261212 9857 solver.cpp:258] Train net output #1: loss_cls = 0.552651 (* 1 = 0.552651 loss) +I0616 05:18:42.261216 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107876 (* 1 = 0.107876 loss) +I0616 05:18:42.261220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0237825 (* 1 = 0.0237825 loss) +I0616 05:18:42.261224 9857 solver.cpp:571] Iteration 16980, lr = 0.001 +speed: 0.681s / iter +I0616 05:18:53.643954 9857 solver.cpp:242] Iteration 17000, loss = 1.34903 +I0616 05:18:53.643996 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205642 (* 1 = 0.205642 loss) +I0616 05:18:53.644001 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370539 (* 1 = 0.370539 loss) +I0616 05:18:53.644006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0462039 (* 1 = 0.0462039 loss) +I0616 05:18:53.644011 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0446696 (* 1 = 0.0446696 loss) +I0616 05:18:53.644014 9857 solver.cpp:571] Iteration 17000, lr = 0.001 +I0616 05:19:05.270222 9857 solver.cpp:242] Iteration 17020, loss = 0.791595 +I0616 05:19:05.270265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18981 (* 1 = 0.18981 loss) +I0616 05:19:05.270270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.458439 (* 1 = 0.458439 loss) +I0616 05:19:05.270275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0661968 (* 1 = 0.0661968 loss) +I0616 05:19:05.270278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207552 (* 1 = 0.0207552 loss) +I0616 05:19:05.270282 9857 solver.cpp:571] Iteration 17020, lr = 0.001 +I0616 05:19:16.515418 9857 solver.cpp:242] Iteration 17040, loss = 1.06741 +I0616 05:19:16.515460 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209773 (* 1 = 0.209773 loss) +I0616 05:19:16.515465 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310116 (* 1 = 0.310116 loss) +I0616 05:19:16.515470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190375 (* 1 = 0.190375 loss) +I0616 05:19:16.515473 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00397368 (* 1 = 0.00397368 loss) +I0616 05:19:16.515480 9857 solver.cpp:571] Iteration 17040, lr = 0.001 +I0616 05:19:28.036517 9857 solver.cpp:242] Iteration 17060, loss = 1.10075 +I0616 05:19:28.036561 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260914 (* 1 = 0.260914 loss) +I0616 05:19:28.036566 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562585 (* 1 = 0.562585 loss) +I0616 05:19:28.036571 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0443085 (* 1 = 0.0443085 loss) +I0616 05:19:28.036574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0606554 (* 1 = 0.0606554 loss) +I0616 05:19:28.036578 9857 solver.cpp:571] Iteration 17060, lr = 0.001 +I0616 05:19:39.353265 9857 solver.cpp:242] Iteration 17080, loss = 2.00068 +I0616 05:19:39.353303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.501719 (* 1 = 0.501719 loss) +I0616 05:19:39.353309 9857 solver.cpp:258] Train net output #1: loss_cls = 1.1483 (* 1 = 1.1483 loss) +I0616 05:19:39.353313 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.403699 (* 1 = 0.403699 loss) +I0616 05:19:39.353317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.214748 (* 1 = 0.214748 loss) +I0616 05:19:39.353320 9857 solver.cpp:571] Iteration 17080, lr = 0.001 +I0616 05:19:50.702273 9857 solver.cpp:242] Iteration 17100, loss = 0.993457 +I0616 05:19:50.702314 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.546771 (* 1 = 0.546771 loss) +I0616 05:19:50.702321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.67166 (* 1 = 0.67166 loss) +I0616 05:19:50.702324 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12889 (* 1 = 0.12889 loss) +I0616 05:19:50.702327 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.152382 (* 1 = 0.152382 loss) +I0616 05:19:50.702332 9857 solver.cpp:571] Iteration 17100, lr = 0.001 +I0616 05:20:02.649199 9857 solver.cpp:242] Iteration 17120, loss = 0.88234 +I0616 05:20:02.649241 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.520838 (* 1 = 0.520838 loss) +I0616 05:20:02.649246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.540348 (* 1 = 0.540348 loss) +I0616 05:20:02.649250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0591085 (* 1 = 0.0591085 loss) +I0616 05:20:02.649255 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.237753 (* 1 = 0.237753 loss) +I0616 05:20:02.649258 9857 solver.cpp:571] Iteration 17120, lr = 0.001 +I0616 05:20:14.166268 9857 solver.cpp:242] Iteration 17140, loss = 0.315624 +I0616 05:20:14.166311 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111167 (* 1 = 0.111167 loss) +I0616 05:20:14.166316 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113001 (* 1 = 0.113001 loss) +I0616 05:20:14.166321 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0221748 (* 1 = 0.0221748 loss) +I0616 05:20:14.166324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0322736 (* 1 = 0.0322736 loss) +I0616 05:20:14.166329 9857 solver.cpp:571] Iteration 17140, lr = 0.001 +I0616 05:20:25.702868 9857 solver.cpp:242] Iteration 17160, loss = 1.33815 +I0616 05:20:25.702911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.378006 (* 1 = 0.378006 loss) +I0616 05:20:25.702916 9857 solver.cpp:258] Train net output #1: loss_cls = 0.493103 (* 1 = 0.493103 loss) +I0616 05:20:25.702921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150733 (* 1 = 0.150733 loss) +I0616 05:20:25.702925 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0218245 (* 1 = 0.0218245 loss) +I0616 05:20:25.702929 9857 solver.cpp:571] Iteration 17160, lr = 0.001 +I0616 05:20:37.006587 9857 solver.cpp:242] Iteration 17180, loss = 0.524314 +I0616 05:20:37.006628 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275456 (* 1 = 0.275456 loss) +I0616 05:20:37.006634 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294557 (* 1 = 0.294557 loss) +I0616 05:20:37.006639 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0368912 (* 1 = 0.0368912 loss) +I0616 05:20:37.006642 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000351695 (* 1 = 0.000351695 loss) +I0616 05:20:37.006646 9857 solver.cpp:571] Iteration 17180, lr = 0.001 +speed: 0.680s / iter +I0616 05:20:48.725636 9857 solver.cpp:242] Iteration 17200, loss = 1.17427 +I0616 05:20:48.725677 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.48159 (* 1 = 0.48159 loss) +I0616 05:20:48.725683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.802415 (* 1 = 0.802415 loss) +I0616 05:20:48.725687 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.460424 (* 1 = 0.460424 loss) +I0616 05:20:48.725692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0882816 (* 1 = 0.0882816 loss) +I0616 05:20:48.725695 9857 solver.cpp:571] Iteration 17200, lr = 0.001 +I0616 05:21:00.511914 9857 solver.cpp:242] Iteration 17220, loss = 0.608898 +I0616 05:21:00.511955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258032 (* 1 = 0.258032 loss) +I0616 05:21:00.511960 9857 solver.cpp:258] Train net output #1: loss_cls = 0.510293 (* 1 = 0.510293 loss) +I0616 05:21:00.511965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0991728 (* 1 = 0.0991728 loss) +I0616 05:21:00.511968 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283675 (* 1 = 0.0283675 loss) +I0616 05:21:00.511972 9857 solver.cpp:571] Iteration 17220, lr = 0.001 +I0616 05:21:12.081179 9857 solver.cpp:242] Iteration 17240, loss = 1.19674 +I0616 05:21:12.081220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.391822 (* 1 = 0.391822 loss) +I0616 05:21:12.081225 9857 solver.cpp:258] Train net output #1: loss_cls = 0.736686 (* 1 = 0.736686 loss) +I0616 05:21:12.081230 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0673064 (* 1 = 0.0673064 loss) +I0616 05:21:12.081234 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0663083 (* 1 = 0.0663083 loss) +I0616 05:21:12.081238 9857 solver.cpp:571] Iteration 17240, lr = 0.001 +I0616 05:21:23.771773 9857 solver.cpp:242] Iteration 17260, loss = 0.575551 +I0616 05:21:23.771816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16443 (* 1 = 0.16443 loss) +I0616 05:21:23.771821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260834 (* 1 = 0.260834 loss) +I0616 05:21:23.771826 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0636291 (* 1 = 0.0636291 loss) +I0616 05:21:23.771831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010919 (* 1 = 0.010919 loss) +I0616 05:21:23.771834 9857 solver.cpp:571] Iteration 17260, lr = 0.001 +I0616 05:21:35.017243 9857 solver.cpp:242] Iteration 17280, loss = 0.920124 +I0616 05:21:35.017284 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27857 (* 1 = 0.27857 loss) +I0616 05:21:35.017289 9857 solver.cpp:258] Train net output #1: loss_cls = 0.477325 (* 1 = 0.477325 loss) +I0616 05:21:35.017294 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0797504 (* 1 = 0.0797504 loss) +I0616 05:21:35.017297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0600196 (* 1 = 0.0600196 loss) +I0616 05:21:35.017302 9857 solver.cpp:571] Iteration 17280, lr = 0.001 +I0616 05:21:46.566247 9857 solver.cpp:242] Iteration 17300, loss = 0.684881 +I0616 05:21:46.566289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313882 (* 1 = 0.313882 loss) +I0616 05:21:46.566294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279856 (* 1 = 0.279856 loss) +I0616 05:21:46.566298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172701 (* 1 = 0.172701 loss) +I0616 05:21:46.566303 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285813 (* 1 = 0.0285813 loss) +I0616 05:21:46.566305 9857 solver.cpp:571] Iteration 17300, lr = 0.001 +I0616 05:21:57.845773 9857 solver.cpp:242] Iteration 17320, loss = 1.2094 +I0616 05:21:57.845815 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379366 (* 1 = 0.379366 loss) +I0616 05:21:57.845821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.428812 (* 1 = 0.428812 loss) +I0616 05:21:57.845825 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104796 (* 1 = 0.104796 loss) +I0616 05:21:57.845829 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00421933 (* 1 = 0.00421933 loss) +I0616 05:21:57.845834 9857 solver.cpp:571] Iteration 17320, lr = 0.001 +I0616 05:22:09.367079 9857 solver.cpp:242] Iteration 17340, loss = 1.17172 +I0616 05:22:09.367120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413105 (* 1 = 0.413105 loss) +I0616 05:22:09.367125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.598387 (* 1 = 0.598387 loss) +I0616 05:22:09.367130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183829 (* 1 = 0.183829 loss) +I0616 05:22:09.367133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130696 (* 1 = 0.0130696 loss) +I0616 05:22:09.367136 9857 solver.cpp:571] Iteration 17340, lr = 0.001 +I0616 05:22:21.010334 9857 solver.cpp:242] Iteration 17360, loss = 0.77615 +I0616 05:22:21.010375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292638 (* 1 = 0.292638 loss) +I0616 05:22:21.010381 9857 solver.cpp:258] Train net output #1: loss_cls = 0.78185 (* 1 = 0.78185 loss) +I0616 05:22:21.010385 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0289767 (* 1 = 0.0289767 loss) +I0616 05:22:21.010390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0544146 (* 1 = 0.0544146 loss) +I0616 05:22:21.010393 9857 solver.cpp:571] Iteration 17360, lr = 0.001 +I0616 05:22:32.810736 9857 solver.cpp:242] Iteration 17380, loss = 1.52328 +I0616 05:22:32.810781 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.560622 (* 1 = 0.560622 loss) +I0616 05:22:32.810787 9857 solver.cpp:258] Train net output #1: loss_cls = 0.560629 (* 1 = 0.560629 loss) +I0616 05:22:32.810791 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.601216 (* 1 = 0.601216 loss) +I0616 05:22:32.810796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.517075 (* 1 = 0.517075 loss) +I0616 05:22:32.810798 9857 solver.cpp:571] Iteration 17380, lr = 0.001 +speed: 0.679s / iter +I0616 05:22:44.396425 9857 solver.cpp:242] Iteration 17400, loss = 0.603507 +I0616 05:22:44.396467 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159144 (* 1 = 0.159144 loss) +I0616 05:22:44.396472 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219238 (* 1 = 0.219238 loss) +I0616 05:22:44.396477 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0931265 (* 1 = 0.0931265 loss) +I0616 05:22:44.396481 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0468677 (* 1 = 0.0468677 loss) +I0616 05:22:44.396484 9857 solver.cpp:571] Iteration 17400, lr = 0.001 +I0616 05:22:55.982322 9857 solver.cpp:242] Iteration 17420, loss = 1.40358 +I0616 05:22:55.982365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.536465 (* 1 = 0.536465 loss) +I0616 05:22:55.982370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.802745 (* 1 = 0.802745 loss) +I0616 05:22:55.982375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.390403 (* 1 = 0.390403 loss) +I0616 05:22:55.982378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.199034 (* 1 = 0.199034 loss) +I0616 05:22:55.982383 9857 solver.cpp:571] Iteration 17420, lr = 0.001 +I0616 05:23:07.297260 9857 solver.cpp:242] Iteration 17440, loss = 0.761694 +I0616 05:23:07.297302 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196273 (* 1 = 0.196273 loss) +I0616 05:23:07.297308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219473 (* 1 = 0.219473 loss) +I0616 05:23:07.297312 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.096905 (* 1 = 0.096905 loss) +I0616 05:23:07.297317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202863 (* 1 = 0.0202863 loss) +I0616 05:23:07.297320 9857 solver.cpp:571] Iteration 17440, lr = 0.001 +I0616 05:23:19.003433 9857 solver.cpp:242] Iteration 17460, loss = 1.14213 +I0616 05:23:19.003474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.569538 (* 1 = 0.569538 loss) +I0616 05:23:19.003480 9857 solver.cpp:258] Train net output #1: loss_cls = 0.437523 (* 1 = 0.437523 loss) +I0616 05:23:19.003484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.309147 (* 1 = 0.309147 loss) +I0616 05:23:19.003489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0592344 (* 1 = 0.0592344 loss) +I0616 05:23:19.003492 9857 solver.cpp:571] Iteration 17460, lr = 0.001 +I0616 05:23:30.438032 9857 solver.cpp:242] Iteration 17480, loss = 1.67428 +I0616 05:23:30.438076 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308183 (* 1 = 0.308183 loss) +I0616 05:23:30.438081 9857 solver.cpp:258] Train net output #1: loss_cls = 0.672166 (* 1 = 0.672166 loss) +I0616 05:23:30.438086 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.221356 (* 1 = 0.221356 loss) +I0616 05:23:30.438089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00589754 (* 1 = 0.00589754 loss) +I0616 05:23:30.438094 9857 solver.cpp:571] Iteration 17480, lr = 0.001 +I0616 05:23:41.848569 9857 solver.cpp:242] Iteration 17500, loss = 0.462318 +I0616 05:23:41.848609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103423 (* 1 = 0.103423 loss) +I0616 05:23:41.848615 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345498 (* 1 = 0.345498 loss) +I0616 05:23:41.848619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102022 (* 1 = 0.102022 loss) +I0616 05:23:41.848623 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103246 (* 1 = 0.0103246 loss) +I0616 05:23:41.848628 9857 solver.cpp:571] Iteration 17500, lr = 0.001 +I0616 05:23:53.045044 9857 solver.cpp:242] Iteration 17520, loss = 1.34104 +I0616 05:23:53.045085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.420042 (* 1 = 0.420042 loss) +I0616 05:23:53.045091 9857 solver.cpp:258] Train net output #1: loss_cls = 0.835412 (* 1 = 0.835412 loss) +I0616 05:23:53.045095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0823736 (* 1 = 0.0823736 loss) +I0616 05:23:53.045099 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306926 (* 1 = 0.0306926 loss) +I0616 05:23:53.045104 9857 solver.cpp:571] Iteration 17520, lr = 0.001 +I0616 05:24:04.330302 9857 solver.cpp:242] Iteration 17540, loss = 0.714106 +I0616 05:24:04.330344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196073 (* 1 = 0.196073 loss) +I0616 05:24:04.330350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.452457 (* 1 = 0.452457 loss) +I0616 05:24:04.330354 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158175 (* 1 = 0.158175 loss) +I0616 05:24:04.330358 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128826 (* 1 = 0.0128826 loss) +I0616 05:24:04.330361 9857 solver.cpp:571] Iteration 17540, lr = 0.001 +I0616 05:24:15.628829 9857 solver.cpp:242] Iteration 17560, loss = 0.965158 +I0616 05:24:15.628870 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.419644 (* 1 = 0.419644 loss) +I0616 05:24:15.628875 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514549 (* 1 = 0.514549 loss) +I0616 05:24:15.628880 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0632643 (* 1 = 0.0632643 loss) +I0616 05:24:15.628883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0643273 (* 1 = 0.0643273 loss) +I0616 05:24:15.628886 9857 solver.cpp:571] Iteration 17560, lr = 0.001 +I0616 05:24:27.410367 9857 solver.cpp:242] Iteration 17580, loss = 1.19633 +I0616 05:24:27.410408 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390041 (* 1 = 0.390041 loss) +I0616 05:24:27.410413 9857 solver.cpp:258] Train net output #1: loss_cls = 0.643553 (* 1 = 0.643553 loss) +I0616 05:24:27.410418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135457 (* 1 = 0.135457 loss) +I0616 05:24:27.410421 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0478018 (* 1 = 0.0478018 loss) +I0616 05:24:27.410425 9857 solver.cpp:571] Iteration 17580, lr = 0.001 +speed: 0.678s / iter +I0616 05:24:38.875469 9857 solver.cpp:242] Iteration 17600, loss = 1.41015 +I0616 05:24:38.875511 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.370513 (* 1 = 0.370513 loss) +I0616 05:24:38.875516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.46585 (* 1 = 0.46585 loss) +I0616 05:24:38.875521 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.292231 (* 1 = 0.292231 loss) +I0616 05:24:38.875524 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.50972 (* 1 = 0.50972 loss) +I0616 05:24:38.875529 9857 solver.cpp:571] Iteration 17600, lr = 0.001 +I0616 05:24:50.468328 9857 solver.cpp:242] Iteration 17620, loss = 0.621882 +I0616 05:24:50.468366 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190297 (* 1 = 0.190297 loss) +I0616 05:24:50.468372 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186488 (* 1 = 0.186488 loss) +I0616 05:24:50.468376 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0643219 (* 1 = 0.0643219 loss) +I0616 05:24:50.468380 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.039986 (* 1 = 0.039986 loss) +I0616 05:24:50.468384 9857 solver.cpp:571] Iteration 17620, lr = 0.001 +I0616 05:25:02.070389 9857 solver.cpp:242] Iteration 17640, loss = 0.845861 +I0616 05:25:02.070431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224501 (* 1 = 0.224501 loss) +I0616 05:25:02.070438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.390517 (* 1 = 0.390517 loss) +I0616 05:25:02.070443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.264184 (* 1 = 0.264184 loss) +I0616 05:25:02.070446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0727909 (* 1 = 0.0727909 loss) +I0616 05:25:02.070451 9857 solver.cpp:571] Iteration 17640, lr = 0.001 +I0616 05:25:13.694142 9857 solver.cpp:242] Iteration 17660, loss = 1.40227 +I0616 05:25:13.694185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.565034 (* 1 = 0.565034 loss) +I0616 05:25:13.694190 9857 solver.cpp:258] Train net output #1: loss_cls = 0.995256 (* 1 = 0.995256 loss) +I0616 05:25:13.694195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.527972 (* 1 = 0.527972 loss) +I0616 05:25:13.694198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.297412 (* 1 = 0.297412 loss) +I0616 05:25:13.694202 9857 solver.cpp:571] Iteration 17660, lr = 0.001 +I0616 05:25:25.483572 9857 solver.cpp:242] Iteration 17680, loss = 1.43248 +I0616 05:25:25.483614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122835 (* 1 = 0.122835 loss) +I0616 05:25:25.483620 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432784 (* 1 = 0.432784 loss) +I0616 05:25:25.483624 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.35532 (* 1 = 0.35532 loss) +I0616 05:25:25.483628 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0296107 (* 1 = 0.0296107 loss) +I0616 05:25:25.483633 9857 solver.cpp:571] Iteration 17680, lr = 0.001 +I0616 05:25:36.932291 9857 solver.cpp:242] Iteration 17700, loss = 0.903216 +I0616 05:25:36.932332 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180015 (* 1 = 0.180015 loss) +I0616 05:25:36.932338 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109115 (* 1 = 0.109115 loss) +I0616 05:25:36.932343 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0930938 (* 1 = 0.0930938 loss) +I0616 05:25:36.932346 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00680389 (* 1 = 0.00680389 loss) +I0616 05:25:36.932349 9857 solver.cpp:571] Iteration 17700, lr = 0.001 +I0616 05:25:48.767688 9857 solver.cpp:242] Iteration 17720, loss = 0.744571 +I0616 05:25:48.767729 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172186 (* 1 = 0.172186 loss) +I0616 05:25:48.767735 9857 solver.cpp:258] Train net output #1: loss_cls = 0.367047 (* 1 = 0.367047 loss) +I0616 05:25:48.767740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.16473 (* 1 = 0.16473 loss) +I0616 05:25:48.767742 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0894196 (* 1 = 0.0894196 loss) +I0616 05:25:48.767746 9857 solver.cpp:571] Iteration 17720, lr = 0.001 +I0616 05:26:00.375468 9857 solver.cpp:242] Iteration 17740, loss = 1.98186 +I0616 05:26:00.375510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352411 (* 1 = 0.352411 loss) +I0616 05:26:00.375515 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00221 (* 1 = 1.00221 loss) +I0616 05:26:00.375519 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.251875 (* 1 = 0.251875 loss) +I0616 05:26:00.375524 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0756679 (* 1 = 0.0756679 loss) +I0616 05:26:00.375530 9857 solver.cpp:571] Iteration 17740, lr = 0.001 +I0616 05:26:12.073987 9857 solver.cpp:242] Iteration 17760, loss = 1.05077 +I0616 05:26:12.074028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228052 (* 1 = 0.228052 loss) +I0616 05:26:12.074034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.544319 (* 1 = 0.544319 loss) +I0616 05:26:12.074038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0485004 (* 1 = 0.0485004 loss) +I0616 05:26:12.074043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.021599 (* 1 = 0.021599 loss) +I0616 05:26:12.074045 9857 solver.cpp:571] Iteration 17760, lr = 0.001 +I0616 05:26:23.559973 9857 solver.cpp:242] Iteration 17780, loss = 0.852479 +I0616 05:26:23.560015 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269686 (* 1 = 0.269686 loss) +I0616 05:26:23.560020 9857 solver.cpp:258] Train net output #1: loss_cls = 0.595147 (* 1 = 0.595147 loss) +I0616 05:26:23.560024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0426966 (* 1 = 0.0426966 loss) +I0616 05:26:23.560029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136024 (* 1 = 0.0136024 loss) +I0616 05:26:23.560032 9857 solver.cpp:571] Iteration 17780, lr = 0.001 +speed: 0.677s / iter +I0616 05:26:35.295343 9857 solver.cpp:242] Iteration 17800, loss = 0.823485 +I0616 05:26:35.295384 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123658 (* 1 = 0.123658 loss) +I0616 05:26:35.295390 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270093 (* 1 = 0.270093 loss) +I0616 05:26:35.295394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164785 (* 1 = 0.164785 loss) +I0616 05:26:35.295397 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.28891 (* 1 = 0.28891 loss) +I0616 05:26:35.295402 9857 solver.cpp:571] Iteration 17800, lr = 0.001 +I0616 05:26:46.769045 9857 solver.cpp:242] Iteration 17820, loss = 1.03231 +I0616 05:26:46.769088 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.398391 (* 1 = 0.398391 loss) +I0616 05:26:46.769094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276619 (* 1 = 0.276619 loss) +I0616 05:26:46.769098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142073 (* 1 = 0.142073 loss) +I0616 05:26:46.769103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0284738 (* 1 = 0.0284738 loss) +I0616 05:26:46.769106 9857 solver.cpp:571] Iteration 17820, lr = 0.001 +I0616 05:26:58.358613 9857 solver.cpp:242] Iteration 17840, loss = 0.87875 +I0616 05:26:58.358655 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0668682 (* 1 = 0.0668682 loss) +I0616 05:26:58.358661 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17069 (* 1 = 0.17069 loss) +I0616 05:26:58.358665 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.055025 (* 1 = 0.055025 loss) +I0616 05:26:58.358669 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00744128 (* 1 = 0.00744128 loss) +I0616 05:26:58.358672 9857 solver.cpp:571] Iteration 17840, lr = 0.001 +I0616 05:27:09.906517 9857 solver.cpp:242] Iteration 17860, loss = 2.55969 +I0616 05:27:09.906560 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.33938 (* 1 = 0.33938 loss) +I0616 05:27:09.906565 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16877 (* 1 = 1.16877 loss) +I0616 05:27:09.906569 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.787555 (* 1 = 0.787555 loss) +I0616 05:27:09.906574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.91416 (* 1 = 0.91416 loss) +I0616 05:27:09.906577 9857 solver.cpp:571] Iteration 17860, lr = 0.001 +I0616 05:27:21.545575 9857 solver.cpp:242] Iteration 17880, loss = 1.98827 +I0616 05:27:21.545617 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.612819 (* 1 = 0.612819 loss) +I0616 05:27:21.545624 9857 solver.cpp:258] Train net output #1: loss_cls = 1.99043 (* 1 = 1.99043 loss) +I0616 05:27:21.545627 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.769619 (* 1 = 0.769619 loss) +I0616 05:27:21.545631 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103168 (* 1 = 0.103168 loss) +I0616 05:27:21.545635 9857 solver.cpp:571] Iteration 17880, lr = 0.001 +I0616 05:27:33.157925 9857 solver.cpp:242] Iteration 17900, loss = 1.13206 +I0616 05:27:33.157966 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408013 (* 1 = 0.408013 loss) +I0616 05:27:33.157973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.752888 (* 1 = 0.752888 loss) +I0616 05:27:33.157976 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.502941 (* 1 = 0.502941 loss) +I0616 05:27:33.157980 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117525 (* 1 = 0.117525 loss) +I0616 05:27:33.157984 9857 solver.cpp:571] Iteration 17900, lr = 0.001 +I0616 05:27:44.713542 9857 solver.cpp:242] Iteration 17920, loss = 0.792279 +I0616 05:27:44.713583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.491022 (* 1 = 0.491022 loss) +I0616 05:27:44.713589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.34186 (* 1 = 0.34186 loss) +I0616 05:27:44.713593 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.260486 (* 1 = 0.260486 loss) +I0616 05:27:44.713598 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.077371 (* 1 = 0.077371 loss) +I0616 05:27:44.713601 9857 solver.cpp:571] Iteration 17920, lr = 0.001 +I0616 05:27:56.326282 9857 solver.cpp:242] Iteration 17940, loss = 1.13975 +I0616 05:27:56.326323 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2186 (* 1 = 0.2186 loss) +I0616 05:27:56.326330 9857 solver.cpp:258] Train net output #1: loss_cls = 0.963184 (* 1 = 0.963184 loss) +I0616 05:27:56.326334 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140725 (* 1 = 0.140725 loss) +I0616 05:27:56.326339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00831406 (* 1 = 0.00831406 loss) +I0616 05:27:56.326342 9857 solver.cpp:571] Iteration 17940, lr = 0.001 +I0616 05:28:08.026710 9857 solver.cpp:242] Iteration 17960, loss = 0.755725 +I0616 05:28:08.026751 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197694 (* 1 = 0.197694 loss) +I0616 05:28:08.026763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.397407 (* 1 = 0.397407 loss) +I0616 05:28:08.026768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120889 (* 1 = 0.120889 loss) +I0616 05:28:08.026772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.20077 (* 1 = 0.20077 loss) +I0616 05:28:08.026777 9857 solver.cpp:571] Iteration 17960, lr = 0.001 +I0616 05:28:19.520254 9857 solver.cpp:242] Iteration 17980, loss = 0.639731 +I0616 05:28:19.520297 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236939 (* 1 = 0.236939 loss) +I0616 05:28:19.520303 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314066 (* 1 = 0.314066 loss) +I0616 05:28:19.520306 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0726767 (* 1 = 0.0726767 loss) +I0616 05:28:19.520310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00948314 (* 1 = 0.00948314 loss) +I0616 05:28:19.520313 9857 solver.cpp:571] Iteration 17980, lr = 0.001 +speed: 0.675s / iter +I0616 05:28:30.936398 9857 solver.cpp:242] Iteration 18000, loss = 1.27439 +I0616 05:28:30.936441 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447541 (* 1 = 0.447541 loss) +I0616 05:28:30.936446 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03663 (* 1 = 1.03663 loss) +I0616 05:28:30.936450 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14642 (* 1 = 0.14642 loss) +I0616 05:28:30.936455 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00883491 (* 1 = 0.00883491 loss) +I0616 05:28:30.936458 9857 solver.cpp:571] Iteration 18000, lr = 0.001 +I0616 05:28:42.765595 9857 solver.cpp:242] Iteration 18020, loss = 0.624644 +I0616 05:28:42.765637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176411 (* 1 = 0.176411 loss) +I0616 05:28:42.765643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.318271 (* 1 = 0.318271 loss) +I0616 05:28:42.765647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129697 (* 1 = 0.129697 loss) +I0616 05:28:42.765651 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0928048 (* 1 = 0.0928048 loss) +I0616 05:28:42.765655 9857 solver.cpp:571] Iteration 18020, lr = 0.001 +I0616 05:28:54.656852 9857 solver.cpp:242] Iteration 18040, loss = 2.15866 +I0616 05:28:54.656896 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.497324 (* 1 = 0.497324 loss) +I0616 05:28:54.656901 9857 solver.cpp:258] Train net output #1: loss_cls = 1.74479 (* 1 = 1.74479 loss) +I0616 05:28:54.656905 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.306874 (* 1 = 0.306874 loss) +I0616 05:28:54.656909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0647596 (* 1 = 0.0647596 loss) +I0616 05:28:54.656913 9857 solver.cpp:571] Iteration 18040, lr = 0.001 +I0616 05:29:06.300998 9857 solver.cpp:242] Iteration 18060, loss = 1.48671 +I0616 05:29:06.301040 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.533799 (* 1 = 0.533799 loss) +I0616 05:29:06.301045 9857 solver.cpp:258] Train net output #1: loss_cls = 0.790041 (* 1 = 0.790041 loss) +I0616 05:29:06.301049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.448342 (* 1 = 0.448342 loss) +I0616 05:29:06.301054 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0869594 (* 1 = 0.0869594 loss) +I0616 05:29:06.301057 9857 solver.cpp:571] Iteration 18060, lr = 0.001 +I0616 05:29:17.805770 9857 solver.cpp:242] Iteration 18080, loss = 1.08058 +I0616 05:29:17.805814 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346013 (* 1 = 0.346013 loss) +I0616 05:29:17.805820 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313133 (* 1 = 0.313133 loss) +I0616 05:29:17.805824 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.021856 (* 1 = 0.021856 loss) +I0616 05:29:17.805827 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00258364 (* 1 = 0.00258364 loss) +I0616 05:29:17.805831 9857 solver.cpp:571] Iteration 18080, lr = 0.001 +I0616 05:29:29.450238 9857 solver.cpp:242] Iteration 18100, loss = 2.0939 +I0616 05:29:29.450279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453705 (* 1 = 0.453705 loss) +I0616 05:29:29.450284 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15008 (* 1 = 1.15008 loss) +I0616 05:29:29.450289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.199825 (* 1 = 0.199825 loss) +I0616 05:29:29.450294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143966 (* 1 = 0.0143966 loss) +I0616 05:29:29.450297 9857 solver.cpp:571] Iteration 18100, lr = 0.001 +I0616 05:29:40.937105 9857 solver.cpp:242] Iteration 18120, loss = 0.397449 +I0616 05:29:40.937147 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107528 (* 1 = 0.107528 loss) +I0616 05:29:40.937153 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182932 (* 1 = 0.182932 loss) +I0616 05:29:40.937157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0662573 (* 1 = 0.0662573 loss) +I0616 05:29:40.937161 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181043 (* 1 = 0.0181043 loss) +I0616 05:29:40.937165 9857 solver.cpp:571] Iteration 18120, lr = 0.001 +I0616 05:29:52.865962 9857 solver.cpp:242] Iteration 18140, loss = 1.69138 +I0616 05:29:52.866003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.456449 (* 1 = 0.456449 loss) +I0616 05:29:52.866008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.982646 (* 1 = 0.982646 loss) +I0616 05:29:52.866013 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.467997 (* 1 = 0.467997 loss) +I0616 05:29:52.866016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.497314 (* 1 = 0.497314 loss) +I0616 05:29:52.866019 9857 solver.cpp:571] Iteration 18140, lr = 0.001 +I0616 05:30:04.484992 9857 solver.cpp:242] Iteration 18160, loss = 0.657315 +I0616 05:30:04.485034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147212 (* 1 = 0.147212 loss) +I0616 05:30:04.485039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.53766 (* 1 = 0.53766 loss) +I0616 05:30:04.485044 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0324651 (* 1 = 0.0324651 loss) +I0616 05:30:04.485047 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0555269 (* 1 = 0.0555269 loss) +I0616 05:30:04.485054 9857 solver.cpp:571] Iteration 18160, lr = 0.001 +I0616 05:30:16.118366 9857 solver.cpp:242] Iteration 18180, loss = 1.95378 +I0616 05:30:16.118409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352458 (* 1 = 0.352458 loss) +I0616 05:30:16.118414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.87653 (* 1 = 0.87653 loss) +I0616 05:30:16.118418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.239429 (* 1 = 0.239429 loss) +I0616 05:30:16.118422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0677571 (* 1 = 0.0677571 loss) +I0616 05:30:16.118427 9857 solver.cpp:571] Iteration 18180, lr = 0.001 +speed: 0.674s / iter +I0616 05:30:27.754484 9857 solver.cpp:242] Iteration 18200, loss = 1.09162 +I0616 05:30:27.754528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.378993 (* 1 = 0.378993 loss) +I0616 05:30:27.754533 9857 solver.cpp:258] Train net output #1: loss_cls = 0.36786 (* 1 = 0.36786 loss) +I0616 05:30:27.754537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0464864 (* 1 = 0.0464864 loss) +I0616 05:30:27.754541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134281 (* 1 = 0.0134281 loss) +I0616 05:30:27.754544 9857 solver.cpp:571] Iteration 18200, lr = 0.001 +I0616 05:30:39.374858 9857 solver.cpp:242] Iteration 18220, loss = 0.70123 +I0616 05:30:39.374900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0672693 (* 1 = 0.0672693 loss) +I0616 05:30:39.374907 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326224 (* 1 = 0.326224 loss) +I0616 05:30:39.374910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0582468 (* 1 = 0.0582468 loss) +I0616 05:30:39.374914 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246602 (* 1 = 0.0246602 loss) +I0616 05:30:39.374919 9857 solver.cpp:571] Iteration 18220, lr = 0.001 +I0616 05:30:51.186687 9857 solver.cpp:242] Iteration 18240, loss = 0.760595 +I0616 05:30:51.186730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.510822 (* 1 = 0.510822 loss) +I0616 05:30:51.186735 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343544 (* 1 = 0.343544 loss) +I0616 05:30:51.186739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.131873 (* 1 = 0.131873 loss) +I0616 05:30:51.186743 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388117 (* 1 = 0.0388117 loss) +I0616 05:30:51.186748 9857 solver.cpp:571] Iteration 18240, lr = 0.001 +I0616 05:31:02.678208 9857 solver.cpp:242] Iteration 18260, loss = 1.21653 +I0616 05:31:02.678252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421 (* 1 = 0.421 loss) +I0616 05:31:02.678257 9857 solver.cpp:258] Train net output #1: loss_cls = 0.663785 (* 1 = 0.663785 loss) +I0616 05:31:02.678261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.476055 (* 1 = 0.476055 loss) +I0616 05:31:02.678266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.436629 (* 1 = 0.436629 loss) +I0616 05:31:02.678269 9857 solver.cpp:571] Iteration 18260, lr = 0.001 +I0616 05:31:14.361057 9857 solver.cpp:242] Iteration 18280, loss = 0.83902 +I0616 05:31:14.361100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201096 (* 1 = 0.201096 loss) +I0616 05:31:14.361105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200761 (* 1 = 0.200761 loss) +I0616 05:31:14.361110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0409454 (* 1 = 0.0409454 loss) +I0616 05:31:14.361114 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0534594 (* 1 = 0.0534594 loss) +I0616 05:31:14.361117 9857 solver.cpp:571] Iteration 18280, lr = 0.001 +I0616 05:31:25.789310 9857 solver.cpp:242] Iteration 18300, loss = 1.36877 +I0616 05:31:25.789352 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225764 (* 1 = 0.225764 loss) +I0616 05:31:25.789357 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302451 (* 1 = 0.302451 loss) +I0616 05:31:25.789361 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.180663 (* 1 = 0.180663 loss) +I0616 05:31:25.789366 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.233443 (* 1 = 0.233443 loss) +I0616 05:31:25.789369 9857 solver.cpp:571] Iteration 18300, lr = 0.001 +I0616 05:31:37.445822 9857 solver.cpp:242] Iteration 18320, loss = 1.30337 +I0616 05:31:37.445863 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329815 (* 1 = 0.329815 loss) +I0616 05:31:37.445868 9857 solver.cpp:258] Train net output #1: loss_cls = 0.561224 (* 1 = 0.561224 loss) +I0616 05:31:37.445873 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.579005 (* 1 = 0.579005 loss) +I0616 05:31:37.445876 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.29702 (* 1 = 0.29702 loss) +I0616 05:31:37.445879 9857 solver.cpp:571] Iteration 18320, lr = 0.001 +I0616 05:31:49.034934 9857 solver.cpp:242] Iteration 18340, loss = 0.980794 +I0616 05:31:49.034976 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.445275 (* 1 = 0.445275 loss) +I0616 05:31:49.034982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.626828 (* 1 = 0.626828 loss) +I0616 05:31:49.034986 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.204679 (* 1 = 0.204679 loss) +I0616 05:31:49.034991 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0352796 (* 1 = 0.0352796 loss) +I0616 05:31:49.034993 9857 solver.cpp:571] Iteration 18340, lr = 0.001 +I0616 05:32:00.539222 9857 solver.cpp:242] Iteration 18360, loss = 0.623196 +I0616 05:32:00.539263 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371518 (* 1 = 0.371518 loss) +I0616 05:32:00.539269 9857 solver.cpp:258] Train net output #1: loss_cls = 0.364052 (* 1 = 0.364052 loss) +I0616 05:32:00.539273 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0298819 (* 1 = 0.0298819 loss) +I0616 05:32:00.539278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00481783 (* 1 = 0.00481783 loss) +I0616 05:32:00.539281 9857 solver.cpp:571] Iteration 18360, lr = 0.001 +I0616 05:32:12.230386 9857 solver.cpp:242] Iteration 18380, loss = 1.59624 +I0616 05:32:12.230427 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.430834 (* 1 = 0.430834 loss) +I0616 05:32:12.230433 9857 solver.cpp:258] Train net output #1: loss_cls = 0.573036 (* 1 = 0.573036 loss) +I0616 05:32:12.230437 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.427622 (* 1 = 0.427622 loss) +I0616 05:32:12.230442 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.334171 (* 1 = 0.334171 loss) +I0616 05:32:12.230444 9857 solver.cpp:571] Iteration 18380, lr = 0.001 +speed: 0.673s / iter +I0616 05:32:23.462565 9857 solver.cpp:242] Iteration 18400, loss = 1.52969 +I0616 05:32:23.462606 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.517098 (* 1 = 0.517098 loss) +I0616 05:32:23.462611 9857 solver.cpp:258] Train net output #1: loss_cls = 0.623116 (* 1 = 0.623116 loss) +I0616 05:32:23.462616 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124532 (* 1 = 0.124532 loss) +I0616 05:32:23.462620 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.131948 (* 1 = 0.131948 loss) +I0616 05:32:23.462623 9857 solver.cpp:571] Iteration 18400, lr = 0.001 +I0616 05:32:35.167626 9857 solver.cpp:242] Iteration 18420, loss = 0.757694 +I0616 05:32:35.167668 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112782 (* 1 = 0.112782 loss) +I0616 05:32:35.167675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381164 (* 1 = 0.381164 loss) +I0616 05:32:35.167680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190073 (* 1 = 0.0190073 loss) +I0616 05:32:35.167682 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00546839 (* 1 = 0.00546839 loss) +I0616 05:32:35.167686 9857 solver.cpp:571] Iteration 18420, lr = 0.001 +I0616 05:32:46.732903 9857 solver.cpp:242] Iteration 18440, loss = 0.565271 +I0616 05:32:46.732944 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265321 (* 1 = 0.265321 loss) +I0616 05:32:46.732950 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189506 (* 1 = 0.189506 loss) +I0616 05:32:46.732954 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0457193 (* 1 = 0.0457193 loss) +I0616 05:32:46.732959 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0493398 (* 1 = 0.0493398 loss) +I0616 05:32:46.732962 9857 solver.cpp:571] Iteration 18440, lr = 0.001 +I0616 05:32:58.128043 9857 solver.cpp:242] Iteration 18460, loss = 1.33617 +I0616 05:32:58.128085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408016 (* 1 = 0.408016 loss) +I0616 05:32:58.128092 9857 solver.cpp:258] Train net output #1: loss_cls = 0.864796 (* 1 = 0.864796 loss) +I0616 05:32:58.128095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.425073 (* 1 = 0.425073 loss) +I0616 05:32:58.128099 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.319281 (* 1 = 0.319281 loss) +I0616 05:32:58.128103 9857 solver.cpp:571] Iteration 18460, lr = 0.001 +I0616 05:33:09.636508 9857 solver.cpp:242] Iteration 18480, loss = 0.442223 +I0616 05:33:09.636549 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138716 (* 1 = 0.138716 loss) +I0616 05:33:09.636555 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0828561 (* 1 = 0.0828561 loss) +I0616 05:33:09.636559 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0451132 (* 1 = 0.0451132 loss) +I0616 05:33:09.636564 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149779 (* 1 = 0.0149779 loss) +I0616 05:33:09.636569 9857 solver.cpp:571] Iteration 18480, lr = 0.001 +I0616 05:33:21.186565 9857 solver.cpp:242] Iteration 18500, loss = 1.42149 +I0616 05:33:21.186607 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283721 (* 1 = 0.283721 loss) +I0616 05:33:21.186612 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270109 (* 1 = 0.270109 loss) +I0616 05:33:21.186617 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168148 (* 1 = 0.168148 loss) +I0616 05:33:21.186621 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152709 (* 1 = 0.0152709 loss) +I0616 05:33:21.186625 9857 solver.cpp:571] Iteration 18500, lr = 0.001 +I0616 05:33:32.706300 9857 solver.cpp:242] Iteration 18520, loss = 0.980493 +I0616 05:33:32.706342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.405252 (* 1 = 0.405252 loss) +I0616 05:33:32.706348 9857 solver.cpp:258] Train net output #1: loss_cls = 0.573606 (* 1 = 0.573606 loss) +I0616 05:33:32.706352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.27206 (* 1 = 0.27206 loss) +I0616 05:33:32.706357 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102677 (* 1 = 0.102677 loss) +I0616 05:33:32.706359 9857 solver.cpp:571] Iteration 18520, lr = 0.001 +I0616 05:33:44.093323 9857 solver.cpp:242] Iteration 18540, loss = 0.792406 +I0616 05:33:44.093366 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2405 (* 1 = 0.2405 loss) +I0616 05:33:44.093372 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288491 (* 1 = 0.288491 loss) +I0616 05:33:44.093376 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0452318 (* 1 = 0.0452318 loss) +I0616 05:33:44.093379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255201 (* 1 = 0.0255201 loss) +I0616 05:33:44.093384 9857 solver.cpp:571] Iteration 18540, lr = 0.001 +I0616 05:33:55.752032 9857 solver.cpp:242] Iteration 18560, loss = 1.01541 +I0616 05:33:55.752074 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.372641 (* 1 = 0.372641 loss) +I0616 05:33:55.752079 9857 solver.cpp:258] Train net output #1: loss_cls = 1.0304 (* 1 = 1.0304 loss) +I0616 05:33:55.752084 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165024 (* 1 = 0.165024 loss) +I0616 05:33:55.752089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0831091 (* 1 = 0.0831091 loss) +I0616 05:33:55.752091 9857 solver.cpp:571] Iteration 18560, lr = 0.001 +I0616 05:34:07.432754 9857 solver.cpp:242] Iteration 18580, loss = 0.851234 +I0616 05:34:07.432796 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184754 (* 1 = 0.184754 loss) +I0616 05:34:07.432802 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202223 (* 1 = 0.202223 loss) +I0616 05:34:07.432806 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0726355 (* 1 = 0.0726355 loss) +I0616 05:34:07.432809 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153534 (* 1 = 0.0153534 loss) +I0616 05:34:07.432813 9857 solver.cpp:571] Iteration 18580, lr = 0.001 +speed: 0.672s / iter +I0616 05:34:19.300462 9857 solver.cpp:242] Iteration 18600, loss = 0.891175 +I0616 05:34:19.300503 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2768 (* 1 = 0.2768 loss) +I0616 05:34:19.300509 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416646 (* 1 = 0.416646 loss) +I0616 05:34:19.300513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0680384 (* 1 = 0.0680384 loss) +I0616 05:34:19.300518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0976556 (* 1 = 0.0976556 loss) +I0616 05:34:19.300521 9857 solver.cpp:571] Iteration 18600, lr = 0.001 +I0616 05:34:30.861171 9857 solver.cpp:242] Iteration 18620, loss = 0.51533 +I0616 05:34:30.861212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12586 (* 1 = 0.12586 loss) +I0616 05:34:30.861218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269975 (* 1 = 0.269975 loss) +I0616 05:34:30.861222 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0360218 (* 1 = 0.0360218 loss) +I0616 05:34:30.861227 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0263144 (* 1 = 0.0263144 loss) +I0616 05:34:30.861229 9857 solver.cpp:571] Iteration 18620, lr = 0.001 +I0616 05:34:42.455906 9857 solver.cpp:242] Iteration 18640, loss = 1.08333 +I0616 05:34:42.455950 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202826 (* 1 = 0.202826 loss) +I0616 05:34:42.455955 9857 solver.cpp:258] Train net output #1: loss_cls = 0.642037 (* 1 = 0.642037 loss) +I0616 05:34:42.455960 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172022 (* 1 = 0.172022 loss) +I0616 05:34:42.455963 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104586 (* 1 = 0.0104586 loss) +I0616 05:34:42.455967 9857 solver.cpp:571] Iteration 18640, lr = 0.001 +I0616 05:34:54.179133 9857 solver.cpp:242] Iteration 18660, loss = 0.914575 +I0616 05:34:54.179172 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0922518 (* 1 = 0.0922518 loss) +I0616 05:34:54.179178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162418 (* 1 = 0.162418 loss) +I0616 05:34:54.179183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0561902 (* 1 = 0.0561902 loss) +I0616 05:34:54.179185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00442809 (* 1 = 0.00442809 loss) +I0616 05:34:54.179189 9857 solver.cpp:571] Iteration 18660, lr = 0.001 +I0616 05:35:05.909670 9857 solver.cpp:242] Iteration 18680, loss = 1.48917 +I0616 05:35:05.909713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421391 (* 1 = 0.421391 loss) +I0616 05:35:05.909719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.807474 (* 1 = 0.807474 loss) +I0616 05:35:05.909723 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.505824 (* 1 = 0.505824 loss) +I0616 05:35:05.909728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.169028 (* 1 = 0.169028 loss) +I0616 05:35:05.909730 9857 solver.cpp:571] Iteration 18680, lr = 0.001 +I0616 05:35:17.629840 9857 solver.cpp:242] Iteration 18700, loss = 1.13325 +I0616 05:35:17.629880 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117354 (* 1 = 0.117354 loss) +I0616 05:35:17.629886 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301016 (* 1 = 0.301016 loss) +I0616 05:35:17.629890 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138712 (* 1 = 0.138712 loss) +I0616 05:35:17.629894 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015147 (* 1 = 0.015147 loss) +I0616 05:35:17.629899 9857 solver.cpp:571] Iteration 18700, lr = 0.001 +I0616 05:35:29.215345 9857 solver.cpp:242] Iteration 18720, loss = 0.778975 +I0616 05:35:29.215387 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28136 (* 1 = 0.28136 loss) +I0616 05:35:29.215394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.753453 (* 1 = 0.753453 loss) +I0616 05:35:29.215397 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0489209 (* 1 = 0.0489209 loss) +I0616 05:35:29.215401 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114044 (* 1 = 0.0114044 loss) +I0616 05:35:29.215404 9857 solver.cpp:571] Iteration 18720, lr = 0.001 +I0616 05:35:40.795219 9857 solver.cpp:242] Iteration 18740, loss = 0.798991 +I0616 05:35:40.795260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350317 (* 1 = 0.350317 loss) +I0616 05:35:40.795266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.585688 (* 1 = 0.585688 loss) +I0616 05:35:40.795270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0433088 (* 1 = 0.0433088 loss) +I0616 05:35:40.795274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0740875 (* 1 = 0.0740875 loss) +I0616 05:35:40.795279 9857 solver.cpp:571] Iteration 18740, lr = 0.001 +I0616 05:35:52.486409 9857 solver.cpp:242] Iteration 18760, loss = 0.543035 +I0616 05:35:52.486450 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0918814 (* 1 = 0.0918814 loss) +I0616 05:35:52.486456 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122067 (* 1 = 0.122067 loss) +I0616 05:35:52.486460 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0492616 (* 1 = 0.0492616 loss) +I0616 05:35:52.486464 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158204 (* 1 = 0.0158204 loss) +I0616 05:35:52.486469 9857 solver.cpp:571] Iteration 18760, lr = 0.001 +I0616 05:36:03.846793 9857 solver.cpp:242] Iteration 18780, loss = 1.05066 +I0616 05:36:03.846837 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215776 (* 1 = 0.215776 loss) +I0616 05:36:03.846843 9857 solver.cpp:258] Train net output #1: loss_cls = 0.2882 (* 1 = 0.2882 loss) +I0616 05:36:03.846846 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0875577 (* 1 = 0.0875577 loss) +I0616 05:36:03.846850 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00800129 (* 1 = 0.00800129 loss) +I0616 05:36:03.846854 9857 solver.cpp:571] Iteration 18780, lr = 0.001 +speed: 0.671s / iter +I0616 05:36:15.429122 9857 solver.cpp:242] Iteration 18800, loss = 0.582661 +I0616 05:36:15.429164 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189841 (* 1 = 0.189841 loss) +I0616 05:36:15.429169 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139057 (* 1 = 0.139057 loss) +I0616 05:36:15.429173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0577045 (* 1 = 0.0577045 loss) +I0616 05:36:15.429177 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194811 (* 1 = 0.0194811 loss) +I0616 05:36:15.429182 9857 solver.cpp:571] Iteration 18800, lr = 0.001 +I0616 05:36:26.953618 9857 solver.cpp:242] Iteration 18820, loss = 0.884528 +I0616 05:36:26.953644 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0871246 (* 1 = 0.0871246 loss) +I0616 05:36:26.953650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136349 (* 1 = 0.136349 loss) +I0616 05:36:26.953655 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0725062 (* 1 = 0.0725062 loss) +I0616 05:36:26.953658 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00455761 (* 1 = 0.00455761 loss) +I0616 05:36:26.953662 9857 solver.cpp:571] Iteration 18820, lr = 0.001 +I0616 05:36:38.533169 9857 solver.cpp:242] Iteration 18840, loss = 1.96469 +I0616 05:36:38.533210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3721 (* 1 = 0.3721 loss) +I0616 05:36:38.533215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.482919 (* 1 = 0.482919 loss) +I0616 05:36:38.533220 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.473582 (* 1 = 0.473582 loss) +I0616 05:36:38.533223 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714276 (* 1 = 0.0714276 loss) +I0616 05:36:38.533227 9857 solver.cpp:571] Iteration 18840, lr = 0.001 +I0616 05:36:50.099695 9857 solver.cpp:242] Iteration 18860, loss = 0.785058 +I0616 05:36:50.099737 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.520959 (* 1 = 0.520959 loss) +I0616 05:36:50.099743 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559033 (* 1 = 0.559033 loss) +I0616 05:36:50.099747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117421 (* 1 = 0.117421 loss) +I0616 05:36:50.099751 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0468053 (* 1 = 0.0468053 loss) +I0616 05:36:50.099755 9857 solver.cpp:571] Iteration 18860, lr = 0.001 +I0616 05:37:01.678158 9857 solver.cpp:242] Iteration 18880, loss = 0.519082 +I0616 05:37:01.678200 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222597 (* 1 = 0.222597 loss) +I0616 05:37:01.678205 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272606 (* 1 = 0.272606 loss) +I0616 05:37:01.678210 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0694597 (* 1 = 0.0694597 loss) +I0616 05:37:01.678212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0270163 (* 1 = 0.0270163 loss) +I0616 05:37:01.678216 9857 solver.cpp:571] Iteration 18880, lr = 0.001 +I0616 05:37:13.375537 9857 solver.cpp:242] Iteration 18900, loss = 0.668013 +I0616 05:37:13.375579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128797 (* 1 = 0.128797 loss) +I0616 05:37:13.375586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.256357 (* 1 = 0.256357 loss) +I0616 05:37:13.375589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12864 (* 1 = 0.12864 loss) +I0616 05:37:13.375593 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288136 (* 1 = 0.0288136 loss) +I0616 05:37:13.375597 9857 solver.cpp:571] Iteration 18900, lr = 0.001 +I0616 05:37:24.959255 9857 solver.cpp:242] Iteration 18920, loss = 1.26369 +I0616 05:37:24.959297 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249591 (* 1 = 0.249591 loss) +I0616 05:37:24.959303 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333027 (* 1 = 0.333027 loss) +I0616 05:37:24.959307 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0624176 (* 1 = 0.0624176 loss) +I0616 05:37:24.959311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0916912 (* 1 = 0.0916912 loss) +I0616 05:37:24.959316 9857 solver.cpp:571] Iteration 18920, lr = 0.001 +I0616 05:37:36.555055 9857 solver.cpp:242] Iteration 18940, loss = 1.29873 +I0616 05:37:36.555097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376406 (* 1 = 0.376406 loss) +I0616 05:37:36.555102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.891742 (* 1 = 0.891742 loss) +I0616 05:37:36.555106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.195624 (* 1 = 0.195624 loss) +I0616 05:37:36.555110 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0940884 (* 1 = 0.0940884 loss) +I0616 05:37:36.555114 9857 solver.cpp:571] Iteration 18940, lr = 0.001 +I0616 05:37:48.426559 9857 solver.cpp:242] Iteration 18960, loss = 0.617931 +I0616 05:37:48.426601 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18413 (* 1 = 0.18413 loss) +I0616 05:37:48.426606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251237 (* 1 = 0.251237 loss) +I0616 05:37:48.426611 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0512062 (* 1 = 0.0512062 loss) +I0616 05:37:48.426615 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00864024 (* 1 = 0.00864024 loss) +I0616 05:37:48.426618 9857 solver.cpp:571] Iteration 18960, lr = 0.001 +I0616 05:37:59.976949 9857 solver.cpp:242] Iteration 18980, loss = 1.29267 +I0616 05:37:59.976991 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.491226 (* 1 = 0.491226 loss) +I0616 05:37:59.976997 9857 solver.cpp:258] Train net output #1: loss_cls = 1.11726 (* 1 = 1.11726 loss) +I0616 05:37:59.977001 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215284 (* 1 = 0.215284 loss) +I0616 05:37:59.977005 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0902414 (* 1 = 0.0902414 loss) +I0616 05:37:59.977008 9857 solver.cpp:571] Iteration 18980, lr = 0.001 +speed: 0.670s / iter +I0616 05:38:11.681213 9857 solver.cpp:242] Iteration 19000, loss = 0.894167 +I0616 05:38:11.681254 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.539888 (* 1 = 0.539888 loss) +I0616 05:38:11.681260 9857 solver.cpp:258] Train net output #1: loss_cls = 0.40233 (* 1 = 0.40233 loss) +I0616 05:38:11.681265 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0405332 (* 1 = 0.0405332 loss) +I0616 05:38:11.681269 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0740505 (* 1 = 0.0740505 loss) +I0616 05:38:11.681274 9857 solver.cpp:571] Iteration 19000, lr = 0.001 +I0616 05:38:23.085559 9857 solver.cpp:242] Iteration 19020, loss = 1.24836 +I0616 05:38:23.085602 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347454 (* 1 = 0.347454 loss) +I0616 05:38:23.085608 9857 solver.cpp:258] Train net output #1: loss_cls = 0.443734 (* 1 = 0.443734 loss) +I0616 05:38:23.085611 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115428 (* 1 = 0.115428 loss) +I0616 05:38:23.085615 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147257 (* 1 = 0.0147257 loss) +I0616 05:38:23.085618 9857 solver.cpp:571] Iteration 19020, lr = 0.001 +I0616 05:38:34.498821 9857 solver.cpp:242] Iteration 19040, loss = 1.61928 +I0616 05:38:34.498862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.555273 (* 1 = 0.555273 loss) +I0616 05:38:34.498867 9857 solver.cpp:258] Train net output #1: loss_cls = 1.57556 (* 1 = 1.57556 loss) +I0616 05:38:34.498872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.534729 (* 1 = 0.534729 loss) +I0616 05:38:34.498875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.173784 (* 1 = 0.173784 loss) +I0616 05:38:34.498878 9857 solver.cpp:571] Iteration 19040, lr = 0.001 +I0616 05:38:46.158695 9857 solver.cpp:242] Iteration 19060, loss = 1.07193 +I0616 05:38:46.158737 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.375043 (* 1 = 0.375043 loss) +I0616 05:38:46.158742 9857 solver.cpp:258] Train net output #1: loss_cls = 0.520131 (* 1 = 0.520131 loss) +I0616 05:38:46.158747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.223925 (* 1 = 0.223925 loss) +I0616 05:38:46.158751 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.260176 (* 1 = 0.260176 loss) +I0616 05:38:46.158754 9857 solver.cpp:571] Iteration 19060, lr = 0.001 +I0616 05:38:57.884846 9857 solver.cpp:242] Iteration 19080, loss = 0.703704 +I0616 05:38:57.884887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192785 (* 1 = 0.192785 loss) +I0616 05:38:57.884892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274855 (* 1 = 0.274855 loss) +I0616 05:38:57.884896 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0658733 (* 1 = 0.0658733 loss) +I0616 05:38:57.884901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0314282 (* 1 = 0.0314282 loss) +I0616 05:38:57.884904 9857 solver.cpp:571] Iteration 19080, lr = 0.001 +I0616 05:39:09.398555 9857 solver.cpp:242] Iteration 19100, loss = 0.93662 +I0616 05:39:09.398597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182884 (* 1 = 0.182884 loss) +I0616 05:39:09.398603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20102 (* 1 = 0.20102 loss) +I0616 05:39:09.398608 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0799253 (* 1 = 0.0799253 loss) +I0616 05:39:09.398612 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253261 (* 1 = 0.0253261 loss) +I0616 05:39:09.398615 9857 solver.cpp:571] Iteration 19100, lr = 0.001 +I0616 05:39:20.963644 9857 solver.cpp:242] Iteration 19120, loss = 0.629304 +I0616 05:39:20.963685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.317236 (* 1 = 0.317236 loss) +I0616 05:39:20.963690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.489823 (* 1 = 0.489823 loss) +I0616 05:39:20.963696 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0677929 (* 1 = 0.0677929 loss) +I0616 05:39:20.963698 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0346866 (* 1 = 0.0346866 loss) +I0616 05:39:20.963702 9857 solver.cpp:571] Iteration 19120, lr = 0.001 +I0616 05:39:32.647948 9857 solver.cpp:242] Iteration 19140, loss = 1.1699 +I0616 05:39:32.647986 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182619 (* 1 = 0.182619 loss) +I0616 05:39:32.647992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231967 (* 1 = 0.231967 loss) +I0616 05:39:32.647996 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184566 (* 1 = 0.184566 loss) +I0616 05:39:32.648000 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020815 (* 1 = 0.020815 loss) +I0616 05:39:32.648005 9857 solver.cpp:571] Iteration 19140, lr = 0.001 +I0616 05:39:43.803834 9857 solver.cpp:242] Iteration 19160, loss = 0.711994 +I0616 05:39:43.803876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190008 (* 1 = 0.190008 loss) +I0616 05:39:43.803881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204899 (* 1 = 0.204899 loss) +I0616 05:39:43.803885 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124153 (* 1 = 0.124153 loss) +I0616 05:39:43.803889 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122207 (* 1 = 0.0122207 loss) +I0616 05:39:43.803894 9857 solver.cpp:571] Iteration 19160, lr = 0.001 +I0616 05:39:55.322970 9857 solver.cpp:242] Iteration 19180, loss = 1.51089 +I0616 05:39:55.323012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.454188 (* 1 = 0.454188 loss) +I0616 05:39:55.323019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.792365 (* 1 = 0.792365 loss) +I0616 05:39:55.323022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.318501 (* 1 = 0.318501 loss) +I0616 05:39:55.323025 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.587836 (* 1 = 0.587836 loss) +I0616 05:39:55.323029 9857 solver.cpp:571] Iteration 19180, lr = 0.001 +speed: 0.669s / iter +I0616 05:40:06.743144 9857 solver.cpp:242] Iteration 19200, loss = 0.60471 +I0616 05:40:06.743185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191804 (* 1 = 0.191804 loss) +I0616 05:40:06.743191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.422757 (* 1 = 0.422757 loss) +I0616 05:40:06.743196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100919 (* 1 = 0.100919 loss) +I0616 05:40:06.743198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0754871 (* 1 = 0.0754871 loss) +I0616 05:40:06.743202 9857 solver.cpp:571] Iteration 19200, lr = 0.001 +I0616 05:40:18.365613 9857 solver.cpp:242] Iteration 19220, loss = 1.37632 +I0616 05:40:18.365669 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.377373 (* 1 = 0.377373 loss) +I0616 05:40:18.365675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.410973 (* 1 = 0.410973 loss) +I0616 05:40:18.365679 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.334565 (* 1 = 0.334565 loss) +I0616 05:40:18.365684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.482706 (* 1 = 0.482706 loss) +I0616 05:40:18.365687 9857 solver.cpp:571] Iteration 19220, lr = 0.001 +I0616 05:40:29.830025 9857 solver.cpp:242] Iteration 19240, loss = 0.642227 +I0616 05:40:29.830068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133092 (* 1 = 0.133092 loss) +I0616 05:40:29.830075 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300969 (* 1 = 0.300969 loss) +I0616 05:40:29.830080 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0859701 (* 1 = 0.0859701 loss) +I0616 05:40:29.830082 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00678591 (* 1 = 0.00678591 loss) +I0616 05:40:29.830086 9857 solver.cpp:571] Iteration 19240, lr = 0.001 +I0616 05:40:41.340873 9857 solver.cpp:242] Iteration 19260, loss = 0.614594 +I0616 05:40:41.340914 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367111 (* 1 = 0.367111 loss) +I0616 05:40:41.340919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.434149 (* 1 = 0.434149 loss) +I0616 05:40:41.340924 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107108 (* 1 = 0.107108 loss) +I0616 05:40:41.340927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00649642 (* 1 = 0.00649642 loss) +I0616 05:40:41.340931 9857 solver.cpp:571] Iteration 19260, lr = 0.001 +I0616 05:40:53.087628 9857 solver.cpp:242] Iteration 19280, loss = 1.00506 +I0616 05:40:53.087671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.457805 (* 1 = 0.457805 loss) +I0616 05:40:53.087676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31418 (* 1 = 0.31418 loss) +I0616 05:40:53.087679 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0983556 (* 1 = 0.0983556 loss) +I0616 05:40:53.087683 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0303238 (* 1 = 0.0303238 loss) +I0616 05:40:53.087687 9857 solver.cpp:571] Iteration 19280, lr = 0.001 +I0616 05:41:04.864055 9857 solver.cpp:242] Iteration 19300, loss = 0.870444 +I0616 05:41:04.864097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276343 (* 1 = 0.276343 loss) +I0616 05:41:04.864102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255223 (* 1 = 0.255223 loss) +I0616 05:41:04.864106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.353661 (* 1 = 0.353661 loss) +I0616 05:41:04.864110 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108959 (* 1 = 0.108959 loss) +I0616 05:41:04.864114 9857 solver.cpp:571] Iteration 19300, lr = 0.001 +I0616 05:41:16.282255 9857 solver.cpp:242] Iteration 19320, loss = 0.982091 +I0616 05:41:16.282294 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488415 (* 1 = 0.488415 loss) +I0616 05:41:16.282299 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413516 (* 1 = 0.413516 loss) +I0616 05:41:16.282302 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165246 (* 1 = 0.165246 loss) +I0616 05:41:16.282306 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0262188 (* 1 = 0.0262188 loss) +I0616 05:41:16.282310 9857 solver.cpp:571] Iteration 19320, lr = 0.001 +I0616 05:41:27.739792 9857 solver.cpp:242] Iteration 19340, loss = 2.05207 +I0616 05:41:27.739835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409824 (* 1 = 0.409824 loss) +I0616 05:41:27.739840 9857 solver.cpp:258] Train net output #1: loss_cls = 1.11539 (* 1 = 1.11539 loss) +I0616 05:41:27.739845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126877 (* 1 = 0.126877 loss) +I0616 05:41:27.739848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169968 (* 1 = 0.0169968 loss) +I0616 05:41:27.739852 9857 solver.cpp:571] Iteration 19340, lr = 0.001 +I0616 05:41:39.231843 9857 solver.cpp:242] Iteration 19360, loss = 0.798705 +I0616 05:41:39.231884 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154404 (* 1 = 0.154404 loss) +I0616 05:41:39.231890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.361662 (* 1 = 0.361662 loss) +I0616 05:41:39.231894 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0330159 (* 1 = 0.0330159 loss) +I0616 05:41:39.231899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159656 (* 1 = 0.0159656 loss) +I0616 05:41:39.231901 9857 solver.cpp:571] Iteration 19360, lr = 0.001 +I0616 05:41:50.471384 9857 solver.cpp:242] Iteration 19380, loss = 1.18367 +I0616 05:41:50.471422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173023 (* 1 = 0.173023 loss) +I0616 05:41:50.471427 9857 solver.cpp:258] Train net output #1: loss_cls = 0.659154 (* 1 = 0.659154 loss) +I0616 05:41:50.471432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105689 (* 1 = 0.105689 loss) +I0616 05:41:50.471436 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00722481 (* 1 = 0.00722481 loss) +I0616 05:41:50.471439 9857 solver.cpp:571] Iteration 19380, lr = 0.001 +speed: 0.668s / iter +I0616 05:42:02.175109 9857 solver.cpp:242] Iteration 19400, loss = 1.64911 +I0616 05:42:02.175151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.301492 (* 1 = 0.301492 loss) +I0616 05:42:02.175156 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295536 (* 1 = 0.295536 loss) +I0616 05:42:02.175161 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.418125 (* 1 = 0.418125 loss) +I0616 05:42:02.175164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0698407 (* 1 = 0.0698407 loss) +I0616 05:42:02.175168 9857 solver.cpp:571] Iteration 19400, lr = 0.001 +I0616 05:42:13.580704 9857 solver.cpp:242] Iteration 19420, loss = 1.38598 +I0616 05:42:13.580746 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0119582 (* 1 = 0.0119582 loss) +I0616 05:42:13.580752 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404019 (* 1 = 0.404019 loss) +I0616 05:42:13.580756 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.48757 (* 1 = 0.48757 loss) +I0616 05:42:13.580760 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.534799 (* 1 = 0.534799 loss) +I0616 05:42:13.580763 9857 solver.cpp:571] Iteration 19420, lr = 0.001 +I0616 05:42:24.973451 9857 solver.cpp:242] Iteration 19440, loss = 0.755916 +I0616 05:42:24.973492 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202934 (* 1 = 0.202934 loss) +I0616 05:42:24.973498 9857 solver.cpp:258] Train net output #1: loss_cls = 0.352024 (* 1 = 0.352024 loss) +I0616 05:42:24.973502 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0343058 (* 1 = 0.0343058 loss) +I0616 05:42:24.973506 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0953027 (* 1 = 0.0953027 loss) +I0616 05:42:24.973510 9857 solver.cpp:571] Iteration 19440, lr = 0.001 +I0616 05:42:36.831348 9857 solver.cpp:242] Iteration 19460, loss = 1.06875 +I0616 05:42:36.831388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207016 (* 1 = 0.207016 loss) +I0616 05:42:36.831393 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191827 (* 1 = 0.191827 loss) +I0616 05:42:36.831398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.070589 (* 1 = 0.070589 loss) +I0616 05:42:36.831401 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105996 (* 1 = 0.0105996 loss) +I0616 05:42:36.831405 9857 solver.cpp:571] Iteration 19460, lr = 0.001 +I0616 05:42:48.606664 9857 solver.cpp:242] Iteration 19480, loss = 0.897312 +I0616 05:42:48.606706 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164935 (* 1 = 0.164935 loss) +I0616 05:42:48.606711 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246819 (* 1 = 0.246819 loss) +I0616 05:42:48.606716 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0366723 (* 1 = 0.0366723 loss) +I0616 05:42:48.606719 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.021283 (* 1 = 0.021283 loss) +I0616 05:42:48.606724 9857 solver.cpp:571] Iteration 19480, lr = 0.001 +I0616 05:43:00.065486 9857 solver.cpp:242] Iteration 19500, loss = 1.68877 +I0616 05:43:00.065527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406281 (* 1 = 0.406281 loss) +I0616 05:43:00.065533 9857 solver.cpp:258] Train net output #1: loss_cls = 1.16914 (* 1 = 1.16914 loss) +I0616 05:43:00.065537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172742 (* 1 = 0.172742 loss) +I0616 05:43:00.065541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215091 (* 1 = 0.0215091 loss) +I0616 05:43:00.065546 9857 solver.cpp:571] Iteration 19500, lr = 0.001 +I0616 05:43:11.606534 9857 solver.cpp:242] Iteration 19520, loss = 1.15081 +I0616 05:43:11.606575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311599 (* 1 = 0.311599 loss) +I0616 05:43:11.606581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.557032 (* 1 = 0.557032 loss) +I0616 05:43:11.606585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.222865 (* 1 = 0.222865 loss) +I0616 05:43:11.606590 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.121327 (* 1 = 0.121327 loss) +I0616 05:43:11.606593 9857 solver.cpp:571] Iteration 19520, lr = 0.001 +I0616 05:43:23.189069 9857 solver.cpp:242] Iteration 19540, loss = 0.588691 +I0616 05:43:23.189111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10514 (* 1 = 0.10514 loss) +I0616 05:43:23.189117 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18076 (* 1 = 0.18076 loss) +I0616 05:43:23.189121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.350252 (* 1 = 0.350252 loss) +I0616 05:43:23.189126 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0959105 (* 1 = 0.0959105 loss) +I0616 05:43:23.189128 9857 solver.cpp:571] Iteration 19540, lr = 0.001 +I0616 05:43:34.735649 9857 solver.cpp:242] Iteration 19560, loss = 0.742914 +I0616 05:43:34.735692 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337101 (* 1 = 0.337101 loss) +I0616 05:43:34.735697 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416922 (* 1 = 0.416922 loss) +I0616 05:43:34.735702 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.255216 (* 1 = 0.255216 loss) +I0616 05:43:34.735705 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.083905 (* 1 = 0.083905 loss) +I0616 05:43:34.735709 9857 solver.cpp:571] Iteration 19560, lr = 0.001 +I0616 05:43:46.531167 9857 solver.cpp:242] Iteration 19580, loss = 1.2893 +I0616 05:43:46.531194 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373561 (* 1 = 0.373561 loss) +I0616 05:43:46.531200 9857 solver.cpp:258] Train net output #1: loss_cls = 0.892162 (* 1 = 0.892162 loss) +I0616 05:43:46.531204 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.500535 (* 1 = 0.500535 loss) +I0616 05:43:46.531208 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.218713 (* 1 = 0.218713 loss) +I0616 05:43:46.531213 9857 solver.cpp:571] Iteration 19580, lr = 0.001 +speed: 0.668s / iter +I0616 05:43:58.196527 9857 solver.cpp:242] Iteration 19600, loss = 1.1518 +I0616 05:43:58.196566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279288 (* 1 = 0.279288 loss) +I0616 05:43:58.196571 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288622 (* 1 = 0.288622 loss) +I0616 05:43:58.196575 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227741 (* 1 = 0.227741 loss) +I0616 05:43:58.196579 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0672318 (* 1 = 0.0672318 loss) +I0616 05:43:58.196583 9857 solver.cpp:571] Iteration 19600, lr = 0.001 +I0616 05:44:09.734170 9857 solver.cpp:242] Iteration 19620, loss = 0.971603 +I0616 05:44:09.734212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.431008 (* 1 = 0.431008 loss) +I0616 05:44:09.734217 9857 solver.cpp:258] Train net output #1: loss_cls = 0.662187 (* 1 = 0.662187 loss) +I0616 05:44:09.734221 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143466 (* 1 = 0.143466 loss) +I0616 05:44:09.734225 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0387838 (* 1 = 0.0387838 loss) +I0616 05:44:09.734228 9857 solver.cpp:571] Iteration 19620, lr = 0.001 +I0616 05:44:21.225556 9857 solver.cpp:242] Iteration 19640, loss = 0.904229 +I0616 05:44:21.225597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336044 (* 1 = 0.336044 loss) +I0616 05:44:21.225603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562309 (* 1 = 0.562309 loss) +I0616 05:44:21.225608 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0795115 (* 1 = 0.0795115 loss) +I0616 05:44:21.225611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245232 (* 1 = 0.0245232 loss) +I0616 05:44:21.225615 9857 solver.cpp:571] Iteration 19640, lr = 0.001 +I0616 05:44:32.740398 9857 solver.cpp:242] Iteration 19660, loss = 0.867115 +I0616 05:44:32.740442 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200991 (* 1 = 0.200991 loss) +I0616 05:44:32.740447 9857 solver.cpp:258] Train net output #1: loss_cls = 0.410884 (* 1 = 0.410884 loss) +I0616 05:44:32.740452 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0542258 (* 1 = 0.0542258 loss) +I0616 05:44:32.740456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246007 (* 1 = 0.0246007 loss) +I0616 05:44:32.740463 9857 solver.cpp:571] Iteration 19660, lr = 0.001 +I0616 05:44:44.438318 9857 solver.cpp:242] Iteration 19680, loss = 0.803362 +I0616 05:44:44.438360 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201193 (* 1 = 0.201193 loss) +I0616 05:44:44.438365 9857 solver.cpp:258] Train net output #1: loss_cls = 0.509178 (* 1 = 0.509178 loss) +I0616 05:44:44.438369 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168665 (* 1 = 0.168665 loss) +I0616 05:44:44.438374 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0998677 (* 1 = 0.0998677 loss) +I0616 05:44:44.438376 9857 solver.cpp:571] Iteration 19680, lr = 0.001 +I0616 05:44:55.943845 9857 solver.cpp:242] Iteration 19700, loss = 0.979589 +I0616 05:44:55.943888 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283476 (* 1 = 0.283476 loss) +I0616 05:44:55.943893 9857 solver.cpp:258] Train net output #1: loss_cls = 0.458613 (* 1 = 0.458613 loss) +I0616 05:44:55.943898 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138425 (* 1 = 0.138425 loss) +I0616 05:44:55.943902 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0427798 (* 1 = 0.0427798 loss) +I0616 05:44:55.943905 9857 solver.cpp:571] Iteration 19700, lr = 0.001 +I0616 05:45:07.352571 9857 solver.cpp:242] Iteration 19720, loss = 0.976345 +I0616 05:45:07.352614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409839 (* 1 = 0.409839 loss) +I0616 05:45:07.352619 9857 solver.cpp:258] Train net output #1: loss_cls = 0.820171 (* 1 = 0.820171 loss) +I0616 05:45:07.352623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0795688 (* 1 = 0.0795688 loss) +I0616 05:45:07.352627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0287059 (* 1 = 0.0287059 loss) +I0616 05:45:07.352632 9857 solver.cpp:571] Iteration 19720, lr = 0.001 +I0616 05:45:19.235801 9857 solver.cpp:242] Iteration 19740, loss = 0.520782 +I0616 05:45:19.235844 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125347 (* 1 = 0.125347 loss) +I0616 05:45:19.235849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123912 (* 1 = 0.123912 loss) +I0616 05:45:19.235853 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0631108 (* 1 = 0.0631108 loss) +I0616 05:45:19.235857 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00977944 (* 1 = 0.00977944 loss) +I0616 05:45:19.235862 9857 solver.cpp:571] Iteration 19740, lr = 0.001 +I0616 05:45:30.877288 9857 solver.cpp:242] Iteration 19760, loss = 0.903262 +I0616 05:45:30.877331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355842 (* 1 = 0.355842 loss) +I0616 05:45:30.877336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.540724 (* 1 = 0.540724 loss) +I0616 05:45:30.877341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.047865 (* 1 = 0.047865 loss) +I0616 05:45:30.877344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0096715 (* 1 = 0.0096715 loss) +I0616 05:45:30.877348 9857 solver.cpp:571] Iteration 19760, lr = 0.001 +I0616 05:45:42.294483 9857 solver.cpp:242] Iteration 19780, loss = 0.968276 +I0616 05:45:42.294524 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235436 (* 1 = 0.235436 loss) +I0616 05:45:42.294529 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359621 (* 1 = 0.359621 loss) +I0616 05:45:42.294534 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0901777 (* 1 = 0.0901777 loss) +I0616 05:45:42.294538 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0318166 (* 1 = 0.0318166 loss) +I0616 05:45:42.294541 9857 solver.cpp:571] Iteration 19780, lr = 0.001 +speed: 0.667s / iter +I0616 05:45:53.732396 9857 solver.cpp:242] Iteration 19800, loss = 0.936954 +I0616 05:45:53.732439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297602 (* 1 = 0.297602 loss) +I0616 05:45:53.732445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.507476 (* 1 = 0.507476 loss) +I0616 05:45:53.732448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.271186 (* 1 = 0.271186 loss) +I0616 05:45:53.732452 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108162 (* 1 = 0.108162 loss) +I0616 05:45:53.732456 9857 solver.cpp:571] Iteration 19800, lr = 0.001 +I0616 05:46:05.291380 9857 solver.cpp:242] Iteration 19820, loss = 1.09017 +I0616 05:46:05.291422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.381237 (* 1 = 0.381237 loss) +I0616 05:46:05.291427 9857 solver.cpp:258] Train net output #1: loss_cls = 0.339634 (* 1 = 0.339634 loss) +I0616 05:46:05.291432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167017 (* 1 = 0.167017 loss) +I0616 05:46:05.291436 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.022765 (* 1 = 0.022765 loss) +I0616 05:46:05.291440 9857 solver.cpp:571] Iteration 19820, lr = 0.001 +I0616 05:46:16.862826 9857 solver.cpp:242] Iteration 19840, loss = 0.91919 +I0616 05:46:16.862867 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172562 (* 1 = 0.172562 loss) +I0616 05:46:16.862874 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298103 (* 1 = 0.298103 loss) +I0616 05:46:16.862877 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.288205 (* 1 = 0.288205 loss) +I0616 05:46:16.862881 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0335892 (* 1 = 0.0335892 loss) +I0616 05:46:16.862885 9857 solver.cpp:571] Iteration 19840, lr = 0.001 +I0616 05:46:28.001363 9857 solver.cpp:242] Iteration 19860, loss = 2.08332 +I0616 05:46:28.001405 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.512722 (* 1 = 0.512722 loss) +I0616 05:46:28.001411 9857 solver.cpp:258] Train net output #1: loss_cls = 1.23936 (* 1 = 1.23936 loss) +I0616 05:46:28.001415 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.293012 (* 1 = 0.293012 loss) +I0616 05:46:28.001420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.147687 (* 1 = 0.147687 loss) +I0616 05:46:28.001422 9857 solver.cpp:571] Iteration 19860, lr = 0.001 +I0616 05:46:39.446280 9857 solver.cpp:242] Iteration 19880, loss = 1.26029 +I0616 05:46:39.446322 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.496678 (* 1 = 0.496678 loss) +I0616 05:46:39.446327 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00717 (* 1 = 1.00717 loss) +I0616 05:46:39.446332 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145793 (* 1 = 0.145793 loss) +I0616 05:46:39.446337 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205729 (* 1 = 0.0205729 loss) +I0616 05:46:39.446343 9857 solver.cpp:571] Iteration 19880, lr = 0.001 +I0616 05:46:50.628687 9857 solver.cpp:242] Iteration 19900, loss = 1.78031 +I0616 05:46:50.628731 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305065 (* 1 = 0.305065 loss) +I0616 05:46:50.628737 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416082 (* 1 = 0.416082 loss) +I0616 05:46:50.628741 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.420015 (* 1 = 0.420015 loss) +I0616 05:46:50.628746 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.46056 (* 1 = 0.46056 loss) +I0616 05:46:50.628749 9857 solver.cpp:571] Iteration 19900, lr = 0.001 +I0616 05:47:02.039460 9857 solver.cpp:242] Iteration 19920, loss = 0.846629 +I0616 05:47:02.039504 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227687 (* 1 = 0.227687 loss) +I0616 05:47:02.039508 9857 solver.cpp:258] Train net output #1: loss_cls = 0.412225 (* 1 = 0.412225 loss) +I0616 05:47:02.039512 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.30235 (* 1 = 0.30235 loss) +I0616 05:47:02.039516 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0823492 (* 1 = 0.0823492 loss) +I0616 05:47:02.039520 9857 solver.cpp:571] Iteration 19920, lr = 0.001 +I0616 05:47:13.480655 9857 solver.cpp:242] Iteration 19940, loss = 1.53493 +I0616 05:47:13.480698 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168597 (* 1 = 0.168597 loss) +I0616 05:47:13.480703 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45553 (* 1 = 0.45553 loss) +I0616 05:47:13.480708 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108737 (* 1 = 0.108737 loss) +I0616 05:47:13.480712 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0261789 (* 1 = 0.0261789 loss) +I0616 05:47:13.480716 9857 solver.cpp:571] Iteration 19940, lr = 0.001 +I0616 05:47:25.014751 9857 solver.cpp:242] Iteration 19960, loss = 0.900917 +I0616 05:47:25.014823 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307748 (* 1 = 0.307748 loss) +I0616 05:47:25.014830 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378077 (* 1 = 0.378077 loss) +I0616 05:47:25.014834 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0471592 (* 1 = 0.0471592 loss) +I0616 05:47:25.014854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0937417 (* 1 = 0.0937417 loss) +I0616 05:47:25.014858 9857 solver.cpp:571] Iteration 19960, lr = 0.001 +I0616 05:47:36.806614 9857 solver.cpp:242] Iteration 19980, loss = 0.692709 +I0616 05:47:36.806656 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.361311 (* 1 = 0.361311 loss) +I0616 05:47:36.806663 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372068 (* 1 = 0.372068 loss) +I0616 05:47:36.806666 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0708357 (* 1 = 0.0708357 loss) +I0616 05:47:36.806670 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325999 (* 1 = 0.0325999 loss) +I0616 05:47:36.806674 9857 solver.cpp:571] Iteration 19980, lr = 0.001 +speed: 0.666s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_20000.caffemodel +I0616 05:47:49.952669 9857 solver.cpp:242] Iteration 20000, loss = 1.11535 +I0616 05:47:49.952711 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.37835 (* 1 = 0.37835 loss) +I0616 05:47:49.952716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.424137 (* 1 = 0.424137 loss) +I0616 05:47:49.952720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.176888 (* 1 = 0.176888 loss) +I0616 05:47:49.952724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0635517 (* 1 = 0.0635517 loss) +I0616 05:47:49.952728 9857 solver.cpp:571] Iteration 20000, lr = 0.001 +I0616 05:48:01.389991 9857 solver.cpp:242] Iteration 20020, loss = 0.738044 +I0616 05:48:01.390034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284103 (* 1 = 0.284103 loss) +I0616 05:48:01.390039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279843 (* 1 = 0.279843 loss) +I0616 05:48:01.390043 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105276 (* 1 = 0.105276 loss) +I0616 05:48:01.390048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194929 (* 1 = 0.0194929 loss) +I0616 05:48:01.390051 9857 solver.cpp:571] Iteration 20020, lr = 0.001 +I0616 05:48:12.954710 9857 solver.cpp:242] Iteration 20040, loss = 0.955922 +I0616 05:48:12.954751 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.395117 (* 1 = 0.395117 loss) +I0616 05:48:12.954772 9857 solver.cpp:258] Train net output #1: loss_cls = 0.735295 (* 1 = 0.735295 loss) +I0616 05:48:12.954777 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0665599 (* 1 = 0.0665599 loss) +I0616 05:48:12.954782 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.246209 (* 1 = 0.246209 loss) +I0616 05:48:12.954785 9857 solver.cpp:571] Iteration 20040, lr = 0.001 +I0616 05:48:24.613394 9857 solver.cpp:242] Iteration 20060, loss = 1.35744 +I0616 05:48:24.613435 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.517072 (* 1 = 0.517072 loss) +I0616 05:48:24.613440 9857 solver.cpp:258] Train net output #1: loss_cls = 0.941697 (* 1 = 0.941697 loss) +I0616 05:48:24.613445 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178664 (* 1 = 0.178664 loss) +I0616 05:48:24.613448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0820164 (* 1 = 0.0820164 loss) +I0616 05:48:24.613452 9857 solver.cpp:571] Iteration 20060, lr = 0.001 +I0616 05:48:36.254719 9857 solver.cpp:242] Iteration 20080, loss = 0.794268 +I0616 05:48:36.254763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202943 (* 1 = 0.202943 loss) +I0616 05:48:36.254770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402136 (* 1 = 0.402136 loss) +I0616 05:48:36.254773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0645194 (* 1 = 0.0645194 loss) +I0616 05:48:36.254777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174969 (* 1 = 0.0174969 loss) +I0616 05:48:36.254781 9857 solver.cpp:571] Iteration 20080, lr = 0.001 +I0616 05:48:48.059923 9857 solver.cpp:242] Iteration 20100, loss = 0.800637 +I0616 05:48:48.059964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159877 (* 1 = 0.159877 loss) +I0616 05:48:48.059970 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208818 (* 1 = 0.208818 loss) +I0616 05:48:48.059974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0171739 (* 1 = 0.0171739 loss) +I0616 05:48:48.059978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153871 (* 1 = 0.0153871 loss) +I0616 05:48:48.059983 9857 solver.cpp:571] Iteration 20100, lr = 0.001 +I0616 05:48:59.680076 9857 solver.cpp:242] Iteration 20120, loss = 1.44222 +I0616 05:48:59.680119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0757905 (* 1 = 0.0757905 loss) +I0616 05:48:59.680124 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280939 (* 1 = 0.280939 loss) +I0616 05:48:59.680129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166717 (* 1 = 0.166717 loss) +I0616 05:48:59.680132 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0137925 (* 1 = 0.0137925 loss) +I0616 05:48:59.680136 9857 solver.cpp:571] Iteration 20120, lr = 0.001 +I0616 05:49:11.331485 9857 solver.cpp:242] Iteration 20140, loss = 0.863789 +I0616 05:49:11.331528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.510859 (* 1 = 0.510859 loss) +I0616 05:49:11.331533 9857 solver.cpp:258] Train net output #1: loss_cls = 0.573962 (* 1 = 0.573962 loss) +I0616 05:49:11.331537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154249 (* 1 = 0.154249 loss) +I0616 05:49:11.331542 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0518782 (* 1 = 0.0518782 loss) +I0616 05:49:11.331545 9857 solver.cpp:571] Iteration 20140, lr = 0.001 +I0616 05:49:22.958977 9857 solver.cpp:242] Iteration 20160, loss = 0.884814 +I0616 05:49:22.959019 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266629 (* 1 = 0.266629 loss) +I0616 05:49:22.959024 9857 solver.cpp:258] Train net output #1: loss_cls = 1.02233 (* 1 = 1.02233 loss) +I0616 05:49:22.959029 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0478597 (* 1 = 0.0478597 loss) +I0616 05:49:22.959033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0155071 (* 1 = 0.0155071 loss) +I0616 05:49:22.959036 9857 solver.cpp:571] Iteration 20160, lr = 0.001 +I0616 05:49:34.597748 9857 solver.cpp:242] Iteration 20180, loss = 1.74838 +I0616 05:49:34.597790 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267036 (* 1 = 0.267036 loss) +I0616 05:49:34.597795 9857 solver.cpp:258] Train net output #1: loss_cls = 1.08631 (* 1 = 1.08631 loss) +I0616 05:49:34.597800 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.352138 (* 1 = 0.352138 loss) +I0616 05:49:34.597803 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.873708 (* 1 = 0.873708 loss) +I0616 05:49:34.597807 9857 solver.cpp:571] Iteration 20180, lr = 0.001 +speed: 0.665s / iter +I0616 05:49:46.305058 9857 solver.cpp:242] Iteration 20200, loss = 0.962479 +I0616 05:49:46.305099 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.442059 (* 1 = 0.442059 loss) +I0616 05:49:46.305104 9857 solver.cpp:258] Train net output #1: loss_cls = 0.602805 (* 1 = 0.602805 loss) +I0616 05:49:46.305109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0773154 (* 1 = 0.0773154 loss) +I0616 05:49:46.305112 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0357011 (* 1 = 0.0357011 loss) +I0616 05:49:46.305116 9857 solver.cpp:571] Iteration 20200, lr = 0.001 +I0616 05:49:57.968518 9857 solver.cpp:242] Iteration 20220, loss = 1.35924 +I0616 05:49:57.968557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313622 (* 1 = 0.313622 loss) +I0616 05:49:57.968564 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305616 (* 1 = 0.305616 loss) +I0616 05:49:57.968567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102329 (* 1 = 0.102329 loss) +I0616 05:49:57.968571 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133867 (* 1 = 0.0133867 loss) +I0616 05:49:57.968575 9857 solver.cpp:571] Iteration 20220, lr = 0.001 +I0616 05:50:09.351220 9857 solver.cpp:242] Iteration 20240, loss = 0.404343 +I0616 05:50:09.351264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11023 (* 1 = 0.11023 loss) +I0616 05:50:09.351270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233049 (* 1 = 0.233049 loss) +I0616 05:50:09.351274 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0234588 (* 1 = 0.0234588 loss) +I0616 05:50:09.351279 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149628 (* 1 = 0.0149628 loss) +I0616 05:50:09.351282 9857 solver.cpp:571] Iteration 20240, lr = 0.001 +I0616 05:50:20.947124 9857 solver.cpp:242] Iteration 20260, loss = 0.571323 +I0616 05:50:20.947166 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103995 (* 1 = 0.103995 loss) +I0616 05:50:20.947172 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140893 (* 1 = 0.140893 loss) +I0616 05:50:20.947176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0346476 (* 1 = 0.0346476 loss) +I0616 05:50:20.947180 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0471498 (* 1 = 0.0471498 loss) +I0616 05:50:20.947183 9857 solver.cpp:571] Iteration 20260, lr = 0.001 +I0616 05:50:32.491156 9857 solver.cpp:242] Iteration 20280, loss = 1.78025 +I0616 05:50:32.491199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.542263 (* 1 = 0.542263 loss) +I0616 05:50:32.491204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.840249 (* 1 = 0.840249 loss) +I0616 05:50:32.491209 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.549931 (* 1 = 0.549931 loss) +I0616 05:50:32.491211 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.324266 (* 1 = 0.324266 loss) +I0616 05:50:32.491215 9857 solver.cpp:571] Iteration 20280, lr = 0.001 +I0616 05:50:44.045416 9857 solver.cpp:242] Iteration 20300, loss = 0.862697 +I0616 05:50:44.045459 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154917 (* 1 = 0.154917 loss) +I0616 05:50:44.045464 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0863858 (* 1 = 0.0863858 loss) +I0616 05:50:44.045469 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00899094 (* 1 = 0.00899094 loss) +I0616 05:50:44.045472 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000246845 (* 1 = 0.000246845 loss) +I0616 05:50:44.045476 9857 solver.cpp:571] Iteration 20300, lr = 0.001 +I0616 05:50:55.752969 9857 solver.cpp:242] Iteration 20320, loss = 1.45931 +I0616 05:50:55.753010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45149 (* 1 = 0.45149 loss) +I0616 05:50:55.753015 9857 solver.cpp:258] Train net output #1: loss_cls = 1.717 (* 1 = 1.717 loss) +I0616 05:50:55.753018 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.357542 (* 1 = 0.357542 loss) +I0616 05:50:55.753022 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407459 (* 1 = 0.0407459 loss) +I0616 05:50:55.753026 9857 solver.cpp:571] Iteration 20320, lr = 0.001 +I0616 05:51:07.319198 9857 solver.cpp:242] Iteration 20340, loss = 1.03138 +I0616 05:51:07.319238 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276055 (* 1 = 0.276055 loss) +I0616 05:51:07.319244 9857 solver.cpp:258] Train net output #1: loss_cls = 0.485417 (* 1 = 0.485417 loss) +I0616 05:51:07.319248 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.398953 (* 1 = 0.398953 loss) +I0616 05:51:07.319252 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0750636 (* 1 = 0.0750636 loss) +I0616 05:51:07.319255 9857 solver.cpp:571] Iteration 20340, lr = 0.001 +I0616 05:51:18.889878 9857 solver.cpp:242] Iteration 20360, loss = 0.662702 +I0616 05:51:18.889920 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329693 (* 1 = 0.329693 loss) +I0616 05:51:18.889926 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281512 (* 1 = 0.281512 loss) +I0616 05:51:18.889930 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0356821 (* 1 = 0.0356821 loss) +I0616 05:51:18.889935 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143056 (* 1 = 0.0143056 loss) +I0616 05:51:18.889938 9857 solver.cpp:571] Iteration 20360, lr = 0.001 +I0616 05:51:30.601819 9857 solver.cpp:242] Iteration 20380, loss = 0.740378 +I0616 05:51:30.601860 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109083 (* 1 = 0.109083 loss) +I0616 05:51:30.601866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209089 (* 1 = 0.209089 loss) +I0616 05:51:30.601869 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141387 (* 1 = 0.0141387 loss) +I0616 05:51:30.601872 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00236954 (* 1 = 0.00236954 loss) +I0616 05:51:30.601876 9857 solver.cpp:571] Iteration 20380, lr = 0.001 +speed: 0.664s / iter +I0616 05:51:41.897999 9857 solver.cpp:242] Iteration 20400, loss = 0.572094 +I0616 05:51:41.898041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101606 (* 1 = 0.101606 loss) +I0616 05:51:41.898046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156599 (* 1 = 0.156599 loss) +I0616 05:51:41.898049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0226762 (* 1 = 0.0226762 loss) +I0616 05:51:41.898053 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0738574 (* 1 = 0.0738574 loss) +I0616 05:51:41.898057 9857 solver.cpp:571] Iteration 20400, lr = 0.001 +I0616 05:51:53.235975 9857 solver.cpp:242] Iteration 20420, loss = 1.15727 +I0616 05:51:53.236016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396768 (* 1 = 0.396768 loss) +I0616 05:51:53.236022 9857 solver.cpp:258] Train net output #1: loss_cls = 0.490568 (* 1 = 0.490568 loss) +I0616 05:51:53.236027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.225314 (* 1 = 0.225314 loss) +I0616 05:51:53.236030 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0451689 (* 1 = 0.0451689 loss) +I0616 05:51:53.236034 9857 solver.cpp:571] Iteration 20420, lr = 0.001 +I0616 05:52:04.402865 9857 solver.cpp:242] Iteration 20440, loss = 0.526535 +I0616 05:52:04.402909 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168173 (* 1 = 0.168173 loss) +I0616 05:52:04.402914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208737 (* 1 = 0.208737 loss) +I0616 05:52:04.402918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0233637 (* 1 = 0.0233637 loss) +I0616 05:52:04.402921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00991269 (* 1 = 0.00991269 loss) +I0616 05:52:04.402925 9857 solver.cpp:571] Iteration 20440, lr = 0.001 +I0616 05:52:15.969635 9857 solver.cpp:242] Iteration 20460, loss = 1.42201 +I0616 05:52:15.969678 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0891178 (* 1 = 0.0891178 loss) +I0616 05:52:15.969684 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139641 (* 1 = 0.139641 loss) +I0616 05:52:15.969688 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0245048 (* 1 = 0.0245048 loss) +I0616 05:52:15.969692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00389793 (* 1 = 0.00389793 loss) +I0616 05:52:15.969696 9857 solver.cpp:571] Iteration 20460, lr = 0.001 +I0616 05:52:27.496810 9857 solver.cpp:242] Iteration 20480, loss = 0.935314 +I0616 05:52:27.496853 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.677244 (* 1 = 0.677244 loss) +I0616 05:52:27.496858 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342229 (* 1 = 0.342229 loss) +I0616 05:52:27.496862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0825795 (* 1 = 0.0825795 loss) +I0616 05:52:27.496866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167928 (* 1 = 0.0167928 loss) +I0616 05:52:27.496871 9857 solver.cpp:571] Iteration 20480, lr = 0.001 +I0616 05:52:39.065181 9857 solver.cpp:242] Iteration 20500, loss = 1.17171 +I0616 05:52:39.065222 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146472 (* 1 = 0.146472 loss) +I0616 05:52:39.065228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165726 (* 1 = 0.165726 loss) +I0616 05:52:39.065232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0126594 (* 1 = 0.0126594 loss) +I0616 05:52:39.065235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00816831 (* 1 = 0.00816831 loss) +I0616 05:52:39.065239 9857 solver.cpp:571] Iteration 20500, lr = 0.001 +I0616 05:52:50.744355 9857 solver.cpp:242] Iteration 20520, loss = 0.971729 +I0616 05:52:50.744397 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311355 (* 1 = 0.311355 loss) +I0616 05:52:50.744402 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363354 (* 1 = 0.363354 loss) +I0616 05:52:50.744406 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168133 (* 1 = 0.168133 loss) +I0616 05:52:50.744410 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.12959 (* 1 = 0.12959 loss) +I0616 05:52:50.744415 9857 solver.cpp:571] Iteration 20520, lr = 0.001 +I0616 05:53:02.314028 9857 solver.cpp:242] Iteration 20540, loss = 0.818174 +I0616 05:53:02.314071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.096366 (* 1 = 0.096366 loss) +I0616 05:53:02.314076 9857 solver.cpp:258] Train net output #1: loss_cls = 0.261081 (* 1 = 0.261081 loss) +I0616 05:53:02.314081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0354858 (* 1 = 0.0354858 loss) +I0616 05:53:02.314085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0757757 (* 1 = 0.0757757 loss) +I0616 05:53:02.314088 9857 solver.cpp:571] Iteration 20540, lr = 0.001 +I0616 05:53:13.796893 9857 solver.cpp:242] Iteration 20560, loss = 0.980157 +I0616 05:53:13.796936 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.604186 (* 1 = 0.604186 loss) +I0616 05:53:13.796942 9857 solver.cpp:258] Train net output #1: loss_cls = 0.412554 (* 1 = 0.412554 loss) +I0616 05:53:13.796947 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136737 (* 1 = 0.136737 loss) +I0616 05:53:13.796949 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0745455 (* 1 = 0.0745455 loss) +I0616 05:53:13.796953 9857 solver.cpp:571] Iteration 20560, lr = 0.001 +I0616 05:53:25.562480 9857 solver.cpp:242] Iteration 20580, loss = 0.57232 +I0616 05:53:25.562523 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202613 (* 1 = 0.202613 loss) +I0616 05:53:25.562528 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351782 (* 1 = 0.351782 loss) +I0616 05:53:25.562532 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0876575 (* 1 = 0.0876575 loss) +I0616 05:53:25.562536 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170101 (* 1 = 0.0170101 loss) +I0616 05:53:25.562539 9857 solver.cpp:571] Iteration 20580, lr = 0.001 +speed: 0.663s / iter +I0616 05:53:37.137413 9857 solver.cpp:242] Iteration 20600, loss = 0.61553 +I0616 05:53:37.137455 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0703442 (* 1 = 0.0703442 loss) +I0616 05:53:37.137461 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122543 (* 1 = 0.122543 loss) +I0616 05:53:37.137465 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0241205 (* 1 = 0.0241205 loss) +I0616 05:53:37.137468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01133 (* 1 = 0.01133 loss) +I0616 05:53:37.137472 9857 solver.cpp:571] Iteration 20600, lr = 0.001 +I0616 05:53:48.765794 9857 solver.cpp:242] Iteration 20620, loss = 0.641218 +I0616 05:53:48.765836 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238016 (* 1 = 0.238016 loss) +I0616 05:53:48.765841 9857 solver.cpp:258] Train net output #1: loss_cls = 0.543657 (* 1 = 0.543657 loss) +I0616 05:53:48.765846 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10106 (* 1 = 0.10106 loss) +I0616 05:53:48.765848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247066 (* 1 = 0.0247066 loss) +I0616 05:53:48.765852 9857 solver.cpp:571] Iteration 20620, lr = 0.001 +I0616 05:54:00.307574 9857 solver.cpp:242] Iteration 20640, loss = 1.14279 +I0616 05:54:00.307616 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.423515 (* 1 = 0.423515 loss) +I0616 05:54:00.307622 9857 solver.cpp:258] Train net output #1: loss_cls = 0.492398 (* 1 = 0.492398 loss) +I0616 05:54:00.307626 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.239299 (* 1 = 0.239299 loss) +I0616 05:54:00.307631 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.393923 (* 1 = 0.393923 loss) +I0616 05:54:00.307634 9857 solver.cpp:571] Iteration 20640, lr = 0.001 +I0616 05:54:11.691223 9857 solver.cpp:242] Iteration 20660, loss = 1.61219 +I0616 05:54:11.691267 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396984 (* 1 = 0.396984 loss) +I0616 05:54:11.691272 9857 solver.cpp:258] Train net output #1: loss_cls = 0.67532 (* 1 = 0.67532 loss) +I0616 05:54:11.691277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0842651 (* 1 = 0.0842651 loss) +I0616 05:54:11.691279 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161923 (* 1 = 0.0161923 loss) +I0616 05:54:11.691283 9857 solver.cpp:571] Iteration 20660, lr = 0.001 +I0616 05:54:23.433238 9857 solver.cpp:242] Iteration 20680, loss = 0.906234 +I0616 05:54:23.433264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276195 (* 1 = 0.276195 loss) +I0616 05:54:23.433284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.424981 (* 1 = 0.424981 loss) +I0616 05:54:23.433289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120273 (* 1 = 0.120273 loss) +I0616 05:54:23.433291 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020464 (* 1 = 0.020464 loss) +I0616 05:54:23.433295 9857 solver.cpp:571] Iteration 20680, lr = 0.001 +I0616 05:54:34.823395 9857 solver.cpp:242] Iteration 20700, loss = 0.562356 +I0616 05:54:34.823437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128731 (* 1 = 0.128731 loss) +I0616 05:54:34.823442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161434 (* 1 = 0.161434 loss) +I0616 05:54:34.823447 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00651908 (* 1 = 0.00651908 loss) +I0616 05:54:34.823451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00853615 (* 1 = 0.00853615 loss) +I0616 05:54:34.823454 9857 solver.cpp:571] Iteration 20700, lr = 0.001 +I0616 05:54:46.254127 9857 solver.cpp:242] Iteration 20720, loss = 1.46449 +I0616 05:54:46.254168 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.598095 (* 1 = 0.598095 loss) +I0616 05:54:46.254173 9857 solver.cpp:258] Train net output #1: loss_cls = 1.52105 (* 1 = 1.52105 loss) +I0616 05:54:46.254178 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.337708 (* 1 = 0.337708 loss) +I0616 05:54:46.254181 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0541638 (* 1 = 0.0541638 loss) +I0616 05:54:46.254185 9857 solver.cpp:571] Iteration 20720, lr = 0.001 +I0616 05:54:57.434391 9857 solver.cpp:242] Iteration 20740, loss = 0.327583 +I0616 05:54:57.434433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0983258 (* 1 = 0.0983258 loss) +I0616 05:54:57.434439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119143 (* 1 = 0.119143 loss) +I0616 05:54:57.434443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212141 (* 1 = 0.0212141 loss) +I0616 05:54:57.434448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011894 (* 1 = 0.011894 loss) +I0616 05:54:57.434451 9857 solver.cpp:571] Iteration 20740, lr = 0.001 +I0616 05:55:09.192836 9857 solver.cpp:242] Iteration 20760, loss = 0.995387 +I0616 05:55:09.192878 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.460811 (* 1 = 0.460811 loss) +I0616 05:55:09.192884 9857 solver.cpp:258] Train net output #1: loss_cls = 0.746473 (* 1 = 0.746473 loss) +I0616 05:55:09.192888 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220761 (* 1 = 0.220761 loss) +I0616 05:55:09.192893 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194038 (* 1 = 0.0194038 loss) +I0616 05:55:09.192896 9857 solver.cpp:571] Iteration 20760, lr = 0.001 +I0616 05:55:20.860930 9857 solver.cpp:242] Iteration 20780, loss = 0.412071 +I0616 05:55:20.860970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0957227 (* 1 = 0.0957227 loss) +I0616 05:55:20.860976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166194 (* 1 = 0.166194 loss) +I0616 05:55:20.860980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163869 (* 1 = 0.0163869 loss) +I0616 05:55:20.860985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00563699 (* 1 = 0.00563699 loss) +I0616 05:55:20.860988 9857 solver.cpp:571] Iteration 20780, lr = 0.001 +speed: 0.662s / iter +I0616 05:55:32.598871 9857 solver.cpp:242] Iteration 20800, loss = 0.922218 +I0616 05:55:32.598913 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287488 (* 1 = 0.287488 loss) +I0616 05:55:32.598918 9857 solver.cpp:258] Train net output #1: loss_cls = 0.33295 (* 1 = 0.33295 loss) +I0616 05:55:32.598922 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0677021 (* 1 = 0.0677021 loss) +I0616 05:55:32.598927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0302724 (* 1 = 0.0302724 loss) +I0616 05:55:32.598930 9857 solver.cpp:571] Iteration 20800, lr = 0.001 +I0616 05:55:44.329803 9857 solver.cpp:242] Iteration 20820, loss = 0.623751 +I0616 05:55:44.329843 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110059 (* 1 = 0.110059 loss) +I0616 05:55:44.329849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.329906 (* 1 = 0.329906 loss) +I0616 05:55:44.329854 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111247 (* 1 = 0.111247 loss) +I0616 05:55:44.329856 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127901 (* 1 = 0.0127901 loss) +I0616 05:55:44.329860 9857 solver.cpp:571] Iteration 20820, lr = 0.001 +I0616 05:55:56.007778 9857 solver.cpp:242] Iteration 20840, loss = 1.48922 +I0616 05:55:56.007820 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411812 (* 1 = 0.411812 loss) +I0616 05:55:56.007827 9857 solver.cpp:258] Train net output #1: loss_cls = 0.60885 (* 1 = 0.60885 loss) +I0616 05:55:56.007829 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.53687 (* 1 = 0.53687 loss) +I0616 05:55:56.007833 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.364007 (* 1 = 0.364007 loss) +I0616 05:55:56.007838 9857 solver.cpp:571] Iteration 20840, lr = 0.001 +I0616 05:56:07.537911 9857 solver.cpp:242] Iteration 20860, loss = 0.589082 +I0616 05:56:07.537953 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0575439 (* 1 = 0.0575439 loss) +I0616 05:56:07.537960 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207166 (* 1 = 0.207166 loss) +I0616 05:56:07.537963 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0738781 (* 1 = 0.0738781 loss) +I0616 05:56:07.537966 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00280237 (* 1 = 0.00280237 loss) +I0616 05:56:07.537971 9857 solver.cpp:571] Iteration 20860, lr = 0.001 +I0616 05:56:19.176110 9857 solver.cpp:242] Iteration 20880, loss = 0.970822 +I0616 05:56:19.176151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270056 (* 1 = 0.270056 loss) +I0616 05:56:19.176156 9857 solver.cpp:258] Train net output #1: loss_cls = 0.866231 (* 1 = 0.866231 loss) +I0616 05:56:19.176161 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0628635 (* 1 = 0.0628635 loss) +I0616 05:56:19.176164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139189 (* 1 = 0.0139189 loss) +I0616 05:56:19.176168 9857 solver.cpp:571] Iteration 20880, lr = 0.001 +I0616 05:56:30.766230 9857 solver.cpp:242] Iteration 20900, loss = 0.623329 +I0616 05:56:30.766271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360371 (* 1 = 0.360371 loss) +I0616 05:56:30.766278 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309663 (* 1 = 0.309663 loss) +I0616 05:56:30.766281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0688692 (* 1 = 0.0688692 loss) +I0616 05:56:30.766285 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111253 (* 1 = 0.111253 loss) +I0616 05:56:30.766289 9857 solver.cpp:571] Iteration 20900, lr = 0.001 +I0616 05:56:42.284453 9857 solver.cpp:242] Iteration 20920, loss = 0.770028 +I0616 05:56:42.284497 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399021 (* 1 = 0.399021 loss) +I0616 05:56:42.284502 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416657 (* 1 = 0.416657 loss) +I0616 05:56:42.284507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146314 (* 1 = 0.146314 loss) +I0616 05:56:42.284510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0243248 (* 1 = 0.0243248 loss) +I0616 05:56:42.284513 9857 solver.cpp:571] Iteration 20920, lr = 0.001 +I0616 05:56:53.848256 9857 solver.cpp:242] Iteration 20940, loss = 1.34697 +I0616 05:56:53.848299 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0886617 (* 1 = 0.0886617 loss) +I0616 05:56:53.848304 9857 solver.cpp:258] Train net output #1: loss_cls = 0.297613 (* 1 = 0.297613 loss) +I0616 05:56:53.848309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.722223 (* 1 = 0.722223 loss) +I0616 05:56:53.848311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.656049 (* 1 = 0.656049 loss) +I0616 05:56:53.848316 9857 solver.cpp:571] Iteration 20940, lr = 0.001 +I0616 05:57:05.206589 9857 solver.cpp:242] Iteration 20960, loss = 1.14516 +I0616 05:57:05.206630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22954 (* 1 = 0.22954 loss) +I0616 05:57:05.206636 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559165 (* 1 = 0.559165 loss) +I0616 05:57:05.206640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.323093 (* 1 = 0.323093 loss) +I0616 05:57:05.206645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.379135 (* 1 = 0.379135 loss) +I0616 05:57:05.206648 9857 solver.cpp:571] Iteration 20960, lr = 0.001 +I0616 05:57:16.711807 9857 solver.cpp:242] Iteration 20980, loss = 0.802821 +I0616 05:57:16.711848 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12717 (* 1 = 0.12717 loss) +I0616 05:57:16.711853 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12997 (* 1 = 0.12997 loss) +I0616 05:57:16.711858 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0369689 (* 1 = 0.0369689 loss) +I0616 05:57:16.711861 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105369 (* 1 = 0.0105369 loss) +I0616 05:57:16.711865 9857 solver.cpp:571] Iteration 20980, lr = 0.001 +speed: 0.662s / iter +I0616 05:57:28.451576 9857 solver.cpp:242] Iteration 21000, loss = 1.74424 +I0616 05:57:28.451617 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292101 (* 1 = 0.292101 loss) +I0616 05:57:28.451623 9857 solver.cpp:258] Train net output #1: loss_cls = 0.751104 (* 1 = 0.751104 loss) +I0616 05:57:28.451627 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130029 (* 1 = 0.130029 loss) +I0616 05:57:28.451632 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0611199 (* 1 = 0.0611199 loss) +I0616 05:57:28.451635 9857 solver.cpp:571] Iteration 21000, lr = 0.001 +I0616 05:57:40.525698 9857 solver.cpp:242] Iteration 21020, loss = 0.598627 +I0616 05:57:40.525739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188246 (* 1 = 0.188246 loss) +I0616 05:57:40.525745 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257983 (* 1 = 0.257983 loss) +I0616 05:57:40.525749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0879611 (* 1 = 0.0879611 loss) +I0616 05:57:40.525753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0299507 (* 1 = 0.0299507 loss) +I0616 05:57:40.525758 9857 solver.cpp:571] Iteration 21020, lr = 0.001 +I0616 05:57:52.088673 9857 solver.cpp:242] Iteration 21040, loss = 0.725495 +I0616 05:57:52.088716 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0459873 (* 1 = 0.0459873 loss) +I0616 05:57:52.088721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271499 (* 1 = 0.271499 loss) +I0616 05:57:52.088726 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0614162 (* 1 = 0.0614162 loss) +I0616 05:57:52.088731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0462507 (* 1 = 0.0462507 loss) +I0616 05:57:52.088734 9857 solver.cpp:571] Iteration 21040, lr = 0.001 +I0616 05:58:03.473050 9857 solver.cpp:242] Iteration 21060, loss = 0.713486 +I0616 05:58:03.473093 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22102 (* 1 = 0.22102 loss) +I0616 05:58:03.473098 9857 solver.cpp:258] Train net output #1: loss_cls = 0.25318 (* 1 = 0.25318 loss) +I0616 05:58:03.473103 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0845928 (* 1 = 0.0845928 loss) +I0616 05:58:03.473106 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0374139 (* 1 = 0.0374139 loss) +I0616 05:58:03.473109 9857 solver.cpp:571] Iteration 21060, lr = 0.001 +I0616 05:58:15.102812 9857 solver.cpp:242] Iteration 21080, loss = 0.998764 +I0616 05:58:15.102851 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272644 (* 1 = 0.272644 loss) +I0616 05:58:15.102856 9857 solver.cpp:258] Train net output #1: loss_cls = 0.440245 (* 1 = 0.440245 loss) +I0616 05:58:15.102861 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.262567 (* 1 = 0.262567 loss) +I0616 05:58:15.102865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0343499 (* 1 = 0.0343499 loss) +I0616 05:58:15.102869 9857 solver.cpp:571] Iteration 21080, lr = 0.001 +I0616 05:58:26.696146 9857 solver.cpp:242] Iteration 21100, loss = 1.65154 +I0616 05:58:26.696188 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30497 (* 1 = 0.30497 loss) +I0616 05:58:26.696193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271402 (* 1 = 0.271402 loss) +I0616 05:58:26.696198 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.182782 (* 1 = 0.182782 loss) +I0616 05:58:26.696202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.05846 (* 1 = 1.05846 loss) +I0616 05:58:26.696205 9857 solver.cpp:571] Iteration 21100, lr = 0.001 +I0616 05:58:38.027799 9857 solver.cpp:242] Iteration 21120, loss = 0.987566 +I0616 05:58:38.027842 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.42056 (* 1 = 0.42056 loss) +I0616 05:58:38.027848 9857 solver.cpp:258] Train net output #1: loss_cls = 0.539578 (* 1 = 0.539578 loss) +I0616 05:58:38.027853 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.238518 (* 1 = 0.238518 loss) +I0616 05:58:38.027855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0784775 (* 1 = 0.0784775 loss) +I0616 05:58:38.027859 9857 solver.cpp:571] Iteration 21120, lr = 0.001 +I0616 05:58:49.458390 9857 solver.cpp:242] Iteration 21140, loss = 0.82735 +I0616 05:58:49.458432 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104882 (* 1 = 0.104882 loss) +I0616 05:58:49.458438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13292 (* 1 = 0.13292 loss) +I0616 05:58:49.458442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120671 (* 1 = 0.120671 loss) +I0616 05:58:49.458446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00837999 (* 1 = 0.00837999 loss) +I0616 05:58:49.458451 9857 solver.cpp:571] Iteration 21140, lr = 0.001 +I0616 05:59:01.064858 9857 solver.cpp:242] Iteration 21160, loss = 1.99449 +I0616 05:59:01.064900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399923 (* 1 = 0.399923 loss) +I0616 05:59:01.064905 9857 solver.cpp:258] Train net output #1: loss_cls = 0.491019 (* 1 = 0.491019 loss) +I0616 05:59:01.064909 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.375134 (* 1 = 0.375134 loss) +I0616 05:59:01.064913 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.125076 (* 1 = 0.125076 loss) +I0616 05:59:01.064918 9857 solver.cpp:571] Iteration 21160, lr = 0.001 +I0616 05:59:12.678270 9857 solver.cpp:242] Iteration 21180, loss = 0.542712 +I0616 05:59:12.678313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186949 (* 1 = 0.186949 loss) +I0616 05:59:12.678318 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165654 (* 1 = 0.165654 loss) +I0616 05:59:12.678323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176054 (* 1 = 0.0176054 loss) +I0616 05:59:12.678326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197518 (* 1 = 0.0197518 loss) +I0616 05:59:12.678329 9857 solver.cpp:571] Iteration 21180, lr = 0.001 +speed: 0.661s / iter +I0616 05:59:24.372321 9857 solver.cpp:242] Iteration 21200, loss = 1.13412 +I0616 05:59:24.372364 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.544531 (* 1 = 0.544531 loss) +I0616 05:59:24.372370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.935349 (* 1 = 0.935349 loss) +I0616 05:59:24.372373 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117378 (* 1 = 0.117378 loss) +I0616 05:59:24.372378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028781 (* 1 = 0.028781 loss) +I0616 05:59:24.372381 9857 solver.cpp:571] Iteration 21200, lr = 0.001 +I0616 05:59:35.898133 9857 solver.cpp:242] Iteration 21220, loss = 0.876439 +I0616 05:59:35.898172 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210421 (* 1 = 0.210421 loss) +I0616 05:59:35.898178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239833 (* 1 = 0.239833 loss) +I0616 05:59:35.898182 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0154991 (* 1 = 0.0154991 loss) +I0616 05:59:35.898186 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00368403 (* 1 = 0.00368403 loss) +I0616 05:59:35.898190 9857 solver.cpp:571] Iteration 21220, lr = 0.001 +I0616 05:59:47.421913 9857 solver.cpp:242] Iteration 21240, loss = 1.3605 +I0616 05:59:47.421953 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202789 (* 1 = 0.202789 loss) +I0616 05:59:47.421959 9857 solver.cpp:258] Train net output #1: loss_cls = 1.08769 (* 1 = 1.08769 loss) +I0616 05:59:47.421964 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200035 (* 1 = 0.200035 loss) +I0616 05:59:47.421968 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0293282 (* 1 = 0.0293282 loss) +I0616 05:59:47.421972 9857 solver.cpp:571] Iteration 21240, lr = 0.001 +I0616 05:59:59.120173 9857 solver.cpp:242] Iteration 21260, loss = 0.75359 +I0616 05:59:59.120214 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164994 (* 1 = 0.164994 loss) +I0616 05:59:59.120220 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191342 (* 1 = 0.191342 loss) +I0616 05:59:59.120224 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110786 (* 1 = 0.110786 loss) +I0616 05:59:59.120229 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202554 (* 1 = 0.0202554 loss) +I0616 05:59:59.120232 9857 solver.cpp:571] Iteration 21260, lr = 0.001 +I0616 06:00:10.374193 9857 solver.cpp:242] Iteration 21280, loss = 0.36628 +I0616 06:00:10.374234 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0654933 (* 1 = 0.0654933 loss) +I0616 06:00:10.374240 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164743 (* 1 = 0.164743 loss) +I0616 06:00:10.374244 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0272894 (* 1 = 0.0272894 loss) +I0616 06:00:10.374248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00322343 (* 1 = 0.00322343 loss) +I0616 06:00:10.374251 9857 solver.cpp:571] Iteration 21280, lr = 0.001 +I0616 06:00:21.832393 9857 solver.cpp:242] Iteration 21300, loss = 0.853374 +I0616 06:00:21.832435 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.471277 (* 1 = 0.471277 loss) +I0616 06:00:21.832440 9857 solver.cpp:258] Train net output #1: loss_cls = 0.529845 (* 1 = 0.529845 loss) +I0616 06:00:21.832444 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164005 (* 1 = 0.164005 loss) +I0616 06:00:21.832448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0364216 (* 1 = 0.0364216 loss) +I0616 06:00:21.832453 9857 solver.cpp:571] Iteration 21300, lr = 0.001 +I0616 06:00:33.651078 9857 solver.cpp:242] Iteration 21320, loss = 0.947749 +I0616 06:00:33.651121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.49528 (* 1 = 0.49528 loss) +I0616 06:00:33.651126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.693055 (* 1 = 0.693055 loss) +I0616 06:00:33.651130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147085 (* 1 = 0.147085 loss) +I0616 06:00:33.651134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0647518 (* 1 = 0.0647518 loss) +I0616 06:00:33.651139 9857 solver.cpp:571] Iteration 21320, lr = 0.001 +I0616 06:00:45.309232 9857 solver.cpp:242] Iteration 21340, loss = 0.882645 +I0616 06:00:45.309275 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175304 (* 1 = 0.175304 loss) +I0616 06:00:45.309280 9857 solver.cpp:258] Train net output #1: loss_cls = 0.193256 (* 1 = 0.193256 loss) +I0616 06:00:45.309285 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.032347 (* 1 = 0.032347 loss) +I0616 06:00:45.309288 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00602326 (* 1 = 0.00602326 loss) +I0616 06:00:45.309293 9857 solver.cpp:571] Iteration 21340, lr = 0.001 +I0616 06:00:56.920153 9857 solver.cpp:242] Iteration 21360, loss = 1.12471 +I0616 06:00:56.920195 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210632 (* 1 = 0.210632 loss) +I0616 06:00:56.920200 9857 solver.cpp:258] Train net output #1: loss_cls = 0.640858 (* 1 = 0.640858 loss) +I0616 06:00:56.920204 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0570758 (* 1 = 0.0570758 loss) +I0616 06:00:56.920208 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0233518 (* 1 = 0.0233518 loss) +I0616 06:00:56.920212 9857 solver.cpp:571] Iteration 21360, lr = 0.001 +I0616 06:01:08.498874 9857 solver.cpp:242] Iteration 21380, loss = 1.25861 +I0616 06:01:08.498916 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32839 (* 1 = 0.32839 loss) +I0616 06:01:08.498922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.919043 (* 1 = 0.919043 loss) +I0616 06:01:08.498926 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0883866 (* 1 = 0.0883866 loss) +I0616 06:01:08.498930 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0328484 (* 1 = 0.0328484 loss) +I0616 06:01:08.498934 9857 solver.cpp:571] Iteration 21380, lr = 0.001 +speed: 0.660s / iter +I0616 06:01:20.072993 9857 solver.cpp:242] Iteration 21400, loss = 0.956396 +I0616 06:01:20.073035 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197252 (* 1 = 0.197252 loss) +I0616 06:01:20.073040 9857 solver.cpp:258] Train net output #1: loss_cls = 0.389694 (* 1 = 0.389694 loss) +I0616 06:01:20.073045 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0697591 (* 1 = 0.0697591 loss) +I0616 06:01:20.073048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106787 (* 1 = 0.0106787 loss) +I0616 06:01:20.073052 9857 solver.cpp:571] Iteration 21400, lr = 0.001 +I0616 06:01:31.792937 9857 solver.cpp:242] Iteration 21420, loss = 0.515165 +I0616 06:01:31.792979 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396876 (* 1 = 0.396876 loss) +I0616 06:01:31.792984 9857 solver.cpp:258] Train net output #1: loss_cls = 0.241185 (* 1 = 0.241185 loss) +I0616 06:01:31.792989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0683118 (* 1 = 0.0683118 loss) +I0616 06:01:31.792992 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00978058 (* 1 = 0.00978058 loss) +I0616 06:01:31.792995 9857 solver.cpp:571] Iteration 21420, lr = 0.001 +I0616 06:01:43.443882 9857 solver.cpp:242] Iteration 21440, loss = 0.804248 +I0616 06:01:43.443924 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117219 (* 1 = 0.117219 loss) +I0616 06:01:43.443930 9857 solver.cpp:258] Train net output #1: loss_cls = 0.489832 (* 1 = 0.489832 loss) +I0616 06:01:43.443934 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0670721 (* 1 = 0.0670721 loss) +I0616 06:01:43.443938 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0186518 (* 1 = 0.0186518 loss) +I0616 06:01:43.443943 9857 solver.cpp:571] Iteration 21440, lr = 0.001 +I0616 06:01:54.834723 9857 solver.cpp:242] Iteration 21460, loss = 0.4022 +I0616 06:01:54.834769 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197433 (* 1 = 0.197433 loss) +I0616 06:01:54.834789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170691 (* 1 = 0.170691 loss) +I0616 06:01:54.834794 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.025915 (* 1 = 0.025915 loss) +I0616 06:01:54.834797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0673358 (* 1 = 0.0673358 loss) +I0616 06:01:54.834801 9857 solver.cpp:571] Iteration 21460, lr = 0.001 +I0616 06:02:06.628726 9857 solver.cpp:242] Iteration 21480, loss = 0.780418 +I0616 06:02:06.628767 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300114 (* 1 = 0.300114 loss) +I0616 06:02:06.628772 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305784 (* 1 = 0.305784 loss) +I0616 06:02:06.628777 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1449 (* 1 = 0.1449 loss) +I0616 06:02:06.628780 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0520551 (* 1 = 0.0520551 loss) +I0616 06:02:06.628784 9857 solver.cpp:571] Iteration 21480, lr = 0.001 +I0616 06:02:18.119601 9857 solver.cpp:242] Iteration 21500, loss = 1.53002 +I0616 06:02:18.119642 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435504 (* 1 = 0.435504 loss) +I0616 06:02:18.119648 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49138 (* 1 = 0.49138 loss) +I0616 06:02:18.119652 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294737 (* 1 = 0.294737 loss) +I0616 06:02:18.119655 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.22416 (* 1 = 0.22416 loss) +I0616 06:02:18.119659 9857 solver.cpp:571] Iteration 21500, lr = 0.001 +I0616 06:02:29.561671 9857 solver.cpp:242] Iteration 21520, loss = 1.04954 +I0616 06:02:29.561713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19175 (* 1 = 0.19175 loss) +I0616 06:02:29.561718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288757 (* 1 = 0.288757 loss) +I0616 06:02:29.561723 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0755338 (* 1 = 0.0755338 loss) +I0616 06:02:29.561727 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0470049 (* 1 = 0.0470049 loss) +I0616 06:02:29.561731 9857 solver.cpp:571] Iteration 21520, lr = 0.001 +I0616 06:02:40.896906 9857 solver.cpp:242] Iteration 21540, loss = 0.591945 +I0616 06:02:40.896949 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105024 (* 1 = 0.105024 loss) +I0616 06:02:40.896955 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125096 (* 1 = 0.125096 loss) +I0616 06:02:40.896958 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0581592 (* 1 = 0.0581592 loss) +I0616 06:02:40.896962 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0195802 (* 1 = 0.0195802 loss) +I0616 06:02:40.896966 9857 solver.cpp:571] Iteration 21540, lr = 0.001 +I0616 06:02:52.116752 9857 solver.cpp:242] Iteration 21560, loss = 0.856635 +I0616 06:02:52.116777 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.340307 (* 1 = 0.340307 loss) +I0616 06:02:52.116797 9857 solver.cpp:258] Train net output #1: loss_cls = 0.752982 (* 1 = 0.752982 loss) +I0616 06:02:52.116801 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122235 (* 1 = 0.122235 loss) +I0616 06:02:52.116806 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0696985 (* 1 = 0.0696985 loss) +I0616 06:02:52.116808 9857 solver.cpp:571] Iteration 21560, lr = 0.001 +I0616 06:03:03.799504 9857 solver.cpp:242] Iteration 21580, loss = 1.14933 +I0616 06:03:03.799545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322382 (* 1 = 0.322382 loss) +I0616 06:03:03.799551 9857 solver.cpp:258] Train net output #1: loss_cls = 0.837643 (* 1 = 0.837643 loss) +I0616 06:03:03.799556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0796751 (* 1 = 0.0796751 loss) +I0616 06:03:03.799558 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0505872 (* 1 = 0.0505872 loss) +I0616 06:03:03.799562 9857 solver.cpp:571] Iteration 21580, lr = 0.001 +speed: 0.659s / iter +I0616 06:03:15.333232 9857 solver.cpp:242] Iteration 21600, loss = 1.06566 +I0616 06:03:15.333272 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112408 (* 1 = 0.112408 loss) +I0616 06:03:15.333278 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169061 (* 1 = 0.169061 loss) +I0616 06:03:15.333282 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0727636 (* 1 = 0.0727636 loss) +I0616 06:03:15.333287 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00773102 (* 1 = 0.00773102 loss) +I0616 06:03:15.333292 9857 solver.cpp:571] Iteration 21600, lr = 0.001 +I0616 06:03:27.101377 9857 solver.cpp:242] Iteration 21620, loss = 0.643071 +I0616 06:03:27.101419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147234 (* 1 = 0.147234 loss) +I0616 06:03:27.101425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196655 (* 1 = 0.196655 loss) +I0616 06:03:27.101429 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0179327 (* 1 = 0.0179327 loss) +I0616 06:03:27.101433 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000998386 (* 1 = 0.000998386 loss) +I0616 06:03:27.101438 9857 solver.cpp:571] Iteration 21620, lr = 0.001 +I0616 06:03:38.539144 9857 solver.cpp:242] Iteration 21640, loss = 1.05726 +I0616 06:03:38.539187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0567542 (* 1 = 0.0567542 loss) +I0616 06:03:38.539193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149805 (* 1 = 0.149805 loss) +I0616 06:03:38.539197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0934681 (* 1 = 0.0934681 loss) +I0616 06:03:38.539201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164186 (* 1 = 0.0164186 loss) +I0616 06:03:38.539204 9857 solver.cpp:571] Iteration 21640, lr = 0.001 +I0616 06:03:50.284735 9857 solver.cpp:242] Iteration 21660, loss = 1.04311 +I0616 06:03:50.284776 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290713 (* 1 = 0.290713 loss) +I0616 06:03:50.284781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.795565 (* 1 = 0.795565 loss) +I0616 06:03:50.284786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.283517 (* 1 = 0.283517 loss) +I0616 06:03:50.284790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00336971 (* 1 = 0.00336971 loss) +I0616 06:03:50.284795 9857 solver.cpp:571] Iteration 21660, lr = 0.001 +I0616 06:04:02.090206 9857 solver.cpp:242] Iteration 21680, loss = 0.907437 +I0616 06:04:02.090248 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.385834 (* 1 = 0.385834 loss) +I0616 06:04:02.090255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.355847 (* 1 = 0.355847 loss) +I0616 06:04:02.090260 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10778 (* 1 = 0.10778 loss) +I0616 06:04:02.090262 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00514294 (* 1 = 0.00514294 loss) +I0616 06:04:02.090266 9857 solver.cpp:571] Iteration 21680, lr = 0.001 +I0616 06:04:13.594938 9857 solver.cpp:242] Iteration 21700, loss = 0.520137 +I0616 06:04:13.594980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102858 (* 1 = 0.102858 loss) +I0616 06:04:13.594985 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249183 (* 1 = 0.249183 loss) +I0616 06:04:13.594990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0363737 (* 1 = 0.0363737 loss) +I0616 06:04:13.594993 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0544136 (* 1 = 0.0544136 loss) +I0616 06:04:13.594996 9857 solver.cpp:571] Iteration 21700, lr = 0.001 +I0616 06:04:25.234629 9857 solver.cpp:242] Iteration 21720, loss = 0.620703 +I0616 06:04:25.234670 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146313 (* 1 = 0.146313 loss) +I0616 06:04:25.234676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196293 (* 1 = 0.196293 loss) +I0616 06:04:25.234680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0568258 (* 1 = 0.0568258 loss) +I0616 06:04:25.234684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00333189 (* 1 = 0.00333189 loss) +I0616 06:04:25.234688 9857 solver.cpp:571] Iteration 21720, lr = 0.001 +I0616 06:04:36.873224 9857 solver.cpp:242] Iteration 21740, loss = 1.16736 +I0616 06:04:36.873265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196823 (* 1 = 0.196823 loss) +I0616 06:04:36.873271 9857 solver.cpp:258] Train net output #1: loss_cls = 0.422416 (* 1 = 0.422416 loss) +I0616 06:04:36.873275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167854 (* 1 = 0.167854 loss) +I0616 06:04:36.873280 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0908749 (* 1 = 0.0908749 loss) +I0616 06:04:36.873283 9857 solver.cpp:571] Iteration 21740, lr = 0.001 +I0616 06:04:48.355546 9857 solver.cpp:242] Iteration 21760, loss = 0.67666 +I0616 06:04:48.355589 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.00487619 (* 1 = 0.00487619 loss) +I0616 06:04:48.355595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29214 (* 1 = 0.29214 loss) +I0616 06:04:48.355599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.275952 (* 1 = 0.275952 loss) +I0616 06:04:48.355602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.153076 (* 1 = 0.153076 loss) +I0616 06:04:48.355607 9857 solver.cpp:571] Iteration 21760, lr = 0.001 +I0616 06:04:59.909541 9857 solver.cpp:242] Iteration 21780, loss = 1.29977 +I0616 06:04:59.909584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272392 (* 1 = 0.272392 loss) +I0616 06:04:59.909590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.483165 (* 1 = 0.483165 loss) +I0616 06:04:59.909593 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.233246 (* 1 = 0.233246 loss) +I0616 06:04:59.909596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.350193 (* 1 = 0.350193 loss) +I0616 06:04:59.909601 9857 solver.cpp:571] Iteration 21780, lr = 0.001 +speed: 0.659s / iter +I0616 06:05:11.457825 9857 solver.cpp:242] Iteration 21800, loss = 0.69854 +I0616 06:05:11.457866 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285468 (* 1 = 0.285468 loss) +I0616 06:05:11.457872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266028 (* 1 = 0.266028 loss) +I0616 06:05:11.457876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215538 (* 1 = 0.215538 loss) +I0616 06:05:11.457880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030095 (* 1 = 0.030095 loss) +I0616 06:05:11.457885 9857 solver.cpp:571] Iteration 21800, lr = 0.001 +I0616 06:05:22.786967 9857 solver.cpp:242] Iteration 21820, loss = 0.600164 +I0616 06:05:22.787009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165637 (* 1 = 0.165637 loss) +I0616 06:05:22.787015 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153748 (* 1 = 0.153748 loss) +I0616 06:05:22.787019 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0188234 (* 1 = 0.0188234 loss) +I0616 06:05:22.787024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101289 (* 1 = 0.0101289 loss) +I0616 06:05:22.787027 9857 solver.cpp:571] Iteration 21820, lr = 0.001 +I0616 06:05:34.437937 9857 solver.cpp:242] Iteration 21840, loss = 1.45148 +I0616 06:05:34.437978 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365201 (* 1 = 0.365201 loss) +I0616 06:05:34.437983 9857 solver.cpp:258] Train net output #1: loss_cls = 0.291026 (* 1 = 0.291026 loss) +I0616 06:05:34.437988 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119331 (* 1 = 0.119331 loss) +I0616 06:05:34.437991 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255762 (* 1 = 0.0255762 loss) +I0616 06:05:34.437996 9857 solver.cpp:571] Iteration 21840, lr = 0.001 +I0616 06:05:45.983783 9857 solver.cpp:242] Iteration 21860, loss = 0.578115 +I0616 06:05:45.983826 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178104 (* 1 = 0.178104 loss) +I0616 06:05:45.983831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295218 (* 1 = 0.295218 loss) +I0616 06:05:45.983835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150135 (* 1 = 0.150135 loss) +I0616 06:05:45.983839 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129385 (* 1 = 0.0129385 loss) +I0616 06:05:45.983844 9857 solver.cpp:571] Iteration 21860, lr = 0.001 +I0616 06:05:57.427645 9857 solver.cpp:242] Iteration 21880, loss = 0.673639 +I0616 06:05:57.427687 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.388079 (* 1 = 0.388079 loss) +I0616 06:05:57.427692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.6768 (* 1 = 0.6768 loss) +I0616 06:05:57.427697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0500484 (* 1 = 0.0500484 loss) +I0616 06:05:57.427700 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.018204 (* 1 = 0.018204 loss) +I0616 06:05:57.427705 9857 solver.cpp:571] Iteration 21880, lr = 0.001 +I0616 06:06:08.840149 9857 solver.cpp:242] Iteration 21900, loss = 0.740567 +I0616 06:06:08.840191 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297557 (* 1 = 0.297557 loss) +I0616 06:06:08.840198 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418749 (* 1 = 0.418749 loss) +I0616 06:06:08.840201 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0896095 (* 1 = 0.0896095 loss) +I0616 06:06:08.840205 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0294366 (* 1 = 0.0294366 loss) +I0616 06:06:08.840209 9857 solver.cpp:571] Iteration 21900, lr = 0.001 +I0616 06:06:20.714893 9857 solver.cpp:242] Iteration 21920, loss = 0.941703 +I0616 06:06:20.714934 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376582 (* 1 = 0.376582 loss) +I0616 06:06:20.714941 9857 solver.cpp:258] Train net output #1: loss_cls = 0.488613 (* 1 = 0.488613 loss) +I0616 06:06:20.714944 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249855 (* 1 = 0.249855 loss) +I0616 06:06:20.714948 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0341526 (* 1 = 0.0341526 loss) +I0616 06:06:20.714952 9857 solver.cpp:571] Iteration 21920, lr = 0.001 +I0616 06:06:32.311693 9857 solver.cpp:242] Iteration 21940, loss = 0.812644 +I0616 06:06:32.311734 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331229 (* 1 = 0.331229 loss) +I0616 06:06:32.311740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280824 (* 1 = 0.280824 loss) +I0616 06:06:32.311744 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136966 (* 1 = 0.136966 loss) +I0616 06:06:32.311748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.056123 (* 1 = 0.056123 loss) +I0616 06:06:32.311753 9857 solver.cpp:571] Iteration 21940, lr = 0.001 +I0616 06:06:43.884184 9857 solver.cpp:242] Iteration 21960, loss = 1.29055 +I0616 06:06:43.884227 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389475 (* 1 = 0.389475 loss) +I0616 06:06:43.884232 9857 solver.cpp:258] Train net output #1: loss_cls = 0.946565 (* 1 = 0.946565 loss) +I0616 06:06:43.884238 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0960551 (* 1 = 0.0960551 loss) +I0616 06:06:43.884241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0459278 (* 1 = 0.0459278 loss) +I0616 06:06:43.884245 9857 solver.cpp:571] Iteration 21960, lr = 0.001 +I0616 06:06:55.575700 9857 solver.cpp:242] Iteration 21980, loss = 1.40664 +I0616 06:06:55.575742 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.328983 (* 1 = 0.328983 loss) +I0616 06:06:55.575747 9857 solver.cpp:258] Train net output #1: loss_cls = 0.516054 (* 1 = 0.516054 loss) +I0616 06:06:55.575752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.620213 (* 1 = 0.620213 loss) +I0616 06:06:55.575755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.03387 (* 1 = 1.03387 loss) +I0616 06:06:55.575759 9857 solver.cpp:571] Iteration 21980, lr = 0.001 +speed: 0.658s / iter +I0616 06:07:07.105207 9857 solver.cpp:242] Iteration 22000, loss = 1.10005 +I0616 06:07:07.105249 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287949 (* 1 = 0.287949 loss) +I0616 06:07:07.105255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.809576 (* 1 = 0.809576 loss) +I0616 06:07:07.105259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0782286 (* 1 = 0.0782286 loss) +I0616 06:07:07.105263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0969529 (* 1 = 0.0969529 loss) +I0616 06:07:07.105267 9857 solver.cpp:571] Iteration 22000, lr = 0.001 +I0616 06:07:18.371475 9857 solver.cpp:242] Iteration 22020, loss = 0.820854 +I0616 06:07:18.371517 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211784 (* 1 = 0.211784 loss) +I0616 06:07:18.371523 9857 solver.cpp:258] Train net output #1: loss_cls = 0.613906 (* 1 = 0.613906 loss) +I0616 06:07:18.371527 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0996741 (* 1 = 0.0996741 loss) +I0616 06:07:18.371531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105439 (* 1 = 0.0105439 loss) +I0616 06:07:18.371534 9857 solver.cpp:571] Iteration 22020, lr = 0.001 +I0616 06:07:29.766773 9857 solver.cpp:242] Iteration 22040, loss = 1.64732 +I0616 06:07:29.766816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.472386 (* 1 = 0.472386 loss) +I0616 06:07:29.766821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.724514 (* 1 = 0.724514 loss) +I0616 06:07:29.766825 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.281647 (* 1 = 0.281647 loss) +I0616 06:07:29.766829 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0618013 (* 1 = 0.0618013 loss) +I0616 06:07:29.766834 9857 solver.cpp:571] Iteration 22040, lr = 0.001 +I0616 06:07:41.538663 9857 solver.cpp:242] Iteration 22060, loss = 0.989245 +I0616 06:07:41.538704 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310069 (* 1 = 0.310069 loss) +I0616 06:07:41.538710 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202342 (* 1 = 0.202342 loss) +I0616 06:07:41.538714 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102331 (* 1 = 0.102331 loss) +I0616 06:07:41.538718 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.024464 (* 1 = 0.024464 loss) +I0616 06:07:41.538722 9857 solver.cpp:571] Iteration 22060, lr = 0.001 +I0616 06:07:52.889407 9857 solver.cpp:242] Iteration 22080, loss = 0.837391 +I0616 06:07:52.889448 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.469822 (* 1 = 0.469822 loss) +I0616 06:07:52.889454 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301773 (* 1 = 0.301773 loss) +I0616 06:07:52.889458 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0380829 (* 1 = 0.0380829 loss) +I0616 06:07:52.889462 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107984 (* 1 = 0.0107984 loss) +I0616 06:07:52.889467 9857 solver.cpp:571] Iteration 22080, lr = 0.001 +I0616 06:08:04.600335 9857 solver.cpp:242] Iteration 22100, loss = 1.60404 +I0616 06:08:04.600378 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.496311 (* 1 = 0.496311 loss) +I0616 06:08:04.600383 9857 solver.cpp:258] Train net output #1: loss_cls = 1.33338 (* 1 = 1.33338 loss) +I0616 06:08:04.600388 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.374602 (* 1 = 0.374602 loss) +I0616 06:08:04.600391 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0555632 (* 1 = 0.0555632 loss) +I0616 06:08:04.600395 9857 solver.cpp:571] Iteration 22100, lr = 0.001 +I0616 06:08:15.981778 9857 solver.cpp:242] Iteration 22120, loss = 0.743702 +I0616 06:08:15.981822 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.466864 (* 1 = 0.466864 loss) +I0616 06:08:15.981828 9857 solver.cpp:258] Train net output #1: loss_cls = 0.391903 (* 1 = 0.391903 loss) +I0616 06:08:15.981832 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138117 (* 1 = 0.138117 loss) +I0616 06:08:15.981837 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0237829 (* 1 = 0.0237829 loss) +I0616 06:08:15.981839 9857 solver.cpp:571] Iteration 22120, lr = 0.001 +I0616 06:08:27.627512 9857 solver.cpp:242] Iteration 22140, loss = 1.06613 +I0616 06:08:27.627554 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236064 (* 1 = 0.236064 loss) +I0616 06:08:27.627559 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251515 (* 1 = 0.251515 loss) +I0616 06:08:27.627564 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0624585 (* 1 = 0.0624585 loss) +I0616 06:08:27.627568 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131376 (* 1 = 0.0131376 loss) +I0616 06:08:27.627571 9857 solver.cpp:571] Iteration 22140, lr = 0.001 +I0616 06:08:39.460229 9857 solver.cpp:242] Iteration 22160, loss = 0.807512 +I0616 06:08:39.460271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320347 (* 1 = 0.320347 loss) +I0616 06:08:39.460276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.827701 (* 1 = 0.827701 loss) +I0616 06:08:39.460280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106018 (* 1 = 0.106018 loss) +I0616 06:08:39.460284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00232347 (* 1 = 0.00232347 loss) +I0616 06:08:39.460289 9857 solver.cpp:571] Iteration 22160, lr = 0.001 +I0616 06:08:50.984776 9857 solver.cpp:242] Iteration 22180, loss = 1.12955 +I0616 06:08:50.984819 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.668941 (* 1 = 0.668941 loss) +I0616 06:08:50.984824 9857 solver.cpp:258] Train net output #1: loss_cls = 0.845714 (* 1 = 0.845714 loss) +I0616 06:08:50.984828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0779809 (* 1 = 0.0779809 loss) +I0616 06:08:50.984833 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0709562 (* 1 = 0.0709562 loss) +I0616 06:08:50.984836 9857 solver.cpp:571] Iteration 22180, lr = 0.001 +speed: 0.657s / iter +I0616 06:09:02.500490 9857 solver.cpp:242] Iteration 22200, loss = 0.685533 +I0616 06:09:02.500533 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0650887 (* 1 = 0.0650887 loss) +I0616 06:09:02.500538 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190829 (* 1 = 0.190829 loss) +I0616 06:09:02.500542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.069676 (* 1 = 0.069676 loss) +I0616 06:09:02.500546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0753677 (* 1 = 0.0753677 loss) +I0616 06:09:02.500550 9857 solver.cpp:571] Iteration 22200, lr = 0.001 +I0616 06:09:14.135974 9857 solver.cpp:242] Iteration 22220, loss = 1.27887 +I0616 06:09:14.136016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389447 (* 1 = 0.389447 loss) +I0616 06:09:14.136023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.65323 (* 1 = 0.65323 loss) +I0616 06:09:14.136026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.296795 (* 1 = 0.296795 loss) +I0616 06:09:14.136030 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0672386 (* 1 = 0.0672386 loss) +I0616 06:09:14.136034 9857 solver.cpp:571] Iteration 22220, lr = 0.001 +I0616 06:09:25.743697 9857 solver.cpp:242] Iteration 22240, loss = 1.68468 +I0616 06:09:25.743739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202231 (* 1 = 0.202231 loss) +I0616 06:09:25.743744 9857 solver.cpp:258] Train net output #1: loss_cls = 0.308488 (* 1 = 0.308488 loss) +I0616 06:09:25.743748 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163252 (* 1 = 0.163252 loss) +I0616 06:09:25.743753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0426578 (* 1 = 0.0426578 loss) +I0616 06:09:25.743757 9857 solver.cpp:571] Iteration 22240, lr = 0.001 +I0616 06:09:37.058178 9857 solver.cpp:242] Iteration 22260, loss = 0.952629 +I0616 06:09:37.058220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294232 (* 1 = 0.294232 loss) +I0616 06:09:37.058225 9857 solver.cpp:258] Train net output #1: loss_cls = 0.905114 (* 1 = 0.905114 loss) +I0616 06:09:37.058230 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142992 (* 1 = 0.142992 loss) +I0616 06:09:37.058234 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0405403 (* 1 = 0.0405403 loss) +I0616 06:09:37.058239 9857 solver.cpp:571] Iteration 22260, lr = 0.001 +I0616 06:09:48.400965 9857 solver.cpp:242] Iteration 22280, loss = 1.60025 +I0616 06:09:48.401005 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.469424 (* 1 = 0.469424 loss) +I0616 06:09:48.401011 9857 solver.cpp:258] Train net output #1: loss_cls = 0.940656 (* 1 = 0.940656 loss) +I0616 06:09:48.401015 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.643331 (* 1 = 0.643331 loss) +I0616 06:09:48.401018 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.249316 (* 1 = 0.249316 loss) +I0616 06:09:48.401023 9857 solver.cpp:571] Iteration 22280, lr = 0.001 +I0616 06:10:00.137542 9857 solver.cpp:242] Iteration 22300, loss = 0.835039 +I0616 06:10:00.137583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1267 (* 1 = 0.1267 loss) +I0616 06:10:00.137589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107929 (* 1 = 0.107929 loss) +I0616 06:10:00.137593 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0302323 (* 1 = 0.0302323 loss) +I0616 06:10:00.137596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0172768 (* 1 = 0.0172768 loss) +I0616 06:10:00.137600 9857 solver.cpp:571] Iteration 22300, lr = 0.001 +I0616 06:10:11.886900 9857 solver.cpp:242] Iteration 22320, loss = 0.69967 +I0616 06:10:11.886941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111071 (* 1 = 0.111071 loss) +I0616 06:10:11.886947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262857 (* 1 = 0.262857 loss) +I0616 06:10:11.886951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0396514 (* 1 = 0.0396514 loss) +I0616 06:10:11.886955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00745719 (* 1 = 0.00745719 loss) +I0616 06:10:11.886960 9857 solver.cpp:571] Iteration 22320, lr = 0.001 +I0616 06:10:23.217304 9857 solver.cpp:242] Iteration 22340, loss = 1.40461 +I0616 06:10:23.217344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.459066 (* 1 = 0.459066 loss) +I0616 06:10:23.217350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.600087 (* 1 = 0.600087 loss) +I0616 06:10:23.217353 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.334059 (* 1 = 0.334059 loss) +I0616 06:10:23.217357 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.135854 (* 1 = 0.135854 loss) +I0616 06:10:23.217361 9857 solver.cpp:571] Iteration 22340, lr = 0.001 +I0616 06:10:34.751770 9857 solver.cpp:242] Iteration 22360, loss = 1.90719 +I0616 06:10:34.751811 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488821 (* 1 = 0.488821 loss) +I0616 06:10:34.751816 9857 solver.cpp:258] Train net output #1: loss_cls = 0.676838 (* 1 = 0.676838 loss) +I0616 06:10:34.751821 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.409227 (* 1 = 0.409227 loss) +I0616 06:10:34.751824 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0755284 (* 1 = 0.0755284 loss) +I0616 06:10:34.751828 9857 solver.cpp:571] Iteration 22360, lr = 0.001 +I0616 06:10:46.157153 9857 solver.cpp:242] Iteration 22380, loss = 0.634555 +I0616 06:10:46.157196 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211735 (* 1 = 0.211735 loss) +I0616 06:10:46.157202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314876 (* 1 = 0.314876 loss) +I0616 06:10:46.157205 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0532889 (* 1 = 0.0532889 loss) +I0616 06:10:46.157209 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0228353 (* 1 = 0.0228353 loss) +I0616 06:10:46.157213 9857 solver.cpp:571] Iteration 22380, lr = 0.001 +speed: 0.656s / iter +I0616 06:10:57.617595 9857 solver.cpp:242] Iteration 22400, loss = 0.544565 +I0616 06:10:57.617638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204139 (* 1 = 0.204139 loss) +I0616 06:10:57.617645 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244207 (* 1 = 0.244207 loss) +I0616 06:10:57.617648 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20783 (* 1 = 0.20783 loss) +I0616 06:10:57.617651 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278256 (* 1 = 0.0278256 loss) +I0616 06:10:57.617655 9857 solver.cpp:571] Iteration 22400, lr = 0.001 +I0616 06:11:08.988180 9857 solver.cpp:242] Iteration 22420, loss = 1.04946 +I0616 06:11:08.988224 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.433896 (* 1 = 0.433896 loss) +I0616 06:11:08.988229 9857 solver.cpp:258] Train net output #1: loss_cls = 0.967697 (* 1 = 0.967697 loss) +I0616 06:11:08.988232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.264874 (* 1 = 0.264874 loss) +I0616 06:11:08.988236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.051359 (* 1 = 0.051359 loss) +I0616 06:11:08.988240 9857 solver.cpp:571] Iteration 22420, lr = 0.001 +I0616 06:11:20.701819 9857 solver.cpp:242] Iteration 22440, loss = 1.11255 +I0616 06:11:20.701860 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318347 (* 1 = 0.318347 loss) +I0616 06:11:20.701865 9857 solver.cpp:258] Train net output #1: loss_cls = 0.510374 (* 1 = 0.510374 loss) +I0616 06:11:20.701869 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.283956 (* 1 = 0.283956 loss) +I0616 06:11:20.701874 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108554 (* 1 = 0.108554 loss) +I0616 06:11:20.701877 9857 solver.cpp:571] Iteration 22440, lr = 0.001 +I0616 06:11:32.119657 9857 solver.cpp:242] Iteration 22460, loss = 1.25506 +I0616 06:11:32.119699 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162554 (* 1 = 0.162554 loss) +I0616 06:11:32.119704 9857 solver.cpp:258] Train net output #1: loss_cls = 0.379458 (* 1 = 0.379458 loss) +I0616 06:11:32.119709 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.410478 (* 1 = 0.410478 loss) +I0616 06:11:32.119712 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111048 (* 1 = 0.111048 loss) +I0616 06:11:32.119716 9857 solver.cpp:571] Iteration 22460, lr = 0.001 +I0616 06:11:43.513936 9857 solver.cpp:242] Iteration 22480, loss = 0.982456 +I0616 06:11:43.513977 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.870581 (* 1 = 0.870581 loss) +I0616 06:11:43.513983 9857 solver.cpp:258] Train net output #1: loss_cls = 0.682289 (* 1 = 0.682289 loss) +I0616 06:11:43.513986 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0533256 (* 1 = 0.0533256 loss) +I0616 06:11:43.513990 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0305396 (* 1 = 0.0305396 loss) +I0616 06:11:43.513994 9857 solver.cpp:571] Iteration 22480, lr = 0.001 +I0616 06:11:54.908367 9857 solver.cpp:242] Iteration 22500, loss = 0.896592 +I0616 06:11:54.908411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130532 (* 1 = 0.130532 loss) +I0616 06:11:54.908416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394077 (* 1 = 0.394077 loss) +I0616 06:11:54.908421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0624287 (* 1 = 0.0624287 loss) +I0616 06:11:54.908423 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101536 (* 1 = 0.0101536 loss) +I0616 06:11:54.908427 9857 solver.cpp:571] Iteration 22500, lr = 0.001 +I0616 06:12:06.306273 9857 solver.cpp:242] Iteration 22520, loss = 1.22114 +I0616 06:12:06.306316 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.693077 (* 1 = 0.693077 loss) +I0616 06:12:06.306321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.990273 (* 1 = 0.990273 loss) +I0616 06:12:06.306326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11563 (* 1 = 0.11563 loss) +I0616 06:12:06.306329 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494207 (* 1 = 0.0494207 loss) +I0616 06:12:06.306334 9857 solver.cpp:571] Iteration 22520, lr = 0.001 +I0616 06:12:17.910080 9857 solver.cpp:242] Iteration 22540, loss = 0.738094 +I0616 06:12:17.910122 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261052 (* 1 = 0.261052 loss) +I0616 06:12:17.910127 9857 solver.cpp:258] Train net output #1: loss_cls = 0.671775 (* 1 = 0.671775 loss) +I0616 06:12:17.910131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.180787 (* 1 = 0.180787 loss) +I0616 06:12:17.910135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000851478 (* 1 = 0.000851478 loss) +I0616 06:12:17.910140 9857 solver.cpp:571] Iteration 22540, lr = 0.001 +I0616 06:12:29.501667 9857 solver.cpp:242] Iteration 22560, loss = 1.04753 +I0616 06:12:29.501709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.403553 (* 1 = 0.403553 loss) +I0616 06:12:29.501715 9857 solver.cpp:258] Train net output #1: loss_cls = 0.979235 (* 1 = 0.979235 loss) +I0616 06:12:29.501719 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.300559 (* 1 = 0.300559 loss) +I0616 06:12:29.501724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129497 (* 1 = 0.129497 loss) +I0616 06:12:29.501727 9857 solver.cpp:571] Iteration 22560, lr = 0.001 +I0616 06:12:41.036593 9857 solver.cpp:242] Iteration 22580, loss = 1.28258 +I0616 06:12:41.036636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.544458 (* 1 = 0.544458 loss) +I0616 06:12:41.036641 9857 solver.cpp:258] Train net output #1: loss_cls = 1.05707 (* 1 = 1.05707 loss) +I0616 06:12:41.036645 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159598 (* 1 = 0.159598 loss) +I0616 06:12:41.036649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00968483 (* 1 = 0.00968483 loss) +I0616 06:12:41.036653 9857 solver.cpp:571] Iteration 22580, lr = 0.001 +speed: 0.656s / iter +I0616 06:12:52.871505 9857 solver.cpp:242] Iteration 22600, loss = 1.58844 +I0616 06:12:52.871546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297147 (* 1 = 0.297147 loss) +I0616 06:12:52.871552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.563982 (* 1 = 0.563982 loss) +I0616 06:12:52.871556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197781 (* 1 = 0.197781 loss) +I0616 06:12:52.871561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.324948 (* 1 = 0.324948 loss) +I0616 06:12:52.871564 9857 solver.cpp:571] Iteration 22600, lr = 0.001 +I0616 06:13:04.336612 9857 solver.cpp:242] Iteration 22620, loss = 0.561957 +I0616 06:13:04.336653 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109788 (* 1 = 0.109788 loss) +I0616 06:13:04.336659 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102348 (* 1 = 0.102348 loss) +I0616 06:13:04.336663 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0474641 (* 1 = 0.0474641 loss) +I0616 06:13:04.336668 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00609648 (* 1 = 0.00609648 loss) +I0616 06:13:04.336671 9857 solver.cpp:571] Iteration 22620, lr = 0.001 +I0616 06:13:15.777969 9857 solver.cpp:242] Iteration 22640, loss = 0.631395 +I0616 06:13:15.778012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251922 (* 1 = 0.251922 loss) +I0616 06:13:15.778017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.470328 (* 1 = 0.470328 loss) +I0616 06:13:15.778023 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0812774 (* 1 = 0.0812774 loss) +I0616 06:13:15.778026 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0309465 (* 1 = 0.0309465 loss) +I0616 06:13:15.778029 9857 solver.cpp:571] Iteration 22640, lr = 0.001 +I0616 06:13:27.184166 9857 solver.cpp:242] Iteration 22660, loss = 0.67832 +I0616 06:13:27.184208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180571 (* 1 = 0.180571 loss) +I0616 06:13:27.184213 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197156 (* 1 = 0.197156 loss) +I0616 06:13:27.184218 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0641501 (* 1 = 0.0641501 loss) +I0616 06:13:27.184221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00504554 (* 1 = 0.00504554 loss) +I0616 06:13:27.184226 9857 solver.cpp:571] Iteration 22660, lr = 0.001 +I0616 06:13:38.659435 9857 solver.cpp:242] Iteration 22680, loss = 0.312754 +I0616 06:13:38.659476 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0855971 (* 1 = 0.0855971 loss) +I0616 06:13:38.659482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151546 (* 1 = 0.151546 loss) +I0616 06:13:38.659485 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0235331 (* 1 = 0.0235331 loss) +I0616 06:13:38.659489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00196889 (* 1 = 0.00196889 loss) +I0616 06:13:38.659492 9857 solver.cpp:571] Iteration 22680, lr = 0.001 +I0616 06:13:50.396780 9857 solver.cpp:242] Iteration 22700, loss = 0.935995 +I0616 06:13:50.396822 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359874 (* 1 = 0.359874 loss) +I0616 06:13:50.396827 9857 solver.cpp:258] Train net output #1: loss_cls = 0.422144 (* 1 = 0.422144 loss) +I0616 06:13:50.396831 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118363 (* 1 = 0.118363 loss) +I0616 06:13:50.396836 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0471105 (* 1 = 0.0471105 loss) +I0616 06:13:50.396839 9857 solver.cpp:571] Iteration 22700, lr = 0.001 +I0616 06:14:01.916043 9857 solver.cpp:242] Iteration 22720, loss = 1.12051 +I0616 06:14:01.916085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195732 (* 1 = 0.195732 loss) +I0616 06:14:01.916091 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394676 (* 1 = 0.394676 loss) +I0616 06:14:01.916095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146731 (* 1 = 0.146731 loss) +I0616 06:14:01.916100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00367376 (* 1 = 0.00367376 loss) +I0616 06:14:01.916103 9857 solver.cpp:571] Iteration 22720, lr = 0.001 +I0616 06:14:13.223088 9857 solver.cpp:242] Iteration 22740, loss = 0.497976 +I0616 06:14:13.223130 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304427 (* 1 = 0.304427 loss) +I0616 06:14:13.223136 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300486 (* 1 = 0.300486 loss) +I0616 06:14:13.223140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0934342 (* 1 = 0.0934342 loss) +I0616 06:14:13.223143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205495 (* 1 = 0.0205495 loss) +I0616 06:14:13.223148 9857 solver.cpp:571] Iteration 22740, lr = 0.001 +I0616 06:14:24.972681 9857 solver.cpp:242] Iteration 22760, loss = 0.829986 +I0616 06:14:24.972724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.459269 (* 1 = 0.459269 loss) +I0616 06:14:24.972729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360024 (* 1 = 0.360024 loss) +I0616 06:14:24.972733 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.092214 (* 1 = 0.092214 loss) +I0616 06:14:24.972738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0462238 (* 1 = 0.0462238 loss) +I0616 06:14:24.972741 9857 solver.cpp:571] Iteration 22760, lr = 0.001 +I0616 06:14:36.660946 9857 solver.cpp:242] Iteration 22780, loss = 0.824873 +I0616 06:14:36.660989 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.339127 (* 1 = 0.339127 loss) +I0616 06:14:36.660995 9857 solver.cpp:258] Train net output #1: loss_cls = 0.552989 (* 1 = 0.552989 loss) +I0616 06:14:36.661000 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.083917 (* 1 = 0.083917 loss) +I0616 06:14:36.661002 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0833184 (* 1 = 0.0833184 loss) +I0616 06:14:36.661006 9857 solver.cpp:571] Iteration 22780, lr = 0.001 +speed: 0.655s / iter +I0616 06:14:48.477311 9857 solver.cpp:242] Iteration 22800, loss = 0.502334 +I0616 06:14:48.477352 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.058664 (* 1 = 0.058664 loss) +I0616 06:14:48.477358 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151764 (* 1 = 0.151764 loss) +I0616 06:14:48.477362 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0966539 (* 1 = 0.0966539 loss) +I0616 06:14:48.477365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00770072 (* 1 = 0.00770072 loss) +I0616 06:14:48.477370 9857 solver.cpp:571] Iteration 22800, lr = 0.001 +I0616 06:15:00.107091 9857 solver.cpp:242] Iteration 22820, loss = 0.611746 +I0616 06:15:00.107132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170098 (* 1 = 0.170098 loss) +I0616 06:15:00.107138 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354897 (* 1 = 0.354897 loss) +I0616 06:15:00.107142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124472 (* 1 = 0.124472 loss) +I0616 06:15:00.107146 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205685 (* 1 = 0.0205685 loss) +I0616 06:15:00.107149 9857 solver.cpp:571] Iteration 22820, lr = 0.001 +I0616 06:15:11.610443 9857 solver.cpp:242] Iteration 22840, loss = 1.02133 +I0616 06:15:11.610483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280133 (* 1 = 0.280133 loss) +I0616 06:15:11.610488 9857 solver.cpp:258] Train net output #1: loss_cls = 0.534649 (* 1 = 0.534649 loss) +I0616 06:15:11.610492 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184893 (* 1 = 0.184893 loss) +I0616 06:15:11.610496 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127783 (* 1 = 0.0127783 loss) +I0616 06:15:11.610501 9857 solver.cpp:571] Iteration 22840, lr = 0.001 +I0616 06:15:23.484117 9857 solver.cpp:242] Iteration 22860, loss = 0.811463 +I0616 06:15:23.484159 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355579 (* 1 = 0.355579 loss) +I0616 06:15:23.484164 9857 solver.cpp:258] Train net output #1: loss_cls = 0.475282 (* 1 = 0.475282 loss) +I0616 06:15:23.484169 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.286199 (* 1 = 0.286199 loss) +I0616 06:15:23.484172 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0626763 (* 1 = 0.0626763 loss) +I0616 06:15:23.484176 9857 solver.cpp:571] Iteration 22860, lr = 0.001 +I0616 06:15:35.098002 9857 solver.cpp:242] Iteration 22880, loss = 0.602631 +I0616 06:15:35.098043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143147 (* 1 = 0.143147 loss) +I0616 06:15:35.098048 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192625 (* 1 = 0.192625 loss) +I0616 06:15:35.098052 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0590191 (* 1 = 0.0590191 loss) +I0616 06:15:35.098057 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0035582 (* 1 = 0.0035582 loss) +I0616 06:15:35.098060 9857 solver.cpp:571] Iteration 22880, lr = 0.001 +I0616 06:15:46.667414 9857 solver.cpp:242] Iteration 22900, loss = 0.795794 +I0616 06:15:46.667457 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296676 (* 1 = 0.296676 loss) +I0616 06:15:46.667462 9857 solver.cpp:258] Train net output #1: loss_cls = 0.453112 (* 1 = 0.453112 loss) +I0616 06:15:46.667466 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197544 (* 1 = 0.197544 loss) +I0616 06:15:46.667470 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135691 (* 1 = 0.0135691 loss) +I0616 06:15:46.667474 9857 solver.cpp:571] Iteration 22900, lr = 0.001 +I0616 06:15:58.131083 9857 solver.cpp:242] Iteration 22920, loss = 0.699553 +I0616 06:15:58.131125 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166612 (* 1 = 0.166612 loss) +I0616 06:15:58.131130 9857 solver.cpp:258] Train net output #1: loss_cls = 0.238191 (* 1 = 0.238191 loss) +I0616 06:15:58.131134 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0178302 (* 1 = 0.0178302 loss) +I0616 06:15:58.131139 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240266 (* 1 = 0.0240266 loss) +I0616 06:15:58.131142 9857 solver.cpp:571] Iteration 22920, lr = 0.001 +I0616 06:16:09.523105 9857 solver.cpp:242] Iteration 22940, loss = 0.491357 +I0616 06:16:09.523146 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163796 (* 1 = 0.163796 loss) +I0616 06:16:09.523152 9857 solver.cpp:258] Train net output #1: loss_cls = 0.224718 (* 1 = 0.224718 loss) +I0616 06:16:09.523156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113318 (* 1 = 0.113318 loss) +I0616 06:16:09.523160 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138411 (* 1 = 0.0138411 loss) +I0616 06:16:09.523164 9857 solver.cpp:571] Iteration 22940, lr = 0.001 +I0616 06:16:20.921238 9857 solver.cpp:242] Iteration 22960, loss = 1.19597 +I0616 06:16:20.921280 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318485 (* 1 = 0.318485 loss) +I0616 06:16:20.921286 9857 solver.cpp:258] Train net output #1: loss_cls = 0.886164 (* 1 = 0.886164 loss) +I0616 06:16:20.921290 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0998976 (* 1 = 0.0998976 loss) +I0616 06:16:20.921294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.019829 (* 1 = 0.019829 loss) +I0616 06:16:20.921298 9857 solver.cpp:571] Iteration 22960, lr = 0.001 +I0616 06:16:32.146775 9857 solver.cpp:242] Iteration 22980, loss = 1.0299 +I0616 06:16:32.146816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.518257 (* 1 = 0.518257 loss) +I0616 06:16:32.146822 9857 solver.cpp:258] Train net output #1: loss_cls = 0.719832 (* 1 = 0.719832 loss) +I0616 06:16:32.146826 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.217923 (* 1 = 0.217923 loss) +I0616 06:16:32.146831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483385 (* 1 = 0.0483385 loss) +I0616 06:16:32.146833 9857 solver.cpp:571] Iteration 22980, lr = 0.001 +speed: 0.654s / iter +I0616 06:16:43.739192 9857 solver.cpp:242] Iteration 23000, loss = 0.691642 +I0616 06:16:43.739234 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218306 (* 1 = 0.218306 loss) +I0616 06:16:43.739239 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242017 (* 1 = 0.242017 loss) +I0616 06:16:43.739243 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0383481 (* 1 = 0.0383481 loss) +I0616 06:16:43.739248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0395268 (* 1 = 0.0395268 loss) +I0616 06:16:43.739251 9857 solver.cpp:571] Iteration 23000, lr = 0.001 +I0616 06:16:55.379973 9857 solver.cpp:242] Iteration 23020, loss = 0.54204 +I0616 06:16:55.380017 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0674848 (* 1 = 0.0674848 loss) +I0616 06:16:55.380023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13528 (* 1 = 0.13528 loss) +I0616 06:16:55.380026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0539543 (* 1 = 0.0539543 loss) +I0616 06:16:55.380030 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231049 (* 1 = 0.0231049 loss) +I0616 06:16:55.380033 9857 solver.cpp:571] Iteration 23020, lr = 0.001 +I0616 06:17:06.981673 9857 solver.cpp:242] Iteration 23040, loss = 0.774514 +I0616 06:17:06.981716 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283947 (* 1 = 0.283947 loss) +I0616 06:17:06.981721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333622 (* 1 = 0.333622 loss) +I0616 06:17:06.981725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0219227 (* 1 = 0.0219227 loss) +I0616 06:17:06.981729 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360923 (* 1 = 0.0360923 loss) +I0616 06:17:06.981732 9857 solver.cpp:571] Iteration 23040, lr = 0.001 +I0616 06:17:18.440359 9857 solver.cpp:242] Iteration 23060, loss = 0.440989 +I0616 06:17:18.440402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228305 (* 1 = 0.228305 loss) +I0616 06:17:18.440407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200132 (* 1 = 0.200132 loss) +I0616 06:17:18.440412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0437085 (* 1 = 0.0437085 loss) +I0616 06:17:18.440415 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00115121 (* 1 = 0.00115121 loss) +I0616 06:17:18.440419 9857 solver.cpp:571] Iteration 23060, lr = 0.001 +I0616 06:17:30.355582 9857 solver.cpp:242] Iteration 23080, loss = 0.991276 +I0616 06:17:30.355624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225152 (* 1 = 0.225152 loss) +I0616 06:17:30.355630 9857 solver.cpp:258] Train net output #1: loss_cls = 0.444098 (* 1 = 0.444098 loss) +I0616 06:17:30.355634 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.461824 (* 1 = 0.461824 loss) +I0616 06:17:30.355638 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0428768 (* 1 = 0.0428768 loss) +I0616 06:17:30.355643 9857 solver.cpp:571] Iteration 23080, lr = 0.001 +I0616 06:17:42.033478 9857 solver.cpp:242] Iteration 23100, loss = 0.638535 +I0616 06:17:42.033519 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279686 (* 1 = 0.279686 loss) +I0616 06:17:42.033525 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270364 (* 1 = 0.270364 loss) +I0616 06:17:42.033529 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210011 (* 1 = 0.210011 loss) +I0616 06:17:42.033534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0177096 (* 1 = 0.0177096 loss) +I0616 06:17:42.033537 9857 solver.cpp:571] Iteration 23100, lr = 0.001 +I0616 06:17:53.658509 9857 solver.cpp:242] Iteration 23120, loss = 1.09718 +I0616 06:17:53.658551 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161661 (* 1 = 0.161661 loss) +I0616 06:17:53.658557 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309413 (* 1 = 0.309413 loss) +I0616 06:17:53.658561 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100499 (* 1 = 0.100499 loss) +I0616 06:17:53.658565 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116871 (* 1 = 0.116871 loss) +I0616 06:17:53.658570 9857 solver.cpp:571] Iteration 23120, lr = 0.001 +I0616 06:18:05.293874 9857 solver.cpp:242] Iteration 23140, loss = 1.05528 +I0616 06:18:05.293917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373982 (* 1 = 0.373982 loss) +I0616 06:18:05.293922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.615013 (* 1 = 0.615013 loss) +I0616 06:18:05.293926 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15188 (* 1 = 0.15188 loss) +I0616 06:18:05.293931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.063661 (* 1 = 0.063661 loss) +I0616 06:18:05.293936 9857 solver.cpp:571] Iteration 23140, lr = 0.001 +I0616 06:18:16.857266 9857 solver.cpp:242] Iteration 23160, loss = 0.912614 +I0616 06:18:16.857309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.48231 (* 1 = 0.48231 loss) +I0616 06:18:16.857316 9857 solver.cpp:258] Train net output #1: loss_cls = 0.76116 (* 1 = 0.76116 loss) +I0616 06:18:16.857319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.269597 (* 1 = 0.269597 loss) +I0616 06:18:16.857323 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.034231 (* 1 = 0.034231 loss) +I0616 06:18:16.857326 9857 solver.cpp:571] Iteration 23160, lr = 0.001 +I0616 06:18:28.146350 9857 solver.cpp:242] Iteration 23180, loss = 0.860486 +I0616 06:18:28.146392 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210323 (* 1 = 0.210323 loss) +I0616 06:18:28.146397 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232548 (* 1 = 0.232548 loss) +I0616 06:18:28.146401 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163907 (* 1 = 0.163907 loss) +I0616 06:18:28.146405 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.070261 (* 1 = 0.070261 loss) +I0616 06:18:28.146409 9857 solver.cpp:571] Iteration 23180, lr = 0.001 +speed: 0.654s / iter +I0616 06:18:39.619650 9857 solver.cpp:242] Iteration 23200, loss = 1.21424 +I0616 06:18:39.619690 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108003 (* 1 = 0.108003 loss) +I0616 06:18:39.619696 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155278 (* 1 = 0.155278 loss) +I0616 06:18:39.619700 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537336 (* 1 = 0.0537336 loss) +I0616 06:18:39.619704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00539059 (* 1 = 0.00539059 loss) +I0616 06:18:39.619709 9857 solver.cpp:571] Iteration 23200, lr = 0.001 +I0616 06:18:50.898772 9857 solver.cpp:242] Iteration 23220, loss = 0.855017 +I0616 06:18:50.898814 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.400095 (* 1 = 0.400095 loss) +I0616 06:18:50.898819 9857 solver.cpp:258] Train net output #1: loss_cls = 0.747851 (* 1 = 0.747851 loss) +I0616 06:18:50.898823 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116113 (* 1 = 0.116113 loss) +I0616 06:18:50.898828 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.060748 (* 1 = 0.060748 loss) +I0616 06:18:50.898831 9857 solver.cpp:571] Iteration 23220, lr = 0.001 +I0616 06:19:02.637437 9857 solver.cpp:242] Iteration 23240, loss = 0.499188 +I0616 06:19:02.637480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196666 (* 1 = 0.196666 loss) +I0616 06:19:02.637485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340097 (* 1 = 0.340097 loss) +I0616 06:19:02.637490 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.031621 (* 1 = 0.031621 loss) +I0616 06:19:02.637493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225811 (* 1 = 0.0225811 loss) +I0616 06:19:02.637497 9857 solver.cpp:571] Iteration 23240, lr = 0.001 +I0616 06:19:13.867866 9857 solver.cpp:242] Iteration 23260, loss = 1.02021 +I0616 06:19:13.867908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235919 (* 1 = 0.235919 loss) +I0616 06:19:13.867914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253202 (* 1 = 0.253202 loss) +I0616 06:19:13.867919 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0318147 (* 1 = 0.0318147 loss) +I0616 06:19:13.867923 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0587219 (* 1 = 0.0587219 loss) +I0616 06:19:13.867926 9857 solver.cpp:571] Iteration 23260, lr = 0.001 +I0616 06:19:25.280978 9857 solver.cpp:242] Iteration 23280, loss = 1.18216 +I0616 06:19:25.281020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.465449 (* 1 = 0.465449 loss) +I0616 06:19:25.281025 9857 solver.cpp:258] Train net output #1: loss_cls = 0.69396 (* 1 = 0.69396 loss) +I0616 06:19:25.281029 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.380043 (* 1 = 0.380043 loss) +I0616 06:19:25.281033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0832335 (* 1 = 0.0832335 loss) +I0616 06:19:25.281038 9857 solver.cpp:571] Iteration 23280, lr = 0.001 +I0616 06:19:36.962998 9857 solver.cpp:242] Iteration 23300, loss = 0.865762 +I0616 06:19:36.963039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336093 (* 1 = 0.336093 loss) +I0616 06:19:36.963044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375894 (* 1 = 0.375894 loss) +I0616 06:19:36.963048 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139389 (* 1 = 0.139389 loss) +I0616 06:19:36.963052 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0566373 (* 1 = 0.0566373 loss) +I0616 06:19:36.963057 9857 solver.cpp:571] Iteration 23300, lr = 0.001 +I0616 06:19:48.337318 9857 solver.cpp:242] Iteration 23320, loss = 1.40847 +I0616 06:19:48.337358 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453612 (* 1 = 0.453612 loss) +I0616 06:19:48.337364 9857 solver.cpp:258] Train net output #1: loss_cls = 1.49574 (* 1 = 1.49574 loss) +I0616 06:19:48.337368 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149533 (* 1 = 0.149533 loss) +I0616 06:19:48.337373 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0458877 (* 1 = 0.0458877 loss) +I0616 06:19:48.337376 9857 solver.cpp:571] Iteration 23320, lr = 0.001 +I0616 06:19:59.681092 9857 solver.cpp:242] Iteration 23340, loss = 1.20865 +I0616 06:19:59.681133 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.246447 (* 1 = 0.246447 loss) +I0616 06:19:59.681139 9857 solver.cpp:258] Train net output #1: loss_cls = 0.515323 (* 1 = 0.515323 loss) +I0616 06:19:59.681143 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.170049 (* 1 = 0.170049 loss) +I0616 06:19:59.681148 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.323747 (* 1 = 0.323747 loss) +I0616 06:19:59.681151 9857 solver.cpp:571] Iteration 23340, lr = 0.001 +I0616 06:20:11.350486 9857 solver.cpp:242] Iteration 23360, loss = 0.639299 +I0616 06:20:11.350528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199423 (* 1 = 0.199423 loss) +I0616 06:20:11.350534 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142639 (* 1 = 0.142639 loss) +I0616 06:20:11.350538 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0709591 (* 1 = 0.0709591 loss) +I0616 06:20:11.350541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00786603 (* 1 = 0.00786603 loss) +I0616 06:20:11.350545 9857 solver.cpp:571] Iteration 23360, lr = 0.001 +I0616 06:20:22.978124 9857 solver.cpp:242] Iteration 23380, loss = 0.456902 +I0616 06:20:22.978163 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108933 (* 1 = 0.108933 loss) +I0616 06:20:22.978169 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171401 (* 1 = 0.171401 loss) +I0616 06:20:22.978173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0641423 (* 1 = 0.0641423 loss) +I0616 06:20:22.978176 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175637 (* 1 = 0.0175637 loss) +I0616 06:20:22.978180 9857 solver.cpp:571] Iteration 23380, lr = 0.001 +speed: 0.653s / iter +I0616 06:20:34.551621 9857 solver.cpp:242] Iteration 23400, loss = 1.22678 +I0616 06:20:34.551661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.471705 (* 1 = 0.471705 loss) +I0616 06:20:34.551667 9857 solver.cpp:258] Train net output #1: loss_cls = 0.807645 (* 1 = 0.807645 loss) +I0616 06:20:34.551671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.182484 (* 1 = 0.182484 loss) +I0616 06:20:34.551674 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.229993 (* 1 = 0.229993 loss) +I0616 06:20:34.551678 9857 solver.cpp:571] Iteration 23400, lr = 0.001 +I0616 06:20:46.005046 9857 solver.cpp:242] Iteration 23420, loss = 1.48247 +I0616 06:20:46.005089 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308914 (* 1 = 0.308914 loss) +I0616 06:20:46.005095 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402818 (* 1 = 0.402818 loss) +I0616 06:20:46.005098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136785 (* 1 = 0.136785 loss) +I0616 06:20:46.005102 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0476514 (* 1 = 0.0476514 loss) +I0616 06:20:46.005106 9857 solver.cpp:571] Iteration 23420, lr = 0.001 +I0616 06:20:57.720325 9857 solver.cpp:242] Iteration 23440, loss = 0.84431 +I0616 06:20:57.720368 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355663 (* 1 = 0.355663 loss) +I0616 06:20:57.720373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23069 (* 1 = 0.23069 loss) +I0616 06:20:57.720377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143267 (* 1 = 0.143267 loss) +I0616 06:20:57.720381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0343638 (* 1 = 0.0343638 loss) +I0616 06:20:57.720386 9857 solver.cpp:571] Iteration 23440, lr = 0.001 +I0616 06:21:09.222409 9857 solver.cpp:242] Iteration 23460, loss = 1.02283 +I0616 06:21:09.222450 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341046 (* 1 = 0.341046 loss) +I0616 06:21:09.222455 9857 solver.cpp:258] Train net output #1: loss_cls = 0.683058 (* 1 = 0.683058 loss) +I0616 06:21:09.222460 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133142 (* 1 = 0.133142 loss) +I0616 06:21:09.222463 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0392314 (* 1 = 0.0392314 loss) +I0616 06:21:09.222466 9857 solver.cpp:571] Iteration 23460, lr = 0.001 +I0616 06:21:20.897961 9857 solver.cpp:242] Iteration 23480, loss = 0.739591 +I0616 06:21:20.898003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1052 (* 1 = 0.1052 loss) +I0616 06:21:20.898008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.681547 (* 1 = 0.681547 loss) +I0616 06:21:20.898012 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0377698 (* 1 = 0.0377698 loss) +I0616 06:21:20.898016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156487 (* 1 = 0.0156487 loss) +I0616 06:21:20.898020 9857 solver.cpp:571] Iteration 23480, lr = 0.001 +I0616 06:21:32.402845 9857 solver.cpp:242] Iteration 23500, loss = 0.551899 +I0616 06:21:32.402886 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17691 (* 1 = 0.17691 loss) +I0616 06:21:32.402891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180414 (* 1 = 0.180414 loss) +I0616 06:21:32.402895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0476952 (* 1 = 0.0476952 loss) +I0616 06:21:32.402899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0459078 (* 1 = 0.0459078 loss) +I0616 06:21:32.402904 9857 solver.cpp:571] Iteration 23500, lr = 0.001 +I0616 06:21:43.791174 9857 solver.cpp:242] Iteration 23520, loss = 0.454347 +I0616 06:21:43.791216 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157982 (* 1 = 0.157982 loss) +I0616 06:21:43.791223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16302 (* 1 = 0.16302 loss) +I0616 06:21:43.791226 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209054 (* 1 = 0.0209054 loss) +I0616 06:21:43.791230 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00999556 (* 1 = 0.00999556 loss) +I0616 06:21:43.791234 9857 solver.cpp:571] Iteration 23520, lr = 0.001 +I0616 06:21:55.523795 9857 solver.cpp:242] Iteration 23540, loss = 0.665629 +I0616 06:21:55.523838 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148963 (* 1 = 0.148963 loss) +I0616 06:21:55.523844 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205699 (* 1 = 0.205699 loss) +I0616 06:21:55.523849 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166005 (* 1 = 0.166005 loss) +I0616 06:21:55.523852 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202933 (* 1 = 0.0202933 loss) +I0616 06:21:55.523856 9857 solver.cpp:571] Iteration 23540, lr = 0.001 +I0616 06:22:07.175171 9857 solver.cpp:242] Iteration 23560, loss = 1.81044 +I0616 06:22:07.175215 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39252 (* 1 = 0.39252 loss) +I0616 06:22:07.175220 9857 solver.cpp:258] Train net output #1: loss_cls = 0.414463 (* 1 = 0.414463 loss) +I0616 06:22:07.175225 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118674 (* 1 = 0.118674 loss) +I0616 06:22:07.175228 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.042687 (* 1 = 0.042687 loss) +I0616 06:22:07.175232 9857 solver.cpp:571] Iteration 23560, lr = 0.001 +I0616 06:22:18.649690 9857 solver.cpp:242] Iteration 23580, loss = 0.983203 +I0616 06:22:18.649732 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436549 (* 1 = 0.436549 loss) +I0616 06:22:18.649739 9857 solver.cpp:258] Train net output #1: loss_cls = 0.50424 (* 1 = 0.50424 loss) +I0616 06:22:18.649742 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.095375 (* 1 = 0.095375 loss) +I0616 06:22:18.649745 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.113212 (* 1 = 0.113212 loss) +I0616 06:22:18.649749 9857 solver.cpp:571] Iteration 23580, lr = 0.001 +speed: 0.652s / iter +I0616 06:22:30.433699 9857 solver.cpp:242] Iteration 23600, loss = 0.636307 +I0616 06:22:30.433743 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0828319 (* 1 = 0.0828319 loss) +I0616 06:22:30.433748 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168273 (* 1 = 0.168273 loss) +I0616 06:22:30.433753 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0982252 (* 1 = 0.0982252 loss) +I0616 06:22:30.433756 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247211 (* 1 = 0.0247211 loss) +I0616 06:22:30.433760 9857 solver.cpp:571] Iteration 23600, lr = 0.001 +I0616 06:22:41.997737 9857 solver.cpp:242] Iteration 23620, loss = 0.776354 +I0616 06:22:41.997779 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124231 (* 1 = 0.124231 loss) +I0616 06:22:41.997784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148184 (* 1 = 0.148184 loss) +I0616 06:22:41.997788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0478952 (* 1 = 0.0478952 loss) +I0616 06:22:41.997792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220018 (* 1 = 0.0220018 loss) +I0616 06:22:41.997797 9857 solver.cpp:571] Iteration 23620, lr = 0.001 +I0616 06:22:53.441246 9857 solver.cpp:242] Iteration 23640, loss = 1.0525 +I0616 06:22:53.441288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113912 (* 1 = 0.113912 loss) +I0616 06:22:53.441294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332522 (* 1 = 0.332522 loss) +I0616 06:22:53.441298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.333086 (* 1 = 0.333086 loss) +I0616 06:22:53.441301 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.124306 (* 1 = 0.124306 loss) +I0616 06:22:53.441305 9857 solver.cpp:571] Iteration 23640, lr = 0.001 +I0616 06:23:05.082105 9857 solver.cpp:242] Iteration 23660, loss = 0.591785 +I0616 06:23:05.082146 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106344 (* 1 = 0.106344 loss) +I0616 06:23:05.082151 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10568 (* 1 = 0.10568 loss) +I0616 06:23:05.082156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0548078 (* 1 = 0.0548078 loss) +I0616 06:23:05.082159 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00708084 (* 1 = 0.00708084 loss) +I0616 06:23:05.082164 9857 solver.cpp:571] Iteration 23660, lr = 0.001 +I0616 06:23:16.594918 9857 solver.cpp:242] Iteration 23680, loss = 0.624281 +I0616 06:23:16.594959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217008 (* 1 = 0.217008 loss) +I0616 06:23:16.594964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262607 (* 1 = 0.262607 loss) +I0616 06:23:16.594969 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0129145 (* 1 = 0.0129145 loss) +I0616 06:23:16.594972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00765527 (* 1 = 0.00765527 loss) +I0616 06:23:16.594975 9857 solver.cpp:571] Iteration 23680, lr = 0.001 +I0616 06:23:28.138054 9857 solver.cpp:242] Iteration 23700, loss = 0.571238 +I0616 06:23:28.138095 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129603 (* 1 = 0.129603 loss) +I0616 06:23:28.138101 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237417 (* 1 = 0.237417 loss) +I0616 06:23:28.138104 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12696 (* 1 = 0.12696 loss) +I0616 06:23:28.138108 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0648167 (* 1 = 0.0648167 loss) +I0616 06:23:28.138113 9857 solver.cpp:571] Iteration 23700, lr = 0.001 +I0616 06:23:39.668404 9857 solver.cpp:242] Iteration 23720, loss = 1.24392 +I0616 06:23:39.668447 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213948 (* 1 = 0.213948 loss) +I0616 06:23:39.668452 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315492 (* 1 = 0.315492 loss) +I0616 06:23:39.668457 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.234866 (* 1 = 0.234866 loss) +I0616 06:23:39.668460 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.031187 (* 1 = 0.031187 loss) +I0616 06:23:39.668464 9857 solver.cpp:571] Iteration 23720, lr = 0.001 +I0616 06:23:51.079401 9857 solver.cpp:242] Iteration 23740, loss = 1.54113 +I0616 06:23:51.079443 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.483153 (* 1 = 0.483153 loss) +I0616 06:23:51.079448 9857 solver.cpp:258] Train net output #1: loss_cls = 0.96727 (* 1 = 0.96727 loss) +I0616 06:23:51.079453 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.39503 (* 1 = 0.39503 loss) +I0616 06:23:51.079457 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.40165 (* 1 = 0.40165 loss) +I0616 06:23:51.079460 9857 solver.cpp:571] Iteration 23740, lr = 0.001 +I0616 06:24:02.602296 9857 solver.cpp:242] Iteration 23760, loss = 1.15332 +I0616 06:24:02.602339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29396 (* 1 = 0.29396 loss) +I0616 06:24:02.602344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.369446 (* 1 = 0.369446 loss) +I0616 06:24:02.602349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0252386 (* 1 = 0.0252386 loss) +I0616 06:24:02.602352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0426372 (* 1 = 0.0426372 loss) +I0616 06:24:02.602356 9857 solver.cpp:571] Iteration 23760, lr = 0.001 +I0616 06:24:14.216089 9857 solver.cpp:242] Iteration 23780, loss = 0.575347 +I0616 06:24:14.216131 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17572 (* 1 = 0.17572 loss) +I0616 06:24:14.216136 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168691 (* 1 = 0.168691 loss) +I0616 06:24:14.216141 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0061114 (* 1 = 0.0061114 loss) +I0616 06:24:14.216145 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00519927 (* 1 = 0.00519927 loss) +I0616 06:24:14.216150 9857 solver.cpp:571] Iteration 23780, lr = 0.001 +speed: 0.652s / iter +I0616 06:24:25.839850 9857 solver.cpp:242] Iteration 23800, loss = 0.671956 +I0616 06:24:25.839891 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151324 (* 1 = 0.151324 loss) +I0616 06:24:25.839897 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237072 (* 1 = 0.237072 loss) +I0616 06:24:25.839901 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0883051 (* 1 = 0.0883051 loss) +I0616 06:24:25.839905 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0154421 (* 1 = 0.0154421 loss) +I0616 06:24:25.839910 9857 solver.cpp:571] Iteration 23800, lr = 0.001 +I0616 06:24:37.574300 9857 solver.cpp:242] Iteration 23820, loss = 1.28691 +I0616 06:24:37.574342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.42038 (* 1 = 0.42038 loss) +I0616 06:24:37.574347 9857 solver.cpp:258] Train net output #1: loss_cls = 0.687255 (* 1 = 0.687255 loss) +I0616 06:24:37.574352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.313747 (* 1 = 0.313747 loss) +I0616 06:24:37.574355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129928 (* 1 = 0.129928 loss) +I0616 06:24:37.574359 9857 solver.cpp:571] Iteration 23820, lr = 0.001 +I0616 06:24:49.129842 9857 solver.cpp:242] Iteration 23840, loss = 0.696843 +I0616 06:24:49.129883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115973 (* 1 = 0.115973 loss) +I0616 06:24:49.129889 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160429 (* 1 = 0.160429 loss) +I0616 06:24:49.129892 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0381487 (* 1 = 0.0381487 loss) +I0616 06:24:49.129895 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00791891 (* 1 = 0.00791891 loss) +I0616 06:24:49.129899 9857 solver.cpp:571] Iteration 23840, lr = 0.001 +I0616 06:25:00.671217 9857 solver.cpp:242] Iteration 23860, loss = 0.518544 +I0616 06:25:00.671270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125575 (* 1 = 0.125575 loss) +I0616 06:25:00.671277 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194802 (* 1 = 0.194802 loss) +I0616 06:25:00.671280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0664235 (* 1 = 0.0664235 loss) +I0616 06:25:00.671284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189845 (* 1 = 0.0189845 loss) +I0616 06:25:00.671288 9857 solver.cpp:571] Iteration 23860, lr = 0.001 +I0616 06:25:12.123381 9857 solver.cpp:242] Iteration 23880, loss = 1.09817 +I0616 06:25:12.123423 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.397371 (* 1 = 0.397371 loss) +I0616 06:25:12.123428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.468091 (* 1 = 0.468091 loss) +I0616 06:25:12.123432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119513 (* 1 = 0.119513 loss) +I0616 06:25:12.123436 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116717 (* 1 = 0.116717 loss) +I0616 06:25:12.123442 9857 solver.cpp:571] Iteration 23880, lr = 0.001 +I0616 06:25:23.399538 9857 solver.cpp:242] Iteration 23900, loss = 0.456849 +I0616 06:25:23.399582 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221197 (* 1 = 0.221197 loss) +I0616 06:25:23.399587 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197351 (* 1 = 0.197351 loss) +I0616 06:25:23.399591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0259555 (* 1 = 0.0259555 loss) +I0616 06:25:23.399595 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360642 (* 1 = 0.0360642 loss) +I0616 06:25:23.399598 9857 solver.cpp:571] Iteration 23900, lr = 0.001 +I0616 06:25:34.952973 9857 solver.cpp:242] Iteration 23920, loss = 0.503382 +I0616 06:25:34.953016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120244 (* 1 = 0.120244 loss) +I0616 06:25:34.953021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165392 (* 1 = 0.165392 loss) +I0616 06:25:34.953024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0875468 (* 1 = 0.0875468 loss) +I0616 06:25:34.953028 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00122344 (* 1 = 0.00122344 loss) +I0616 06:25:34.953032 9857 solver.cpp:571] Iteration 23920, lr = 0.001 +I0616 06:25:46.344377 9857 solver.cpp:242] Iteration 23940, loss = 0.791511 +I0616 06:25:46.344419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.426679 (* 1 = 0.426679 loss) +I0616 06:25:46.344425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.347902 (* 1 = 0.347902 loss) +I0616 06:25:46.344429 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0625838 (* 1 = 0.0625838 loss) +I0616 06:25:46.344434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169928 (* 1 = 0.0169928 loss) +I0616 06:25:46.344436 9857 solver.cpp:571] Iteration 23940, lr = 0.001 +I0616 06:25:57.973971 9857 solver.cpp:242] Iteration 23960, loss = 0.99714 +I0616 06:25:57.974012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329961 (* 1 = 0.329961 loss) +I0616 06:25:57.974019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384386 (* 1 = 0.384386 loss) +I0616 06:25:57.974022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197656 (* 1 = 0.197656 loss) +I0616 06:25:57.974026 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0548396 (* 1 = 0.0548396 loss) +I0616 06:25:57.974030 9857 solver.cpp:571] Iteration 23960, lr = 0.001 +I0616 06:26:09.335877 9857 solver.cpp:242] Iteration 23980, loss = 1.31488 +I0616 06:26:09.335919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.617343 (* 1 = 0.617343 loss) +I0616 06:26:09.335925 9857 solver.cpp:258] Train net output #1: loss_cls = 0.578603 (* 1 = 0.578603 loss) +I0616 06:26:09.335929 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.340404 (* 1 = 0.340404 loss) +I0616 06:26:09.335933 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.100736 (* 1 = 0.100736 loss) +I0616 06:26:09.335937 9857 solver.cpp:571] Iteration 23980, lr = 0.001 +speed: 0.651s / iter +I0616 06:26:20.875172 9857 solver.cpp:242] Iteration 24000, loss = 0.555969 +I0616 06:26:20.875214 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0804508 (* 1 = 0.0804508 loss) +I0616 06:26:20.875221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260577 (* 1 = 0.260577 loss) +I0616 06:26:20.875224 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19194 (* 1 = 0.19194 loss) +I0616 06:26:20.875228 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0021118 (* 1 = 0.0021118 loss) +I0616 06:26:20.875232 9857 solver.cpp:571] Iteration 24000, lr = 0.001 +I0616 06:26:32.470288 9857 solver.cpp:242] Iteration 24020, loss = 0.742545 +I0616 06:26:32.470330 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154647 (* 1 = 0.154647 loss) +I0616 06:26:32.470336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244515 (* 1 = 0.244515 loss) +I0616 06:26:32.470340 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175982 (* 1 = 0.175982 loss) +I0616 06:26:32.470345 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183921 (* 1 = 0.0183921 loss) +I0616 06:26:32.470348 9857 solver.cpp:571] Iteration 24020, lr = 0.001 +I0616 06:26:43.797300 9857 solver.cpp:242] Iteration 24040, loss = 1.09538 +I0616 06:26:43.797343 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.245939 (* 1 = 0.245939 loss) +I0616 06:26:43.797348 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28762 (* 1 = 0.28762 loss) +I0616 06:26:43.797353 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194056 (* 1 = 0.194056 loss) +I0616 06:26:43.797356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0434816 (* 1 = 0.0434816 loss) +I0616 06:26:43.797360 9857 solver.cpp:571] Iteration 24040, lr = 0.001 +I0616 06:26:55.430155 9857 solver.cpp:242] Iteration 24060, loss = 1.01735 +I0616 06:26:55.430198 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169207 (* 1 = 0.169207 loss) +I0616 06:26:55.430203 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223 (* 1 = 0.223 loss) +I0616 06:26:55.430207 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.245184 (* 1 = 0.245184 loss) +I0616 06:26:55.430212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.280145 (* 1 = 0.280145 loss) +I0616 06:26:55.430215 9857 solver.cpp:571] Iteration 24060, lr = 0.001 +I0616 06:27:07.172297 9857 solver.cpp:242] Iteration 24080, loss = 1.26174 +I0616 06:27:07.172338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.504084 (* 1 = 0.504084 loss) +I0616 06:27:07.172343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.61898 (* 1 = 0.61898 loss) +I0616 06:27:07.172348 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.248047 (* 1 = 0.248047 loss) +I0616 06:27:07.172351 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0636255 (* 1 = 0.0636255 loss) +I0616 06:27:07.172358 9857 solver.cpp:571] Iteration 24080, lr = 0.001 +I0616 06:27:18.879092 9857 solver.cpp:242] Iteration 24100, loss = 1.24008 +I0616 06:27:18.879133 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.42299 (* 1 = 0.42299 loss) +I0616 06:27:18.879138 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49956 (* 1 = 0.49956 loss) +I0616 06:27:18.879142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.283117 (* 1 = 0.283117 loss) +I0616 06:27:18.879147 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0563711 (* 1 = 0.0563711 loss) +I0616 06:27:18.879150 9857 solver.cpp:571] Iteration 24100, lr = 0.001 +I0616 06:27:30.332185 9857 solver.cpp:242] Iteration 24120, loss = 0.860755 +I0616 06:27:30.332227 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0979228 (* 1 = 0.0979228 loss) +I0616 06:27:30.332232 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130269 (* 1 = 0.130269 loss) +I0616 06:27:30.332237 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0146424 (* 1 = 0.0146424 loss) +I0616 06:27:30.332240 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0141165 (* 1 = 0.0141165 loss) +I0616 06:27:30.332244 9857 solver.cpp:571] Iteration 24120, lr = 0.001 +I0616 06:27:41.677161 9857 solver.cpp:242] Iteration 24140, loss = 0.663004 +I0616 06:27:41.677203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260734 (* 1 = 0.260734 loss) +I0616 06:27:41.677208 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305245 (* 1 = 0.305245 loss) +I0616 06:27:41.677212 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0749863 (* 1 = 0.0749863 loss) +I0616 06:27:41.677217 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00575053 (* 1 = 0.00575053 loss) +I0616 06:27:41.677220 9857 solver.cpp:571] Iteration 24140, lr = 0.001 +I0616 06:27:53.124202 9857 solver.cpp:242] Iteration 24160, loss = 1.13789 +I0616 06:27:53.124244 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331907 (* 1 = 0.331907 loss) +I0616 06:27:53.124249 9857 solver.cpp:258] Train net output #1: loss_cls = 0.793508 (* 1 = 0.793508 loss) +I0616 06:27:53.124253 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151345 (* 1 = 0.151345 loss) +I0616 06:27:53.124258 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.157826 (* 1 = 0.157826 loss) +I0616 06:27:53.124263 9857 solver.cpp:571] Iteration 24160, lr = 0.001 +I0616 06:28:04.648239 9857 solver.cpp:242] Iteration 24180, loss = 0.634828 +I0616 06:28:04.648282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38262 (* 1 = 0.38262 loss) +I0616 06:28:04.648288 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340821 (* 1 = 0.340821 loss) +I0616 06:28:04.648291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0256569 (* 1 = 0.0256569 loss) +I0616 06:28:04.648295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.035696 (* 1 = 0.035696 loss) +I0616 06:28:04.648299 9857 solver.cpp:571] Iteration 24180, lr = 0.001 +speed: 0.650s / iter +I0616 06:28:16.146543 9857 solver.cpp:242] Iteration 24200, loss = 0.712444 +I0616 06:28:16.146584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250226 (* 1 = 0.250226 loss) +I0616 06:28:16.146589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.376091 (* 1 = 0.376091 loss) +I0616 06:28:16.146594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22874 (* 1 = 0.22874 loss) +I0616 06:28:16.146597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.190087 (* 1 = 0.190087 loss) +I0616 06:28:16.146601 9857 solver.cpp:571] Iteration 24200, lr = 0.001 +I0616 06:28:27.845947 9857 solver.cpp:242] Iteration 24220, loss = 1.02283 +I0616 06:28:27.845988 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.451451 (* 1 = 0.451451 loss) +I0616 06:28:27.845994 9857 solver.cpp:258] Train net output #1: loss_cls = 0.851972 (* 1 = 0.851972 loss) +I0616 06:28:27.845999 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155466 (* 1 = 0.155466 loss) +I0616 06:28:27.846002 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0574193 (* 1 = 0.0574193 loss) +I0616 06:28:27.846006 9857 solver.cpp:571] Iteration 24220, lr = 0.001 +I0616 06:28:39.246116 9857 solver.cpp:242] Iteration 24240, loss = 1.17662 +I0616 06:28:39.246160 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.584267 (* 1 = 0.584267 loss) +I0616 06:28:39.246165 9857 solver.cpp:258] Train net output #1: loss_cls = 0.530994 (* 1 = 0.530994 loss) +I0616 06:28:39.246170 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.264813 (* 1 = 0.264813 loss) +I0616 06:28:39.246172 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.183302 (* 1 = 0.183302 loss) +I0616 06:28:39.246176 9857 solver.cpp:571] Iteration 24240, lr = 0.001 +I0616 06:28:50.772588 9857 solver.cpp:242] Iteration 24260, loss = 1.34817 +I0616 06:28:50.772631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.500272 (* 1 = 0.500272 loss) +I0616 06:28:50.772637 9857 solver.cpp:258] Train net output #1: loss_cls = 1.41569 (* 1 = 1.41569 loss) +I0616 06:28:50.772641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179738 (* 1 = 0.179738 loss) +I0616 06:28:50.772645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221593 (* 1 = 0.0221593 loss) +I0616 06:28:50.772650 9857 solver.cpp:571] Iteration 24260, lr = 0.001 +I0616 06:29:02.328589 9857 solver.cpp:242] Iteration 24280, loss = 1.47793 +I0616 06:29:02.328632 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345346 (* 1 = 0.345346 loss) +I0616 06:29:02.328637 9857 solver.cpp:258] Train net output #1: loss_cls = 1.0007 (* 1 = 1.0007 loss) +I0616 06:29:02.328641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.253713 (* 1 = 0.253713 loss) +I0616 06:29:02.328645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.176842 (* 1 = 0.176842 loss) +I0616 06:29:02.328649 9857 solver.cpp:571] Iteration 24280, lr = 0.001 +I0616 06:29:13.727051 9857 solver.cpp:242] Iteration 24300, loss = 0.956262 +I0616 06:29:13.727092 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371898 (* 1 = 0.371898 loss) +I0616 06:29:13.727097 9857 solver.cpp:258] Train net output #1: loss_cls = 0.701799 (* 1 = 0.701799 loss) +I0616 06:29:13.727102 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157335 (* 1 = 0.157335 loss) +I0616 06:29:13.727105 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.049113 (* 1 = 0.049113 loss) +I0616 06:29:13.727109 9857 solver.cpp:571] Iteration 24300, lr = 0.001 +I0616 06:29:25.248394 9857 solver.cpp:242] Iteration 24320, loss = 1.14496 +I0616 06:29:25.248436 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331225 (* 1 = 0.331225 loss) +I0616 06:29:25.248441 9857 solver.cpp:258] Train net output #1: loss_cls = 1.15408 (* 1 = 1.15408 loss) +I0616 06:29:25.248446 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0152662 (* 1 = 0.0152662 loss) +I0616 06:29:25.248450 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285808 (* 1 = 0.0285808 loss) +I0616 06:29:25.248453 9857 solver.cpp:571] Iteration 24320, lr = 0.001 +I0616 06:29:36.938738 9857 solver.cpp:242] Iteration 24340, loss = 1.77957 +I0616 06:29:36.938781 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355188 (* 1 = 0.355188 loss) +I0616 06:29:36.938787 9857 solver.cpp:258] Train net output #1: loss_cls = 0.66113 (* 1 = 0.66113 loss) +I0616 06:29:36.938791 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.269855 (* 1 = 0.269855 loss) +I0616 06:29:36.938796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.435542 (* 1 = 0.435542 loss) +I0616 06:29:36.938799 9857 solver.cpp:571] Iteration 24340, lr = 0.001 +I0616 06:29:48.622797 9857 solver.cpp:242] Iteration 24360, loss = 1.12602 +I0616 06:29:48.622838 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282337 (* 1 = 0.282337 loss) +I0616 06:29:48.622844 9857 solver.cpp:258] Train net output #1: loss_cls = 0.542568 (* 1 = 0.542568 loss) +I0616 06:29:48.622848 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126245 (* 1 = 0.126245 loss) +I0616 06:29:48.622853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00445859 (* 1 = 0.00445859 loss) +I0616 06:29:48.622856 9857 solver.cpp:571] Iteration 24360, lr = 0.001 +I0616 06:30:00.288641 9857 solver.cpp:242] Iteration 24380, loss = 0.704431 +I0616 06:30:00.288683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138208 (* 1 = 0.138208 loss) +I0616 06:30:00.288691 9857 solver.cpp:258] Train net output #1: loss_cls = 0.642289 (* 1 = 0.642289 loss) +I0616 06:30:00.288694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0284101 (* 1 = 0.0284101 loss) +I0616 06:30:00.288698 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108558 (* 1 = 0.0108558 loss) +I0616 06:30:00.288702 9857 solver.cpp:571] Iteration 24380, lr = 0.001 +speed: 0.650s / iter +I0616 06:30:11.889510 9857 solver.cpp:242] Iteration 24400, loss = 0.935113 +I0616 06:30:11.889551 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206143 (* 1 = 0.206143 loss) +I0616 06:30:11.889556 9857 solver.cpp:258] Train net output #1: loss_cls = 0.414765 (* 1 = 0.414765 loss) +I0616 06:30:11.889561 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0322651 (* 1 = 0.0322651 loss) +I0616 06:30:11.889565 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105393 (* 1 = 0.0105393 loss) +I0616 06:30:11.889569 9857 solver.cpp:571] Iteration 24400, lr = 0.001 +I0616 06:30:23.471846 9857 solver.cpp:242] Iteration 24420, loss = 0.83669 +I0616 06:30:23.471889 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200411 (* 1 = 0.200411 loss) +I0616 06:30:23.471894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.77082 (* 1 = 0.77082 loss) +I0616 06:30:23.471897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0992282 (* 1 = 0.0992282 loss) +I0616 06:30:23.471901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0190681 (* 1 = 0.0190681 loss) +I0616 06:30:23.471904 9857 solver.cpp:571] Iteration 24420, lr = 0.001 +I0616 06:30:35.148638 9857 solver.cpp:242] Iteration 24440, loss = 0.789999 +I0616 06:30:35.148679 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157038 (* 1 = 0.157038 loss) +I0616 06:30:35.148684 9857 solver.cpp:258] Train net output #1: loss_cls = 0.433437 (* 1 = 0.433437 loss) +I0616 06:30:35.148689 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.187655 (* 1 = 0.187655 loss) +I0616 06:30:35.148692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0277136 (* 1 = 0.0277136 loss) +I0616 06:30:35.148696 9857 solver.cpp:571] Iteration 24440, lr = 0.001 +I0616 06:30:46.862696 9857 solver.cpp:242] Iteration 24460, loss = 1.15386 +I0616 06:30:46.862738 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.469539 (* 1 = 0.469539 loss) +I0616 06:30:46.862743 9857 solver.cpp:258] Train net output #1: loss_cls = 0.828022 (* 1 = 0.828022 loss) +I0616 06:30:46.862747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164688 (* 1 = 0.164688 loss) +I0616 06:30:46.862751 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0486281 (* 1 = 0.0486281 loss) +I0616 06:30:46.862759 9857 solver.cpp:571] Iteration 24460, lr = 0.001 +I0616 06:30:58.205523 9857 solver.cpp:242] Iteration 24480, loss = 0.797019 +I0616 06:30:58.205566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120966 (* 1 = 0.120966 loss) +I0616 06:30:58.205571 9857 solver.cpp:258] Train net output #1: loss_cls = 0.336325 (* 1 = 0.336325 loss) +I0616 06:30:58.205575 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.221583 (* 1 = 0.221583 loss) +I0616 06:30:58.205579 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.12709 (* 1 = 0.12709 loss) +I0616 06:30:58.205584 9857 solver.cpp:571] Iteration 24480, lr = 0.001 +I0616 06:31:09.727131 9857 solver.cpp:242] Iteration 24500, loss = 0.447294 +I0616 06:31:09.727174 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122056 (* 1 = 0.122056 loss) +I0616 06:31:09.727180 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266394 (* 1 = 0.266394 loss) +I0616 06:31:09.727183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141268 (* 1 = 0.141268 loss) +I0616 06:31:09.727186 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189985 (* 1 = 0.0189985 loss) +I0616 06:31:09.727190 9857 solver.cpp:571] Iteration 24500, lr = 0.001 +I0616 06:31:21.278713 9857 solver.cpp:242] Iteration 24520, loss = 0.95944 +I0616 06:31:21.278759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0688456 (* 1 = 0.0688456 loss) +I0616 06:31:21.278764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187332 (* 1 = 0.187332 loss) +I0616 06:31:21.278769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555997 (* 1 = 0.0555997 loss) +I0616 06:31:21.278772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170405 (* 1 = 0.0170405 loss) +I0616 06:31:21.278776 9857 solver.cpp:571] Iteration 24520, lr = 0.001 +I0616 06:31:32.441289 9857 solver.cpp:242] Iteration 24540, loss = 1.0504 +I0616 06:31:32.441330 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373483 (* 1 = 0.373483 loss) +I0616 06:31:32.441335 9857 solver.cpp:258] Train net output #1: loss_cls = 0.652942 (* 1 = 0.652942 loss) +I0616 06:31:32.441339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.240558 (* 1 = 0.240558 loss) +I0616 06:31:32.441344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.248156 (* 1 = 0.248156 loss) +I0616 06:31:32.441349 9857 solver.cpp:571] Iteration 24540, lr = 0.001 +I0616 06:31:44.169834 9857 solver.cpp:242] Iteration 24560, loss = 1.73168 +I0616 06:31:44.169879 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396266 (* 1 = 0.396266 loss) +I0616 06:31:44.169899 9857 solver.cpp:258] Train net output #1: loss_cls = 0.754669 (* 1 = 0.754669 loss) +I0616 06:31:44.169903 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117591 (* 1 = 0.117591 loss) +I0616 06:31:44.169909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0176806 (* 1 = 0.0176806 loss) +I0616 06:31:44.169912 9857 solver.cpp:571] Iteration 24560, lr = 0.001 +I0616 06:31:55.753039 9857 solver.cpp:242] Iteration 24580, loss = 0.821413 +I0616 06:31:55.753080 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234614 (* 1 = 0.234614 loss) +I0616 06:31:55.753087 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351316 (* 1 = 0.351316 loss) +I0616 06:31:55.753090 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294809 (* 1 = 0.294809 loss) +I0616 06:31:55.753093 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.046554 (* 1 = 0.046554 loss) +I0616 06:31:55.753098 9857 solver.cpp:571] Iteration 24580, lr = 0.001 +speed: 0.649s / iter +I0616 06:32:07.255585 9857 solver.cpp:242] Iteration 24600, loss = 1.28518 +I0616 06:32:07.255627 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.495921 (* 1 = 0.495921 loss) +I0616 06:32:07.255632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.794359 (* 1 = 0.794359 loss) +I0616 06:32:07.255636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.270509 (* 1 = 0.270509 loss) +I0616 06:32:07.255640 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0557437 (* 1 = 0.0557437 loss) +I0616 06:32:07.255645 9857 solver.cpp:571] Iteration 24600, lr = 0.001 +I0616 06:32:18.694900 9857 solver.cpp:242] Iteration 24620, loss = 1.04171 +I0616 06:32:18.694942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.46 (* 1 = 0.46 loss) +I0616 06:32:18.694948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401973 (* 1 = 0.401973 loss) +I0616 06:32:18.694952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235367 (* 1 = 0.235367 loss) +I0616 06:32:18.694955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0530709 (* 1 = 0.0530709 loss) +I0616 06:32:18.694959 9857 solver.cpp:571] Iteration 24620, lr = 0.001 +I0616 06:32:30.442175 9857 solver.cpp:242] Iteration 24640, loss = 1.04447 +I0616 06:32:30.442217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354323 (* 1 = 0.354323 loss) +I0616 06:32:30.442224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.602319 (* 1 = 0.602319 loss) +I0616 06:32:30.442227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.421949 (* 1 = 0.421949 loss) +I0616 06:32:30.442231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.316123 (* 1 = 0.316123 loss) +I0616 06:32:30.442234 9857 solver.cpp:571] Iteration 24640, lr = 0.001 +I0616 06:32:42.073902 9857 solver.cpp:242] Iteration 24660, loss = 0.615247 +I0616 06:32:42.073942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0938536 (* 1 = 0.0938536 loss) +I0616 06:32:42.073948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104845 (* 1 = 0.104845 loss) +I0616 06:32:42.073952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0411623 (* 1 = 0.0411623 loss) +I0616 06:32:42.073956 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00840968 (* 1 = 0.00840968 loss) +I0616 06:32:42.073961 9857 solver.cpp:571] Iteration 24660, lr = 0.001 +I0616 06:32:53.690562 9857 solver.cpp:242] Iteration 24680, loss = 0.477967 +I0616 06:32:53.690603 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17086 (* 1 = 0.17086 loss) +I0616 06:32:53.690608 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280951 (* 1 = 0.280951 loss) +I0616 06:32:53.690613 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0826531 (* 1 = 0.0826531 loss) +I0616 06:32:53.690616 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334063 (* 1 = 0.0334063 loss) +I0616 06:32:53.690619 9857 solver.cpp:571] Iteration 24680, lr = 0.001 +I0616 06:33:05.342396 9857 solver.cpp:242] Iteration 24700, loss = 0.524083 +I0616 06:33:05.342437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167013 (* 1 = 0.167013 loss) +I0616 06:33:05.342442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27766 (* 1 = 0.27766 loss) +I0616 06:33:05.342447 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154297 (* 1 = 0.154297 loss) +I0616 06:33:05.342450 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0343303 (* 1 = 0.0343303 loss) +I0616 06:33:05.342454 9857 solver.cpp:571] Iteration 24700, lr = 0.001 +I0616 06:33:16.937443 9857 solver.cpp:242] Iteration 24720, loss = 0.532607 +I0616 06:33:16.937484 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222599 (* 1 = 0.222599 loss) +I0616 06:33:16.937489 9857 solver.cpp:258] Train net output #1: loss_cls = 0.398804 (* 1 = 0.398804 loss) +I0616 06:33:16.937494 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166689 (* 1 = 0.166689 loss) +I0616 06:33:16.937497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0574242 (* 1 = 0.0574242 loss) +I0616 06:33:16.937500 9857 solver.cpp:571] Iteration 24720, lr = 0.001 +I0616 06:33:28.563845 9857 solver.cpp:242] Iteration 24740, loss = 1.04371 +I0616 06:33:28.563887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270974 (* 1 = 0.270974 loss) +I0616 06:33:28.563894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.557484 (* 1 = 0.557484 loss) +I0616 06:33:28.563897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104228 (* 1 = 0.104228 loss) +I0616 06:33:28.563901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0238071 (* 1 = 0.0238071 loss) +I0616 06:33:28.563905 9857 solver.cpp:571] Iteration 24740, lr = 0.001 +I0616 06:33:40.227193 9857 solver.cpp:242] Iteration 24760, loss = 1.50885 +I0616 06:33:40.227236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.483987 (* 1 = 0.483987 loss) +I0616 06:33:40.227241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.75644 (* 1 = 0.75644 loss) +I0616 06:33:40.227244 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121288 (* 1 = 0.121288 loss) +I0616 06:33:40.227248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224819 (* 1 = 0.0224819 loss) +I0616 06:33:40.227252 9857 solver.cpp:571] Iteration 24760, lr = 0.001 +I0616 06:33:52.126035 9857 solver.cpp:242] Iteration 24780, loss = 1.23904 +I0616 06:33:52.126077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338469 (* 1 = 0.338469 loss) +I0616 06:33:52.126083 9857 solver.cpp:258] Train net output #1: loss_cls = 0.734395 (* 1 = 0.734395 loss) +I0616 06:33:52.126087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.330398 (* 1 = 0.330398 loss) +I0616 06:33:52.126091 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.48174 (* 1 = 0.48174 loss) +I0616 06:33:52.126094 9857 solver.cpp:571] Iteration 24780, lr = 0.001 +speed: 0.649s / iter +I0616 06:34:03.544402 9857 solver.cpp:242] Iteration 24800, loss = 1.01742 +I0616 06:34:03.544441 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199407 (* 1 = 0.199407 loss) +I0616 06:34:03.544447 9857 solver.cpp:258] Train net output #1: loss_cls = 0.439839 (* 1 = 0.439839 loss) +I0616 06:34:03.544451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.208899 (* 1 = 0.208899 loss) +I0616 06:34:03.544456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106645 (* 1 = 0.0106645 loss) +I0616 06:34:03.544459 9857 solver.cpp:571] Iteration 24800, lr = 0.001 +I0616 06:34:15.167840 9857 solver.cpp:242] Iteration 24820, loss = 1.07609 +I0616 06:34:15.167881 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.330138 (* 1 = 0.330138 loss) +I0616 06:34:15.167886 9857 solver.cpp:258] Train net output #1: loss_cls = 0.674834 (* 1 = 0.674834 loss) +I0616 06:34:15.167891 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.398908 (* 1 = 0.398908 loss) +I0616 06:34:15.167893 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.278712 (* 1 = 0.278712 loss) +I0616 06:34:15.167897 9857 solver.cpp:571] Iteration 24820, lr = 0.001 +I0616 06:34:26.676502 9857 solver.cpp:242] Iteration 24840, loss = 0.60683 +I0616 06:34:26.676544 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218045 (* 1 = 0.218045 loss) +I0616 06:34:26.676549 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354388 (* 1 = 0.354388 loss) +I0616 06:34:26.676553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.088753 (* 1 = 0.088753 loss) +I0616 06:34:26.676556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0931642 (* 1 = 0.0931642 loss) +I0616 06:34:26.676561 9857 solver.cpp:571] Iteration 24840, lr = 0.001 +I0616 06:34:38.264246 9857 solver.cpp:242] Iteration 24860, loss = 0.437397 +I0616 06:34:38.264288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232207 (* 1 = 0.232207 loss) +I0616 06:34:38.264294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239214 (* 1 = 0.239214 loss) +I0616 06:34:38.264298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0174487 (* 1 = 0.0174487 loss) +I0616 06:34:38.264302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00904908 (* 1 = 0.00904908 loss) +I0616 06:34:38.264307 9857 solver.cpp:571] Iteration 24860, lr = 0.001 +I0616 06:34:49.672252 9857 solver.cpp:242] Iteration 24880, loss = 1.13627 +I0616 06:34:49.672291 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28642 (* 1 = 0.28642 loss) +I0616 06:34:49.672296 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298767 (* 1 = 0.298767 loss) +I0616 06:34:49.672299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.187155 (* 1 = 0.187155 loss) +I0616 06:34:49.672303 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222567 (* 1 = 0.0222567 loss) +I0616 06:34:49.672307 9857 solver.cpp:571] Iteration 24880, lr = 0.001 +I0616 06:35:01.192071 9857 solver.cpp:242] Iteration 24900, loss = 1.21012 +I0616 06:35:01.192112 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.541075 (* 1 = 0.541075 loss) +I0616 06:35:01.192117 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559452 (* 1 = 0.559452 loss) +I0616 06:35:01.192121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.214905 (* 1 = 0.214905 loss) +I0616 06:35:01.192126 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158818 (* 1 = 0.0158818 loss) +I0616 06:35:01.192129 9857 solver.cpp:571] Iteration 24900, lr = 0.001 +I0616 06:35:12.602928 9857 solver.cpp:242] Iteration 24920, loss = 1.49426 +I0616 06:35:12.602970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237529 (* 1 = 0.237529 loss) +I0616 06:35:12.602975 9857 solver.cpp:258] Train net output #1: loss_cls = 0.674667 (* 1 = 0.674667 loss) +I0616 06:35:12.602979 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.244267 (* 1 = 0.244267 loss) +I0616 06:35:12.602982 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.451246 (* 1 = 0.451246 loss) +I0616 06:35:12.602987 9857 solver.cpp:571] Iteration 24920, lr = 0.001 +I0616 06:35:23.784046 9857 solver.cpp:242] Iteration 24940, loss = 1.01332 +I0616 06:35:23.784087 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315176 (* 1 = 0.315176 loss) +I0616 06:35:23.784093 9857 solver.cpp:258] Train net output #1: loss_cls = 0.534636 (* 1 = 0.534636 loss) +I0616 06:35:23.784097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.230049 (* 1 = 0.230049 loss) +I0616 06:35:23.784101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.403138 (* 1 = 0.403138 loss) +I0616 06:35:23.784104 9857 solver.cpp:571] Iteration 24940, lr = 0.001 +I0616 06:35:35.489599 9857 solver.cpp:242] Iteration 24960, loss = 0.30089 +I0616 06:35:35.489642 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0758501 (* 1 = 0.0758501 loss) +I0616 06:35:35.489648 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0894211 (* 1 = 0.0894211 loss) +I0616 06:35:35.489652 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103625 (* 1 = 0.103625 loss) +I0616 06:35:35.489656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112052 (* 1 = 0.0112052 loss) +I0616 06:35:35.489660 9857 solver.cpp:571] Iteration 24960, lr = 0.001 +I0616 06:35:46.861784 9857 solver.cpp:242] Iteration 24980, loss = 1.78713 +I0616 06:35:46.861826 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308477 (* 1 = 0.308477 loss) +I0616 06:35:46.861831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.602976 (* 1 = 0.602976 loss) +I0616 06:35:46.861835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.315258 (* 1 = 0.315258 loss) +I0616 06:35:46.861838 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.565883 (* 1 = 0.565883 loss) +I0616 06:35:46.861842 9857 solver.cpp:571] Iteration 24980, lr = 0.001 +speed: 0.648s / iter +I0616 06:35:58.352130 9857 solver.cpp:242] Iteration 25000, loss = 1.2461 +I0616 06:35:58.352171 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202236 (* 1 = 0.202236 loss) +I0616 06:35:58.352179 9857 solver.cpp:258] Train net output #1: loss_cls = 0.449265 (* 1 = 0.449265 loss) +I0616 06:35:58.352183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0385672 (* 1 = 0.0385672 loss) +I0616 06:35:58.352186 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0281498 (* 1 = 0.0281498 loss) +I0616 06:35:58.352190 9857 solver.cpp:571] Iteration 25000, lr = 0.001 +I0616 06:36:09.757035 9857 solver.cpp:242] Iteration 25020, loss = 0.373371 +I0616 06:36:09.757077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0621352 (* 1 = 0.0621352 loss) +I0616 06:36:09.757082 9857 solver.cpp:258] Train net output #1: loss_cls = 0.062466 (* 1 = 0.062466 loss) +I0616 06:36:09.757086 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0143517 (* 1 = 0.0143517 loss) +I0616 06:36:09.757091 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0036119 (* 1 = 0.0036119 loss) +I0616 06:36:09.757094 9857 solver.cpp:571] Iteration 25020, lr = 0.001 +I0616 06:36:21.491430 9857 solver.cpp:242] Iteration 25040, loss = 0.465137 +I0616 06:36:21.491471 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143645 (* 1 = 0.143645 loss) +I0616 06:36:21.491475 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124319 (* 1 = 0.124319 loss) +I0616 06:36:21.491480 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0538949 (* 1 = 0.0538949 loss) +I0616 06:36:21.491483 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224732 (* 1 = 0.0224732 loss) +I0616 06:36:21.491487 9857 solver.cpp:571] Iteration 25040, lr = 0.001 +I0616 06:36:33.161149 9857 solver.cpp:242] Iteration 25060, loss = 0.943571 +I0616 06:36:33.161191 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45436 (* 1 = 0.45436 loss) +I0616 06:36:33.161196 9857 solver.cpp:258] Train net output #1: loss_cls = 0.448854 (* 1 = 0.448854 loss) +I0616 06:36:33.161201 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.357476 (* 1 = 0.357476 loss) +I0616 06:36:33.161204 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0986253 (* 1 = 0.0986253 loss) +I0616 06:36:33.161208 9857 solver.cpp:571] Iteration 25060, lr = 0.001 +I0616 06:36:44.722030 9857 solver.cpp:242] Iteration 25080, loss = 0.797373 +I0616 06:36:44.722072 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389992 (* 1 = 0.389992 loss) +I0616 06:36:44.722077 9857 solver.cpp:258] Train net output #1: loss_cls = 0.444129 (* 1 = 0.444129 loss) +I0616 06:36:44.722081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0441331 (* 1 = 0.0441331 loss) +I0616 06:36:44.722085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323643 (* 1 = 0.0323643 loss) +I0616 06:36:44.722090 9857 solver.cpp:571] Iteration 25080, lr = 0.001 +I0616 06:36:56.251859 9857 solver.cpp:242] Iteration 25100, loss = 0.91022 +I0616 06:36:56.251900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320955 (* 1 = 0.320955 loss) +I0616 06:36:56.251905 9857 solver.cpp:258] Train net output #1: loss_cls = 1.02725 (* 1 = 1.02725 loss) +I0616 06:36:56.251909 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0936662 (* 1 = 0.0936662 loss) +I0616 06:36:56.251914 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0588335 (* 1 = 0.0588335 loss) +I0616 06:36:56.251917 9857 solver.cpp:571] Iteration 25100, lr = 0.001 +I0616 06:37:07.892385 9857 solver.cpp:242] Iteration 25120, loss = 0.593271 +I0616 06:37:07.892427 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100741 (* 1 = 0.100741 loss) +I0616 06:37:07.892432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147667 (* 1 = 0.147667 loss) +I0616 06:37:07.892436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0372765 (* 1 = 0.0372765 loss) +I0616 06:37:07.892441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00578746 (* 1 = 0.00578746 loss) +I0616 06:37:07.892444 9857 solver.cpp:571] Iteration 25120, lr = 0.001 +I0616 06:37:19.312336 9857 solver.cpp:242] Iteration 25140, loss = 0.572839 +I0616 06:37:19.312376 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.370361 (* 1 = 0.370361 loss) +I0616 06:37:19.312381 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351302 (* 1 = 0.351302 loss) +I0616 06:37:19.312386 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395306 (* 1 = 0.0395306 loss) +I0616 06:37:19.312389 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224612 (* 1 = 0.0224612 loss) +I0616 06:37:19.312393 9857 solver.cpp:571] Iteration 25140, lr = 0.001 +I0616 06:37:30.804437 9857 solver.cpp:242] Iteration 25160, loss = 1.00891 +I0616 06:37:30.804478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379147 (* 1 = 0.379147 loss) +I0616 06:37:30.804483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.454213 (* 1 = 0.454213 loss) +I0616 06:37:30.804488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124885 (* 1 = 0.124885 loss) +I0616 06:37:30.804491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10877 (* 1 = 0.10877 loss) +I0616 06:37:30.804496 9857 solver.cpp:571] Iteration 25160, lr = 0.001 +I0616 06:37:42.378422 9857 solver.cpp:242] Iteration 25180, loss = 0.401131 +I0616 06:37:42.378464 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0872015 (* 1 = 0.0872015 loss) +I0616 06:37:42.378470 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160587 (* 1 = 0.160587 loss) +I0616 06:37:42.378474 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0373739 (* 1 = 0.0373739 loss) +I0616 06:37:42.378478 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246776 (* 1 = 0.0246776 loss) +I0616 06:37:42.378481 9857 solver.cpp:571] Iteration 25180, lr = 0.001 +speed: 0.648s / iter +I0616 06:37:53.852136 9857 solver.cpp:242] Iteration 25200, loss = 0.950659 +I0616 06:37:53.852177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.410518 (* 1 = 0.410518 loss) +I0616 06:37:53.852183 9857 solver.cpp:258] Train net output #1: loss_cls = 0.693752 (* 1 = 0.693752 loss) +I0616 06:37:53.852187 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224886 (* 1 = 0.224886 loss) +I0616 06:37:53.852191 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0485488 (* 1 = 0.0485488 loss) +I0616 06:37:53.852195 9857 solver.cpp:571] Iteration 25200, lr = 0.001 +I0616 06:38:05.529065 9857 solver.cpp:242] Iteration 25220, loss = 0.755282 +I0616 06:38:05.529108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345296 (* 1 = 0.345296 loss) +I0616 06:38:05.529112 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49008 (* 1 = 0.49008 loss) +I0616 06:38:05.529116 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108867 (* 1 = 0.108867 loss) +I0616 06:38:05.529120 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0332892 (* 1 = 0.0332892 loss) +I0616 06:38:05.529124 9857 solver.cpp:571] Iteration 25220, lr = 0.001 +I0616 06:38:16.881227 9857 solver.cpp:242] Iteration 25240, loss = 1.37071 +I0616 06:38:16.881270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.216405 (* 1 = 0.216405 loss) +I0616 06:38:16.881275 9857 solver.cpp:258] Train net output #1: loss_cls = 0.693883 (* 1 = 0.693883 loss) +I0616 06:38:16.881279 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.449391 (* 1 = 0.449391 loss) +I0616 06:38:16.881283 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0583679 (* 1 = 0.0583679 loss) +I0616 06:38:16.881288 9857 solver.cpp:571] Iteration 25240, lr = 0.001 +I0616 06:38:28.594650 9857 solver.cpp:242] Iteration 25260, loss = 1.09663 +I0616 06:38:28.594693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.362114 (* 1 = 0.362114 loss) +I0616 06:38:28.594699 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304664 (* 1 = 0.304664 loss) +I0616 06:38:28.594703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0614554 (* 1 = 0.0614554 loss) +I0616 06:38:28.594707 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0465244 (* 1 = 0.0465244 loss) +I0616 06:38:28.594710 9857 solver.cpp:571] Iteration 25260, lr = 0.001 +I0616 06:38:39.929417 9857 solver.cpp:242] Iteration 25280, loss = 0.861716 +I0616 06:38:39.929461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107902 (* 1 = 0.107902 loss) +I0616 06:38:39.929466 9857 solver.cpp:258] Train net output #1: loss_cls = 0.51656 (* 1 = 0.51656 loss) +I0616 06:38:39.929469 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0105503 (* 1 = 0.0105503 loss) +I0616 06:38:39.929473 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00344765 (* 1 = 0.00344765 loss) +I0616 06:38:39.929477 9857 solver.cpp:571] Iteration 25280, lr = 0.001 +I0616 06:38:51.250828 9857 solver.cpp:242] Iteration 25300, loss = 1.07553 +I0616 06:38:51.250866 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263111 (* 1 = 0.263111 loss) +I0616 06:38:51.250872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.497026 (* 1 = 0.497026 loss) +I0616 06:38:51.250876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.21711 (* 1 = 0.21711 loss) +I0616 06:38:51.250880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0296769 (* 1 = 0.0296769 loss) +I0616 06:38:51.250885 9857 solver.cpp:571] Iteration 25300, lr = 0.001 +I0616 06:39:02.949587 9857 solver.cpp:242] Iteration 25320, loss = 0.771433 +I0616 06:39:02.949628 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13785 (* 1 = 0.13785 loss) +I0616 06:39:02.949633 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199667 (* 1 = 0.199667 loss) +I0616 06:39:02.949638 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0277109 (* 1 = 0.0277109 loss) +I0616 06:39:02.949641 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254918 (* 1 = 0.0254918 loss) +I0616 06:39:02.949645 9857 solver.cpp:571] Iteration 25320, lr = 0.001 +I0616 06:39:14.624589 9857 solver.cpp:242] Iteration 25340, loss = 0.696684 +I0616 06:39:14.624629 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172572 (* 1 = 0.172572 loss) +I0616 06:39:14.624635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239364 (* 1 = 0.239364 loss) +I0616 06:39:14.624639 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0384866 (* 1 = 0.0384866 loss) +I0616 06:39:14.624644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260562 (* 1 = 0.0260562 loss) +I0616 06:39:14.624647 9857 solver.cpp:571] Iteration 25340, lr = 0.001 +I0616 06:39:26.251443 9857 solver.cpp:242] Iteration 25360, loss = 1.09936 +I0616 06:39:26.251483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255427 (* 1 = 0.255427 loss) +I0616 06:39:26.251489 9857 solver.cpp:258] Train net output #1: loss_cls = 1.07872 (* 1 = 1.07872 loss) +I0616 06:39:26.251493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0758672 (* 1 = 0.0758672 loss) +I0616 06:39:26.251497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00620651 (* 1 = 0.00620651 loss) +I0616 06:39:26.251502 9857 solver.cpp:571] Iteration 25360, lr = 0.001 +I0616 06:39:37.760082 9857 solver.cpp:242] Iteration 25380, loss = 0.78741 +I0616 06:39:37.760123 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.63529 (* 1 = 0.63529 loss) +I0616 06:39:37.760128 9857 solver.cpp:258] Train net output #1: loss_cls = 0.458796 (* 1 = 0.458796 loss) +I0616 06:39:37.760133 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0361161 (* 1 = 0.0361161 loss) +I0616 06:39:37.760138 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0560976 (* 1 = 0.0560976 loss) +I0616 06:39:37.760140 9857 solver.cpp:571] Iteration 25380, lr = 0.001 +speed: 0.647s / iter +I0616 06:39:49.225297 9857 solver.cpp:242] Iteration 25400, loss = 0.579585 +I0616 06:39:49.225338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214129 (* 1 = 0.214129 loss) +I0616 06:39:49.225343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29346 (* 1 = 0.29346 loss) +I0616 06:39:49.225347 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0543626 (* 1 = 0.0543626 loss) +I0616 06:39:49.225352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00725237 (* 1 = 0.00725237 loss) +I0616 06:39:49.225355 9857 solver.cpp:571] Iteration 25400, lr = 0.001 +I0616 06:40:00.958029 9857 solver.cpp:242] Iteration 25420, loss = 0.76837 +I0616 06:40:00.958072 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.478459 (* 1 = 0.478459 loss) +I0616 06:40:00.958077 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394441 (* 1 = 0.394441 loss) +I0616 06:40:00.958081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0642703 (* 1 = 0.0642703 loss) +I0616 06:40:00.958086 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0261776 (* 1 = 0.0261776 loss) +I0616 06:40:00.958089 9857 solver.cpp:571] Iteration 25420, lr = 0.001 +I0616 06:40:12.779896 9857 solver.cpp:242] Iteration 25440, loss = 1.39418 +I0616 06:40:12.779937 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.489224 (* 1 = 0.489224 loss) +I0616 06:40:12.779943 9857 solver.cpp:258] Train net output #1: loss_cls = 0.561607 (* 1 = 0.561607 loss) +I0616 06:40:12.779947 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.359287 (* 1 = 0.359287 loss) +I0616 06:40:12.779950 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159243 (* 1 = 0.159243 loss) +I0616 06:40:12.779954 9857 solver.cpp:571] Iteration 25440, lr = 0.001 +I0616 06:40:24.367660 9857 solver.cpp:242] Iteration 25460, loss = 0.738805 +I0616 06:40:24.367702 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308589 (* 1 = 0.308589 loss) +I0616 06:40:24.367707 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266877 (* 1 = 0.266877 loss) +I0616 06:40:24.367712 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125177 (* 1 = 0.125177 loss) +I0616 06:40:24.367714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114145 (* 1 = 0.0114145 loss) +I0616 06:40:24.367718 9857 solver.cpp:571] Iteration 25460, lr = 0.001 +I0616 06:40:35.781502 9857 solver.cpp:242] Iteration 25480, loss = 0.657103 +I0616 06:40:35.781543 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206595 (* 1 = 0.206595 loss) +I0616 06:40:35.781549 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305105 (* 1 = 0.305105 loss) +I0616 06:40:35.781553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11773 (* 1 = 0.11773 loss) +I0616 06:40:35.781558 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0172986 (* 1 = 0.0172986 loss) +I0616 06:40:35.781561 9857 solver.cpp:571] Iteration 25480, lr = 0.001 +I0616 06:40:47.084161 9857 solver.cpp:242] Iteration 25500, loss = 0.530381 +I0616 06:40:47.084203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255681 (* 1 = 0.255681 loss) +I0616 06:40:47.084208 9857 solver.cpp:258] Train net output #1: loss_cls = 0.291556 (* 1 = 0.291556 loss) +I0616 06:40:47.084213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.06793 (* 1 = 0.06793 loss) +I0616 06:40:47.084216 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0582907 (* 1 = 0.0582907 loss) +I0616 06:40:47.084220 9857 solver.cpp:571] Iteration 25500, lr = 0.001 +I0616 06:40:58.714412 9857 solver.cpp:242] Iteration 25520, loss = 0.457712 +I0616 06:40:58.714454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167275 (* 1 = 0.167275 loss) +I0616 06:40:58.714459 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165588 (* 1 = 0.165588 loss) +I0616 06:40:58.714463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00827533 (* 1 = 0.00827533 loss) +I0616 06:40:58.714468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102381 (* 1 = 0.102381 loss) +I0616 06:40:58.714471 9857 solver.cpp:571] Iteration 25520, lr = 0.001 +I0616 06:41:10.385538 9857 solver.cpp:242] Iteration 25540, loss = 0.805787 +I0616 06:41:10.385581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144994 (* 1 = 0.144994 loss) +I0616 06:41:10.385586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206925 (* 1 = 0.206925 loss) +I0616 06:41:10.385591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0364131 (* 1 = 0.0364131 loss) +I0616 06:41:10.385594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0290263 (* 1 = 0.0290263 loss) +I0616 06:41:10.385598 9857 solver.cpp:571] Iteration 25540, lr = 0.001 +I0616 06:41:21.931617 9857 solver.cpp:242] Iteration 25560, loss = 1.01653 +I0616 06:41:21.931658 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249671 (* 1 = 0.249671 loss) +I0616 06:41:21.931663 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21729 (* 1 = 0.21729 loss) +I0616 06:41:21.931668 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0332798 (* 1 = 0.0332798 loss) +I0616 06:41:21.931671 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144766 (* 1 = 0.0144766 loss) +I0616 06:41:21.931675 9857 solver.cpp:571] Iteration 25560, lr = 0.001 +I0616 06:41:33.667585 9857 solver.cpp:242] Iteration 25580, loss = 0.35969 +I0616 06:41:33.667626 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129071 (* 1 = 0.129071 loss) +I0616 06:41:33.667632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205381 (* 1 = 0.205381 loss) +I0616 06:41:33.667636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0829321 (* 1 = 0.0829321 loss) +I0616 06:41:33.667639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0250959 (* 1 = 0.0250959 loss) +I0616 06:41:33.667644 9857 solver.cpp:571] Iteration 25580, lr = 0.001 +speed: 0.646s / iter +I0616 06:41:45.336709 9857 solver.cpp:242] Iteration 25600, loss = 1.50357 +I0616 06:41:45.336750 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.400799 (* 1 = 0.400799 loss) +I0616 06:41:45.336755 9857 solver.cpp:258] Train net output #1: loss_cls = 0.609017 (* 1 = 0.609017 loss) +I0616 06:41:45.336760 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0981777 (* 1 = 0.0981777 loss) +I0616 06:41:45.336763 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0530386 (* 1 = 0.0530386 loss) +I0616 06:41:45.336767 9857 solver.cpp:571] Iteration 25600, lr = 0.001 +I0616 06:41:56.748054 9857 solver.cpp:242] Iteration 25620, loss = 0.540094 +I0616 06:41:56.748096 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186421 (* 1 = 0.186421 loss) +I0616 06:41:56.748101 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161069 (* 1 = 0.161069 loss) +I0616 06:41:56.748106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100727 (* 1 = 0.100727 loss) +I0616 06:41:56.748109 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0536852 (* 1 = 0.0536852 loss) +I0616 06:41:56.748113 9857 solver.cpp:571] Iteration 25620, lr = 0.001 +I0616 06:42:08.410142 9857 solver.cpp:242] Iteration 25640, loss = 0.351802 +I0616 06:42:08.410184 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0862433 (* 1 = 0.0862433 loss) +I0616 06:42:08.410190 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187329 (* 1 = 0.187329 loss) +I0616 06:42:08.410194 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0657473 (* 1 = 0.0657473 loss) +I0616 06:42:08.410198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273503 (* 1 = 0.0273503 loss) +I0616 06:42:08.410202 9857 solver.cpp:571] Iteration 25640, lr = 0.001 +I0616 06:42:19.974474 9857 solver.cpp:242] Iteration 25660, loss = 0.89392 +I0616 06:42:19.974516 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252556 (* 1 = 0.252556 loss) +I0616 06:42:19.974522 9857 solver.cpp:258] Train net output #1: loss_cls = 0.430397 (* 1 = 0.430397 loss) +I0616 06:42:19.974526 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386321 (* 1 = 0.0386321 loss) +I0616 06:42:19.974530 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198561 (* 1 = 0.0198561 loss) +I0616 06:42:19.974534 9857 solver.cpp:571] Iteration 25660, lr = 0.001 +I0616 06:42:31.737936 9857 solver.cpp:242] Iteration 25680, loss = 2.16422 +I0616 06:42:31.737977 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.489257 (* 1 = 0.489257 loss) +I0616 06:42:31.737982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.444455 (* 1 = 0.444455 loss) +I0616 06:42:31.737987 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.422883 (* 1 = 0.422883 loss) +I0616 06:42:31.737990 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.628507 (* 1 = 0.628507 loss) +I0616 06:42:31.737994 9857 solver.cpp:571] Iteration 25680, lr = 0.001 +I0616 06:42:43.420215 9857 solver.cpp:242] Iteration 25700, loss = 1.4395 +I0616 06:42:43.420256 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.471182 (* 1 = 0.471182 loss) +I0616 06:42:43.420263 9857 solver.cpp:258] Train net output #1: loss_cls = 1.43439 (* 1 = 1.43439 loss) +I0616 06:42:43.420266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.368715 (* 1 = 0.368715 loss) +I0616 06:42:43.420270 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0516315 (* 1 = 0.0516315 loss) +I0616 06:42:43.420274 9857 solver.cpp:571] Iteration 25700, lr = 0.001 +I0616 06:42:54.955874 9857 solver.cpp:242] Iteration 25720, loss = 1.34413 +I0616 06:42:54.955916 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.468617 (* 1 = 0.468617 loss) +I0616 06:42:54.955921 9857 solver.cpp:258] Train net output #1: loss_cls = 0.515953 (* 1 = 0.515953 loss) +I0616 06:42:54.955925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20846 (* 1 = 0.20846 loss) +I0616 06:42:54.955929 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.15224 (* 1 = 0.15224 loss) +I0616 06:42:54.955934 9857 solver.cpp:571] Iteration 25720, lr = 0.001 +I0616 06:43:06.604223 9857 solver.cpp:242] Iteration 25740, loss = 0.633608 +I0616 06:43:06.604264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354025 (* 1 = 0.354025 loss) +I0616 06:43:06.604269 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368772 (* 1 = 0.368772 loss) +I0616 06:43:06.604274 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0466818 (* 1 = 0.0466818 loss) +I0616 06:43:06.604277 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028156 (* 1 = 0.028156 loss) +I0616 06:43:06.604281 9857 solver.cpp:571] Iteration 25740, lr = 0.001 +I0616 06:43:17.838176 9857 solver.cpp:242] Iteration 25760, loss = 0.573023 +I0616 06:43:17.838217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.06639 (* 1 = 0.06639 loss) +I0616 06:43:17.838223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11524 (* 1 = 0.11524 loss) +I0616 06:43:17.838227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0279797 (* 1 = 0.0279797 loss) +I0616 06:43:17.838232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014969 (* 1 = 0.014969 loss) +I0616 06:43:17.838235 9857 solver.cpp:571] Iteration 25760, lr = 0.001 +I0616 06:43:29.363306 9857 solver.cpp:242] Iteration 25780, loss = 1.67971 +I0616 06:43:29.363346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.418456 (* 1 = 0.418456 loss) +I0616 06:43:29.363353 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459107 (* 1 = 0.459107 loss) +I0616 06:43:29.363370 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22589 (* 1 = 0.22589 loss) +I0616 06:43:29.363374 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.101346 (* 1 = 0.101346 loss) +I0616 06:43:29.363379 9857 solver.cpp:571] Iteration 25780, lr = 0.001 +speed: 0.646s / iter +I0616 06:43:40.973240 9857 solver.cpp:242] Iteration 25800, loss = 0.511255 +I0616 06:43:40.973283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186432 (* 1 = 0.186432 loss) +I0616 06:43:40.973289 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287784 (* 1 = 0.287784 loss) +I0616 06:43:40.973292 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0164495 (* 1 = 0.0164495 loss) +I0616 06:43:40.973296 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0484604 (* 1 = 0.0484604 loss) +I0616 06:43:40.973299 9857 solver.cpp:571] Iteration 25800, lr = 0.001 +I0616 06:43:52.626497 9857 solver.cpp:242] Iteration 25820, loss = 1.8047 +I0616 06:43:52.626538 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.531937 (* 1 = 0.531937 loss) +I0616 06:43:52.626544 9857 solver.cpp:258] Train net output #1: loss_cls = 1.07153 (* 1 = 1.07153 loss) +I0616 06:43:52.626549 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.557385 (* 1 = 0.557385 loss) +I0616 06:43:52.626552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.403767 (* 1 = 0.403767 loss) +I0616 06:43:52.626556 9857 solver.cpp:571] Iteration 25820, lr = 0.001 +I0616 06:44:03.748559 9857 solver.cpp:242] Iteration 25840, loss = 0.60227 +I0616 06:44:03.748601 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155677 (* 1 = 0.155677 loss) +I0616 06:44:03.748606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239599 (* 1 = 0.239599 loss) +I0616 06:44:03.748610 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151747 (* 1 = 0.151747 loss) +I0616 06:44:03.748615 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0495805 (* 1 = 0.0495805 loss) +I0616 06:44:03.748618 9857 solver.cpp:571] Iteration 25840, lr = 0.001 +I0616 06:44:15.289463 9857 solver.cpp:242] Iteration 25860, loss = 0.612967 +I0616 06:44:15.289504 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.34661 (* 1 = 0.34661 loss) +I0616 06:44:15.289510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.50097 (* 1 = 0.50097 loss) +I0616 06:44:15.289513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0628755 (* 1 = 0.0628755 loss) +I0616 06:44:15.289517 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00255923 (* 1 = 0.00255923 loss) +I0616 06:44:15.289521 9857 solver.cpp:571] Iteration 25860, lr = 0.001 +I0616 06:44:26.966709 9857 solver.cpp:242] Iteration 25880, loss = 0.879208 +I0616 06:44:26.966752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.245161 (* 1 = 0.245161 loss) +I0616 06:44:26.966759 9857 solver.cpp:258] Train net output #1: loss_cls = 0.434827 (* 1 = 0.434827 loss) +I0616 06:44:26.966764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0643651 (* 1 = 0.0643651 loss) +I0616 06:44:26.966768 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00821678 (* 1 = 0.00821678 loss) +I0616 06:44:26.966771 9857 solver.cpp:571] Iteration 25880, lr = 0.001 +I0616 06:44:38.247684 9857 solver.cpp:242] Iteration 25900, loss = 1.10635 +I0616 06:44:38.247714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.356655 (* 1 = 0.356655 loss) +I0616 06:44:38.247719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404377 (* 1 = 0.404377 loss) +I0616 06:44:38.247722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395127 (* 1 = 0.0395127 loss) +I0616 06:44:38.247726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00283719 (* 1 = 0.00283719 loss) +I0616 06:44:38.247730 9857 solver.cpp:571] Iteration 25900, lr = 0.001 +I0616 06:44:50.058722 9857 solver.cpp:242] Iteration 25920, loss = 0.928315 +I0616 06:44:50.058766 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394919 (* 1 = 0.394919 loss) +I0616 06:44:50.058773 9857 solver.cpp:258] Train net output #1: loss_cls = 0.395892 (* 1 = 0.395892 loss) +I0616 06:44:50.058776 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0936774 (* 1 = 0.0936774 loss) +I0616 06:44:50.058780 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0498118 (* 1 = 0.0498118 loss) +I0616 06:44:50.058784 9857 solver.cpp:571] Iteration 25920, lr = 0.001 +I0616 06:45:01.592633 9857 solver.cpp:242] Iteration 25940, loss = 0.962685 +I0616 06:45:01.592674 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211374 (* 1 = 0.211374 loss) +I0616 06:45:01.592679 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363489 (* 1 = 0.363489 loss) +I0616 06:45:01.592684 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0277092 (* 1 = 0.0277092 loss) +I0616 06:45:01.592686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020111 (* 1 = 0.020111 loss) +I0616 06:45:01.592691 9857 solver.cpp:571] Iteration 25940, lr = 0.001 +I0616 06:45:13.174985 9857 solver.cpp:242] Iteration 25960, loss = 1.26711 +I0616 06:45:13.175030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254354 (* 1 = 0.254354 loss) +I0616 06:45:13.175037 9857 solver.cpp:258] Train net output #1: loss_cls = 0.491061 (* 1 = 0.491061 loss) +I0616 06:45:13.175043 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.319145 (* 1 = 0.319145 loss) +I0616 06:45:13.175048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.201652 (* 1 = 0.201652 loss) +I0616 06:45:13.175052 9857 solver.cpp:571] Iteration 25960, lr = 0.001 +I0616 06:45:24.468415 9857 solver.cpp:242] Iteration 25980, loss = 0.754631 +I0616 06:45:24.468456 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176029 (* 1 = 0.176029 loss) +I0616 06:45:24.468461 9857 solver.cpp:258] Train net output #1: loss_cls = 0.541581 (* 1 = 0.541581 loss) +I0616 06:45:24.468466 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0991867 (* 1 = 0.0991867 loss) +I0616 06:45:24.468468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0373003 (* 1 = 0.0373003 loss) +I0616 06:45:24.468472 9857 solver.cpp:571] Iteration 25980, lr = 0.001 +speed: 0.645s / iter +I0616 06:45:35.980834 9857 solver.cpp:242] Iteration 26000, loss = 0.844011 +I0616 06:45:35.980875 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244527 (* 1 = 0.244527 loss) +I0616 06:45:35.980881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221136 (* 1 = 0.221136 loss) +I0616 06:45:35.980885 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0418379 (* 1 = 0.0418379 loss) +I0616 06:45:35.980888 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109606 (* 1 = 0.0109606 loss) +I0616 06:45:35.980892 9857 solver.cpp:571] Iteration 26000, lr = 0.001 +I0616 06:45:47.551355 9857 solver.cpp:242] Iteration 26020, loss = 0.535751 +I0616 06:45:47.551396 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238381 (* 1 = 0.238381 loss) +I0616 06:45:47.551403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.421496 (* 1 = 0.421496 loss) +I0616 06:45:47.551406 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0235911 (* 1 = 0.0235911 loss) +I0616 06:45:47.551410 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010748 (* 1 = 0.010748 loss) +I0616 06:45:47.551414 9857 solver.cpp:571] Iteration 26020, lr = 0.001 +I0616 06:45:58.725186 9857 solver.cpp:242] Iteration 26040, loss = 0.451753 +I0616 06:45:58.725225 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303857 (* 1 = 0.303857 loss) +I0616 06:45:58.725231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23973 (* 1 = 0.23973 loss) +I0616 06:45:58.725235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0488378 (* 1 = 0.0488378 loss) +I0616 06:45:58.725239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.04993 (* 1 = 0.04993 loss) +I0616 06:45:58.725244 9857 solver.cpp:571] Iteration 26040, lr = 0.001 +I0616 06:46:10.308959 9857 solver.cpp:242] Iteration 26060, loss = 1.22717 +I0616 06:46:10.309000 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.402856 (* 1 = 0.402856 loss) +I0616 06:46:10.309005 9857 solver.cpp:258] Train net output #1: loss_cls = 0.536847 (* 1 = 0.536847 loss) +I0616 06:46:10.309010 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133024 (* 1 = 0.133024 loss) +I0616 06:46:10.309013 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0688011 (* 1 = 0.0688011 loss) +I0616 06:46:10.309018 9857 solver.cpp:571] Iteration 26060, lr = 0.001 +I0616 06:46:21.698623 9857 solver.cpp:242] Iteration 26080, loss = 1.67957 +I0616 06:46:21.698664 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366208 (* 1 = 0.366208 loss) +I0616 06:46:21.698670 9857 solver.cpp:258] Train net output #1: loss_cls = 0.891999 (* 1 = 0.891999 loss) +I0616 06:46:21.698674 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130836 (* 1 = 0.130836 loss) +I0616 06:46:21.698678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156564 (* 1 = 0.156564 loss) +I0616 06:46:21.698681 9857 solver.cpp:571] Iteration 26080, lr = 0.001 +I0616 06:46:33.183017 9857 solver.cpp:242] Iteration 26100, loss = 0.456535 +I0616 06:46:33.183058 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107729 (* 1 = 0.107729 loss) +I0616 06:46:33.183063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142191 (* 1 = 0.142191 loss) +I0616 06:46:33.183068 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0151689 (* 1 = 0.0151689 loss) +I0616 06:46:33.183070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00865523 (* 1 = 0.00865523 loss) +I0616 06:46:33.183075 9857 solver.cpp:571] Iteration 26100, lr = 0.001 +I0616 06:46:44.660567 9857 solver.cpp:242] Iteration 26120, loss = 1.27741 +I0616 06:46:44.660609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.433307 (* 1 = 0.433307 loss) +I0616 06:46:44.660614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.676067 (* 1 = 0.676067 loss) +I0616 06:46:44.660617 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.266065 (* 1 = 0.266065 loss) +I0616 06:46:44.660621 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128343 (* 1 = 0.128343 loss) +I0616 06:46:44.660625 9857 solver.cpp:571] Iteration 26120, lr = 0.001 +I0616 06:46:56.169118 9857 solver.cpp:242] Iteration 26140, loss = 1.69325 +I0616 06:46:56.169162 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.34366 (* 1 = 0.34366 loss) +I0616 06:46:56.169168 9857 solver.cpp:258] Train net output #1: loss_cls = 0.396366 (* 1 = 0.396366 loss) +I0616 06:46:56.169173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.452766 (* 1 = 0.452766 loss) +I0616 06:46:56.169176 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.648595 (* 1 = 0.648595 loss) +I0616 06:46:56.169180 9857 solver.cpp:571] Iteration 26140, lr = 0.001 +I0616 06:47:07.594483 9857 solver.cpp:242] Iteration 26160, loss = 0.925504 +I0616 06:47:07.594526 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193842 (* 1 = 0.193842 loss) +I0616 06:47:07.594530 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191807 (* 1 = 0.191807 loss) +I0616 06:47:07.594534 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0192818 (* 1 = 0.0192818 loss) +I0616 06:47:07.594538 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00110339 (* 1 = 0.00110339 loss) +I0616 06:47:07.594542 9857 solver.cpp:571] Iteration 26160, lr = 0.001 +I0616 06:47:18.991525 9857 solver.cpp:242] Iteration 26180, loss = 0.851052 +I0616 06:47:18.991567 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.541055 (* 1 = 0.541055 loss) +I0616 06:47:18.991574 9857 solver.cpp:258] Train net output #1: loss_cls = 0.788732 (* 1 = 0.788732 loss) +I0616 06:47:18.991577 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0223645 (* 1 = 0.0223645 loss) +I0616 06:47:18.991581 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152168 (* 1 = 0.0152168 loss) +I0616 06:47:18.991585 9857 solver.cpp:571] Iteration 26180, lr = 0.001 +speed: 0.645s / iter +I0616 06:47:30.547389 9857 solver.cpp:242] Iteration 26200, loss = 0.92664 +I0616 06:47:30.547430 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287951 (* 1 = 0.287951 loss) +I0616 06:47:30.547435 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313248 (* 1 = 0.313248 loss) +I0616 06:47:30.547440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0631082 (* 1 = 0.0631082 loss) +I0616 06:47:30.547442 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0077922 (* 1 = 0.0077922 loss) +I0616 06:47:30.547446 9857 solver.cpp:571] Iteration 26200, lr = 0.001 +I0616 06:47:42.156762 9857 solver.cpp:242] Iteration 26220, loss = 0.628366 +I0616 06:47:42.156805 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0606667 (* 1 = 0.0606667 loss) +I0616 06:47:42.156810 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183 (* 1 = 0.183 loss) +I0616 06:47:42.156813 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0719439 (* 1 = 0.0719439 loss) +I0616 06:47:42.156817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0270696 (* 1 = 0.0270696 loss) +I0616 06:47:42.156821 9857 solver.cpp:571] Iteration 26220, lr = 0.001 +I0616 06:47:53.622321 9857 solver.cpp:242] Iteration 26240, loss = 0.458571 +I0616 06:47:53.622364 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.142238 (* 1 = 0.142238 loss) +I0616 06:47:53.622370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180751 (* 1 = 0.180751 loss) +I0616 06:47:53.622375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0339478 (* 1 = 0.0339478 loss) +I0616 06:47:53.622377 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00545052 (* 1 = 0.00545052 loss) +I0616 06:47:53.622383 9857 solver.cpp:571] Iteration 26240, lr = 0.001 +I0616 06:48:04.822928 9857 solver.cpp:242] Iteration 26260, loss = 0.670041 +I0616 06:48:04.822971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32832 (* 1 = 0.32832 loss) +I0616 06:48:04.822976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.580237 (* 1 = 0.580237 loss) +I0616 06:48:04.822981 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0953081 (* 1 = 0.0953081 loss) +I0616 06:48:04.822985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245154 (* 1 = 0.0245154 loss) +I0616 06:48:04.822988 9857 solver.cpp:571] Iteration 26260, lr = 0.001 +I0616 06:48:16.483525 9857 solver.cpp:242] Iteration 26280, loss = 1.15307 +I0616 06:48:16.483566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436384 (* 1 = 0.436384 loss) +I0616 06:48:16.483572 9857 solver.cpp:258] Train net output #1: loss_cls = 0.850066 (* 1 = 0.850066 loss) +I0616 06:48:16.483577 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.241756 (* 1 = 0.241756 loss) +I0616 06:48:16.483580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.088622 (* 1 = 0.088622 loss) +I0616 06:48:16.483583 9857 solver.cpp:571] Iteration 26280, lr = 0.001 +I0616 06:48:27.945024 9857 solver.cpp:242] Iteration 26300, loss = 1.54321 +I0616 06:48:27.945067 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.493636 (* 1 = 0.493636 loss) +I0616 06:48:27.945073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.79311 (* 1 = 0.79311 loss) +I0616 06:48:27.945077 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.375186 (* 1 = 0.375186 loss) +I0616 06:48:27.945080 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.631175 (* 1 = 0.631175 loss) +I0616 06:48:27.945085 9857 solver.cpp:571] Iteration 26300, lr = 0.001 +I0616 06:48:39.609407 9857 solver.cpp:242] Iteration 26320, loss = 0.46585 +I0616 06:48:39.609447 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110891 (* 1 = 0.110891 loss) +I0616 06:48:39.609453 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166932 (* 1 = 0.166932 loss) +I0616 06:48:39.609457 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159952 (* 1 = 0.159952 loss) +I0616 06:48:39.609460 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104398 (* 1 = 0.104398 loss) +I0616 06:48:39.609464 9857 solver.cpp:571] Iteration 26320, lr = 0.001 +I0616 06:48:51.096207 9857 solver.cpp:242] Iteration 26340, loss = 0.653596 +I0616 06:48:51.096251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164055 (* 1 = 0.164055 loss) +I0616 06:48:51.096256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.542139 (* 1 = 0.542139 loss) +I0616 06:48:51.096259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122294 (* 1 = 0.122294 loss) +I0616 06:48:51.096263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0280152 (* 1 = 0.0280152 loss) +I0616 06:48:51.096267 9857 solver.cpp:571] Iteration 26340, lr = 0.001 +I0616 06:49:02.775058 9857 solver.cpp:242] Iteration 26360, loss = 1.45042 +I0616 06:49:02.775099 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.491209 (* 1 = 0.491209 loss) +I0616 06:49:02.775104 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10178 (* 1 = 1.10178 loss) +I0616 06:49:02.775107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0864442 (* 1 = 0.0864442 loss) +I0616 06:49:02.775111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.222249 (* 1 = 0.222249 loss) +I0616 06:49:02.775115 9857 solver.cpp:571] Iteration 26360, lr = 0.001 +I0616 06:49:14.339432 9857 solver.cpp:242] Iteration 26380, loss = 0.966886 +I0616 06:49:14.339474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25081 (* 1 = 0.25081 loss) +I0616 06:49:14.339479 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337263 (* 1 = 0.337263 loss) +I0616 06:49:14.339483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0704927 (* 1 = 0.0704927 loss) +I0616 06:49:14.339488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00355799 (* 1 = 0.00355799 loss) +I0616 06:49:14.339491 9857 solver.cpp:571] Iteration 26380, lr = 0.001 +speed: 0.644s / iter +I0616 06:49:25.990489 9857 solver.cpp:242] Iteration 26400, loss = 0.391917 +I0616 06:49:25.990530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157051 (* 1 = 0.157051 loss) +I0616 06:49:25.990535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132183 (* 1 = 0.132183 loss) +I0616 06:49:25.990538 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106535 (* 1 = 0.106535 loss) +I0616 06:49:25.990542 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199632 (* 1 = 0.0199632 loss) +I0616 06:49:25.990546 9857 solver.cpp:571] Iteration 26400, lr = 0.001 +I0616 06:49:37.463775 9857 solver.cpp:242] Iteration 26420, loss = 1.10696 +I0616 06:49:37.463816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390662 (* 1 = 0.390662 loss) +I0616 06:49:37.463822 9857 solver.cpp:258] Train net output #1: loss_cls = 0.511464 (* 1 = 0.511464 loss) +I0616 06:49:37.463826 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.171734 (* 1 = 0.171734 loss) +I0616 06:49:37.463830 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0848231 (* 1 = 0.0848231 loss) +I0616 06:49:37.463835 9857 solver.cpp:571] Iteration 26420, lr = 0.001 +I0616 06:49:49.027979 9857 solver.cpp:242] Iteration 26440, loss = 0.925083 +I0616 06:49:49.028022 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296788 (* 1 = 0.296788 loss) +I0616 06:49:49.028028 9857 solver.cpp:258] Train net output #1: loss_cls = 0.472795 (* 1 = 0.472795 loss) +I0616 06:49:49.028031 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0999028 (* 1 = 0.0999028 loss) +I0616 06:49:49.028034 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0402318 (* 1 = 0.0402318 loss) +I0616 06:49:49.028038 9857 solver.cpp:571] Iteration 26440, lr = 0.001 +I0616 06:50:00.266512 9857 solver.cpp:242] Iteration 26460, loss = 0.930457 +I0616 06:50:00.266553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237513 (* 1 = 0.237513 loss) +I0616 06:50:00.266559 9857 solver.cpp:258] Train net output #1: loss_cls = 0.668265 (* 1 = 0.668265 loss) +I0616 06:50:00.266563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155883 (* 1 = 0.155883 loss) +I0616 06:50:00.266567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120058 (* 1 = 0.0120058 loss) +I0616 06:50:00.266571 9857 solver.cpp:571] Iteration 26460, lr = 0.001 +I0616 06:50:11.641752 9857 solver.cpp:242] Iteration 26480, loss = 0.585006 +I0616 06:50:11.641794 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132165 (* 1 = 0.132165 loss) +I0616 06:50:11.641800 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157588 (* 1 = 0.157588 loss) +I0616 06:50:11.641804 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0630685 (* 1 = 0.0630685 loss) +I0616 06:50:11.641808 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189955 (* 1 = 0.0189955 loss) +I0616 06:50:11.641811 9857 solver.cpp:571] Iteration 26480, lr = 0.001 +I0616 06:50:23.287055 9857 solver.cpp:242] Iteration 26500, loss = 0.823013 +I0616 06:50:23.287096 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125783 (* 1 = 0.125783 loss) +I0616 06:50:23.287102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132245 (* 1 = 0.132245 loss) +I0616 06:50:23.287107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.015365 (* 1 = 0.015365 loss) +I0616 06:50:23.287111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00561821 (* 1 = 0.00561821 loss) +I0616 06:50:23.287114 9857 solver.cpp:571] Iteration 26500, lr = 0.001 +I0616 06:50:34.512737 9857 solver.cpp:242] Iteration 26520, loss = 0.883617 +I0616 06:50:34.512776 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27268 (* 1 = 0.27268 loss) +I0616 06:50:34.512783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.662025 (* 1 = 0.662025 loss) +I0616 06:50:34.512786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0547276 (* 1 = 0.0547276 loss) +I0616 06:50:34.512790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0540143 (* 1 = 0.0540143 loss) +I0616 06:50:34.512794 9857 solver.cpp:571] Iteration 26520, lr = 0.001 +I0616 06:50:46.194941 9857 solver.cpp:242] Iteration 26540, loss = 0.632703 +I0616 06:50:46.194980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0969563 (* 1 = 0.0969563 loss) +I0616 06:50:46.194986 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14522 (* 1 = 0.14522 loss) +I0616 06:50:46.194990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125442 (* 1 = 0.125442 loss) +I0616 06:50:46.194994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131 (* 1 = 0.0131 loss) +I0616 06:50:46.194999 9857 solver.cpp:571] Iteration 26540, lr = 0.001 +I0616 06:50:57.603322 9857 solver.cpp:242] Iteration 26560, loss = 1.34324 +I0616 06:50:57.603364 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.687498 (* 1 = 0.687498 loss) +I0616 06:50:57.603370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.879187 (* 1 = 0.879187 loss) +I0616 06:50:57.603374 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.525806 (* 1 = 0.525806 loss) +I0616 06:50:57.603377 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108914 (* 1 = 0.108914 loss) +I0616 06:50:57.603382 9857 solver.cpp:571] Iteration 26560, lr = 0.001 +I0616 06:51:09.310834 9857 solver.cpp:242] Iteration 26580, loss = 1.25867 +I0616 06:51:09.310876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.697406 (* 1 = 0.697406 loss) +I0616 06:51:09.310881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.530113 (* 1 = 0.530113 loss) +I0616 06:51:09.310885 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0640559 (* 1 = 0.0640559 loss) +I0616 06:51:09.310890 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240073 (* 1 = 0.0240073 loss) +I0616 06:51:09.310894 9857 solver.cpp:571] Iteration 26580, lr = 0.001 +speed: 0.644s / iter +I0616 06:51:21.295536 9857 solver.cpp:242] Iteration 26600, loss = 0.621566 +I0616 06:51:21.295578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.223002 (* 1 = 0.223002 loss) +I0616 06:51:21.295583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242873 (* 1 = 0.242873 loss) +I0616 06:51:21.295588 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227928 (* 1 = 0.227928 loss) +I0616 06:51:21.295591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.100269 (* 1 = 0.100269 loss) +I0616 06:51:21.295594 9857 solver.cpp:571] Iteration 26600, lr = 0.001 +I0616 06:51:32.831550 9857 solver.cpp:242] Iteration 26620, loss = 1.54641 +I0616 06:51:32.831593 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343959 (* 1 = 0.343959 loss) +I0616 06:51:32.831598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32678 (* 1 = 0.32678 loss) +I0616 06:51:32.831603 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141308 (* 1 = 0.141308 loss) +I0616 06:51:32.831606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0546678 (* 1 = 0.0546678 loss) +I0616 06:51:32.831609 9857 solver.cpp:571] Iteration 26620, lr = 0.001 +I0616 06:51:44.362298 9857 solver.cpp:242] Iteration 26640, loss = 0.471799 +I0616 06:51:44.362340 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0855785 (* 1 = 0.0855785 loss) +I0616 06:51:44.362346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134783 (* 1 = 0.134783 loss) +I0616 06:51:44.362350 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0260881 (* 1 = 0.0260881 loss) +I0616 06:51:44.362354 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313626 (* 1 = 0.0313626 loss) +I0616 06:51:44.362359 9857 solver.cpp:571] Iteration 26640, lr = 0.001 +I0616 06:51:55.937527 9857 solver.cpp:242] Iteration 26660, loss = 1.15836 +I0616 06:51:55.937568 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256079 (* 1 = 0.256079 loss) +I0616 06:51:55.937574 9857 solver.cpp:258] Train net output #1: loss_cls = 0.307051 (* 1 = 0.307051 loss) +I0616 06:51:55.937578 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0520547 (* 1 = 0.0520547 loss) +I0616 06:51:55.937582 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170764 (* 1 = 0.0170764 loss) +I0616 06:51:55.937587 9857 solver.cpp:571] Iteration 26660, lr = 0.001 +I0616 06:52:07.365658 9857 solver.cpp:242] Iteration 26680, loss = 1.5295 +I0616 06:52:07.365700 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.417739 (* 1 = 0.417739 loss) +I0616 06:52:07.365705 9857 solver.cpp:258] Train net output #1: loss_cls = 0.800987 (* 1 = 0.800987 loss) +I0616 06:52:07.365710 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.465313 (* 1 = 0.465313 loss) +I0616 06:52:07.365713 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.113603 (* 1 = 0.113603 loss) +I0616 06:52:07.365717 9857 solver.cpp:571] Iteration 26680, lr = 0.001 +I0616 06:52:18.664610 9857 solver.cpp:242] Iteration 26700, loss = 1.87736 +I0616 06:52:18.664652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.55767 (* 1 = 0.55767 loss) +I0616 06:52:18.664657 9857 solver.cpp:258] Train net output #1: loss_cls = 1.80026 (* 1 = 1.80026 loss) +I0616 06:52:18.664661 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.516466 (* 1 = 0.516466 loss) +I0616 06:52:18.664665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.204192 (* 1 = 0.204192 loss) +I0616 06:52:18.664669 9857 solver.cpp:571] Iteration 26700, lr = 0.001 +I0616 06:52:30.358247 9857 solver.cpp:242] Iteration 26720, loss = 1.01085 +I0616 06:52:30.358289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287944 (* 1 = 0.287944 loss) +I0616 06:52:30.358294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.659436 (* 1 = 0.659436 loss) +I0616 06:52:30.358299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.185882 (* 1 = 0.185882 loss) +I0616 06:52:30.358301 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181315 (* 1 = 0.0181315 loss) +I0616 06:52:30.358305 9857 solver.cpp:571] Iteration 26720, lr = 0.001 +I0616 06:52:41.906903 9857 solver.cpp:242] Iteration 26740, loss = 1.23053 +I0616 06:52:41.906944 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.393368 (* 1 = 0.393368 loss) +I0616 06:52:41.906951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.906392 (* 1 = 0.906392 loss) +I0616 06:52:41.906955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.295922 (* 1 = 0.295922 loss) +I0616 06:52:41.906960 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0865902 (* 1 = 0.0865902 loss) +I0616 06:52:41.906963 9857 solver.cpp:571] Iteration 26740, lr = 0.001 +I0616 06:52:53.434492 9857 solver.cpp:242] Iteration 26760, loss = 0.845297 +I0616 06:52:53.434530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265141 (* 1 = 0.265141 loss) +I0616 06:52:53.434535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.293775 (* 1 = 0.293775 loss) +I0616 06:52:53.434540 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.097837 (* 1 = 0.097837 loss) +I0616 06:52:53.434543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0151358 (* 1 = 0.0151358 loss) +I0616 06:52:53.434547 9857 solver.cpp:571] Iteration 26760, lr = 0.001 +I0616 06:53:05.080351 9857 solver.cpp:242] Iteration 26780, loss = 1.28482 +I0616 06:53:05.080391 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230886 (* 1 = 0.230886 loss) +I0616 06:53:05.080396 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323493 (* 1 = 0.323493 loss) +I0616 06:53:05.080400 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.209465 (* 1 = 0.209465 loss) +I0616 06:53:05.080404 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.463559 (* 1 = 0.463559 loss) +I0616 06:53:05.080409 9857 solver.cpp:571] Iteration 26780, lr = 0.001 +speed: 0.643s / iter +I0616 06:53:16.676193 9857 solver.cpp:242] Iteration 26800, loss = 0.955951 +I0616 06:53:16.676235 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394931 (* 1 = 0.394931 loss) +I0616 06:53:16.676240 9857 solver.cpp:258] Train net output #1: loss_cls = 0.369217 (* 1 = 0.369217 loss) +I0616 06:53:16.676244 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144771 (* 1 = 0.144771 loss) +I0616 06:53:16.676249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00578207 (* 1 = 0.00578207 loss) +I0616 06:53:16.676252 9857 solver.cpp:571] Iteration 26800, lr = 0.001 +I0616 06:53:28.048684 9857 solver.cpp:242] Iteration 26820, loss = 1.61556 +I0616 06:53:28.048727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.606981 (* 1 = 0.606981 loss) +I0616 06:53:28.048732 9857 solver.cpp:258] Train net output #1: loss_cls = 1.25324 (* 1 = 1.25324 loss) +I0616 06:53:28.048735 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11296 (* 1 = 0.11296 loss) +I0616 06:53:28.048739 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122786 (* 1 = 0.0122786 loss) +I0616 06:53:28.048743 9857 solver.cpp:571] Iteration 26820, lr = 0.001 +I0616 06:53:39.510743 9857 solver.cpp:242] Iteration 26840, loss = 0.769114 +I0616 06:53:39.510788 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409158 (* 1 = 0.409158 loss) +I0616 06:53:39.510793 9857 solver.cpp:258] Train net output #1: loss_cls = 0.568642 (* 1 = 0.568642 loss) +I0616 06:53:39.510797 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.148889 (* 1 = 0.148889 loss) +I0616 06:53:39.510802 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0646783 (* 1 = 0.0646783 loss) +I0616 06:53:39.510805 9857 solver.cpp:571] Iteration 26840, lr = 0.001 +I0616 06:53:51.311691 9857 solver.cpp:242] Iteration 26860, loss = 1.11317 +I0616 06:53:51.311734 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.301752 (* 1 = 0.301752 loss) +I0616 06:53:51.311739 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459249 (* 1 = 0.459249 loss) +I0616 06:53:51.311743 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153109 (* 1 = 0.153109 loss) +I0616 06:53:51.311748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.220774 (* 1 = 0.220774 loss) +I0616 06:53:51.311751 9857 solver.cpp:571] Iteration 26860, lr = 0.001 +I0616 06:54:02.999186 9857 solver.cpp:242] Iteration 26880, loss = 0.932699 +I0616 06:54:02.999228 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353497 (* 1 = 0.353497 loss) +I0616 06:54:02.999234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.40348 (* 1 = 0.40348 loss) +I0616 06:54:02.999238 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140737 (* 1 = 0.140737 loss) +I0616 06:54:02.999243 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0539769 (* 1 = 0.0539769 loss) +I0616 06:54:02.999246 9857 solver.cpp:571] Iteration 26880, lr = 0.001 +I0616 06:54:14.728075 9857 solver.cpp:242] Iteration 26900, loss = 0.349683 +I0616 06:54:14.728116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162175 (* 1 = 0.162175 loss) +I0616 06:54:14.728122 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101464 (* 1 = 0.101464 loss) +I0616 06:54:14.728127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.043772 (* 1 = 0.043772 loss) +I0616 06:54:14.728130 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0238867 (* 1 = 0.0238867 loss) +I0616 06:54:14.728134 9857 solver.cpp:571] Iteration 26900, lr = 0.001 +I0616 06:54:26.219960 9857 solver.cpp:242] Iteration 26920, loss = 1.03756 +I0616 06:54:26.220001 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213777 (* 1 = 0.213777 loss) +I0616 06:54:26.220007 9857 solver.cpp:258] Train net output #1: loss_cls = 0.436185 (* 1 = 0.436185 loss) +I0616 06:54:26.220011 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0836007 (* 1 = 0.0836007 loss) +I0616 06:54:26.220016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.1204 (* 1 = 0.1204 loss) +I0616 06:54:26.220019 9857 solver.cpp:571] Iteration 26920, lr = 0.001 +I0616 06:54:37.689716 9857 solver.cpp:242] Iteration 26940, loss = 0.426459 +I0616 06:54:37.689759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234306 (* 1 = 0.234306 loss) +I0616 06:54:37.689764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164242 (* 1 = 0.164242 loss) +I0616 06:54:37.689769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100188 (* 1 = 0.100188 loss) +I0616 06:54:37.689771 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0517482 (* 1 = 0.0517482 loss) +I0616 06:54:37.689775 9857 solver.cpp:571] Iteration 26940, lr = 0.001 +I0616 06:54:49.126170 9857 solver.cpp:242] Iteration 26960, loss = 0.848067 +I0616 06:54:49.126212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.466888 (* 1 = 0.466888 loss) +I0616 06:54:49.126217 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416864 (* 1 = 0.416864 loss) +I0616 06:54:49.126221 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.273017 (* 1 = 0.273017 loss) +I0616 06:54:49.126225 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.17984 (* 1 = 0.17984 loss) +I0616 06:54:49.126229 9857 solver.cpp:571] Iteration 26960, lr = 0.001 +I0616 06:55:00.681311 9857 solver.cpp:242] Iteration 26980, loss = 0.490087 +I0616 06:55:00.681354 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247136 (* 1 = 0.247136 loss) +I0616 06:55:00.681360 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232138 (* 1 = 0.232138 loss) +I0616 06:55:00.681365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10557 (* 1 = 0.10557 loss) +I0616 06:55:00.681368 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0375005 (* 1 = 0.0375005 loss) +I0616 06:55:00.681371 9857 solver.cpp:571] Iteration 26980, lr = 0.001 +speed: 0.643s / iter +I0616 06:55:12.544757 9857 solver.cpp:242] Iteration 27000, loss = 1.74275 +I0616 06:55:12.544800 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249459 (* 1 = 0.249459 loss) +I0616 06:55:12.544806 9857 solver.cpp:258] Train net output #1: loss_cls = 0.382878 (* 1 = 0.382878 loss) +I0616 06:55:12.544809 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.046301 (* 1 = 0.046301 loss) +I0616 06:55:12.544813 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179575 (* 1 = 0.0179575 loss) +I0616 06:55:12.544817 9857 solver.cpp:571] Iteration 27000, lr = 0.001 +I0616 06:55:24.122711 9857 solver.cpp:242] Iteration 27020, loss = 1.19812 +I0616 06:55:24.122752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.448959 (* 1 = 0.448959 loss) +I0616 06:55:24.122761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229738 (* 1 = 0.229738 loss) +I0616 06:55:24.122779 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.378807 (* 1 = 0.378807 loss) +I0616 06:55:24.122783 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.45349 (* 1 = 0.45349 loss) +I0616 06:55:24.122802 9857 solver.cpp:571] Iteration 27020, lr = 0.001 +I0616 06:55:35.786172 9857 solver.cpp:242] Iteration 27040, loss = 0.801787 +I0616 06:55:35.786214 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0346149 (* 1 = 0.0346149 loss) +I0616 06:55:35.786221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166839 (* 1 = 0.166839 loss) +I0616 06:55:35.786224 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0766667 (* 1 = 0.0766667 loss) +I0616 06:55:35.786228 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0294928 (* 1 = 0.0294928 loss) +I0616 06:55:35.786232 9857 solver.cpp:571] Iteration 27040, lr = 0.001 +I0616 06:55:47.370390 9857 solver.cpp:242] Iteration 27060, loss = 1.17938 +I0616 06:55:47.370431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.491175 (* 1 = 0.491175 loss) +I0616 06:55:47.370437 9857 solver.cpp:258] Train net output #1: loss_cls = 0.567708 (* 1 = 0.567708 loss) +I0616 06:55:47.370441 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183512 (* 1 = 0.183512 loss) +I0616 06:55:47.370445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0822604 (* 1 = 0.0822604 loss) +I0616 06:55:47.370450 9857 solver.cpp:571] Iteration 27060, lr = 0.001 +I0616 06:55:58.796375 9857 solver.cpp:242] Iteration 27080, loss = 0.94545 +I0616 06:55:58.796417 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111334 (* 1 = 0.111334 loss) +I0616 06:55:58.796422 9857 solver.cpp:258] Train net output #1: loss_cls = 0.615474 (* 1 = 0.615474 loss) +I0616 06:55:58.796427 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.025897 (* 1 = 0.025897 loss) +I0616 06:55:58.796430 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0422558 (* 1 = 0.0422558 loss) +I0616 06:55:58.796434 9857 solver.cpp:571] Iteration 27080, lr = 0.001 +I0616 06:56:10.406132 9857 solver.cpp:242] Iteration 27100, loss = 0.542764 +I0616 06:56:10.406173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268897 (* 1 = 0.268897 loss) +I0616 06:56:10.406179 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360792 (* 1 = 0.360792 loss) +I0616 06:56:10.406183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175955 (* 1 = 0.175955 loss) +I0616 06:56:10.406188 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00888653 (* 1 = 0.00888653 loss) +I0616 06:56:10.406191 9857 solver.cpp:571] Iteration 27100, lr = 0.001 +I0616 06:56:21.755494 9857 solver.cpp:242] Iteration 27120, loss = 1.10611 +I0616 06:56:21.755537 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10093 (* 1 = 0.10093 loss) +I0616 06:56:21.755542 9857 solver.cpp:258] Train net output #1: loss_cls = 0.412837 (* 1 = 0.412837 loss) +I0616 06:56:21.755547 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0751822 (* 1 = 0.0751822 loss) +I0616 06:56:21.755550 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101384 (* 1 = 0.0101384 loss) +I0616 06:56:21.755553 9857 solver.cpp:571] Iteration 27120, lr = 0.001 +I0616 06:56:33.350199 9857 solver.cpp:242] Iteration 27140, loss = 0.920073 +I0616 06:56:33.350242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11122 (* 1 = 0.11122 loss) +I0616 06:56:33.350249 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0808298 (* 1 = 0.0808298 loss) +I0616 06:56:33.350252 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14518 (* 1 = 0.14518 loss) +I0616 06:56:33.350255 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245085 (* 1 = 0.0245085 loss) +I0616 06:56:33.350260 9857 solver.cpp:571] Iteration 27140, lr = 0.001 +I0616 06:56:44.959394 9857 solver.cpp:242] Iteration 27160, loss = 1.35146 +I0616 06:56:44.959437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298377 (* 1 = 0.298377 loss) +I0616 06:56:44.959444 9857 solver.cpp:258] Train net output #1: loss_cls = 0.716744 (* 1 = 0.716744 loss) +I0616 06:56:44.959447 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114782 (* 1 = 0.114782 loss) +I0616 06:56:44.959451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.031807 (* 1 = 0.031807 loss) +I0616 06:56:44.959455 9857 solver.cpp:571] Iteration 27160, lr = 0.001 +I0616 06:56:56.683727 9857 solver.cpp:242] Iteration 27180, loss = 1.01263 +I0616 06:56:56.683768 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179533 (* 1 = 0.179533 loss) +I0616 06:56:56.683774 9857 solver.cpp:258] Train net output #1: loss_cls = 0.329565 (* 1 = 0.329565 loss) +I0616 06:56:56.683779 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0819572 (* 1 = 0.0819572 loss) +I0616 06:56:56.683782 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.146825 (* 1 = 0.146825 loss) +I0616 06:56:56.683786 9857 solver.cpp:571] Iteration 27180, lr = 0.001 +speed: 0.642s / iter +I0616 06:57:08.210530 9857 solver.cpp:242] Iteration 27200, loss = 0.520578 +I0616 06:57:08.210572 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139201 (* 1 = 0.139201 loss) +I0616 06:57:08.210577 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211961 (* 1 = 0.211961 loss) +I0616 06:57:08.210582 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105857 (* 1 = 0.105857 loss) +I0616 06:57:08.210585 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225622 (* 1 = 0.0225622 loss) +I0616 06:57:08.210588 9857 solver.cpp:571] Iteration 27200, lr = 0.001 +I0616 06:57:19.766891 9857 solver.cpp:242] Iteration 27220, loss = 0.78468 +I0616 06:57:19.766932 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311527 (* 1 = 0.311527 loss) +I0616 06:57:19.766938 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358697 (* 1 = 0.358697 loss) +I0616 06:57:19.766943 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129539 (* 1 = 0.129539 loss) +I0616 06:57:19.766947 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.11938 (* 1 = 0.11938 loss) +I0616 06:57:19.766950 9857 solver.cpp:571] Iteration 27220, lr = 0.001 +I0616 06:57:31.386443 9857 solver.cpp:242] Iteration 27240, loss = 0.726789 +I0616 06:57:31.386484 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350297 (* 1 = 0.350297 loss) +I0616 06:57:31.386490 9857 solver.cpp:258] Train net output #1: loss_cls = 0.454437 (* 1 = 0.454437 loss) +I0616 06:57:31.386494 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112073 (* 1 = 0.112073 loss) +I0616 06:57:31.386498 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0289686 (* 1 = 0.0289686 loss) +I0616 06:57:31.386518 9857 solver.cpp:571] Iteration 27240, lr = 0.001 +I0616 06:57:42.671141 9857 solver.cpp:242] Iteration 27260, loss = 0.79068 +I0616 06:57:42.671182 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0517931 (* 1 = 0.0517931 loss) +I0616 06:57:42.671187 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292655 (* 1 = 0.292655 loss) +I0616 06:57:42.671192 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0908748 (* 1 = 0.0908748 loss) +I0616 06:57:42.671195 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00235853 (* 1 = 0.00235853 loss) +I0616 06:57:42.671200 9857 solver.cpp:571] Iteration 27260, lr = 0.001 +I0616 06:57:54.301906 9857 solver.cpp:242] Iteration 27280, loss = 0.717835 +I0616 06:57:54.301947 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346595 (* 1 = 0.346595 loss) +I0616 06:57:54.301954 9857 solver.cpp:258] Train net output #1: loss_cls = 0.491866 (* 1 = 0.491866 loss) +I0616 06:57:54.301957 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0718152 (* 1 = 0.0718152 loss) +I0616 06:57:54.301961 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119246 (* 1 = 0.119246 loss) +I0616 06:57:54.301964 9857 solver.cpp:571] Iteration 27280, lr = 0.001 +I0616 06:58:05.797693 9857 solver.cpp:242] Iteration 27300, loss = 0.807366 +I0616 06:58:05.797735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0383199 (* 1 = 0.0383199 loss) +I0616 06:58:05.797741 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130063 (* 1 = 0.130063 loss) +I0616 06:58:05.797745 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0820435 (* 1 = 0.0820435 loss) +I0616 06:58:05.797749 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.022879 (* 1 = 0.022879 loss) +I0616 06:58:05.797752 9857 solver.cpp:571] Iteration 27300, lr = 0.001 +I0616 06:58:17.497627 9857 solver.cpp:242] Iteration 27320, loss = 0.728664 +I0616 06:58:17.497669 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212218 (* 1 = 0.212218 loss) +I0616 06:58:17.497675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.631013 (* 1 = 0.631013 loss) +I0616 06:58:17.497679 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0674442 (* 1 = 0.0674442 loss) +I0616 06:58:17.497684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0475093 (* 1 = 0.0475093 loss) +I0616 06:58:17.497687 9857 solver.cpp:571] Iteration 27320, lr = 0.001 +I0616 06:58:29.079382 9857 solver.cpp:242] Iteration 27340, loss = 0.990014 +I0616 06:58:29.079423 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488484 (* 1 = 0.488484 loss) +I0616 06:58:29.079429 9857 solver.cpp:258] Train net output #1: loss_cls = 0.598198 (* 1 = 0.598198 loss) +I0616 06:58:29.079433 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159045 (* 1 = 0.159045 loss) +I0616 06:58:29.079437 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.067943 (* 1 = 0.067943 loss) +I0616 06:58:29.079442 9857 solver.cpp:571] Iteration 27340, lr = 0.001 +I0616 06:58:40.591835 9857 solver.cpp:242] Iteration 27360, loss = 1.07726 +I0616 06:58:40.591876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.502663 (* 1 = 0.502663 loss) +I0616 06:58:40.591882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.634027 (* 1 = 0.634027 loss) +I0616 06:58:40.591886 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.232205 (* 1 = 0.232205 loss) +I0616 06:58:40.591889 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0527287 (* 1 = 0.0527287 loss) +I0616 06:58:40.591894 9857 solver.cpp:571] Iteration 27360, lr = 0.001 +I0616 06:58:52.131103 9857 solver.cpp:242] Iteration 27380, loss = 0.799018 +I0616 06:58:52.131145 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.449887 (* 1 = 0.449887 loss) +I0616 06:58:52.131150 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431754 (* 1 = 0.431754 loss) +I0616 06:58:52.131155 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188417 (* 1 = 0.188417 loss) +I0616 06:58:52.131158 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.035255 (* 1 = 0.035255 loss) +I0616 06:58:52.131162 9857 solver.cpp:571] Iteration 27380, lr = 0.001 +speed: 0.642s / iter +I0616 06:59:03.676790 9857 solver.cpp:242] Iteration 27400, loss = 1.1353 +I0616 06:59:03.676833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146142 (* 1 = 0.146142 loss) +I0616 06:59:03.676839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268257 (* 1 = 0.268257 loss) +I0616 06:59:03.676843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.084209 (* 1 = 0.084209 loss) +I0616 06:59:03.676847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201315 (* 1 = 0.0201315 loss) +I0616 06:59:03.676851 9857 solver.cpp:571] Iteration 27400, lr = 0.001 +I0616 06:59:15.115700 9857 solver.cpp:242] Iteration 27420, loss = 0.784555 +I0616 06:59:15.115741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247964 (* 1 = 0.247964 loss) +I0616 06:59:15.115746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236083 (* 1 = 0.236083 loss) +I0616 06:59:15.115751 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0823408 (* 1 = 0.0823408 loss) +I0616 06:59:15.115754 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0268093 (* 1 = 0.0268093 loss) +I0616 06:59:15.115758 9857 solver.cpp:571] Iteration 27420, lr = 0.001 +I0616 06:59:26.619460 9857 solver.cpp:242] Iteration 27440, loss = 1.2815 +I0616 06:59:26.619501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296142 (* 1 = 0.296142 loss) +I0616 06:59:26.619506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.720544 (* 1 = 0.720544 loss) +I0616 06:59:26.619510 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.248998 (* 1 = 0.248998 loss) +I0616 06:59:26.619514 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128739 (* 1 = 0.0128739 loss) +I0616 06:59:26.619518 9857 solver.cpp:571] Iteration 27440, lr = 0.001 +I0616 06:59:37.924247 9857 solver.cpp:242] Iteration 27460, loss = 1.20548 +I0616 06:59:37.924288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0305517 (* 1 = 0.0305517 loss) +I0616 06:59:37.924293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.506384 (* 1 = 0.506384 loss) +I0616 06:59:37.924298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.522143 (* 1 = 0.522143 loss) +I0616 06:59:37.924301 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.58692 (* 1 = 0.58692 loss) +I0616 06:59:37.924305 9857 solver.cpp:571] Iteration 27460, lr = 0.001 +I0616 06:59:49.564198 9857 solver.cpp:242] Iteration 27480, loss = 0.872708 +I0616 06:59:49.564240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233211 (* 1 = 0.233211 loss) +I0616 06:59:49.564245 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26915 (* 1 = 0.26915 loss) +I0616 06:59:49.564250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0830007 (* 1 = 0.0830007 loss) +I0616 06:59:49.564254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105019 (* 1 = 0.0105019 loss) +I0616 06:59:49.564257 9857 solver.cpp:571] Iteration 27480, lr = 0.001 +I0616 07:00:01.041507 9857 solver.cpp:242] Iteration 27500, loss = 1.0204 +I0616 07:00:01.041548 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193766 (* 1 = 0.193766 loss) +I0616 07:00:01.041553 9857 solver.cpp:258] Train net output #1: loss_cls = 0.720002 (* 1 = 0.720002 loss) +I0616 07:00:01.041558 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.173955 (* 1 = 0.173955 loss) +I0616 07:00:01.041561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0347959 (* 1 = 0.0347959 loss) +I0616 07:00:01.041565 9857 solver.cpp:571] Iteration 27500, lr = 0.001 +I0616 07:00:12.396632 9857 solver.cpp:242] Iteration 27520, loss = 0.468523 +I0616 07:00:12.396673 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140738 (* 1 = 0.140738 loss) +I0616 07:00:12.396679 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309216 (* 1 = 0.309216 loss) +I0616 07:00:12.396683 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0764804 (* 1 = 0.0764804 loss) +I0616 07:00:12.396687 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00834618 (* 1 = 0.00834618 loss) +I0616 07:00:12.396692 9857 solver.cpp:571] Iteration 27520, lr = 0.001 +I0616 07:00:23.872582 9857 solver.cpp:242] Iteration 27540, loss = 0.843916 +I0616 07:00:23.872622 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413517 (* 1 = 0.413517 loss) +I0616 07:00:23.872627 9857 solver.cpp:258] Train net output #1: loss_cls = 0.588194 (* 1 = 0.588194 loss) +I0616 07:00:23.872632 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.063542 (* 1 = 0.063542 loss) +I0616 07:00:23.872634 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.20297 (* 1 = 0.20297 loss) +I0616 07:00:23.872638 9857 solver.cpp:571] Iteration 27540, lr = 0.001 +I0616 07:00:35.469816 9857 solver.cpp:242] Iteration 27560, loss = 1.21496 +I0616 07:00:35.469858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.505086 (* 1 = 0.505086 loss) +I0616 07:00:35.469863 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354175 (* 1 = 0.354175 loss) +I0616 07:00:35.469868 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154296 (* 1 = 0.154296 loss) +I0616 07:00:35.469871 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145446 (* 1 = 0.0145446 loss) +I0616 07:00:35.469876 9857 solver.cpp:571] Iteration 27560, lr = 0.001 +I0616 07:00:46.985247 9857 solver.cpp:242] Iteration 27580, loss = 0.471499 +I0616 07:00:46.985290 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210898 (* 1 = 0.210898 loss) +I0616 07:00:46.985294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219732 (* 1 = 0.219732 loss) +I0616 07:00:46.985298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151229 (* 1 = 0.151229 loss) +I0616 07:00:46.985302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0492556 (* 1 = 0.0492556 loss) +I0616 07:00:46.985306 9857 solver.cpp:571] Iteration 27580, lr = 0.001 +speed: 0.641s / iter +I0616 07:00:58.583791 9857 solver.cpp:242] Iteration 27600, loss = 0.508634 +I0616 07:00:58.583832 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308417 (* 1 = 0.308417 loss) +I0616 07:00:58.583837 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155793 (* 1 = 0.155793 loss) +I0616 07:00:58.583842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.198263 (* 1 = 0.198263 loss) +I0616 07:00:58.583845 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313337 (* 1 = 0.0313337 loss) +I0616 07:00:58.583848 9857 solver.cpp:571] Iteration 27600, lr = 0.001 +I0616 07:01:10.335131 9857 solver.cpp:242] Iteration 27620, loss = 1.30885 +I0616 07:01:10.335173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177395 (* 1 = 0.177395 loss) +I0616 07:01:10.335180 9857 solver.cpp:258] Train net output #1: loss_cls = 0.611345 (* 1 = 0.611345 loss) +I0616 07:01:10.335183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109492 (* 1 = 0.109492 loss) +I0616 07:01:10.335187 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138568 (* 1 = 0.0138568 loss) +I0616 07:01:10.335191 9857 solver.cpp:571] Iteration 27620, lr = 0.001 +I0616 07:01:21.738306 9857 solver.cpp:242] Iteration 27640, loss = 1.30697 +I0616 07:01:21.738346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.43203 (* 1 = 0.43203 loss) +I0616 07:01:21.738353 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45886 (* 1 = 0.45886 loss) +I0616 07:01:21.738356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22599 (* 1 = 0.22599 loss) +I0616 07:01:21.738360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.256055 (* 1 = 0.256055 loss) +I0616 07:01:21.738364 9857 solver.cpp:571] Iteration 27640, lr = 0.001 +I0616 07:01:33.259636 9857 solver.cpp:242] Iteration 27660, loss = 1.67776 +I0616 07:01:33.259680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386982 (* 1 = 0.386982 loss) +I0616 07:01:33.259685 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381559 (* 1 = 0.381559 loss) +I0616 07:01:33.259690 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00657669 (* 1 = 0.00657669 loss) +I0616 07:01:33.259693 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00179168 (* 1 = 0.00179168 loss) +I0616 07:01:33.259696 9857 solver.cpp:571] Iteration 27660, lr = 0.001 +I0616 07:01:44.762585 9857 solver.cpp:242] Iteration 27680, loss = 0.961309 +I0616 07:01:44.762626 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158393 (* 1 = 0.158393 loss) +I0616 07:01:44.762632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.212307 (* 1 = 0.212307 loss) +I0616 07:01:44.762636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0562556 (* 1 = 0.0562556 loss) +I0616 07:01:44.762639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165405 (* 1 = 0.0165405 loss) +I0616 07:01:44.762644 9857 solver.cpp:571] Iteration 27680, lr = 0.001 +I0616 07:01:56.398717 9857 solver.cpp:242] Iteration 27700, loss = 0.665696 +I0616 07:01:56.398763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0660848 (* 1 = 0.0660848 loss) +I0616 07:01:56.398769 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110213 (* 1 = 0.110213 loss) +I0616 07:01:56.398774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0695979 (* 1 = 0.0695979 loss) +I0616 07:01:56.398777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239146 (* 1 = 0.0239146 loss) +I0616 07:01:56.398782 9857 solver.cpp:571] Iteration 27700, lr = 0.001 +I0616 07:02:07.743783 9857 solver.cpp:242] Iteration 27720, loss = 1.47549 +I0616 07:02:07.743825 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323613 (* 1 = 0.323613 loss) +I0616 07:02:07.743831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.576448 (* 1 = 0.576448 loss) +I0616 07:02:07.743835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200205 (* 1 = 0.200205 loss) +I0616 07:02:07.743839 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0705595 (* 1 = 0.0705595 loss) +I0616 07:02:07.743844 9857 solver.cpp:571] Iteration 27720, lr = 0.001 +I0616 07:02:19.160101 9857 solver.cpp:242] Iteration 27740, loss = 0.574958 +I0616 07:02:19.160142 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350369 (* 1 = 0.350369 loss) +I0616 07:02:19.160148 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264445 (* 1 = 0.264445 loss) +I0616 07:02:19.160152 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0435377 (* 1 = 0.0435377 loss) +I0616 07:02:19.160156 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182124 (* 1 = 0.0182124 loss) +I0616 07:02:19.160161 9857 solver.cpp:571] Iteration 27740, lr = 0.001 +I0616 07:02:30.353071 9857 solver.cpp:242] Iteration 27760, loss = 0.845787 +I0616 07:02:30.353113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389212 (* 1 = 0.389212 loss) +I0616 07:02:30.353119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.530098 (* 1 = 0.530098 loss) +I0616 07:02:30.353123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183349 (* 1 = 0.183349 loss) +I0616 07:02:30.353127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.130097 (* 1 = 0.130097 loss) +I0616 07:02:30.353132 9857 solver.cpp:571] Iteration 27760, lr = 0.001 +I0616 07:02:42.221818 9857 solver.cpp:242] Iteration 27780, loss = 0.945491 +I0616 07:02:42.221863 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264459 (* 1 = 0.264459 loss) +I0616 07:02:42.221868 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300825 (* 1 = 0.300825 loss) +I0616 07:02:42.221871 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224657 (* 1 = 0.224657 loss) +I0616 07:02:42.221875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0464473 (* 1 = 0.0464473 loss) +I0616 07:02:42.221879 9857 solver.cpp:571] Iteration 27780, lr = 0.001 +speed: 0.641s / iter +I0616 07:02:53.779870 9857 solver.cpp:242] Iteration 27800, loss = 0.604894 +I0616 07:02:53.779912 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259383 (* 1 = 0.259383 loss) +I0616 07:02:53.779918 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366351 (* 1 = 0.366351 loss) +I0616 07:02:53.779922 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0975477 (* 1 = 0.0975477 loss) +I0616 07:02:53.779927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00827204 (* 1 = 0.00827204 loss) +I0616 07:02:53.779930 9857 solver.cpp:571] Iteration 27800, lr = 0.001 +I0616 07:03:05.615126 9857 solver.cpp:242] Iteration 27820, loss = 0.70675 +I0616 07:03:05.615167 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233267 (* 1 = 0.233267 loss) +I0616 07:03:05.615172 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270717 (* 1 = 0.270717 loss) +I0616 07:03:05.615176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0146515 (* 1 = 0.0146515 loss) +I0616 07:03:05.615180 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00720318 (* 1 = 0.00720318 loss) +I0616 07:03:05.615185 9857 solver.cpp:571] Iteration 27820, lr = 0.001 +I0616 07:03:16.899983 9857 solver.cpp:242] Iteration 27840, loss = 0.373148 +I0616 07:03:16.900025 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175129 (* 1 = 0.175129 loss) +I0616 07:03:16.900030 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209548 (* 1 = 0.209548 loss) +I0616 07:03:16.900034 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0884895 (* 1 = 0.0884895 loss) +I0616 07:03:16.900038 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252625 (* 1 = 0.0252625 loss) +I0616 07:03:16.900043 9857 solver.cpp:571] Iteration 27840, lr = 0.001 +I0616 07:03:28.523429 9857 solver.cpp:242] Iteration 27860, loss = 0.49974 +I0616 07:03:28.523470 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0837868 (* 1 = 0.0837868 loss) +I0616 07:03:28.523476 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151851 (* 1 = 0.151851 loss) +I0616 07:03:28.523480 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0479996 (* 1 = 0.0479996 loss) +I0616 07:03:28.523484 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00765378 (* 1 = 0.00765378 loss) +I0616 07:03:28.523488 9857 solver.cpp:571] Iteration 27860, lr = 0.001 +I0616 07:03:40.108347 9857 solver.cpp:242] Iteration 27880, loss = 0.547109 +I0616 07:03:40.108388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0891991 (* 1 = 0.0891991 loss) +I0616 07:03:40.108394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126534 (* 1 = 0.126534 loss) +I0616 07:03:40.108398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141478 (* 1 = 0.0141478 loss) +I0616 07:03:40.108402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000749515 (* 1 = 0.000749515 loss) +I0616 07:03:40.108407 9857 solver.cpp:571] Iteration 27880, lr = 0.001 +I0616 07:03:51.757347 9857 solver.cpp:242] Iteration 27900, loss = 0.598975 +I0616 07:03:51.757388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28563 (* 1 = 0.28563 loss) +I0616 07:03:51.757395 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221172 (* 1 = 0.221172 loss) +I0616 07:03:51.757398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101719 (* 1 = 0.101719 loss) +I0616 07:03:51.757402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.101765 (* 1 = 0.101765 loss) +I0616 07:03:51.757406 9857 solver.cpp:571] Iteration 27900, lr = 0.001 +I0616 07:04:03.575917 9857 solver.cpp:242] Iteration 27920, loss = 1.38865 +I0616 07:04:03.575955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.412899 (* 1 = 0.412899 loss) +I0616 07:04:03.575961 9857 solver.cpp:258] Train net output #1: loss_cls = 0.898406 (* 1 = 0.898406 loss) +I0616 07:04:03.575965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.390786 (* 1 = 0.390786 loss) +I0616 07:04:03.575969 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0651143 (* 1 = 0.0651143 loss) +I0616 07:04:03.575973 9857 solver.cpp:571] Iteration 27920, lr = 0.001 +I0616 07:04:15.038022 9857 solver.cpp:242] Iteration 27940, loss = 0.664394 +I0616 07:04:15.038061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113913 (* 1 = 0.113913 loss) +I0616 07:04:15.038067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366048 (* 1 = 0.366048 loss) +I0616 07:04:15.038071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0426124 (* 1 = 0.0426124 loss) +I0616 07:04:15.038075 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167495 (* 1 = 0.0167495 loss) +I0616 07:04:15.038079 9857 solver.cpp:571] Iteration 27940, lr = 0.001 +I0616 07:04:26.482673 9857 solver.cpp:242] Iteration 27960, loss = 0.322478 +I0616 07:04:26.482717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13289 (* 1 = 0.13289 loss) +I0616 07:04:26.482722 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230281 (* 1 = 0.230281 loss) +I0616 07:04:26.482727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0285196 (* 1 = 0.0285196 loss) +I0616 07:04:26.482730 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0422297 (* 1 = 0.0422297 loss) +I0616 07:04:26.482734 9857 solver.cpp:571] Iteration 27960, lr = 0.001 +I0616 07:04:38.194952 9857 solver.cpp:242] Iteration 27980, loss = 0.67708 +I0616 07:04:38.194994 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447428 (* 1 = 0.447428 loss) +I0616 07:04:38.194999 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235211 (* 1 = 0.235211 loss) +I0616 07:04:38.195004 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113914 (* 1 = 0.113914 loss) +I0616 07:04:38.195008 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.025749 (* 1 = 0.025749 loss) +I0616 07:04:38.195011 9857 solver.cpp:571] Iteration 27980, lr = 0.001 +speed: 0.641s / iter +I0616 07:04:49.965673 9857 solver.cpp:242] Iteration 28000, loss = 1.79192 +I0616 07:04:49.965714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.497305 (* 1 = 0.497305 loss) +I0616 07:04:49.965720 9857 solver.cpp:258] Train net output #1: loss_cls = 0.653155 (* 1 = 0.653155 loss) +I0616 07:04:49.965724 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294091 (* 1 = 0.294091 loss) +I0616 07:04:49.965728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.061612 (* 1 = 0.061612 loss) +I0616 07:04:49.965733 9857 solver.cpp:571] Iteration 28000, lr = 0.001 +I0616 07:05:01.540369 9857 solver.cpp:242] Iteration 28020, loss = 0.806921 +I0616 07:05:01.540410 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0410672 (* 1 = 0.0410672 loss) +I0616 07:05:01.540416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.227683 (* 1 = 0.227683 loss) +I0616 07:05:01.540421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0355971 (* 1 = 0.0355971 loss) +I0616 07:05:01.540424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483874 (* 1 = 0.0483874 loss) +I0616 07:05:01.540427 9857 solver.cpp:571] Iteration 28020, lr = 0.001 +I0616 07:05:13.297197 9857 solver.cpp:242] Iteration 28040, loss = 0.811685 +I0616 07:05:13.297242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0978463 (* 1 = 0.0978463 loss) +I0616 07:05:13.297250 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432525 (* 1 = 0.432525 loss) +I0616 07:05:13.297255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0895977 (* 1 = 0.0895977 loss) +I0616 07:05:13.297260 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.03236 (* 1 = 0.03236 loss) +I0616 07:05:13.297266 9857 solver.cpp:571] Iteration 28040, lr = 0.001 +I0616 07:05:25.095705 9857 solver.cpp:242] Iteration 28060, loss = 1.15679 +I0616 07:05:25.095747 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316629 (* 1 = 0.316629 loss) +I0616 07:05:25.095753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.471906 (* 1 = 0.471906 loss) +I0616 07:05:25.095757 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.23245 (* 1 = 0.23245 loss) +I0616 07:05:25.095762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.060897 (* 1 = 0.060897 loss) +I0616 07:05:25.095764 9857 solver.cpp:571] Iteration 28060, lr = 0.001 +I0616 07:05:36.475019 9857 solver.cpp:242] Iteration 28080, loss = 2.1767 +I0616 07:05:36.475061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290025 (* 1 = 0.290025 loss) +I0616 07:05:36.475066 9857 solver.cpp:258] Train net output #1: loss_cls = 1.52425 (* 1 = 1.52425 loss) +I0616 07:05:36.475070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 1.59949 (* 1 = 1.59949 loss) +I0616 07:05:36.475075 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.597357 (* 1 = 0.597357 loss) +I0616 07:05:36.475080 9857 solver.cpp:571] Iteration 28080, lr = 0.001 +I0616 07:05:48.020087 9857 solver.cpp:242] Iteration 28100, loss = 0.656797 +I0616 07:05:48.020128 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152511 (* 1 = 0.152511 loss) +I0616 07:05:48.020134 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260971 (* 1 = 0.260971 loss) +I0616 07:05:48.020138 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0250171 (* 1 = 0.0250171 loss) +I0616 07:05:48.020143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0200405 (* 1 = 0.0200405 loss) +I0616 07:05:48.020148 9857 solver.cpp:571] Iteration 28100, lr = 0.001 +I0616 07:05:59.697620 9857 solver.cpp:242] Iteration 28120, loss = 0.964453 +I0616 07:05:59.697661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150191 (* 1 = 0.150191 loss) +I0616 07:05:59.697666 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21924 (* 1 = 0.21924 loss) +I0616 07:05:59.697670 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.036031 (* 1 = 0.036031 loss) +I0616 07:05:59.697674 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165197 (* 1 = 0.0165197 loss) +I0616 07:05:59.697679 9857 solver.cpp:571] Iteration 28120, lr = 0.001 +I0616 07:06:11.004875 9857 solver.cpp:242] Iteration 28140, loss = 0.267702 +I0616 07:06:11.004915 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0416511 (* 1 = 0.0416511 loss) +I0616 07:06:11.004921 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118695 (* 1 = 0.118695 loss) +I0616 07:06:11.004925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0707065 (* 1 = 0.0707065 loss) +I0616 07:06:11.004930 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0118261 (* 1 = 0.0118261 loss) +I0616 07:06:11.004933 9857 solver.cpp:571] Iteration 28140, lr = 0.001 +I0616 07:06:22.526039 9857 solver.cpp:242] Iteration 28160, loss = 0.912968 +I0616 07:06:22.526080 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.429373 (* 1 = 0.429373 loss) +I0616 07:06:22.526087 9857 solver.cpp:258] Train net output #1: loss_cls = 0.602951 (* 1 = 0.602951 loss) +I0616 07:06:22.526090 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101744 (* 1 = 0.101744 loss) +I0616 07:06:22.526094 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0393509 (* 1 = 0.0393509 loss) +I0616 07:06:22.526098 9857 solver.cpp:571] Iteration 28160, lr = 0.001 +I0616 07:06:33.980303 9857 solver.cpp:242] Iteration 28180, loss = 1.05039 +I0616 07:06:33.980345 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399117 (* 1 = 0.399117 loss) +I0616 07:06:33.980350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366179 (* 1 = 0.366179 loss) +I0616 07:06:33.980353 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0944041 (* 1 = 0.0944041 loss) +I0616 07:06:33.980357 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0362154 (* 1 = 0.0362154 loss) +I0616 07:06:33.980361 9857 solver.cpp:571] Iteration 28180, lr = 0.001 +speed: 0.640s / iter +I0616 07:06:45.544373 9857 solver.cpp:242] Iteration 28200, loss = 0.561782 +I0616 07:06:45.544412 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203507 (* 1 = 0.203507 loss) +I0616 07:06:45.544417 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345771 (* 1 = 0.345771 loss) +I0616 07:06:45.544421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0503607 (* 1 = 0.0503607 loss) +I0616 07:06:45.544425 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0331344 (* 1 = 0.0331344 loss) +I0616 07:06:45.544430 9857 solver.cpp:571] Iteration 28200, lr = 0.001 +I0616 07:06:57.391412 9857 solver.cpp:242] Iteration 28220, loss = 0.706045 +I0616 07:06:57.391453 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149613 (* 1 = 0.149613 loss) +I0616 07:06:57.391458 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149882 (* 1 = 0.149882 loss) +I0616 07:06:57.391463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0535771 (* 1 = 0.0535771 loss) +I0616 07:06:57.391466 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114033 (* 1 = 0.0114033 loss) +I0616 07:06:57.391469 9857 solver.cpp:571] Iteration 28220, lr = 0.001 +I0616 07:07:08.907229 9857 solver.cpp:242] Iteration 28240, loss = 0.455302 +I0616 07:07:08.907271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178538 (* 1 = 0.178538 loss) +I0616 07:07:08.907276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176756 (* 1 = 0.176756 loss) +I0616 07:07:08.907281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00737215 (* 1 = 0.00737215 loss) +I0616 07:07:08.907285 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00293271 (* 1 = 0.00293271 loss) +I0616 07:07:08.907289 9857 solver.cpp:571] Iteration 28240, lr = 0.001 +I0616 07:07:20.574543 9857 solver.cpp:242] Iteration 28260, loss = 1.06507 +I0616 07:07:20.574584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27674 (* 1 = 0.27674 loss) +I0616 07:07:20.574589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426509 (* 1 = 0.426509 loss) +I0616 07:07:20.574594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210777 (* 1 = 0.210777 loss) +I0616 07:07:20.574597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0394828 (* 1 = 0.0394828 loss) +I0616 07:07:20.574601 9857 solver.cpp:571] Iteration 28260, lr = 0.001 +I0616 07:07:32.149955 9857 solver.cpp:242] Iteration 28280, loss = 0.499426 +I0616 07:07:32.149996 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138104 (* 1 = 0.138104 loss) +I0616 07:07:32.150002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237902 (* 1 = 0.237902 loss) +I0616 07:07:32.150007 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0345048 (* 1 = 0.0345048 loss) +I0616 07:07:32.150009 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00635108 (* 1 = 0.00635108 loss) +I0616 07:07:32.150013 9857 solver.cpp:571] Iteration 28280, lr = 0.001 +I0616 07:07:43.564657 9857 solver.cpp:242] Iteration 28300, loss = 0.81933 +I0616 07:07:43.564697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112935 (* 1 = 0.112935 loss) +I0616 07:07:43.564702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110376 (* 1 = 0.110376 loss) +I0616 07:07:43.564705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0120298 (* 1 = 0.0120298 loss) +I0616 07:07:43.564709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239897 (* 1 = 0.0239897 loss) +I0616 07:07:43.564713 9857 solver.cpp:571] Iteration 28300, lr = 0.001 +I0616 07:07:55.047018 9857 solver.cpp:242] Iteration 28320, loss = 0.845453 +I0616 07:07:55.047056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308641 (* 1 = 0.308641 loss) +I0616 07:07:55.047061 9857 solver.cpp:258] Train net output #1: loss_cls = 0.390358 (* 1 = 0.390358 loss) +I0616 07:07:55.047065 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.171449 (* 1 = 0.171449 loss) +I0616 07:07:55.047070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165498 (* 1 = 0.0165498 loss) +I0616 07:07:55.047073 9857 solver.cpp:571] Iteration 28320, lr = 0.001 +I0616 07:08:06.613656 9857 solver.cpp:242] Iteration 28340, loss = 0.496802 +I0616 07:08:06.613697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132266 (* 1 = 0.132266 loss) +I0616 07:08:06.613701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135467 (* 1 = 0.135467 loss) +I0616 07:08:06.613705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0821076 (* 1 = 0.0821076 loss) +I0616 07:08:06.613709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0449869 (* 1 = 0.0449869 loss) +I0616 07:08:06.613713 9857 solver.cpp:571] Iteration 28340, lr = 0.001 +I0616 07:08:18.085260 9857 solver.cpp:242] Iteration 28360, loss = 0.900278 +I0616 07:08:18.085302 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275392 (* 1 = 0.275392 loss) +I0616 07:08:18.085307 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296637 (* 1 = 0.296637 loss) +I0616 07:08:18.085312 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0756303 (* 1 = 0.0756303 loss) +I0616 07:08:18.085315 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0580815 (* 1 = 0.0580815 loss) +I0616 07:08:18.085319 9857 solver.cpp:571] Iteration 28360, lr = 0.001 +I0616 07:08:29.655028 9857 solver.cpp:242] Iteration 28380, loss = 1.29071 +I0616 07:08:29.655071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134483 (* 1 = 0.134483 loss) +I0616 07:08:29.655076 9857 solver.cpp:258] Train net output #1: loss_cls = 0.25276 (* 1 = 0.25276 loss) +I0616 07:08:29.655079 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124431 (* 1 = 0.124431 loss) +I0616 07:08:29.655083 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215212 (* 1 = 0.0215212 loss) +I0616 07:08:29.655087 9857 solver.cpp:571] Iteration 28380, lr = 0.001 +speed: 0.640s / iter +I0616 07:08:41.091297 9857 solver.cpp:242] Iteration 28400, loss = 1.34699 +I0616 07:08:41.091337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262231 (* 1 = 0.262231 loss) +I0616 07:08:41.091342 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392363 (* 1 = 0.392363 loss) +I0616 07:08:41.091346 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.228196 (* 1 = 0.228196 loss) +I0616 07:08:41.091351 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231895 (* 1 = 0.0231895 loss) +I0616 07:08:41.091354 9857 solver.cpp:571] Iteration 28400, lr = 0.001 +I0616 07:08:52.715834 9857 solver.cpp:242] Iteration 28420, loss = 0.992285 +I0616 07:08:52.715875 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210197 (* 1 = 0.210197 loss) +I0616 07:08:52.715881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259823 (* 1 = 0.259823 loss) +I0616 07:08:52.715885 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.318241 (* 1 = 0.318241 loss) +I0616 07:08:52.715888 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.75114 (* 1 = 0.75114 loss) +I0616 07:08:52.715893 9857 solver.cpp:571] Iteration 28420, lr = 0.001 +I0616 07:09:04.606674 9857 solver.cpp:242] Iteration 28440, loss = 1.51428 +I0616 07:09:04.606716 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.361433 (* 1 = 0.361433 loss) +I0616 07:09:04.606721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.460712 (* 1 = 0.460712 loss) +I0616 07:09:04.606725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0316509 (* 1 = 0.0316509 loss) +I0616 07:09:04.606729 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.195068 (* 1 = 0.195068 loss) +I0616 07:09:04.606734 9857 solver.cpp:571] Iteration 28440, lr = 0.001 +I0616 07:09:16.126986 9857 solver.cpp:242] Iteration 28460, loss = 0.703041 +I0616 07:09:16.127027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120371 (* 1 = 0.120371 loss) +I0616 07:09:16.127032 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459872 (* 1 = 0.459872 loss) +I0616 07:09:16.127038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395606 (* 1 = 0.0395606 loss) +I0616 07:09:16.127040 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107944 (* 1 = 0.0107944 loss) +I0616 07:09:16.127044 9857 solver.cpp:571] Iteration 28460, lr = 0.001 +I0616 07:09:27.694710 9857 solver.cpp:242] Iteration 28480, loss = 1.30137 +I0616 07:09:27.694753 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373949 (* 1 = 0.373949 loss) +I0616 07:09:27.694766 9857 solver.cpp:258] Train net output #1: loss_cls = 0.65563 (* 1 = 0.65563 loss) +I0616 07:09:27.694769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.244934 (* 1 = 0.244934 loss) +I0616 07:09:27.694773 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.419171 (* 1 = 0.419171 loss) +I0616 07:09:27.694777 9857 solver.cpp:571] Iteration 28480, lr = 0.001 +I0616 07:09:39.488117 9857 solver.cpp:242] Iteration 28500, loss = 2.18497 +I0616 07:09:39.488160 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174539 (* 1 = 0.174539 loss) +I0616 07:09:39.488167 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313175 (* 1 = 0.313175 loss) +I0616 07:09:39.488170 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0581041 (* 1 = 0.0581041 loss) +I0616 07:09:39.488174 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00527581 (* 1 = 0.00527581 loss) +I0616 07:09:39.488178 9857 solver.cpp:571] Iteration 28500, lr = 0.001 +I0616 07:09:50.849822 9857 solver.cpp:242] Iteration 28520, loss = 0.803109 +I0616 07:09:50.849865 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244898 (* 1 = 0.244898 loss) +I0616 07:09:50.849870 9857 solver.cpp:258] Train net output #1: loss_cls = 0.307949 (* 1 = 0.307949 loss) +I0616 07:09:50.849874 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0751498 (* 1 = 0.0751498 loss) +I0616 07:09:50.849879 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0496272 (* 1 = 0.0496272 loss) +I0616 07:09:50.849882 9857 solver.cpp:571] Iteration 28520, lr = 0.001 +I0616 07:10:02.429677 9857 solver.cpp:242] Iteration 28540, loss = 1.04426 +I0616 07:10:02.429718 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.524632 (* 1 = 0.524632 loss) +I0616 07:10:02.429723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.926729 (* 1 = 0.926729 loss) +I0616 07:10:02.429728 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166533 (* 1 = 0.166533 loss) +I0616 07:10:02.429731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0792365 (* 1 = 0.0792365 loss) +I0616 07:10:02.429735 9857 solver.cpp:571] Iteration 28540, lr = 0.001 +I0616 07:10:13.984689 9857 solver.cpp:242] Iteration 28560, loss = 0.413916 +I0616 07:10:13.984730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185825 (* 1 = 0.185825 loss) +I0616 07:10:13.984735 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287757 (* 1 = 0.287757 loss) +I0616 07:10:13.984740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0801094 (* 1 = 0.0801094 loss) +I0616 07:10:13.984743 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00485067 (* 1 = 0.00485067 loss) +I0616 07:10:13.984747 9857 solver.cpp:571] Iteration 28560, lr = 0.001 +I0616 07:10:25.422791 9857 solver.cpp:242] Iteration 28580, loss = 1.62888 +I0616 07:10:25.422832 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145629 (* 1 = 0.145629 loss) +I0616 07:10:25.422837 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313757 (* 1 = 0.313757 loss) +I0616 07:10:25.422842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.514477 (* 1 = 0.514477 loss) +I0616 07:10:25.422845 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.07351 (* 1 = 1.07351 loss) +I0616 07:10:25.422849 9857 solver.cpp:571] Iteration 28580, lr = 0.001 +speed: 0.639s / iter +I0616 07:10:36.787547 9857 solver.cpp:242] Iteration 28600, loss = 0.499386 +I0616 07:10:36.787590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138964 (* 1 = 0.138964 loss) +I0616 07:10:36.787595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18931 (* 1 = 0.18931 loss) +I0616 07:10:36.787600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0194384 (* 1 = 0.0194384 loss) +I0616 07:10:36.787602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105571 (* 1 = 0.0105571 loss) +I0616 07:10:36.787606 9857 solver.cpp:571] Iteration 28600, lr = 0.001 +I0616 07:10:48.299312 9857 solver.cpp:242] Iteration 28620, loss = 0.719404 +I0616 07:10:48.299350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327247 (* 1 = 0.327247 loss) +I0616 07:10:48.299355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213419 (* 1 = 0.213419 loss) +I0616 07:10:48.299360 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0568887 (* 1 = 0.0568887 loss) +I0616 07:10:48.299363 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0307726 (* 1 = 0.0307726 loss) +I0616 07:10:48.299367 9857 solver.cpp:571] Iteration 28620, lr = 0.001 +I0616 07:11:00.205305 9857 solver.cpp:242] Iteration 28640, loss = 0.543038 +I0616 07:11:00.205346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241286 (* 1 = 0.241286 loss) +I0616 07:11:00.205351 9857 solver.cpp:258] Train net output #1: loss_cls = 0.258262 (* 1 = 0.258262 loss) +I0616 07:11:00.205355 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0958335 (* 1 = 0.0958335 loss) +I0616 07:11:00.205359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0241766 (* 1 = 0.0241766 loss) +I0616 07:11:00.205363 9857 solver.cpp:571] Iteration 28640, lr = 0.001 +I0616 07:11:11.773547 9857 solver.cpp:242] Iteration 28660, loss = 1.21128 +I0616 07:11:11.773589 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.325931 (* 1 = 0.325931 loss) +I0616 07:11:11.773596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.428527 (* 1 = 0.428527 loss) +I0616 07:11:11.773599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.393496 (* 1 = 0.393496 loss) +I0616 07:11:11.773602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.192654 (* 1 = 0.192654 loss) +I0616 07:11:11.773607 9857 solver.cpp:571] Iteration 28660, lr = 0.001 +I0616 07:11:23.223870 9857 solver.cpp:242] Iteration 28680, loss = 1.35378 +I0616 07:11:23.223912 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.499539 (* 1 = 0.499539 loss) +I0616 07:11:23.223917 9857 solver.cpp:258] Train net output #1: loss_cls = 1.0822 (* 1 = 1.0822 loss) +I0616 07:11:23.223922 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.283508 (* 1 = 0.283508 loss) +I0616 07:11:23.223924 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.257685 (* 1 = 0.257685 loss) +I0616 07:11:23.223928 9857 solver.cpp:571] Iteration 28680, lr = 0.001 +I0616 07:11:34.774986 9857 solver.cpp:242] Iteration 28700, loss = 1.03433 +I0616 07:11:34.775028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30892 (* 1 = 0.30892 loss) +I0616 07:11:34.775034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.612123 (* 1 = 0.612123 loss) +I0616 07:11:34.775038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146153 (* 1 = 0.146153 loss) +I0616 07:11:34.775043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0236279 (* 1 = 0.0236279 loss) +I0616 07:11:34.775045 9857 solver.cpp:571] Iteration 28700, lr = 0.001 +I0616 07:11:46.397239 9857 solver.cpp:242] Iteration 28720, loss = 0.664796 +I0616 07:11:46.397282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161701 (* 1 = 0.161701 loss) +I0616 07:11:46.397287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340081 (* 1 = 0.340081 loss) +I0616 07:11:46.397291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161901 (* 1 = 0.161901 loss) +I0616 07:11:46.397295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0541856 (* 1 = 0.0541856 loss) +I0616 07:11:46.397300 9857 solver.cpp:571] Iteration 28720, lr = 0.001 +I0616 07:11:57.780774 9857 solver.cpp:242] Iteration 28740, loss = 1.12322 +I0616 07:11:57.780818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132732 (* 1 = 0.132732 loss) +I0616 07:11:57.780823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.480952 (* 1 = 0.480952 loss) +I0616 07:11:57.780827 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.299333 (* 1 = 0.299333 loss) +I0616 07:11:57.780832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.288748 (* 1 = 0.288748 loss) +I0616 07:11:57.780835 9857 solver.cpp:571] Iteration 28740, lr = 0.001 +I0616 07:12:09.245869 9857 solver.cpp:242] Iteration 28760, loss = 1.62373 +I0616 07:12:09.245911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386272 (* 1 = 0.386272 loss) +I0616 07:12:09.245918 9857 solver.cpp:258] Train net output #1: loss_cls = 1.0234 (* 1 = 1.0234 loss) +I0616 07:12:09.245921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108063 (* 1 = 0.108063 loss) +I0616 07:12:09.245925 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135219 (* 1 = 0.0135219 loss) +I0616 07:12:09.245931 9857 solver.cpp:571] Iteration 28760, lr = 0.001 +I0616 07:12:20.938763 9857 solver.cpp:242] Iteration 28780, loss = 1.6264 +I0616 07:12:20.938805 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232588 (* 1 = 0.232588 loss) +I0616 07:12:20.938810 9857 solver.cpp:258] Train net output #1: loss_cls = 0.798623 (* 1 = 0.798623 loss) +I0616 07:12:20.938814 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386277 (* 1 = 0.0386277 loss) +I0616 07:12:20.938818 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285032 (* 1 = 0.0285032 loss) +I0616 07:12:20.938822 9857 solver.cpp:571] Iteration 28780, lr = 0.001 +speed: 0.639s / iter +I0616 07:12:32.332245 9857 solver.cpp:242] Iteration 28800, loss = 0.486643 +I0616 07:12:32.332288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209348 (* 1 = 0.209348 loss) +I0616 07:12:32.332293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23682 (* 1 = 0.23682 loss) +I0616 07:12:32.332298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134068 (* 1 = 0.134068 loss) +I0616 07:12:32.332300 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161917 (* 1 = 0.0161917 loss) +I0616 07:12:32.332305 9857 solver.cpp:571] Iteration 28800, lr = 0.001 +I0616 07:12:43.858676 9857 solver.cpp:242] Iteration 28820, loss = 1.57048 +I0616 07:12:43.858718 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.490086 (* 1 = 0.490086 loss) +I0616 07:12:43.858723 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10207 (* 1 = 1.10207 loss) +I0616 07:12:43.858727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114257 (* 1 = 0.114257 loss) +I0616 07:12:43.858731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00958531 (* 1 = 0.00958531 loss) +I0616 07:12:43.858736 9857 solver.cpp:571] Iteration 28820, lr = 0.001 +I0616 07:12:55.473474 9857 solver.cpp:242] Iteration 28840, loss = 0.422889 +I0616 07:12:55.473515 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153482 (* 1 = 0.153482 loss) +I0616 07:12:55.473520 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191314 (* 1 = 0.191314 loss) +I0616 07:12:55.473525 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0115506 (* 1 = 0.0115506 loss) +I0616 07:12:55.473528 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132642 (* 1 = 0.0132642 loss) +I0616 07:12:55.473532 9857 solver.cpp:571] Iteration 28840, lr = 0.001 +I0616 07:13:07.100886 9857 solver.cpp:242] Iteration 28860, loss = 0.901855 +I0616 07:13:07.100929 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.35301 (* 1 = 0.35301 loss) +I0616 07:13:07.100934 9857 solver.cpp:258] Train net output #1: loss_cls = 0.532556 (* 1 = 0.532556 loss) +I0616 07:13:07.100937 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0841692 (* 1 = 0.0841692 loss) +I0616 07:13:07.100941 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0303398 (* 1 = 0.0303398 loss) +I0616 07:13:07.100945 9857 solver.cpp:571] Iteration 28860, lr = 0.001 +I0616 07:13:18.622181 9857 solver.cpp:242] Iteration 28880, loss = 0.648697 +I0616 07:13:18.622221 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21437 (* 1 = 0.21437 loss) +I0616 07:13:18.622226 9857 solver.cpp:258] Train net output #1: loss_cls = 0.468239 (* 1 = 0.468239 loss) +I0616 07:13:18.622231 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0524927 (* 1 = 0.0524927 loss) +I0616 07:13:18.622234 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128812 (* 1 = 0.0128812 loss) +I0616 07:13:18.622238 9857 solver.cpp:571] Iteration 28880, lr = 0.001 +I0616 07:13:30.360488 9857 solver.cpp:242] Iteration 28900, loss = 0.622155 +I0616 07:13:30.360530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323306 (* 1 = 0.323306 loss) +I0616 07:13:30.360537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370551 (* 1 = 0.370551 loss) +I0616 07:13:30.360540 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0368062 (* 1 = 0.0368062 loss) +I0616 07:13:30.360544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012278 (* 1 = 0.012278 loss) +I0616 07:13:30.360550 9857 solver.cpp:571] Iteration 28900, lr = 0.001 +I0616 07:13:41.949641 9857 solver.cpp:242] Iteration 28920, loss = 1.07223 +I0616 07:13:41.949683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210175 (* 1 = 0.210175 loss) +I0616 07:13:41.949688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247624 (* 1 = 0.247624 loss) +I0616 07:13:41.949692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125577 (* 1 = 0.125577 loss) +I0616 07:13:41.949697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000858624 (* 1 = 0.000858624 loss) +I0616 07:13:41.949700 9857 solver.cpp:571] Iteration 28920, lr = 0.001 +I0616 07:13:53.480829 9857 solver.cpp:242] Iteration 28940, loss = 0.682783 +I0616 07:13:53.480871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371953 (* 1 = 0.371953 loss) +I0616 07:13:53.480877 9857 solver.cpp:258] Train net output #1: loss_cls = 0.513494 (* 1 = 0.513494 loss) +I0616 07:13:53.480881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20362 (* 1 = 0.20362 loss) +I0616 07:13:53.480885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230759 (* 1 = 0.0230759 loss) +I0616 07:13:53.480890 9857 solver.cpp:571] Iteration 28940, lr = 0.001 +I0616 07:14:04.916641 9857 solver.cpp:242] Iteration 28960, loss = 0.580196 +I0616 07:14:04.916684 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138171 (* 1 = 0.138171 loss) +I0616 07:14:04.916690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368525 (* 1 = 0.368525 loss) +I0616 07:14:04.916694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0373116 (* 1 = 0.0373116 loss) +I0616 07:14:04.916698 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00686485 (* 1 = 0.00686485 loss) +I0616 07:14:04.916702 9857 solver.cpp:571] Iteration 28960, lr = 0.001 +I0616 07:14:16.712258 9857 solver.cpp:242] Iteration 28980, loss = 1.58757 +I0616 07:14:16.712301 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0889491 (* 1 = 0.0889491 loss) +I0616 07:14:16.712306 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143981 (* 1 = 0.143981 loss) +I0616 07:14:16.712311 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 1.00732 (* 1 = 1.00732 loss) +I0616 07:14:16.712314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.834399 (* 1 = 0.834399 loss) +I0616 07:14:16.712317 9857 solver.cpp:571] Iteration 28980, lr = 0.001 +speed: 0.638s / iter +I0616 07:14:28.167107 9857 solver.cpp:242] Iteration 29000, loss = 1.21563 +I0616 07:14:28.167150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.321906 (* 1 = 0.321906 loss) +I0616 07:14:28.167155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.475284 (* 1 = 0.475284 loss) +I0616 07:14:28.167160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.23103 (* 1 = 0.23103 loss) +I0616 07:14:28.167163 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.151434 (* 1 = 0.151434 loss) +I0616 07:14:28.167167 9857 solver.cpp:571] Iteration 29000, lr = 0.001 +I0616 07:14:39.981647 9857 solver.cpp:242] Iteration 29020, loss = 1.14528 +I0616 07:14:39.981690 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242551 (* 1 = 0.242551 loss) +I0616 07:14:39.981695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.535912 (* 1 = 0.535912 loss) +I0616 07:14:39.981699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10965 (* 1 = 0.10965 loss) +I0616 07:14:39.981703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0764826 (* 1 = 0.0764826 loss) +I0616 07:14:39.981708 9857 solver.cpp:571] Iteration 29020, lr = 0.001 +I0616 07:14:51.605767 9857 solver.cpp:242] Iteration 29040, loss = 0.847559 +I0616 07:14:51.605806 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244119 (* 1 = 0.244119 loss) +I0616 07:14:51.605811 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343725 (* 1 = 0.343725 loss) +I0616 07:14:51.605816 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18385 (* 1 = 0.18385 loss) +I0616 07:14:51.605819 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000394718 (* 1 = 0.000394718 loss) +I0616 07:14:51.605823 9857 solver.cpp:571] Iteration 29040, lr = 0.001 +I0616 07:15:03.114492 9857 solver.cpp:242] Iteration 29060, loss = 0.824845 +I0616 07:15:03.114534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212662 (* 1 = 0.212662 loss) +I0616 07:15:03.114540 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314005 (* 1 = 0.314005 loss) +I0616 07:15:03.114544 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0308281 (* 1 = 0.0308281 loss) +I0616 07:15:03.114548 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0016312 (* 1 = 0.0016312 loss) +I0616 07:15:03.114552 9857 solver.cpp:571] Iteration 29060, lr = 0.001 +I0616 07:15:14.836493 9857 solver.cpp:242] Iteration 29080, loss = 0.536021 +I0616 07:15:14.836534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151503 (* 1 = 0.151503 loss) +I0616 07:15:14.836539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221218 (* 1 = 0.221218 loss) +I0616 07:15:14.836544 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10251 (* 1 = 0.10251 loss) +I0616 07:15:14.836547 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0211419 (* 1 = 0.0211419 loss) +I0616 07:15:14.836551 9857 solver.cpp:571] Iteration 29080, lr = 0.001 +I0616 07:15:26.625967 9857 solver.cpp:242] Iteration 29100, loss = 1.14627 +I0616 07:15:26.626009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0785977 (* 1 = 0.0785977 loss) +I0616 07:15:26.626015 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147939 (* 1 = 0.147939 loss) +I0616 07:15:26.626019 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0955874 (* 1 = 0.0955874 loss) +I0616 07:15:26.626024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0379269 (* 1 = 0.0379269 loss) +I0616 07:15:26.626027 9857 solver.cpp:571] Iteration 29100, lr = 0.001 +I0616 07:15:38.054468 9857 solver.cpp:242] Iteration 29120, loss = 0.651173 +I0616 07:15:38.054509 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21382 (* 1 = 0.21382 loss) +I0616 07:15:38.054515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.454644 (* 1 = 0.454644 loss) +I0616 07:15:38.054519 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124156 (* 1 = 0.124156 loss) +I0616 07:15:38.054523 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0264895 (* 1 = 0.0264895 loss) +I0616 07:15:38.054527 9857 solver.cpp:571] Iteration 29120, lr = 0.001 +I0616 07:15:49.649197 9857 solver.cpp:242] Iteration 29140, loss = 0.965916 +I0616 07:15:49.649240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447503 (* 1 = 0.447503 loss) +I0616 07:15:49.649245 9857 solver.cpp:258] Train net output #1: loss_cls = 0.554606 (* 1 = 0.554606 loss) +I0616 07:15:49.649250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.321288 (* 1 = 0.321288 loss) +I0616 07:15:49.649253 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0594989 (* 1 = 0.0594989 loss) +I0616 07:15:49.649257 9857 solver.cpp:571] Iteration 29140, lr = 0.001 +I0616 07:16:01.182147 9857 solver.cpp:242] Iteration 29160, loss = 1.00799 +I0616 07:16:01.182190 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202079 (* 1 = 0.202079 loss) +I0616 07:16:01.182195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179744 (* 1 = 0.179744 loss) +I0616 07:16:01.182199 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141141 (* 1 = 0.0141141 loss) +I0616 07:16:01.182204 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00281654 (* 1 = 0.00281654 loss) +I0616 07:16:01.182207 9857 solver.cpp:571] Iteration 29160, lr = 0.001 +I0616 07:16:12.759881 9857 solver.cpp:242] Iteration 29180, loss = 1.15959 +I0616 07:16:12.759923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168779 (* 1 = 0.168779 loss) +I0616 07:16:12.759928 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138179 (* 1 = 0.138179 loss) +I0616 07:16:12.759932 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0697637 (* 1 = 0.0697637 loss) +I0616 07:16:12.759937 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.031037 (* 1 = 0.031037 loss) +I0616 07:16:12.759940 9857 solver.cpp:571] Iteration 29180, lr = 0.001 +speed: 0.638s / iter +I0616 07:16:24.708549 9857 solver.cpp:242] Iteration 29200, loss = 0.642294 +I0616 07:16:24.708591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.245228 (* 1 = 0.245228 loss) +I0616 07:16:24.708597 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279645 (* 1 = 0.279645 loss) +I0616 07:16:24.708601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117764 (* 1 = 0.117764 loss) +I0616 07:16:24.708606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0699493 (* 1 = 0.0699493 loss) +I0616 07:16:24.708609 9857 solver.cpp:571] Iteration 29200, lr = 0.001 +I0616 07:16:36.208099 9857 solver.cpp:242] Iteration 29220, loss = 0.469805 +I0616 07:16:36.208140 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228122 (* 1 = 0.228122 loss) +I0616 07:16:36.208145 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266956 (* 1 = 0.266956 loss) +I0616 07:16:36.208149 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0724283 (* 1 = 0.0724283 loss) +I0616 07:16:36.208153 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189479 (* 1 = 0.0189479 loss) +I0616 07:16:36.208158 9857 solver.cpp:571] Iteration 29220, lr = 0.001 +I0616 07:16:47.793638 9857 solver.cpp:242] Iteration 29240, loss = 0.951548 +I0616 07:16:47.793679 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408593 (* 1 = 0.408593 loss) +I0616 07:16:47.793684 9857 solver.cpp:258] Train net output #1: loss_cls = 0.660825 (* 1 = 0.660825 loss) +I0616 07:16:47.793689 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190102 (* 1 = 0.190102 loss) +I0616 07:16:47.793692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221292 (* 1 = 0.0221292 loss) +I0616 07:16:47.793696 9857 solver.cpp:571] Iteration 29240, lr = 0.001 +I0616 07:16:59.329731 9857 solver.cpp:242] Iteration 29260, loss = 1.04401 +I0616 07:16:59.329773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.445194 (* 1 = 0.445194 loss) +I0616 07:16:59.329778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.605764 (* 1 = 0.605764 loss) +I0616 07:16:59.329782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.223477 (* 1 = 0.223477 loss) +I0616 07:16:59.329787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0333593 (* 1 = 0.0333593 loss) +I0616 07:16:59.329792 9857 solver.cpp:571] Iteration 29260, lr = 0.001 +I0616 07:17:10.962993 9857 solver.cpp:242] Iteration 29280, loss = 0.752035 +I0616 07:17:10.963035 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263475 (* 1 = 0.263475 loss) +I0616 07:17:10.963042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300733 (* 1 = 0.300733 loss) +I0616 07:17:10.963045 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169287 (* 1 = 0.169287 loss) +I0616 07:17:10.963049 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397392 (* 1 = 0.0397392 loss) +I0616 07:17:10.963052 9857 solver.cpp:571] Iteration 29280, lr = 0.001 +I0616 07:17:22.396358 9857 solver.cpp:242] Iteration 29300, loss = 0.818774 +I0616 07:17:22.396401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358689 (* 1 = 0.358689 loss) +I0616 07:17:22.396406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.767142 (* 1 = 0.767142 loss) +I0616 07:17:22.396411 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.073743 (* 1 = 0.073743 loss) +I0616 07:17:22.396414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0382089 (* 1 = 0.0382089 loss) +I0616 07:17:22.396419 9857 solver.cpp:571] Iteration 29300, lr = 0.001 +I0616 07:17:34.055512 9857 solver.cpp:242] Iteration 29320, loss = 0.753785 +I0616 07:17:34.055553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200928 (* 1 = 0.200928 loss) +I0616 07:17:34.055559 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232245 (* 1 = 0.232245 loss) +I0616 07:17:34.055563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106182 (* 1 = 0.106182 loss) +I0616 07:17:34.055567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00144891 (* 1 = 0.00144891 loss) +I0616 07:17:34.055572 9857 solver.cpp:571] Iteration 29320, lr = 0.001 +I0616 07:17:45.862447 9857 solver.cpp:242] Iteration 29340, loss = 0.533667 +I0616 07:17:45.862488 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0612718 (* 1 = 0.0612718 loss) +I0616 07:17:45.862494 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12084 (* 1 = 0.12084 loss) +I0616 07:17:45.862498 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0126133 (* 1 = 0.0126133 loss) +I0616 07:17:45.862503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00302327 (* 1 = 0.00302327 loss) +I0616 07:17:45.862506 9857 solver.cpp:571] Iteration 29340, lr = 0.001 +I0616 07:17:57.508282 9857 solver.cpp:242] Iteration 29360, loss = 0.924639 +I0616 07:17:57.508324 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.465611 (* 1 = 0.465611 loss) +I0616 07:17:57.508329 9857 solver.cpp:258] Train net output #1: loss_cls = 0.64215 (* 1 = 0.64215 loss) +I0616 07:17:57.508333 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.265703 (* 1 = 0.265703 loss) +I0616 07:17:57.508337 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0330649 (* 1 = 0.0330649 loss) +I0616 07:17:57.508342 9857 solver.cpp:571] Iteration 29360, lr = 0.001 +I0616 07:18:08.881613 9857 solver.cpp:242] Iteration 29380, loss = 1.76295 +I0616 07:18:08.881654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.621774 (* 1 = 0.621774 loss) +I0616 07:18:08.881660 9857 solver.cpp:258] Train net output #1: loss_cls = 1.81875 (* 1 = 1.81875 loss) +I0616 07:18:08.881664 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.307574 (* 1 = 0.307574 loss) +I0616 07:18:08.881669 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.115639 (* 1 = 0.115639 loss) +I0616 07:18:08.881672 9857 solver.cpp:571] Iteration 29380, lr = 0.001 +speed: 0.638s / iter +I0616 07:18:20.558230 9857 solver.cpp:242] Iteration 29400, loss = 0.47317 +I0616 07:18:20.558270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110447 (* 1 = 0.110447 loss) +I0616 07:18:20.558276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.407795 (* 1 = 0.407795 loss) +I0616 07:18:20.558280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0689902 (* 1 = 0.0689902 loss) +I0616 07:18:20.558284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292854 (* 1 = 0.0292854 loss) +I0616 07:18:20.558289 9857 solver.cpp:571] Iteration 29400, lr = 0.001 +I0616 07:18:32.099834 9857 solver.cpp:242] Iteration 29420, loss = 0.632831 +I0616 07:18:32.099874 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136795 (* 1 = 0.136795 loss) +I0616 07:18:32.099879 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162525 (* 1 = 0.162525 loss) +I0616 07:18:32.099882 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125065 (* 1 = 0.125065 loss) +I0616 07:18:32.099886 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00442977 (* 1 = 0.00442977 loss) +I0616 07:18:32.099890 9857 solver.cpp:571] Iteration 29420, lr = 0.001 +I0616 07:18:43.640022 9857 solver.cpp:242] Iteration 29440, loss = 1.13415 +I0616 07:18:43.640063 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265717 (* 1 = 0.265717 loss) +I0616 07:18:43.640067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.923447 (* 1 = 0.923447 loss) +I0616 07:18:43.640072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0710935 (* 1 = 0.0710935 loss) +I0616 07:18:43.640075 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00838141 (* 1 = 0.00838141 loss) +I0616 07:18:43.640079 9857 solver.cpp:571] Iteration 29440, lr = 0.001 +I0616 07:18:55.188860 9857 solver.cpp:242] Iteration 29460, loss = 0.480292 +I0616 07:18:55.188900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268405 (* 1 = 0.268405 loss) +I0616 07:18:55.188905 9857 solver.cpp:258] Train net output #1: loss_cls = 0.238148 (* 1 = 0.238148 loss) +I0616 07:18:55.188910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0425153 (* 1 = 0.0425153 loss) +I0616 07:18:55.188913 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00903695 (* 1 = 0.00903695 loss) +I0616 07:18:55.188918 9857 solver.cpp:571] Iteration 29460, lr = 0.001 +I0616 07:19:06.421855 9857 solver.cpp:242] Iteration 29480, loss = 0.621944 +I0616 07:19:06.421898 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0096575 (* 1 = 0.0096575 loss) +I0616 07:19:06.421905 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127989 (* 1 = 0.127989 loss) +I0616 07:19:06.421908 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120356 (* 1 = 0.120356 loss) +I0616 07:19:06.421912 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0997115 (* 1 = 0.0997115 loss) +I0616 07:19:06.421916 9857 solver.cpp:571] Iteration 29480, lr = 0.001 +I0616 07:19:17.746158 9857 solver.cpp:242] Iteration 29500, loss = 0.934924 +I0616 07:19:17.746201 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304002 (* 1 = 0.304002 loss) +I0616 07:19:17.746206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.353811 (* 1 = 0.353811 loss) +I0616 07:19:17.746211 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15652 (* 1 = 0.15652 loss) +I0616 07:19:17.746214 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.472915 (* 1 = 0.472915 loss) +I0616 07:19:17.746220 9857 solver.cpp:571] Iteration 29500, lr = 0.001 +I0616 07:19:29.266050 9857 solver.cpp:242] Iteration 29520, loss = 0.611941 +I0616 07:19:29.266093 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171966 (* 1 = 0.171966 loss) +I0616 07:19:29.266098 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370122 (* 1 = 0.370122 loss) +I0616 07:19:29.266103 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0349732 (* 1 = 0.0349732 loss) +I0616 07:19:29.266106 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0651389 (* 1 = 0.0651389 loss) +I0616 07:19:29.266109 9857 solver.cpp:571] Iteration 29520, lr = 0.001 +I0616 07:19:40.756521 9857 solver.cpp:242] Iteration 29540, loss = 1.34739 +I0616 07:19:40.756563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.577014 (* 1 = 0.577014 loss) +I0616 07:19:40.756568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.649205 (* 1 = 0.649205 loss) +I0616 07:19:40.756572 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0768767 (* 1 = 0.0768767 loss) +I0616 07:19:40.756577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.13521 (* 1 = 0.13521 loss) +I0616 07:19:40.756580 9857 solver.cpp:571] Iteration 29540, lr = 0.001 +I0616 07:19:52.172952 9857 solver.cpp:242] Iteration 29560, loss = 0.704416 +I0616 07:19:52.172992 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.317148 (* 1 = 0.317148 loss) +I0616 07:19:52.172997 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295558 (* 1 = 0.295558 loss) +I0616 07:19:52.173002 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123039 (* 1 = 0.123039 loss) +I0616 07:19:52.173005 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103917 (* 1 = 0.103917 loss) +I0616 07:19:52.173009 9857 solver.cpp:571] Iteration 29560, lr = 0.001 +I0616 07:20:03.888445 9857 solver.cpp:242] Iteration 29580, loss = 0.660044 +I0616 07:20:03.888486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215384 (* 1 = 0.215384 loss) +I0616 07:20:03.888494 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154149 (* 1 = 0.154149 loss) +I0616 07:20:03.888497 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141701 (* 1 = 0.141701 loss) +I0616 07:20:03.888501 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0366931 (* 1 = 0.0366931 loss) +I0616 07:20:03.888505 9857 solver.cpp:571] Iteration 29580, lr = 0.001 +speed: 0.637s / iter +I0616 07:20:15.465879 9857 solver.cpp:242] Iteration 29600, loss = 0.906764 +I0616 07:20:15.465921 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146884 (* 1 = 0.146884 loss) +I0616 07:20:15.465927 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276113 (* 1 = 0.276113 loss) +I0616 07:20:15.465931 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0864946 (* 1 = 0.0864946 loss) +I0616 07:20:15.465934 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.147731 (* 1 = 0.147731 loss) +I0616 07:20:15.465939 9857 solver.cpp:571] Iteration 29600, lr = 0.001 +I0616 07:20:27.014909 9857 solver.cpp:242] Iteration 29620, loss = 1.06853 +I0616 07:20:27.014951 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379381 (* 1 = 0.379381 loss) +I0616 07:20:27.014957 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216108 (* 1 = 0.216108 loss) +I0616 07:20:27.014961 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0672118 (* 1 = 0.0672118 loss) +I0616 07:20:27.014964 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255524 (* 1 = 0.0255524 loss) +I0616 07:20:27.014968 9857 solver.cpp:571] Iteration 29620, lr = 0.001 +I0616 07:20:38.586140 9857 solver.cpp:242] Iteration 29640, loss = 0.772255 +I0616 07:20:38.586181 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193543 (* 1 = 0.193543 loss) +I0616 07:20:38.586187 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181607 (* 1 = 0.181607 loss) +I0616 07:20:38.586191 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0385649 (* 1 = 0.0385649 loss) +I0616 07:20:38.586194 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147138 (* 1 = 0.0147138 loss) +I0616 07:20:38.586199 9857 solver.cpp:571] Iteration 29640, lr = 0.001 +I0616 07:20:50.311112 9857 solver.cpp:242] Iteration 29660, loss = 1.06724 +I0616 07:20:50.311156 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336717 (* 1 = 0.336717 loss) +I0616 07:20:50.311161 9857 solver.cpp:258] Train net output #1: loss_cls = 0.509586 (* 1 = 0.509586 loss) +I0616 07:20:50.311166 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0618902 (* 1 = 0.0618902 loss) +I0616 07:20:50.311169 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0737131 (* 1 = 0.0737131 loss) +I0616 07:20:50.311173 9857 solver.cpp:571] Iteration 29660, lr = 0.001 +I0616 07:21:02.094975 9857 solver.cpp:242] Iteration 29680, loss = 1.03844 +I0616 07:21:02.095016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.397482 (* 1 = 0.397482 loss) +I0616 07:21:02.095021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459353 (* 1 = 0.459353 loss) +I0616 07:21:02.095026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14568 (* 1 = 0.14568 loss) +I0616 07:21:02.095029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.017879 (* 1 = 0.017879 loss) +I0616 07:21:02.095033 9857 solver.cpp:571] Iteration 29680, lr = 0.001 +I0616 07:21:13.764430 9857 solver.cpp:242] Iteration 29700, loss = 0.813223 +I0616 07:21:13.764472 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.34562 (* 1 = 0.34562 loss) +I0616 07:21:13.764478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243011 (* 1 = 0.243011 loss) +I0616 07:21:13.764482 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141864 (* 1 = 0.141864 loss) +I0616 07:21:13.764487 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.007833 (* 1 = 0.007833 loss) +I0616 07:21:13.764490 9857 solver.cpp:571] Iteration 29700, lr = 0.001 +I0616 07:21:25.433743 9857 solver.cpp:242] Iteration 29720, loss = 0.553488 +I0616 07:21:25.433786 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1864 (* 1 = 0.1864 loss) +I0616 07:21:25.433792 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179532 (* 1 = 0.179532 loss) +I0616 07:21:25.433796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0131763 (* 1 = 0.0131763 loss) +I0616 07:21:25.433800 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0034747 (* 1 = 0.0034747 loss) +I0616 07:21:25.433804 9857 solver.cpp:571] Iteration 29720, lr = 0.001 +I0616 07:21:36.921535 9857 solver.cpp:242] Iteration 29740, loss = 0.800331 +I0616 07:21:36.921577 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222845 (* 1 = 0.222845 loss) +I0616 07:21:36.921582 9857 solver.cpp:258] Train net output #1: loss_cls = 0.439006 (* 1 = 0.439006 loss) +I0616 07:21:36.921586 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109556 (* 1 = 0.109556 loss) +I0616 07:21:36.921591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.114809 (* 1 = 0.114809 loss) +I0616 07:21:36.921594 9857 solver.cpp:571] Iteration 29740, lr = 0.001 +I0616 07:21:48.254776 9857 solver.cpp:242] Iteration 29760, loss = 0.451345 +I0616 07:21:48.254818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14388 (* 1 = 0.14388 loss) +I0616 07:21:48.254823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237284 (* 1 = 0.237284 loss) +I0616 07:21:48.254828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0494128 (* 1 = 0.0494128 loss) +I0616 07:21:48.254832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109856 (* 1 = 0.0109856 loss) +I0616 07:21:48.254835 9857 solver.cpp:571] Iteration 29760, lr = 0.001 +I0616 07:21:59.790393 9857 solver.cpp:242] Iteration 29780, loss = 0.684767 +I0616 07:21:59.790436 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351975 (* 1 = 0.351975 loss) +I0616 07:21:59.790442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.585246 (* 1 = 0.585246 loss) +I0616 07:21:59.790446 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0487664 (* 1 = 0.0487664 loss) +I0616 07:21:59.790451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0754514 (* 1 = 0.0754514 loss) +I0616 07:21:59.790454 9857 solver.cpp:571] Iteration 29780, lr = 0.001 +speed: 0.637s / iter +I0616 07:22:11.159183 9857 solver.cpp:242] Iteration 29800, loss = 0.827811 +I0616 07:22:11.159226 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232319 (* 1 = 0.232319 loss) +I0616 07:22:11.159231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321001 (* 1 = 0.321001 loss) +I0616 07:22:11.159235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.203723 (* 1 = 0.203723 loss) +I0616 07:22:11.159240 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.342859 (* 1 = 0.342859 loss) +I0616 07:22:11.159243 9857 solver.cpp:571] Iteration 29800, lr = 0.001 +I0616 07:22:22.869154 9857 solver.cpp:242] Iteration 29820, loss = 0.623226 +I0616 07:22:22.869195 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162739 (* 1 = 0.162739 loss) +I0616 07:22:22.869200 9857 solver.cpp:258] Train net output #1: loss_cls = 0.299689 (* 1 = 0.299689 loss) +I0616 07:22:22.869204 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100609 (* 1 = 0.100609 loss) +I0616 07:22:22.869209 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00245244 (* 1 = 0.00245244 loss) +I0616 07:22:22.869212 9857 solver.cpp:571] Iteration 29820, lr = 0.001 +I0616 07:22:34.391736 9857 solver.cpp:242] Iteration 29840, loss = 0.925891 +I0616 07:22:34.391778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181829 (* 1 = 0.181829 loss) +I0616 07:22:34.391783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178916 (* 1 = 0.178916 loss) +I0616 07:22:34.391788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.084064 (* 1 = 0.084064 loss) +I0616 07:22:34.391791 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126488 (* 1 = 0.0126488 loss) +I0616 07:22:34.391795 9857 solver.cpp:571] Iteration 29840, lr = 0.001 +I0616 07:22:45.955721 9857 solver.cpp:242] Iteration 29860, loss = 0.620726 +I0616 07:22:45.955763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124415 (* 1 = 0.124415 loss) +I0616 07:22:45.955770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186902 (* 1 = 0.186902 loss) +I0616 07:22:45.955773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.061881 (* 1 = 0.061881 loss) +I0616 07:22:45.955777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00503455 (* 1 = 0.00503455 loss) +I0616 07:22:45.955781 9857 solver.cpp:571] Iteration 29860, lr = 0.001 +I0616 07:22:57.084126 9857 solver.cpp:242] Iteration 29880, loss = 0.582138 +I0616 07:22:57.084167 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327467 (* 1 = 0.327467 loss) +I0616 07:22:57.084187 9857 solver.cpp:258] Train net output #1: loss_cls = 0.447126 (* 1 = 0.447126 loss) +I0616 07:22:57.084190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0712837 (* 1 = 0.0712837 loss) +I0616 07:22:57.084194 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156091 (* 1 = 0.0156091 loss) +I0616 07:22:57.084198 9857 solver.cpp:571] Iteration 29880, lr = 0.001 +I0616 07:23:08.719959 9857 solver.cpp:242] Iteration 29900, loss = 1.00758 +I0616 07:23:08.719986 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384459 (* 1 = 0.384459 loss) +I0616 07:23:08.719992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337256 (* 1 = 0.337256 loss) +I0616 07:23:08.719996 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0416761 (* 1 = 0.0416761 loss) +I0616 07:23:08.720000 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0208482 (* 1 = 0.0208482 loss) +I0616 07:23:08.720005 9857 solver.cpp:571] Iteration 29900, lr = 0.001 +I0616 07:23:20.320401 9857 solver.cpp:242] Iteration 29920, loss = 1.10789 +I0616 07:23:20.320441 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250056 (* 1 = 0.250056 loss) +I0616 07:23:20.320447 9857 solver.cpp:258] Train net output #1: loss_cls = 0.903179 (* 1 = 0.903179 loss) +I0616 07:23:20.320451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224869 (* 1 = 0.224869 loss) +I0616 07:23:20.320456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.216378 (* 1 = 0.216378 loss) +I0616 07:23:20.320461 9857 solver.cpp:571] Iteration 29920, lr = 0.001 +I0616 07:23:31.881435 9857 solver.cpp:242] Iteration 29940, loss = 1.16135 +I0616 07:23:31.881477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133134 (* 1 = 0.133134 loss) +I0616 07:23:31.881482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.364296 (* 1 = 0.364296 loss) +I0616 07:23:31.881486 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0864344 (* 1 = 0.0864344 loss) +I0616 07:23:31.881490 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173321 (* 1 = 0.0173321 loss) +I0616 07:23:31.881494 9857 solver.cpp:571] Iteration 29940, lr = 0.001 +I0616 07:23:43.140916 9857 solver.cpp:242] Iteration 29960, loss = 1.22162 +I0616 07:23:43.140959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278483 (* 1 = 0.278483 loss) +I0616 07:23:43.140964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275899 (* 1 = 0.275899 loss) +I0616 07:23:43.140967 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162945 (* 1 = 0.162945 loss) +I0616 07:23:43.140970 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.253017 (* 1 = 0.253017 loss) +I0616 07:23:43.140974 9857 solver.cpp:571] Iteration 29960, lr = 0.001 +I0616 07:23:54.799525 9857 solver.cpp:242] Iteration 29980, loss = 1.52068 +I0616 07:23:54.799566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.41255 (* 1 = 0.41255 loss) +I0616 07:23:54.799571 9857 solver.cpp:258] Train net output #1: loss_cls = 0.991719 (* 1 = 0.991719 loss) +I0616 07:23:54.799576 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249406 (* 1 = 0.249406 loss) +I0616 07:23:54.799579 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0350108 (* 1 = 0.0350108 loss) +I0616 07:23:54.799583 9857 solver.cpp:571] Iteration 29980, lr = 0.001 +speed: 0.636s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_30000.caffemodel +I0616 07:24:07.992784 9857 solver.cpp:242] Iteration 30000, loss = 0.589286 +I0616 07:24:07.992825 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0921212 (* 1 = 0.0921212 loss) +I0616 07:24:07.992830 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226715 (* 1 = 0.226715 loss) +I0616 07:24:07.992835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.023153 (* 1 = 0.023153 loss) +I0616 07:24:07.992837 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129093 (* 1 = 0.0129093 loss) +I0616 07:24:07.992841 9857 solver.cpp:571] Iteration 30000, lr = 0.001 +I0616 07:24:19.321713 9857 solver.cpp:242] Iteration 30020, loss = 1.16359 +I0616 07:24:19.321755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.398876 (* 1 = 0.398876 loss) +I0616 07:24:19.321761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276657 (* 1 = 0.276657 loss) +I0616 07:24:19.321765 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0269132 (* 1 = 0.0269132 loss) +I0616 07:24:19.321769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103132 (* 1 = 0.0103132 loss) +I0616 07:24:19.321774 9857 solver.cpp:571] Iteration 30020, lr = 0.001 +I0616 07:24:30.907218 9857 solver.cpp:242] Iteration 30040, loss = 0.900464 +I0616 07:24:30.907261 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333387 (* 1 = 0.333387 loss) +I0616 07:24:30.907268 9857 solver.cpp:258] Train net output #1: loss_cls = 0.447833 (* 1 = 0.447833 loss) +I0616 07:24:30.907271 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117643 (* 1 = 0.117643 loss) +I0616 07:24:30.907275 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0318584 (* 1 = 0.0318584 loss) +I0616 07:24:30.907279 9857 solver.cpp:571] Iteration 30040, lr = 0.001 +I0616 07:24:42.620357 9857 solver.cpp:242] Iteration 30060, loss = 0.760429 +I0616 07:24:42.620398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278508 (* 1 = 0.278508 loss) +I0616 07:24:42.620404 9857 solver.cpp:258] Train net output #1: loss_cls = 0.544022 (* 1 = 0.544022 loss) +I0616 07:24:42.620409 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128749 (* 1 = 0.128749 loss) +I0616 07:24:42.620411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.036028 (* 1 = 0.036028 loss) +I0616 07:24:42.620415 9857 solver.cpp:571] Iteration 30060, lr = 0.001 +I0616 07:24:54.076275 9857 solver.cpp:242] Iteration 30080, loss = 0.497231 +I0616 07:24:54.076316 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0865584 (* 1 = 0.0865584 loss) +I0616 07:24:54.076321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252343 (* 1 = 0.252343 loss) +I0616 07:24:54.076325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0169969 (* 1 = 0.0169969 loss) +I0616 07:24:54.076329 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0362723 (* 1 = 0.0362723 loss) +I0616 07:24:54.076333 9857 solver.cpp:571] Iteration 30080, lr = 0.001 +I0616 07:25:05.418570 9857 solver.cpp:242] Iteration 30100, loss = 0.644072 +I0616 07:25:05.418613 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38866 (* 1 = 0.38866 loss) +I0616 07:25:05.418618 9857 solver.cpp:258] Train net output #1: loss_cls = 0.374014 (* 1 = 0.374014 loss) +I0616 07:25:05.418623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0567785 (* 1 = 0.0567785 loss) +I0616 07:25:05.418627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0191907 (* 1 = 0.0191907 loss) +I0616 07:25:05.418632 9857 solver.cpp:571] Iteration 30100, lr = 0.001 +I0616 07:25:17.148032 9857 solver.cpp:242] Iteration 30120, loss = 0.670658 +I0616 07:25:17.148074 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179541 (* 1 = 0.179541 loss) +I0616 07:25:17.148079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.35361 (* 1 = 0.35361 loss) +I0616 07:25:17.148083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0717535 (* 1 = 0.0717535 loss) +I0616 07:25:17.148087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117053 (* 1 = 0.0117053 loss) +I0616 07:25:17.148092 9857 solver.cpp:571] Iteration 30120, lr = 0.001 +I0616 07:25:28.936275 9857 solver.cpp:242] Iteration 30140, loss = 0.95385 +I0616 07:25:28.936319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.525298 (* 1 = 0.525298 loss) +I0616 07:25:28.936324 9857 solver.cpp:258] Train net output #1: loss_cls = 0.644325 (* 1 = 0.644325 loss) +I0616 07:25:28.936328 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.218298 (* 1 = 0.218298 loss) +I0616 07:25:28.936332 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0969782 (* 1 = 0.0969782 loss) +I0616 07:25:28.936336 9857 solver.cpp:571] Iteration 30140, lr = 0.001 +I0616 07:25:40.531123 9857 solver.cpp:242] Iteration 30160, loss = 0.748474 +I0616 07:25:40.531165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205467 (* 1 = 0.205467 loss) +I0616 07:25:40.531170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246578 (* 1 = 0.246578 loss) +I0616 07:25:40.531174 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128003 (* 1 = 0.128003 loss) +I0616 07:25:40.531178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0196803 (* 1 = 0.0196803 loss) +I0616 07:25:40.531183 9857 solver.cpp:571] Iteration 30160, lr = 0.001 +I0616 07:25:52.206645 9857 solver.cpp:242] Iteration 30180, loss = 0.762411 +I0616 07:25:52.206687 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227533 (* 1 = 0.227533 loss) +I0616 07:25:52.206692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.555694 (* 1 = 0.555694 loss) +I0616 07:25:52.206696 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0760295 (* 1 = 0.0760295 loss) +I0616 07:25:52.206701 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147434 (* 1 = 0.0147434 loss) +I0616 07:25:52.206704 9857 solver.cpp:571] Iteration 30180, lr = 0.001 +speed: 0.636s / iter +I0616 07:26:03.852887 9857 solver.cpp:242] Iteration 30200, loss = 1.64804 +I0616 07:26:03.852931 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.50917 (* 1 = 0.50917 loss) +I0616 07:26:03.852936 9857 solver.cpp:258] Train net output #1: loss_cls = 0.633018 (* 1 = 0.633018 loss) +I0616 07:26:03.852939 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.36064 (* 1 = 0.36064 loss) +I0616 07:26:03.852943 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.037985 (* 1 = 0.037985 loss) +I0616 07:26:03.852947 9857 solver.cpp:571] Iteration 30200, lr = 0.001 +I0616 07:26:15.160264 9857 solver.cpp:242] Iteration 30220, loss = 1.00577 +I0616 07:26:15.160305 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18488 (* 1 = 0.18488 loss) +I0616 07:26:15.160310 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19916 (* 1 = 0.19916 loss) +I0616 07:26:15.160315 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0748781 (* 1 = 0.0748781 loss) +I0616 07:26:15.160317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00860608 (* 1 = 0.00860608 loss) +I0616 07:26:15.160321 9857 solver.cpp:571] Iteration 30220, lr = 0.001 +I0616 07:26:26.838910 9857 solver.cpp:242] Iteration 30240, loss = 0.395367 +I0616 07:26:26.838953 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0619509 (* 1 = 0.0619509 loss) +I0616 07:26:26.838959 9857 solver.cpp:258] Train net output #1: loss_cls = 0.117749 (* 1 = 0.117749 loss) +I0616 07:26:26.838963 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0150856 (* 1 = 0.0150856 loss) +I0616 07:26:26.838966 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013757 (* 1 = 0.013757 loss) +I0616 07:26:26.838970 9857 solver.cpp:571] Iteration 30240, lr = 0.001 +I0616 07:26:38.271189 9857 solver.cpp:242] Iteration 30260, loss = 0.455093 +I0616 07:26:38.271232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332305 (* 1 = 0.332305 loss) +I0616 07:26:38.271239 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23492 (* 1 = 0.23492 loss) +I0616 07:26:38.271242 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0480712 (* 1 = 0.0480712 loss) +I0616 07:26:38.271246 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00360582 (* 1 = 0.00360582 loss) +I0616 07:26:38.271250 9857 solver.cpp:571] Iteration 30260, lr = 0.001 +I0616 07:26:49.432906 9857 solver.cpp:242] Iteration 30280, loss = 1.15955 +I0616 07:26:49.432947 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327773 (* 1 = 0.327773 loss) +I0616 07:26:49.432952 9857 solver.cpp:258] Train net output #1: loss_cls = 0.781486 (* 1 = 0.781486 loss) +I0616 07:26:49.432956 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.457654 (* 1 = 0.457654 loss) +I0616 07:26:49.432960 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.172575 (* 1 = 0.172575 loss) +I0616 07:26:49.432965 9857 solver.cpp:571] Iteration 30280, lr = 0.001 +I0616 07:27:01.086995 9857 solver.cpp:242] Iteration 30300, loss = 1.31094 +I0616 07:27:01.087041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258406 (* 1 = 0.258406 loss) +I0616 07:27:01.087049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.669695 (* 1 = 0.669695 loss) +I0616 07:27:01.087054 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.265384 (* 1 = 0.265384 loss) +I0616 07:27:01.087059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.382278 (* 1 = 0.382278 loss) +I0616 07:27:01.087064 9857 solver.cpp:571] Iteration 30300, lr = 0.001 +I0616 07:27:12.630648 9857 solver.cpp:242] Iteration 30320, loss = 1.06412 +I0616 07:27:12.630691 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286248 (* 1 = 0.286248 loss) +I0616 07:27:12.630695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31513 (* 1 = 0.31513 loss) +I0616 07:27:12.630700 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11394 (* 1 = 0.11394 loss) +I0616 07:27:12.630703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.023625 (* 1 = 0.023625 loss) +I0616 07:27:12.630707 9857 solver.cpp:571] Iteration 30320, lr = 0.001 +I0616 07:27:24.162092 9857 solver.cpp:242] Iteration 30340, loss = 1.08796 +I0616 07:27:24.162134 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0980616 (* 1 = 0.0980616 loss) +I0616 07:27:24.162140 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111806 (* 1 = 0.111806 loss) +I0616 07:27:24.162144 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0903497 (* 1 = 0.0903497 loss) +I0616 07:27:24.162148 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00237495 (* 1 = 0.00237495 loss) +I0616 07:27:24.162152 9857 solver.cpp:571] Iteration 30340, lr = 0.001 +I0616 07:27:35.650391 9857 solver.cpp:242] Iteration 30360, loss = 0.555252 +I0616 07:27:35.650434 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153871 (* 1 = 0.153871 loss) +I0616 07:27:35.650439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.509112 (* 1 = 0.509112 loss) +I0616 07:27:35.650444 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.047526 (* 1 = 0.047526 loss) +I0616 07:27:35.650446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00763326 (* 1 = 0.00763326 loss) +I0616 07:27:35.650450 9857 solver.cpp:571] Iteration 30360, lr = 0.001 +I0616 07:27:47.098428 9857 solver.cpp:242] Iteration 30380, loss = 1.48068 +I0616 07:27:47.098469 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.412862 (* 1 = 0.412862 loss) +I0616 07:27:47.098475 9857 solver.cpp:258] Train net output #1: loss_cls = 0.734147 (* 1 = 0.734147 loss) +I0616 07:27:47.098479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.281194 (* 1 = 0.281194 loss) +I0616 07:27:47.098484 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.127748 (* 1 = 0.127748 loss) +I0616 07:27:47.098487 9857 solver.cpp:571] Iteration 30380, lr = 0.001 +speed: 0.636s / iter +I0616 07:27:58.776975 9857 solver.cpp:242] Iteration 30400, loss = 0.940738 +I0616 07:27:58.777019 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29041 (* 1 = 0.29041 loss) +I0616 07:27:58.777024 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209331 (* 1 = 0.209331 loss) +I0616 07:27:58.777027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0328948 (* 1 = 0.0328948 loss) +I0616 07:27:58.777031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0484028 (* 1 = 0.0484028 loss) +I0616 07:27:58.777035 9857 solver.cpp:571] Iteration 30400, lr = 0.001 +I0616 07:28:10.172194 9857 solver.cpp:242] Iteration 30420, loss = 0.824835 +I0616 07:28:10.172236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352637 (* 1 = 0.352637 loss) +I0616 07:28:10.172242 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372772 (* 1 = 0.372772 loss) +I0616 07:28:10.172246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0697725 (* 1 = 0.0697725 loss) +I0616 07:28:10.172250 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0605459 (* 1 = 0.0605459 loss) +I0616 07:28:10.172253 9857 solver.cpp:571] Iteration 30420, lr = 0.001 +I0616 07:28:21.834238 9857 solver.cpp:242] Iteration 30440, loss = 1.35411 +I0616 07:28:21.834278 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.377303 (* 1 = 0.377303 loss) +I0616 07:28:21.834285 9857 solver.cpp:258] Train net output #1: loss_cls = 0.798165 (* 1 = 0.798165 loss) +I0616 07:28:21.834288 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210037 (* 1 = 0.210037 loss) +I0616 07:28:21.834292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10547 (* 1 = 0.10547 loss) +I0616 07:28:21.834296 9857 solver.cpp:571] Iteration 30440, lr = 0.001 +I0616 07:28:33.402196 9857 solver.cpp:242] Iteration 30460, loss = 0.842887 +I0616 07:28:33.402237 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.348029 (* 1 = 0.348029 loss) +I0616 07:28:33.402243 9857 solver.cpp:258] Train net output #1: loss_cls = 0.320188 (* 1 = 0.320188 loss) +I0616 07:28:33.402247 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.340785 (* 1 = 0.340785 loss) +I0616 07:28:33.402251 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0518469 (* 1 = 0.0518469 loss) +I0616 07:28:33.402256 9857 solver.cpp:571] Iteration 30460, lr = 0.001 +I0616 07:28:44.884676 9857 solver.cpp:242] Iteration 30480, loss = 1.20177 +I0616 07:28:44.884718 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127853 (* 1 = 0.127853 loss) +I0616 07:28:44.884724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196687 (* 1 = 0.196687 loss) +I0616 07:28:44.884728 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.052074 (* 1 = 0.052074 loss) +I0616 07:28:44.884732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139693 (* 1 = 0.0139693 loss) +I0616 07:28:44.884735 9857 solver.cpp:571] Iteration 30480, lr = 0.001 +I0616 07:28:56.348474 9857 solver.cpp:242] Iteration 30500, loss = 0.483761 +I0616 07:28:56.348513 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202853 (* 1 = 0.202853 loss) +I0616 07:28:56.348520 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210241 (* 1 = 0.210241 loss) +I0616 07:28:56.348523 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0961268 (* 1 = 0.0961268 loss) +I0616 07:28:56.348527 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0417846 (* 1 = 0.0417846 loss) +I0616 07:28:56.348531 9857 solver.cpp:571] Iteration 30500, lr = 0.001 +I0616 07:29:07.892781 9857 solver.cpp:242] Iteration 30520, loss = 0.501963 +I0616 07:29:07.892823 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213187 (* 1 = 0.213187 loss) +I0616 07:29:07.892829 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371399 (* 1 = 0.371399 loss) +I0616 07:29:07.892833 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0380474 (* 1 = 0.0380474 loss) +I0616 07:29:07.892838 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291924 (* 1 = 0.0291924 loss) +I0616 07:29:07.892841 9857 solver.cpp:571] Iteration 30520, lr = 0.001 +I0616 07:29:19.527081 9857 solver.cpp:242] Iteration 30540, loss = 0.572208 +I0616 07:29:19.527122 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29256 (* 1 = 0.29256 loss) +I0616 07:29:19.527128 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221616 (* 1 = 0.221616 loss) +I0616 07:29:19.527132 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0752616 (* 1 = 0.0752616 loss) +I0616 07:29:19.527137 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.052633 (* 1 = 0.052633 loss) +I0616 07:29:19.527140 9857 solver.cpp:571] Iteration 30540, lr = 0.001 +I0616 07:29:31.026093 9857 solver.cpp:242] Iteration 30560, loss = 0.860406 +I0616 07:29:31.026135 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271811 (* 1 = 0.271811 loss) +I0616 07:29:31.026140 9857 solver.cpp:258] Train net output #1: loss_cls = 0.414783 (* 1 = 0.414783 loss) +I0616 07:29:31.026145 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0903631 (* 1 = 0.0903631 loss) +I0616 07:29:31.026149 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0918231 (* 1 = 0.0918231 loss) +I0616 07:29:31.026154 9857 solver.cpp:571] Iteration 30560, lr = 0.001 +I0616 07:29:42.669356 9857 solver.cpp:242] Iteration 30580, loss = 0.901925 +I0616 07:29:42.669397 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244539 (* 1 = 0.244539 loss) +I0616 07:29:42.669404 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236594 (* 1 = 0.236594 loss) +I0616 07:29:42.669407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0304127 (* 1 = 0.0304127 loss) +I0616 07:29:42.669411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0339493 (* 1 = 0.0339493 loss) +I0616 07:29:42.669414 9857 solver.cpp:571] Iteration 30580, lr = 0.001 +speed: 0.635s / iter +I0616 07:29:54.222302 9857 solver.cpp:242] Iteration 30600, loss = 1.18782 +I0616 07:29:54.222344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270724 (* 1 = 0.270724 loss) +I0616 07:29:54.222350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.560994 (* 1 = 0.560994 loss) +I0616 07:29:54.222354 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113453 (* 1 = 0.113453 loss) +I0616 07:29:54.222358 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0730046 (* 1 = 0.0730046 loss) +I0616 07:29:54.222362 9857 solver.cpp:571] Iteration 30600, lr = 0.001 +I0616 07:30:05.669839 9857 solver.cpp:242] Iteration 30620, loss = 1.67439 +I0616 07:30:05.669884 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.498465 (* 1 = 0.498465 loss) +I0616 07:30:05.669889 9857 solver.cpp:258] Train net output #1: loss_cls = 1.34396 (* 1 = 1.34396 loss) +I0616 07:30:05.669893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.447722 (* 1 = 0.447722 loss) +I0616 07:30:05.669898 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.174275 (* 1 = 0.174275 loss) +I0616 07:30:05.669901 9857 solver.cpp:571] Iteration 30620, lr = 0.001 +I0616 07:30:17.273929 9857 solver.cpp:242] Iteration 30640, loss = 0.961492 +I0616 07:30:17.273972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0948621 (* 1 = 0.0948621 loss) +I0616 07:30:17.273977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148164 (* 1 = 0.148164 loss) +I0616 07:30:17.273980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117982 (* 1 = 0.117982 loss) +I0616 07:30:17.273984 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115874 (* 1 = 0.0115874 loss) +I0616 07:30:17.273989 9857 solver.cpp:571] Iteration 30640, lr = 0.001 +I0616 07:30:28.874886 9857 solver.cpp:242] Iteration 30660, loss = 0.290425 +I0616 07:30:28.874913 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0689351 (* 1 = 0.0689351 loss) +I0616 07:30:28.874918 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191992 (* 1 = 0.191992 loss) +I0616 07:30:28.874922 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0413701 (* 1 = 0.0413701 loss) +I0616 07:30:28.874927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114091 (* 1 = 0.0114091 loss) +I0616 07:30:28.874930 9857 solver.cpp:571] Iteration 30660, lr = 0.001 +I0616 07:30:40.283175 9857 solver.cpp:242] Iteration 30680, loss = 1.41768 +I0616 07:30:40.283217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.446921 (* 1 = 0.446921 loss) +I0616 07:30:40.283223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.512121 (* 1 = 0.512121 loss) +I0616 07:30:40.283227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.341338 (* 1 = 0.341338 loss) +I0616 07:30:40.283231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0929684 (* 1 = 0.0929684 loss) +I0616 07:30:40.283236 9857 solver.cpp:571] Iteration 30680, lr = 0.001 +I0616 07:30:51.961125 9857 solver.cpp:242] Iteration 30700, loss = 0.457126 +I0616 07:30:51.961169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190576 (* 1 = 0.190576 loss) +I0616 07:30:51.961174 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170807 (* 1 = 0.170807 loss) +I0616 07:30:51.961179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605525 (* 1 = 0.0605525 loss) +I0616 07:30:51.961182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134188 (* 1 = 0.0134188 loss) +I0616 07:30:51.961186 9857 solver.cpp:571] Iteration 30700, lr = 0.001 +I0616 07:31:03.674419 9857 solver.cpp:242] Iteration 30720, loss = 0.527021 +I0616 07:31:03.674460 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.088647 (* 1 = 0.088647 loss) +I0616 07:31:03.674466 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221359 (* 1 = 0.221359 loss) +I0616 07:31:03.674470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00924027 (* 1 = 0.00924027 loss) +I0616 07:31:03.674474 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125583 (* 1 = 0.0125583 loss) +I0616 07:31:03.674479 9857 solver.cpp:571] Iteration 30720, lr = 0.001 +I0616 07:31:15.036737 9857 solver.cpp:242] Iteration 30740, loss = 0.726777 +I0616 07:31:15.036777 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138533 (* 1 = 0.138533 loss) +I0616 07:31:15.036782 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263148 (* 1 = 0.263148 loss) +I0616 07:31:15.036787 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162271 (* 1 = 0.162271 loss) +I0616 07:31:15.036789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.2872 (* 1 = 0.2872 loss) +I0616 07:31:15.036793 9857 solver.cpp:571] Iteration 30740, lr = 0.001 +I0616 07:31:26.587600 9857 solver.cpp:242] Iteration 30760, loss = 0.334471 +I0616 07:31:26.587642 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0689452 (* 1 = 0.0689452 loss) +I0616 07:31:26.587647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150046 (* 1 = 0.150046 loss) +I0616 07:31:26.587651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0464824 (* 1 = 0.0464824 loss) +I0616 07:31:26.587656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0265341 (* 1 = 0.0265341 loss) +I0616 07:31:26.587659 9857 solver.cpp:571] Iteration 30760, lr = 0.001 +I0616 07:31:38.168489 9857 solver.cpp:242] Iteration 30780, loss = 1.05959 +I0616 07:31:38.168532 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185497 (* 1 = 0.185497 loss) +I0616 07:31:38.168539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.995779 (* 1 = 0.995779 loss) +I0616 07:31:38.168542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138241 (* 1 = 0.138241 loss) +I0616 07:31:38.168546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0401485 (* 1 = 0.0401485 loss) +I0616 07:31:38.168550 9857 solver.cpp:571] Iteration 30780, lr = 0.001 +speed: 0.635s / iter +I0616 07:31:49.958068 9857 solver.cpp:242] Iteration 30800, loss = 0.961084 +I0616 07:31:49.958111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.383368 (* 1 = 0.383368 loss) +I0616 07:31:49.958115 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392513 (* 1 = 0.392513 loss) +I0616 07:31:49.958120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169399 (* 1 = 0.169399 loss) +I0616 07:31:49.958124 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209771 (* 1 = 0.0209771 loss) +I0616 07:31:49.958127 9857 solver.cpp:571] Iteration 30800, lr = 0.001 +I0616 07:32:01.294692 9857 solver.cpp:242] Iteration 30820, loss = 0.708222 +I0616 07:32:01.294735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21083 (* 1 = 0.21083 loss) +I0616 07:32:01.294741 9857 solver.cpp:258] Train net output #1: loss_cls = 0.467637 (* 1 = 0.467637 loss) +I0616 07:32:01.294745 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105171 (* 1 = 0.105171 loss) +I0616 07:32:01.294749 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00891859 (* 1 = 0.00891859 loss) +I0616 07:32:01.294754 9857 solver.cpp:571] Iteration 30820, lr = 0.001 +I0616 07:32:12.639117 9857 solver.cpp:242] Iteration 30840, loss = 0.962403 +I0616 07:32:12.639158 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189056 (* 1 = 0.189056 loss) +I0616 07:32:12.639164 9857 solver.cpp:258] Train net output #1: loss_cls = 0.869514 (* 1 = 0.869514 loss) +I0616 07:32:12.639168 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0967744 (* 1 = 0.0967744 loss) +I0616 07:32:12.639173 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.031694 (* 1 = 0.031694 loss) +I0616 07:32:12.639176 9857 solver.cpp:571] Iteration 30840, lr = 0.001 +I0616 07:32:23.912178 9857 solver.cpp:242] Iteration 30860, loss = 1.34129 +I0616 07:32:23.912219 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.517135 (* 1 = 0.517135 loss) +I0616 07:32:23.912225 9857 solver.cpp:258] Train net output #1: loss_cls = 0.710766 (* 1 = 0.710766 loss) +I0616 07:32:23.912228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.225617 (* 1 = 0.225617 loss) +I0616 07:32:23.912232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.032307 (* 1 = 0.032307 loss) +I0616 07:32:23.912235 9857 solver.cpp:571] Iteration 30860, lr = 0.001 +I0616 07:32:35.793670 9857 solver.cpp:242] Iteration 30880, loss = 1.70661 +I0616 07:32:35.793712 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.429971 (* 1 = 0.429971 loss) +I0616 07:32:35.793718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.761068 (* 1 = 0.761068 loss) +I0616 07:32:35.793722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.305059 (* 1 = 0.305059 loss) +I0616 07:32:35.793726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.058727 (* 1 = 0.058727 loss) +I0616 07:32:35.793730 9857 solver.cpp:571] Iteration 30880, lr = 0.001 +I0616 07:32:47.310825 9857 solver.cpp:242] Iteration 30900, loss = 0.505019 +I0616 07:32:47.310868 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129806 (* 1 = 0.129806 loss) +I0616 07:32:47.310873 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173432 (* 1 = 0.173432 loss) +I0616 07:32:47.310878 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0296631 (* 1 = 0.0296631 loss) +I0616 07:32:47.310880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193383 (* 1 = 0.0193383 loss) +I0616 07:32:47.310885 9857 solver.cpp:571] Iteration 30900, lr = 0.001 +I0616 07:32:58.764922 9857 solver.cpp:242] Iteration 30920, loss = 0.962529 +I0616 07:32:58.764964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338841 (* 1 = 0.338841 loss) +I0616 07:32:58.764971 9857 solver.cpp:258] Train net output #1: loss_cls = 0.759952 (* 1 = 0.759952 loss) +I0616 07:32:58.764974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135103 (* 1 = 0.135103 loss) +I0616 07:32:58.764978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0657815 (* 1 = 0.0657815 loss) +I0616 07:32:58.764982 9857 solver.cpp:571] Iteration 30920, lr = 0.001 +I0616 07:33:10.338382 9857 solver.cpp:242] Iteration 30940, loss = 0.456089 +I0616 07:33:10.338419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274818 (* 1 = 0.274818 loss) +I0616 07:33:10.338425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264945 (* 1 = 0.264945 loss) +I0616 07:33:10.338429 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0487411 (* 1 = 0.0487411 loss) +I0616 07:33:10.338433 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0032198 (* 1 = 0.0032198 loss) +I0616 07:33:10.338436 9857 solver.cpp:571] Iteration 30940, lr = 0.001 +I0616 07:33:22.054316 9857 solver.cpp:242] Iteration 30960, loss = 0.547869 +I0616 07:33:22.054358 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0977454 (* 1 = 0.0977454 loss) +I0616 07:33:22.054363 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253008 (* 1 = 0.253008 loss) +I0616 07:33:22.054368 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0410246 (* 1 = 0.0410246 loss) +I0616 07:33:22.054371 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388459 (* 1 = 0.0388459 loss) +I0616 07:33:22.054374 9857 solver.cpp:571] Iteration 30960, lr = 0.001 +I0616 07:33:33.151968 9857 solver.cpp:242] Iteration 30980, loss = 0.754506 +I0616 07:33:33.152011 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113325 (* 1 = 0.113325 loss) +I0616 07:33:33.152016 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0859102 (* 1 = 0.0859102 loss) +I0616 07:33:33.152020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0464238 (* 1 = 0.0464238 loss) +I0616 07:33:33.152024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0191918 (* 1 = 0.0191918 loss) +I0616 07:33:33.152029 9857 solver.cpp:571] Iteration 30980, lr = 0.001 +speed: 0.634s / iter +I0616 07:33:44.645892 9857 solver.cpp:242] Iteration 31000, loss = 0.851114 +I0616 07:33:44.645933 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174956 (* 1 = 0.174956 loss) +I0616 07:33:44.645938 9857 solver.cpp:258] Train net output #1: loss_cls = 0.465366 (* 1 = 0.465366 loss) +I0616 07:33:44.645942 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0860091 (* 1 = 0.0860091 loss) +I0616 07:33:44.645946 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112528 (* 1 = 0.0112528 loss) +I0616 07:33:44.645951 9857 solver.cpp:571] Iteration 31000, lr = 0.001 +I0616 07:33:56.198639 9857 solver.cpp:242] Iteration 31020, loss = 0.759569 +I0616 07:33:56.198683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.37135 (* 1 = 0.37135 loss) +I0616 07:33:56.198688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292848 (* 1 = 0.292848 loss) +I0616 07:33:56.198693 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0982104 (* 1 = 0.0982104 loss) +I0616 07:33:56.198695 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286226 (* 1 = 0.0286226 loss) +I0616 07:33:56.198699 9857 solver.cpp:571] Iteration 31020, lr = 0.001 +I0616 07:34:07.597954 9857 solver.cpp:242] Iteration 31040, loss = 0.64741 +I0616 07:34:07.597996 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.378198 (* 1 = 0.378198 loss) +I0616 07:34:07.598002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426731 (* 1 = 0.426731 loss) +I0616 07:34:07.598006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.053383 (* 1 = 0.053383 loss) +I0616 07:34:07.598009 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223908 (* 1 = 0.0223908 loss) +I0616 07:34:07.598013 9857 solver.cpp:571] Iteration 31040, lr = 0.001 +I0616 07:34:19.152384 9857 solver.cpp:242] Iteration 31060, loss = 0.486989 +I0616 07:34:19.152426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137981 (* 1 = 0.137981 loss) +I0616 07:34:19.152431 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172344 (* 1 = 0.172344 loss) +I0616 07:34:19.152436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0943323 (* 1 = 0.0943323 loss) +I0616 07:34:19.152439 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00470433 (* 1 = 0.00470433 loss) +I0616 07:34:19.152443 9857 solver.cpp:571] Iteration 31060, lr = 0.001 +I0616 07:34:30.592594 9857 solver.cpp:242] Iteration 31080, loss = 1.02186 +I0616 07:34:30.592638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215439 (* 1 = 0.215439 loss) +I0616 07:34:30.592643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.517153 (* 1 = 0.517153 loss) +I0616 07:34:30.592646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164131 (* 1 = 0.164131 loss) +I0616 07:34:30.592650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.343742 (* 1 = 0.343742 loss) +I0616 07:34:30.592654 9857 solver.cpp:571] Iteration 31080, lr = 0.001 +I0616 07:34:42.204098 9857 solver.cpp:242] Iteration 31100, loss = 0.936008 +I0616 07:34:42.204139 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288478 (* 1 = 0.288478 loss) +I0616 07:34:42.204144 9857 solver.cpp:258] Train net output #1: loss_cls = 0.682593 (* 1 = 0.682593 loss) +I0616 07:34:42.204149 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0608404 (* 1 = 0.0608404 loss) +I0616 07:34:42.204152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0671246 (* 1 = 0.0671246 loss) +I0616 07:34:42.204156 9857 solver.cpp:571] Iteration 31100, lr = 0.001 +I0616 07:34:53.817934 9857 solver.cpp:242] Iteration 31120, loss = 0.786484 +I0616 07:34:53.817975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115628 (* 1 = 0.115628 loss) +I0616 07:34:53.817981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164352 (* 1 = 0.164352 loss) +I0616 07:34:53.817986 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605852 (* 1 = 0.0605852 loss) +I0616 07:34:53.817989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143571 (* 1 = 0.0143571 loss) +I0616 07:34:53.817992 9857 solver.cpp:571] Iteration 31120, lr = 0.001 +I0616 07:35:05.266201 9857 solver.cpp:242] Iteration 31140, loss = 0.917264 +I0616 07:35:05.266242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257701 (* 1 = 0.257701 loss) +I0616 07:35:05.266248 9857 solver.cpp:258] Train net output #1: loss_cls = 0.794845 (* 1 = 0.794845 loss) +I0616 07:35:05.266252 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113949 (* 1 = 0.113949 loss) +I0616 07:35:05.266257 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0332717 (* 1 = 0.0332717 loss) +I0616 07:35:05.266260 9857 solver.cpp:571] Iteration 31140, lr = 0.001 +I0616 07:35:16.929694 9857 solver.cpp:242] Iteration 31160, loss = 0.974173 +I0616 07:35:16.929735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221231 (* 1 = 0.221231 loss) +I0616 07:35:16.929741 9857 solver.cpp:258] Train net output #1: loss_cls = 0.395857 (* 1 = 0.395857 loss) +I0616 07:35:16.929745 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0489435 (* 1 = 0.0489435 loss) +I0616 07:35:16.929749 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.153263 (* 1 = 0.153263 loss) +I0616 07:35:16.929752 9857 solver.cpp:571] Iteration 31160, lr = 0.001 +I0616 07:35:28.451854 9857 solver.cpp:242] Iteration 31180, loss = 0.440593 +I0616 07:35:28.451896 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0694473 (* 1 = 0.0694473 loss) +I0616 07:35:28.451902 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234458 (* 1 = 0.234458 loss) +I0616 07:35:28.451906 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386255 (* 1 = 0.0386255 loss) +I0616 07:35:28.451910 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101028 (* 1 = 0.0101028 loss) +I0616 07:35:28.451913 9857 solver.cpp:571] Iteration 31180, lr = 0.001 +speed: 0.634s / iter +I0616 07:35:39.961813 9857 solver.cpp:242] Iteration 31200, loss = 0.799704 +I0616 07:35:39.961855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260028 (* 1 = 0.260028 loss) +I0616 07:35:39.961860 9857 solver.cpp:258] Train net output #1: loss_cls = 0.330235 (* 1 = 0.330235 loss) +I0616 07:35:39.961865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0744309 (* 1 = 0.0744309 loss) +I0616 07:35:39.961869 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114915 (* 1 = 0.0114915 loss) +I0616 07:35:39.961872 9857 solver.cpp:571] Iteration 31200, lr = 0.001 +I0616 07:35:51.200448 9857 solver.cpp:242] Iteration 31220, loss = 1.21307 +I0616 07:35:51.200491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265203 (* 1 = 0.265203 loss) +I0616 07:35:51.200496 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196722 (* 1 = 0.196722 loss) +I0616 07:35:51.200500 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0128451 (* 1 = 0.0128451 loss) +I0616 07:35:51.200505 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000823167 (* 1 = 0.000823167 loss) +I0616 07:35:51.200508 9857 solver.cpp:571] Iteration 31220, lr = 0.001 +I0616 07:36:02.711597 9857 solver.cpp:242] Iteration 31240, loss = 0.769689 +I0616 07:36:02.711638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.319564 (* 1 = 0.319564 loss) +I0616 07:36:02.711644 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392226 (* 1 = 0.392226 loss) +I0616 07:36:02.711648 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122954 (* 1 = 0.122954 loss) +I0616 07:36:02.711652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306733 (* 1 = 0.0306733 loss) +I0616 07:36:02.711657 9857 solver.cpp:571] Iteration 31240, lr = 0.001 +I0616 07:36:14.202002 9857 solver.cpp:242] Iteration 31260, loss = 1.33103 +I0616 07:36:14.202045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.400677 (* 1 = 0.400677 loss) +I0616 07:36:14.202050 9857 solver.cpp:258] Train net output #1: loss_cls = 0.940022 (* 1 = 0.940022 loss) +I0616 07:36:14.202054 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0686783 (* 1 = 0.0686783 loss) +I0616 07:36:14.202059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286679 (* 1 = 0.0286679 loss) +I0616 07:36:14.202062 9857 solver.cpp:571] Iteration 31260, lr = 0.001 +I0616 07:36:25.356371 9857 solver.cpp:242] Iteration 31280, loss = 0.918625 +I0616 07:36:25.356415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401001 (* 1 = 0.401001 loss) +I0616 07:36:25.356420 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413413 (* 1 = 0.413413 loss) +I0616 07:36:25.356423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.261923 (* 1 = 0.261923 loss) +I0616 07:36:25.356427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.093644 (* 1 = 0.093644 loss) +I0616 07:36:25.356431 9857 solver.cpp:571] Iteration 31280, lr = 0.001 +I0616 07:36:36.980937 9857 solver.cpp:242] Iteration 31300, loss = 0.718589 +I0616 07:36:36.980980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105674 (* 1 = 0.105674 loss) +I0616 07:36:36.980986 9857 solver.cpp:258] Train net output #1: loss_cls = 0.607636 (* 1 = 0.607636 loss) +I0616 07:36:36.980990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0307461 (* 1 = 0.0307461 loss) +I0616 07:36:36.980994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143802 (* 1 = 0.0143802 loss) +I0616 07:36:36.980998 9857 solver.cpp:571] Iteration 31300, lr = 0.001 +I0616 07:36:48.511015 9857 solver.cpp:242] Iteration 31320, loss = 0.729258 +I0616 07:36:48.511056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22761 (* 1 = 0.22761 loss) +I0616 07:36:48.511062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215278 (* 1 = 0.215278 loss) +I0616 07:36:48.511066 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102271 (* 1 = 0.102271 loss) +I0616 07:36:48.511070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216102 (* 1 = 0.0216102 loss) +I0616 07:36:48.511075 9857 solver.cpp:571] Iteration 31320, lr = 0.001 +I0616 07:36:59.849023 9857 solver.cpp:242] Iteration 31340, loss = 1.59816 +I0616 07:36:59.849066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389855 (* 1 = 0.389855 loss) +I0616 07:36:59.849072 9857 solver.cpp:258] Train net output #1: loss_cls = 1.46151 (* 1 = 1.46151 loss) +I0616 07:36:59.849076 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.399365 (* 1 = 0.399365 loss) +I0616 07:36:59.849079 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.402539 (* 1 = 0.402539 loss) +I0616 07:36:59.849083 9857 solver.cpp:571] Iteration 31340, lr = 0.001 +I0616 07:37:11.370654 9857 solver.cpp:242] Iteration 31360, loss = 0.76619 +I0616 07:37:11.370697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347013 (* 1 = 0.347013 loss) +I0616 07:37:11.370702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338403 (* 1 = 0.338403 loss) +I0616 07:37:11.370707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227832 (* 1 = 0.227832 loss) +I0616 07:37:11.370710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0418643 (* 1 = 0.0418643 loss) +I0616 07:37:11.370714 9857 solver.cpp:571] Iteration 31360, lr = 0.001 +I0616 07:37:22.831380 9857 solver.cpp:242] Iteration 31380, loss = 1.08869 +I0616 07:37:22.831421 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368733 (* 1 = 0.368733 loss) +I0616 07:37:22.831428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4341 (* 1 = 0.4341 loss) +I0616 07:37:22.831431 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159543 (* 1 = 0.159543 loss) +I0616 07:37:22.831435 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0513923 (* 1 = 0.0513923 loss) +I0616 07:37:22.831439 9857 solver.cpp:571] Iteration 31380, lr = 0.001 +speed: 0.634s / iter +I0616 07:37:34.539785 9857 solver.cpp:242] Iteration 31400, loss = 1.15893 +I0616 07:37:34.539827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.441786 (* 1 = 0.441786 loss) +I0616 07:37:34.539832 9857 solver.cpp:258] Train net output #1: loss_cls = 0.74147 (* 1 = 0.74147 loss) +I0616 07:37:34.539836 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194435 (* 1 = 0.194435 loss) +I0616 07:37:34.539840 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0497892 (* 1 = 0.0497892 loss) +I0616 07:37:34.539844 9857 solver.cpp:571] Iteration 31400, lr = 0.001 +I0616 07:37:46.111776 9857 solver.cpp:242] Iteration 31420, loss = 1.05401 +I0616 07:37:46.111819 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11272 (* 1 = 0.11272 loss) +I0616 07:37:46.111824 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144656 (* 1 = 0.144656 loss) +I0616 07:37:46.111829 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0120971 (* 1 = 0.0120971 loss) +I0616 07:37:46.111832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0178042 (* 1 = 0.0178042 loss) +I0616 07:37:46.111836 9857 solver.cpp:571] Iteration 31420, lr = 0.001 +I0616 07:37:57.295353 9857 solver.cpp:242] Iteration 31440, loss = 0.575522 +I0616 07:37:57.295395 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10855 (* 1 = 0.10855 loss) +I0616 07:37:57.295400 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119428 (* 1 = 0.119428 loss) +I0616 07:37:57.295404 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0562048 (* 1 = 0.0562048 loss) +I0616 07:37:57.295408 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0124429 (* 1 = 0.0124429 loss) +I0616 07:37:57.295414 9857 solver.cpp:571] Iteration 31440, lr = 0.001 +I0616 07:38:08.797883 9857 solver.cpp:242] Iteration 31460, loss = 0.824697 +I0616 07:38:08.797925 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202474 (* 1 = 0.202474 loss) +I0616 07:38:08.797930 9857 solver.cpp:258] Train net output #1: loss_cls = 0.515575 (* 1 = 0.515575 loss) +I0616 07:38:08.797935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0990816 (* 1 = 0.0990816 loss) +I0616 07:38:08.797938 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0385692 (* 1 = 0.0385692 loss) +I0616 07:38:08.797942 9857 solver.cpp:571] Iteration 31460, lr = 0.001 +I0616 07:38:20.406597 9857 solver.cpp:242] Iteration 31480, loss = 1.26604 +I0616 07:38:20.406638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.520439 (* 1 = 0.520439 loss) +I0616 07:38:20.406643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.665601 (* 1 = 0.665601 loss) +I0616 07:38:20.406647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220855 (* 1 = 0.220855 loss) +I0616 07:38:20.406651 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0896983 (* 1 = 0.0896983 loss) +I0616 07:38:20.406656 9857 solver.cpp:571] Iteration 31480, lr = 0.001 +I0616 07:38:31.855355 9857 solver.cpp:242] Iteration 31500, loss = 0.714531 +I0616 07:38:31.855397 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306 (* 1 = 0.306 loss) +I0616 07:38:31.855403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.670434 (* 1 = 0.670434 loss) +I0616 07:38:31.855406 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140858 (* 1 = 0.140858 loss) +I0616 07:38:31.855411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254065 (* 1 = 0.0254065 loss) +I0616 07:38:31.855414 9857 solver.cpp:571] Iteration 31500, lr = 0.001 +I0616 07:38:43.498590 9857 solver.cpp:242] Iteration 31520, loss = 0.809799 +I0616 07:38:43.498633 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214278 (* 1 = 0.214278 loss) +I0616 07:38:43.498638 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199131 (* 1 = 0.199131 loss) +I0616 07:38:43.498642 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.047565 (* 1 = 0.047565 loss) +I0616 07:38:43.498646 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00430338 (* 1 = 0.00430338 loss) +I0616 07:38:43.498649 9857 solver.cpp:571] Iteration 31520, lr = 0.001 +I0616 07:38:54.846489 9857 solver.cpp:242] Iteration 31540, loss = 0.583367 +I0616 07:38:54.846531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202228 (* 1 = 0.202228 loss) +I0616 07:38:54.846537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296859 (* 1 = 0.296859 loss) +I0616 07:38:54.846541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0732094 (* 1 = 0.0732094 loss) +I0616 07:38:54.846545 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00714239 (* 1 = 0.00714239 loss) +I0616 07:38:54.846549 9857 solver.cpp:571] Iteration 31540, lr = 0.001 +I0616 07:39:06.623833 9857 solver.cpp:242] Iteration 31560, loss = 0.865606 +I0616 07:39:06.623875 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238643 (* 1 = 0.238643 loss) +I0616 07:39:06.623880 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278562 (* 1 = 0.278562 loss) +I0616 07:39:06.623884 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108834 (* 1 = 0.108834 loss) +I0616 07:39:06.623888 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0317835 (* 1 = 0.0317835 loss) +I0616 07:39:06.623893 9857 solver.cpp:571] Iteration 31560, lr = 0.001 +I0616 07:39:18.290669 9857 solver.cpp:242] Iteration 31580, loss = 0.83996 +I0616 07:39:18.290711 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155252 (* 1 = 0.155252 loss) +I0616 07:39:18.290717 9857 solver.cpp:258] Train net output #1: loss_cls = 0.566274 (* 1 = 0.566274 loss) +I0616 07:39:18.290721 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0438909 (* 1 = 0.0438909 loss) +I0616 07:39:18.290724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407441 (* 1 = 0.0407441 loss) +I0616 07:39:18.290729 9857 solver.cpp:571] Iteration 31580, lr = 0.001 +speed: 0.633s / iter +I0616 07:39:29.700464 9857 solver.cpp:242] Iteration 31600, loss = 0.546611 +I0616 07:39:29.700506 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270301 (* 1 = 0.270301 loss) +I0616 07:39:29.700512 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257208 (* 1 = 0.257208 loss) +I0616 07:39:29.700516 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0398923 (* 1 = 0.0398923 loss) +I0616 07:39:29.700520 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278383 (* 1 = 0.0278383 loss) +I0616 07:39:29.700523 9857 solver.cpp:571] Iteration 31600, lr = 0.001 +I0616 07:39:41.079941 9857 solver.cpp:242] Iteration 31620, loss = 1.02643 +I0616 07:39:41.079982 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315669 (* 1 = 0.315669 loss) +I0616 07:39:41.079988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.58355 (* 1 = 0.58355 loss) +I0616 07:39:41.079993 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.275131 (* 1 = 0.275131 loss) +I0616 07:39:41.079996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.187106 (* 1 = 0.187106 loss) +I0616 07:39:41.080000 9857 solver.cpp:571] Iteration 31620, lr = 0.001 +I0616 07:39:52.643668 9857 solver.cpp:242] Iteration 31640, loss = 1.2967 +I0616 07:39:52.643712 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155866 (* 1 = 0.155866 loss) +I0616 07:39:52.643718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384129 (* 1 = 0.384129 loss) +I0616 07:39:52.643721 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0997917 (* 1 = 0.0997917 loss) +I0616 07:39:52.643725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000547376 (* 1 = 0.000547376 loss) +I0616 07:39:52.643729 9857 solver.cpp:571] Iteration 31640, lr = 0.001 +I0616 07:40:04.202286 9857 solver.cpp:242] Iteration 31660, loss = 0.761558 +I0616 07:40:04.202329 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0769515 (* 1 = 0.0769515 loss) +I0616 07:40:04.202335 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359113 (* 1 = 0.359113 loss) +I0616 07:40:04.202339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0405615 (* 1 = 0.0405615 loss) +I0616 07:40:04.202343 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00939931 (* 1 = 0.00939931 loss) +I0616 07:40:04.202347 9857 solver.cpp:571] Iteration 31660, lr = 0.001 +I0616 07:40:15.787443 9857 solver.cpp:242] Iteration 31680, loss = 1.11335 +I0616 07:40:15.787487 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102588 (* 1 = 0.102588 loss) +I0616 07:40:15.787492 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162733 (* 1 = 0.162733 loss) +I0616 07:40:15.787497 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0226723 (* 1 = 0.0226723 loss) +I0616 07:40:15.787500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0268367 (* 1 = 0.0268367 loss) +I0616 07:40:15.787503 9857 solver.cpp:571] Iteration 31680, lr = 0.001 +I0616 07:40:27.528540 9857 solver.cpp:242] Iteration 31700, loss = 0.646828 +I0616 07:40:27.528581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239767 (* 1 = 0.239767 loss) +I0616 07:40:27.528586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.508506 (* 1 = 0.508506 loss) +I0616 07:40:27.528590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100561 (* 1 = 0.100561 loss) +I0616 07:40:27.528594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323765 (* 1 = 0.0323765 loss) +I0616 07:40:27.528599 9857 solver.cpp:571] Iteration 31700, lr = 0.001 +I0616 07:40:39.176743 9857 solver.cpp:242] Iteration 31720, loss = 1.03045 +I0616 07:40:39.176785 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389497 (* 1 = 0.389497 loss) +I0616 07:40:39.176791 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259955 (* 1 = 0.259955 loss) +I0616 07:40:39.176795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0778861 (* 1 = 0.0778861 loss) +I0616 07:40:39.176798 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0702039 (* 1 = 0.0702039 loss) +I0616 07:40:39.176802 9857 solver.cpp:571] Iteration 31720, lr = 0.001 +I0616 07:40:50.760726 9857 solver.cpp:242] Iteration 31740, loss = 0.945919 +I0616 07:40:50.760766 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.33078 (* 1 = 0.33078 loss) +I0616 07:40:50.760771 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335604 (* 1 = 0.335604 loss) +I0616 07:40:50.760776 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.226369 (* 1 = 0.226369 loss) +I0616 07:40:50.760779 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0450671 (* 1 = 0.0450671 loss) +I0616 07:40:50.760783 9857 solver.cpp:571] Iteration 31740, lr = 0.001 +I0616 07:41:02.311328 9857 solver.cpp:242] Iteration 31760, loss = 1.16302 +I0616 07:41:02.311370 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159734 (* 1 = 0.159734 loss) +I0616 07:41:02.311377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368334 (* 1 = 0.368334 loss) +I0616 07:41:02.311380 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209021 (* 1 = 0.0209021 loss) +I0616 07:41:02.311385 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012065 (* 1 = 0.012065 loss) +I0616 07:41:02.311388 9857 solver.cpp:571] Iteration 31760, lr = 0.001 +I0616 07:41:13.881646 9857 solver.cpp:242] Iteration 31780, loss = 0.980239 +I0616 07:41:13.881687 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184278 (* 1 = 0.184278 loss) +I0616 07:41:13.881693 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199417 (* 1 = 0.199417 loss) +I0616 07:41:13.881697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0810685 (* 1 = 0.0810685 loss) +I0616 07:41:13.881701 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163335 (* 1 = 0.0163335 loss) +I0616 07:41:13.881705 9857 solver.cpp:571] Iteration 31780, lr = 0.001 +speed: 0.633s / iter +I0616 07:41:25.119045 9857 solver.cpp:242] Iteration 31800, loss = 0.573177 +I0616 07:41:25.119086 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102786 (* 1 = 0.102786 loss) +I0616 07:41:25.119091 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249738 (* 1 = 0.249738 loss) +I0616 07:41:25.119096 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0959925 (* 1 = 0.0959925 loss) +I0616 07:41:25.119099 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136846 (* 1 = 0.0136846 loss) +I0616 07:41:25.119103 9857 solver.cpp:571] Iteration 31800, lr = 0.001 +I0616 07:41:36.461745 9857 solver.cpp:242] Iteration 31820, loss = 0.853674 +I0616 07:41:36.461786 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.46345 (* 1 = 0.46345 loss) +I0616 07:41:36.461791 9857 solver.cpp:258] Train net output #1: loss_cls = 0.593724 (* 1 = 0.593724 loss) +I0616 07:41:36.461796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.209921 (* 1 = 0.209921 loss) +I0616 07:41:36.461799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0374893 (* 1 = 0.0374893 loss) +I0616 07:41:36.461803 9857 solver.cpp:571] Iteration 31820, lr = 0.001 +I0616 07:41:48.047917 9857 solver.cpp:242] Iteration 31840, loss = 1.37881 +I0616 07:41:48.047961 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.610881 (* 1 = 0.610881 loss) +I0616 07:41:48.047966 9857 solver.cpp:258] Train net output #1: loss_cls = 0.72113 (* 1 = 0.72113 loss) +I0616 07:41:48.047971 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11413 (* 1 = 0.11413 loss) +I0616 07:41:48.047974 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.120137 (* 1 = 0.120137 loss) +I0616 07:41:48.047978 9857 solver.cpp:571] Iteration 31840, lr = 0.001 +I0616 07:41:59.714550 9857 solver.cpp:242] Iteration 31860, loss = 1.19452 +I0616 07:41:59.714591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.472524 (* 1 = 0.472524 loss) +I0616 07:41:59.714596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.689848 (* 1 = 0.689848 loss) +I0616 07:41:59.714601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0819213 (* 1 = 0.0819213 loss) +I0616 07:41:59.714604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.201793 (* 1 = 0.201793 loss) +I0616 07:41:59.714608 9857 solver.cpp:571] Iteration 31860, lr = 0.001 +I0616 07:42:11.281390 9857 solver.cpp:242] Iteration 31880, loss = 1.70256 +I0616 07:42:11.281433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.470357 (* 1 = 0.470357 loss) +I0616 07:42:11.281440 9857 solver.cpp:258] Train net output #1: loss_cls = 0.931597 (* 1 = 0.931597 loss) +I0616 07:42:11.281443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0740258 (* 1 = 0.0740258 loss) +I0616 07:42:11.281447 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0696476 (* 1 = 0.0696476 loss) +I0616 07:42:11.281451 9857 solver.cpp:571] Iteration 31880, lr = 0.001 +I0616 07:42:22.847472 9857 solver.cpp:242] Iteration 31900, loss = 0.389151 +I0616 07:42:22.847515 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120153 (* 1 = 0.120153 loss) +I0616 07:42:22.847522 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246177 (* 1 = 0.246177 loss) +I0616 07:42:22.847525 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389348 (* 1 = 0.0389348 loss) +I0616 07:42:22.847529 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00767531 (* 1 = 0.00767531 loss) +I0616 07:42:22.847533 9857 solver.cpp:571] Iteration 31900, lr = 0.001 +I0616 07:42:34.447216 9857 solver.cpp:242] Iteration 31920, loss = 1.60627 +I0616 07:42:34.447259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.567852 (* 1 = 0.567852 loss) +I0616 07:42:34.447265 9857 solver.cpp:258] Train net output #1: loss_cls = 0.811709 (* 1 = 0.811709 loss) +I0616 07:42:34.447269 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.380192 (* 1 = 0.380192 loss) +I0616 07:42:34.447273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.16908 (* 1 = 0.16908 loss) +I0616 07:42:34.447278 9857 solver.cpp:571] Iteration 31920, lr = 0.001 +I0616 07:42:46.109446 9857 solver.cpp:242] Iteration 31940, loss = 0.867974 +I0616 07:42:46.109486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376539 (* 1 = 0.376539 loss) +I0616 07:42:46.109493 9857 solver.cpp:258] Train net output #1: loss_cls = 0.64199 (* 1 = 0.64199 loss) +I0616 07:42:46.109496 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.27039 (* 1 = 0.27039 loss) +I0616 07:42:46.109499 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0652273 (* 1 = 0.0652273 loss) +I0616 07:42:46.109503 9857 solver.cpp:571] Iteration 31940, lr = 0.001 +I0616 07:42:57.597323 9857 solver.cpp:242] Iteration 31960, loss = 0.940673 +I0616 07:42:57.597367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.487606 (* 1 = 0.487606 loss) +I0616 07:42:57.597371 9857 solver.cpp:258] Train net output #1: loss_cls = 0.442556 (* 1 = 0.442556 loss) +I0616 07:42:57.597375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.21089 (* 1 = 0.21089 loss) +I0616 07:42:57.597379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235825 (* 1 = 0.0235825 loss) +I0616 07:42:57.597383 9857 solver.cpp:571] Iteration 31960, lr = 0.001 +I0616 07:43:09.111413 9857 solver.cpp:242] Iteration 31980, loss = 0.925065 +I0616 07:43:09.111454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101037 (* 1 = 0.101037 loss) +I0616 07:43:09.111459 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185453 (* 1 = 0.185453 loss) +I0616 07:43:09.111464 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12937 (* 1 = 0.12937 loss) +I0616 07:43:09.111467 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00513281 (* 1 = 0.00513281 loss) +I0616 07:43:09.111471 9857 solver.cpp:571] Iteration 31980, lr = 0.001 +speed: 0.633s / iter +I0616 07:43:20.963850 9857 solver.cpp:242] Iteration 32000, loss = 0.747373 +I0616 07:43:20.963891 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297317 (* 1 = 0.297317 loss) +I0616 07:43:20.963896 9857 solver.cpp:258] Train net output #1: loss_cls = 0.484657 (* 1 = 0.484657 loss) +I0616 07:43:20.963899 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0889383 (* 1 = 0.0889383 loss) +I0616 07:43:20.963903 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.018055 (* 1 = 0.018055 loss) +I0616 07:43:20.963907 9857 solver.cpp:571] Iteration 32000, lr = 0.001 +I0616 07:43:32.525985 9857 solver.cpp:242] Iteration 32020, loss = 0.689439 +I0616 07:43:32.526028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160147 (* 1 = 0.160147 loss) +I0616 07:43:32.526033 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218414 (* 1 = 0.218414 loss) +I0616 07:43:32.526037 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129585 (* 1 = 0.129585 loss) +I0616 07:43:32.526041 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00862367 (* 1 = 0.00862367 loss) +I0616 07:43:32.526046 9857 solver.cpp:571] Iteration 32020, lr = 0.001 +I0616 07:43:44.098731 9857 solver.cpp:242] Iteration 32040, loss = 0.709332 +I0616 07:43:44.098776 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329638 (* 1 = 0.329638 loss) +I0616 07:43:44.098783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.341673 (* 1 = 0.341673 loss) +I0616 07:43:44.098788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0716087 (* 1 = 0.0716087 loss) +I0616 07:43:44.098791 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286297 (* 1 = 0.0286297 loss) +I0616 07:43:44.098795 9857 solver.cpp:571] Iteration 32040, lr = 0.001 +I0616 07:43:55.750737 9857 solver.cpp:242] Iteration 32060, loss = 0.450857 +I0616 07:43:55.750784 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204139 (* 1 = 0.204139 loss) +I0616 07:43:55.750792 9857 solver.cpp:258] Train net output #1: loss_cls = 0.240331 (* 1 = 0.240331 loss) +I0616 07:43:55.750795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105026 (* 1 = 0.105026 loss) +I0616 07:43:55.750799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0217234 (* 1 = 0.0217234 loss) +I0616 07:43:55.750803 9857 solver.cpp:571] Iteration 32060, lr = 0.001 +I0616 07:44:07.249328 9857 solver.cpp:242] Iteration 32080, loss = 1.88612 +I0616 07:44:07.249372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.344738 (* 1 = 0.344738 loss) +I0616 07:44:07.249377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.556974 (* 1 = 0.556974 loss) +I0616 07:44:07.249382 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15259 (* 1 = 0.15259 loss) +I0616 07:44:07.249385 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247505 (* 1 = 0.0247505 loss) +I0616 07:44:07.249389 9857 solver.cpp:571] Iteration 32080, lr = 0.001 +I0616 07:44:18.867547 9857 solver.cpp:242] Iteration 32100, loss = 0.828496 +I0616 07:44:18.867590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192093 (* 1 = 0.192093 loss) +I0616 07:44:18.867595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.361335 (* 1 = 0.361335 loss) +I0616 07:44:18.867599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.248151 (* 1 = 0.248151 loss) +I0616 07:44:18.867604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193142 (* 1 = 0.0193142 loss) +I0616 07:44:18.867607 9857 solver.cpp:571] Iteration 32100, lr = 0.001 +I0616 07:44:30.595791 9857 solver.cpp:242] Iteration 32120, loss = 0.903565 +I0616 07:44:30.595834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.321515 (* 1 = 0.321515 loss) +I0616 07:44:30.595839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.685457 (* 1 = 0.685457 loss) +I0616 07:44:30.595844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.065854 (* 1 = 0.065854 loss) +I0616 07:44:30.595847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0364883 (* 1 = 0.0364883 loss) +I0616 07:44:30.595851 9857 solver.cpp:571] Iteration 32120, lr = 0.001 +I0616 07:44:42.370976 9857 solver.cpp:242] Iteration 32140, loss = 0.632627 +I0616 07:44:42.371017 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.497433 (* 1 = 0.497433 loss) +I0616 07:44:42.371023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243683 (* 1 = 0.243683 loss) +I0616 07:44:42.371027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.048272 (* 1 = 0.048272 loss) +I0616 07:44:42.371031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0519008 (* 1 = 0.0519008 loss) +I0616 07:44:42.371036 9857 solver.cpp:571] Iteration 32140, lr = 0.001 +I0616 07:44:54.032487 9857 solver.cpp:242] Iteration 32160, loss = 0.997261 +I0616 07:44:54.032529 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389488 (* 1 = 0.389488 loss) +I0616 07:44:54.032536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.613331 (* 1 = 0.613331 loss) +I0616 07:44:54.032539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.320311 (* 1 = 0.320311 loss) +I0616 07:44:54.032543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119833 (* 1 = 0.119833 loss) +I0616 07:44:54.032546 9857 solver.cpp:571] Iteration 32160, lr = 0.001 +I0616 07:45:05.410871 9857 solver.cpp:242] Iteration 32180, loss = 0.651612 +I0616 07:45:05.410910 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193238 (* 1 = 0.193238 loss) +I0616 07:45:05.410915 9857 solver.cpp:258] Train net output #1: loss_cls = 0.614176 (* 1 = 0.614176 loss) +I0616 07:45:05.410920 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0418444 (* 1 = 0.0418444 loss) +I0616 07:45:05.410923 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240142 (* 1 = 0.0240142 loss) +I0616 07:45:05.410928 9857 solver.cpp:571] Iteration 32180, lr = 0.001 +speed: 0.632s / iter +I0616 07:45:16.869732 9857 solver.cpp:242] Iteration 32200, loss = 0.831667 +I0616 07:45:16.869774 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189572 (* 1 = 0.189572 loss) +I0616 07:45:16.869781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384606 (* 1 = 0.384606 loss) +I0616 07:45:16.869786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0669404 (* 1 = 0.0669404 loss) +I0616 07:45:16.869789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00365572 (* 1 = 0.00365572 loss) +I0616 07:45:16.869793 9857 solver.cpp:571] Iteration 32200, lr = 0.001 +I0616 07:45:28.702309 9857 solver.cpp:242] Iteration 32220, loss = 0.954451 +I0616 07:45:28.702339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.451551 (* 1 = 0.451551 loss) +I0616 07:45:28.702347 9857 solver.cpp:258] Train net output #1: loss_cls = 0.708188 (* 1 = 0.708188 loss) +I0616 07:45:28.702353 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0967325 (* 1 = 0.0967325 loss) +I0616 07:45:28.702359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.085923 (* 1 = 0.085923 loss) +I0616 07:45:28.702368 9857 solver.cpp:571] Iteration 32220, lr = 0.001 +I0616 07:45:40.132391 9857 solver.cpp:242] Iteration 32240, loss = 0.475984 +I0616 07:45:40.132422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115861 (* 1 = 0.115861 loss) +I0616 07:45:40.132429 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32943 (* 1 = 0.32943 loss) +I0616 07:45:40.132436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0837543 (* 1 = 0.0837543 loss) +I0616 07:45:40.132441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.166159 (* 1 = 0.166159 loss) +I0616 07:45:40.132448 9857 solver.cpp:571] Iteration 32240, lr = 0.001 +I0616 07:45:51.530216 9857 solver.cpp:242] Iteration 32260, loss = 0.490485 +I0616 07:45:51.530246 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0809871 (* 1 = 0.0809871 loss) +I0616 07:45:51.530254 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158175 (* 1 = 0.158175 loss) +I0616 07:45:51.530259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0184668 (* 1 = 0.0184668 loss) +I0616 07:45:51.530266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00222501 (* 1 = 0.00222501 loss) +I0616 07:45:51.530275 9857 solver.cpp:571] Iteration 32260, lr = 0.001 +I0616 07:46:03.188050 9857 solver.cpp:242] Iteration 32280, loss = 1.3578 +I0616 07:46:03.188079 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435403 (* 1 = 0.435403 loss) +I0616 07:46:03.188086 9857 solver.cpp:258] Train net output #1: loss_cls = 1.43186 (* 1 = 1.43186 loss) +I0616 07:46:03.188107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0441845 (* 1 = 0.0441845 loss) +I0616 07:46:03.188113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404272 (* 1 = 0.0404272 loss) +I0616 07:46:03.188119 9857 solver.cpp:571] Iteration 32280, lr = 0.001 +I0616 07:46:14.824331 9857 solver.cpp:242] Iteration 32300, loss = 0.705198 +I0616 07:46:14.824362 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247126 (* 1 = 0.247126 loss) +I0616 07:46:14.824368 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335976 (* 1 = 0.335976 loss) +I0616 07:46:14.824374 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.243934 (* 1 = 0.243934 loss) +I0616 07:46:14.824380 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0743597 (* 1 = 0.0743597 loss) +I0616 07:46:14.824388 9857 solver.cpp:571] Iteration 32300, lr = 0.001 +I0616 07:46:26.423738 9857 solver.cpp:242] Iteration 32320, loss = 0.995253 +I0616 07:46:26.423766 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309929 (* 1 = 0.309929 loss) +I0616 07:46:26.423774 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498037 (* 1 = 0.498037 loss) +I0616 07:46:26.423779 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139255 (* 1 = 0.139255 loss) +I0616 07:46:26.423785 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0490387 (* 1 = 0.0490387 loss) +I0616 07:46:26.423791 9857 solver.cpp:571] Iteration 32320, lr = 0.001 +I0616 07:46:37.933208 9857 solver.cpp:242] Iteration 32340, loss = 0.878626 +I0616 07:46:37.933238 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.442685 (* 1 = 0.442685 loss) +I0616 07:46:37.933245 9857 solver.cpp:258] Train net output #1: loss_cls = 0.67563 (* 1 = 0.67563 loss) +I0616 07:46:37.933251 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144453 (* 1 = 0.144453 loss) +I0616 07:46:37.933257 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0415893 (* 1 = 0.0415893 loss) +I0616 07:46:37.933266 9857 solver.cpp:571] Iteration 32340, lr = 0.001 +I0616 07:46:49.622165 9857 solver.cpp:242] Iteration 32360, loss = 0.72453 +I0616 07:46:49.622195 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322418 (* 1 = 0.322418 loss) +I0616 07:46:49.622201 9857 solver.cpp:258] Train net output #1: loss_cls = 0.595088 (* 1 = 0.595088 loss) +I0616 07:46:49.622208 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120858 (* 1 = 0.120858 loss) +I0616 07:46:49.622218 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0771804 (* 1 = 0.0771804 loss) +I0616 07:46:49.622226 9857 solver.cpp:571] Iteration 32360, lr = 0.001 +I0616 07:47:01.301563 9857 solver.cpp:242] Iteration 32380, loss = 0.656282 +I0616 07:47:01.301605 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263283 (* 1 = 0.263283 loss) +I0616 07:47:01.301611 9857 solver.cpp:258] Train net output #1: loss_cls = 0.341002 (* 1 = 0.341002 loss) +I0616 07:47:01.301615 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178949 (* 1 = 0.178949 loss) +I0616 07:47:01.301620 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127927 (* 1 = 0.0127927 loss) +I0616 07:47:01.301623 9857 solver.cpp:571] Iteration 32380, lr = 0.001 +speed: 0.632s / iter +I0616 07:47:12.737776 9857 solver.cpp:242] Iteration 32400, loss = 0.894182 +I0616 07:47:12.737818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143558 (* 1 = 0.143558 loss) +I0616 07:47:12.737823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215615 (* 1 = 0.215615 loss) +I0616 07:47:12.737828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138666 (* 1 = 0.138666 loss) +I0616 07:47:12.737831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00518237 (* 1 = 0.00518237 loss) +I0616 07:47:12.737835 9857 solver.cpp:571] Iteration 32400, lr = 0.001 +I0616 07:47:24.325947 9857 solver.cpp:242] Iteration 32420, loss = 1.32733 +I0616 07:47:24.325987 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274737 (* 1 = 0.274737 loss) +I0616 07:47:24.325994 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431985 (* 1 = 0.431985 loss) +I0616 07:47:24.325997 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.208782 (* 1 = 0.208782 loss) +I0616 07:47:24.326001 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129049 (* 1 = 0.129049 loss) +I0616 07:47:24.326004 9857 solver.cpp:571] Iteration 32420, lr = 0.001 +I0616 07:47:35.894562 9857 solver.cpp:242] Iteration 32440, loss = 0.450764 +I0616 07:47:35.894604 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12757 (* 1 = 0.12757 loss) +I0616 07:47:35.894610 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159484 (* 1 = 0.159484 loss) +I0616 07:47:35.894614 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0177783 (* 1 = 0.0177783 loss) +I0616 07:47:35.894618 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0736913 (* 1 = 0.0736913 loss) +I0616 07:47:35.894621 9857 solver.cpp:571] Iteration 32440, lr = 0.001 +I0616 07:47:47.470829 9857 solver.cpp:242] Iteration 32460, loss = 1.26886 +I0616 07:47:47.470871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.443392 (* 1 = 0.443392 loss) +I0616 07:47:47.470877 9857 solver.cpp:258] Train net output #1: loss_cls = 0.713428 (* 1 = 0.713428 loss) +I0616 07:47:47.470881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.314798 (* 1 = 0.314798 loss) +I0616 07:47:47.470885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0924919 (* 1 = 0.0924919 loss) +I0616 07:47:47.470890 9857 solver.cpp:571] Iteration 32460, lr = 0.001 +I0616 07:47:59.285890 9857 solver.cpp:242] Iteration 32480, loss = 0.627938 +I0616 07:47:59.285933 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.398088 (* 1 = 0.398088 loss) +I0616 07:47:59.285938 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232927 (* 1 = 0.232927 loss) +I0616 07:47:59.285943 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.174489 (* 1 = 0.174489 loss) +I0616 07:47:59.285945 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.214539 (* 1 = 0.214539 loss) +I0616 07:47:59.285949 9857 solver.cpp:571] Iteration 32480, lr = 0.001 +I0616 07:48:10.673002 9857 solver.cpp:242] Iteration 32500, loss = 0.528189 +I0616 07:48:10.673045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105741 (* 1 = 0.105741 loss) +I0616 07:48:10.673051 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175479 (* 1 = 0.175479 loss) +I0616 07:48:10.673055 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0506544 (* 1 = 0.0506544 loss) +I0616 07:48:10.673059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0146661 (* 1 = 0.0146661 loss) +I0616 07:48:10.673063 9857 solver.cpp:571] Iteration 32500, lr = 0.001 +I0616 07:48:22.140758 9857 solver.cpp:242] Iteration 32520, loss = 0.607513 +I0616 07:48:22.140799 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30139 (* 1 = 0.30139 loss) +I0616 07:48:22.140805 9857 solver.cpp:258] Train net output #1: loss_cls = 0.240982 (* 1 = 0.240982 loss) +I0616 07:48:22.140810 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0880276 (* 1 = 0.0880276 loss) +I0616 07:48:22.140813 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0208926 (* 1 = 0.0208926 loss) +I0616 07:48:22.140817 9857 solver.cpp:571] Iteration 32520, lr = 0.001 +I0616 07:48:33.445478 9857 solver.cpp:242] Iteration 32540, loss = 1.00274 +I0616 07:48:33.445519 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326321 (* 1 = 0.326321 loss) +I0616 07:48:33.445525 9857 solver.cpp:258] Train net output #1: loss_cls = 0.297273 (* 1 = 0.297273 loss) +I0616 07:48:33.445529 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.240106 (* 1 = 0.240106 loss) +I0616 07:48:33.445533 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.410831 (* 1 = 0.410831 loss) +I0616 07:48:33.445538 9857 solver.cpp:571] Iteration 32540, lr = 0.001 +I0616 07:48:44.979167 9857 solver.cpp:242] Iteration 32560, loss = 0.53135 +I0616 07:48:44.979210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145451 (* 1 = 0.145451 loss) +I0616 07:48:44.979217 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213627 (* 1 = 0.213627 loss) +I0616 07:48:44.979220 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0953648 (* 1 = 0.0953648 loss) +I0616 07:48:44.979224 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0386523 (* 1 = 0.0386523 loss) +I0616 07:48:44.979228 9857 solver.cpp:571] Iteration 32560, lr = 0.001 +I0616 07:48:56.509095 9857 solver.cpp:242] Iteration 32580, loss = 0.794713 +I0616 07:48:56.509136 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0921481 (* 1 = 0.0921481 loss) +I0616 07:48:56.509142 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225789 (* 1 = 0.225789 loss) +I0616 07:48:56.509146 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0372649 (* 1 = 0.0372649 loss) +I0616 07:48:56.509150 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.070284 (* 1 = 0.070284 loss) +I0616 07:48:56.509153 9857 solver.cpp:571] Iteration 32580, lr = 0.001 +speed: 0.632s / iter +I0616 07:49:08.058199 9857 solver.cpp:242] Iteration 32600, loss = 1.14639 +I0616 07:49:08.058241 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.361399 (* 1 = 0.361399 loss) +I0616 07:49:08.058246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.436807 (* 1 = 0.436807 loss) +I0616 07:49:08.058250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0602812 (* 1 = 0.0602812 loss) +I0616 07:49:08.058254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158069 (* 1 = 0.0158069 loss) +I0616 07:49:08.058259 9857 solver.cpp:571] Iteration 32600, lr = 0.001 +I0616 07:49:19.673871 9857 solver.cpp:242] Iteration 32620, loss = 0.336924 +I0616 07:49:19.673914 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163754 (* 1 = 0.163754 loss) +I0616 07:49:19.673919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202581 (* 1 = 0.202581 loss) +I0616 07:49:19.673924 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386539 (* 1 = 0.0386539 loss) +I0616 07:49:19.673928 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0229454 (* 1 = 0.0229454 loss) +I0616 07:49:19.673931 9857 solver.cpp:571] Iteration 32620, lr = 0.001 +I0616 07:49:31.143806 9857 solver.cpp:242] Iteration 32640, loss = 0.998661 +I0616 07:49:31.143848 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269686 (* 1 = 0.269686 loss) +I0616 07:49:31.143853 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337925 (* 1 = 0.337925 loss) +I0616 07:49:31.143857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.171743 (* 1 = 0.171743 loss) +I0616 07:49:31.143862 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286144 (* 1 = 0.0286144 loss) +I0616 07:49:31.143865 9857 solver.cpp:571] Iteration 32640, lr = 0.001 +I0616 07:49:42.872618 9857 solver.cpp:242] Iteration 32660, loss = 0.960218 +I0616 07:49:42.872659 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.31019 (* 1 = 0.31019 loss) +I0616 07:49:42.872665 9857 solver.cpp:258] Train net output #1: loss_cls = 0.504795 (* 1 = 0.504795 loss) +I0616 07:49:42.872669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0531545 (* 1 = 0.0531545 loss) +I0616 07:49:42.872673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239202 (* 1 = 0.0239202 loss) +I0616 07:49:42.872678 9857 solver.cpp:571] Iteration 32660, lr = 0.001 +I0616 07:49:54.524440 9857 solver.cpp:242] Iteration 32680, loss = 0.897737 +I0616 07:49:54.524480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360282 (* 1 = 0.360282 loss) +I0616 07:49:54.524487 9857 solver.cpp:258] Train net output #1: loss_cls = 0.776112 (* 1 = 0.776112 loss) +I0616 07:49:54.524490 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.049997 (* 1 = 0.049997 loss) +I0616 07:49:54.524494 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0986922 (* 1 = 0.0986922 loss) +I0616 07:49:54.524497 9857 solver.cpp:571] Iteration 32680, lr = 0.001 +I0616 07:50:06.055739 9857 solver.cpp:242] Iteration 32700, loss = 0.901231 +I0616 07:50:06.055783 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13545 (* 1 = 0.13545 loss) +I0616 07:50:06.055788 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127649 (* 1 = 0.127649 loss) +I0616 07:50:06.055793 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0183817 (* 1 = 0.0183817 loss) +I0616 07:50:06.055796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145701 (* 1 = 0.0145701 loss) +I0616 07:50:06.055800 9857 solver.cpp:571] Iteration 32700, lr = 0.001 +I0616 07:50:17.565676 9857 solver.cpp:242] Iteration 32720, loss = 0.477876 +I0616 07:50:17.565717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103761 (* 1 = 0.103761 loss) +I0616 07:50:17.565723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19848 (* 1 = 0.19848 loss) +I0616 07:50:17.565727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0658864 (* 1 = 0.0658864 loss) +I0616 07:50:17.565732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0266581 (* 1 = 0.0266581 loss) +I0616 07:50:17.565735 9857 solver.cpp:571] Iteration 32720, lr = 0.001 +I0616 07:50:29.210489 9857 solver.cpp:242] Iteration 32740, loss = 1.16879 +I0616 07:50:29.210531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.525944 (* 1 = 0.525944 loss) +I0616 07:50:29.210536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.594256 (* 1 = 0.594256 loss) +I0616 07:50:29.210541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235942 (* 1 = 0.235942 loss) +I0616 07:50:29.210544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.062829 (* 1 = 0.062829 loss) +I0616 07:50:29.210549 9857 solver.cpp:571] Iteration 32740, lr = 0.001 +I0616 07:50:40.899749 9857 solver.cpp:242] Iteration 32760, loss = 0.972846 +I0616 07:50:40.899790 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133778 (* 1 = 0.133778 loss) +I0616 07:50:40.899796 9857 solver.cpp:258] Train net output #1: loss_cls = 0.256956 (* 1 = 0.256956 loss) +I0616 07:50:40.899799 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0456104 (* 1 = 0.0456104 loss) +I0616 07:50:40.899803 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167767 (* 1 = 0.0167767 loss) +I0616 07:50:40.899807 9857 solver.cpp:571] Iteration 32760, lr = 0.001 +I0616 07:50:52.143834 9857 solver.cpp:242] Iteration 32780, loss = 0.753226 +I0616 07:50:52.143877 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39705 (* 1 = 0.39705 loss) +I0616 07:50:52.143882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.35372 (* 1 = 0.35372 loss) +I0616 07:50:52.143887 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0715765 (* 1 = 0.0715765 loss) +I0616 07:50:52.143889 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114211 (* 1 = 0.0114211 loss) +I0616 07:50:52.143893 9857 solver.cpp:571] Iteration 32780, lr = 0.001 +speed: 0.631s / iter +I0616 07:51:03.629056 9857 solver.cpp:242] Iteration 32800, loss = 0.764263 +I0616 07:51:03.629099 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199441 (* 1 = 0.199441 loss) +I0616 07:51:03.629106 9857 solver.cpp:258] Train net output #1: loss_cls = 0.346118 (* 1 = 0.346118 loss) +I0616 07:51:03.629109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151175 (* 1 = 0.151175 loss) +I0616 07:51:03.629113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407031 (* 1 = 0.0407031 loss) +I0616 07:51:03.629117 9857 solver.cpp:571] Iteration 32800, lr = 0.001 +I0616 07:51:15.168429 9857 solver.cpp:242] Iteration 32820, loss = 1.07953 +I0616 07:51:15.168470 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.423403 (* 1 = 0.423403 loss) +I0616 07:51:15.168476 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559678 (* 1 = 0.559678 loss) +I0616 07:51:15.168480 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162335 (* 1 = 0.162335 loss) +I0616 07:51:15.168484 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0338936 (* 1 = 0.0338936 loss) +I0616 07:51:15.168488 9857 solver.cpp:571] Iteration 32820, lr = 0.001 +I0616 07:51:26.633419 9857 solver.cpp:242] Iteration 32840, loss = 1.73046 +I0616 07:51:26.633462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408204 (* 1 = 0.408204 loss) +I0616 07:51:26.633468 9857 solver.cpp:258] Train net output #1: loss_cls = 0.669417 (* 1 = 0.669417 loss) +I0616 07:51:26.633472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.448511 (* 1 = 0.448511 loss) +I0616 07:51:26.633476 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.345427 (* 1 = 0.345427 loss) +I0616 07:51:26.633479 9857 solver.cpp:571] Iteration 32840, lr = 0.001 +I0616 07:51:38.271045 9857 solver.cpp:242] Iteration 32860, loss = 0.546791 +I0616 07:51:38.271087 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221182 (* 1 = 0.221182 loss) +I0616 07:51:38.271092 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229837 (* 1 = 0.229837 loss) +I0616 07:51:38.271096 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0426763 (* 1 = 0.0426763 loss) +I0616 07:51:38.271100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0057271 (* 1 = 0.0057271 loss) +I0616 07:51:38.271105 9857 solver.cpp:571] Iteration 32860, lr = 0.001 +I0616 07:51:49.611752 9857 solver.cpp:242] Iteration 32880, loss = 0.625287 +I0616 07:51:49.611793 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0989633 (* 1 = 0.0989633 loss) +I0616 07:51:49.611799 9857 solver.cpp:258] Train net output #1: loss_cls = 0.238909 (* 1 = 0.238909 loss) +I0616 07:51:49.611802 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0899927 (* 1 = 0.0899927 loss) +I0616 07:51:49.611806 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0314969 (* 1 = 0.0314969 loss) +I0616 07:51:49.611810 9857 solver.cpp:571] Iteration 32880, lr = 0.001 +I0616 07:52:00.925591 9857 solver.cpp:242] Iteration 32900, loss = 1.20338 +I0616 07:52:00.925633 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.414211 (* 1 = 0.414211 loss) +I0616 07:52:00.925639 9857 solver.cpp:258] Train net output #1: loss_cls = 0.675648 (* 1 = 0.675648 loss) +I0616 07:52:00.925643 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0927877 (* 1 = 0.0927877 loss) +I0616 07:52:00.925647 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0410837 (* 1 = 0.0410837 loss) +I0616 07:52:00.925652 9857 solver.cpp:571] Iteration 32900, lr = 0.001 +I0616 07:52:12.171954 9857 solver.cpp:242] Iteration 32920, loss = 1.05075 +I0616 07:52:12.171998 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106066 (* 1 = 0.106066 loss) +I0616 07:52:12.172003 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24024 (* 1 = 0.24024 loss) +I0616 07:52:12.172006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0475437 (* 1 = 0.0475437 loss) +I0616 07:52:12.172010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325674 (* 1 = 0.0325674 loss) +I0616 07:52:12.172014 9857 solver.cpp:571] Iteration 32920, lr = 0.001 +I0616 07:52:23.822399 9857 solver.cpp:242] Iteration 32940, loss = 0.810959 +I0616 07:52:23.822443 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174044 (* 1 = 0.174044 loss) +I0616 07:52:23.822448 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275047 (* 1 = 0.275047 loss) +I0616 07:52:23.822453 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0385738 (* 1 = 0.0385738 loss) +I0616 07:52:23.822456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119432 (* 1 = 0.0119432 loss) +I0616 07:52:23.822460 9857 solver.cpp:571] Iteration 32940, lr = 0.001 +I0616 07:52:35.251956 9857 solver.cpp:242] Iteration 32960, loss = 1.16605 +I0616 07:52:35.251999 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338622 (* 1 = 0.338622 loss) +I0616 07:52:35.252004 9857 solver.cpp:258] Train net output #1: loss_cls = 0.391166 (* 1 = 0.391166 loss) +I0616 07:52:35.252008 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.414886 (* 1 = 0.414886 loss) +I0616 07:52:35.252013 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0692781 (* 1 = 0.0692781 loss) +I0616 07:52:35.252017 9857 solver.cpp:571] Iteration 32960, lr = 0.001 +I0616 07:52:46.785920 9857 solver.cpp:242] Iteration 32980, loss = 0.454513 +I0616 07:52:46.785964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110899 (* 1 = 0.110899 loss) +I0616 07:52:46.785969 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254153 (* 1 = 0.254153 loss) +I0616 07:52:46.785972 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123678 (* 1 = 0.123678 loss) +I0616 07:52:46.785976 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030917 (* 1 = 0.030917 loss) +I0616 07:52:46.785980 9857 solver.cpp:571] Iteration 32980, lr = 0.001 +speed: 0.631s / iter +I0616 07:52:58.425827 9857 solver.cpp:242] Iteration 33000, loss = 1.08483 +I0616 07:52:58.425870 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112589 (* 1 = 0.112589 loss) +I0616 07:52:58.425876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314089 (* 1 = 0.314089 loss) +I0616 07:52:58.425880 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0636498 (* 1 = 0.0636498 loss) +I0616 07:52:58.425884 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00446346 (* 1 = 0.00446346 loss) +I0616 07:52:58.425887 9857 solver.cpp:571] Iteration 33000, lr = 0.001 +I0616 07:53:09.873261 9857 solver.cpp:242] Iteration 33020, loss = 1.3787 +I0616 07:53:09.873301 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281113 (* 1 = 0.281113 loss) +I0616 07:53:09.873307 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332445 (* 1 = 0.332445 loss) +I0616 07:53:09.873311 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.395622 (* 1 = 0.395622 loss) +I0616 07:53:09.873314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.203002 (* 1 = 0.203002 loss) +I0616 07:53:09.873318 9857 solver.cpp:571] Iteration 33020, lr = 0.001 +I0616 07:53:21.395731 9857 solver.cpp:242] Iteration 33040, loss = 0.770373 +I0616 07:53:21.395773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355577 (* 1 = 0.355577 loss) +I0616 07:53:21.395778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316883 (* 1 = 0.316883 loss) +I0616 07:53:21.395782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0542015 (* 1 = 0.0542015 loss) +I0616 07:53:21.395787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00629029 (* 1 = 0.00629029 loss) +I0616 07:53:21.395790 9857 solver.cpp:571] Iteration 33040, lr = 0.001 +I0616 07:53:32.652784 9857 solver.cpp:242] Iteration 33060, loss = 1.16991 +I0616 07:53:32.652827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254633 (* 1 = 0.254633 loss) +I0616 07:53:32.652833 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310334 (* 1 = 0.310334 loss) +I0616 07:53:32.652837 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107006 (* 1 = 0.107006 loss) +I0616 07:53:32.652842 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.317031 (* 1 = 0.317031 loss) +I0616 07:53:32.652845 9857 solver.cpp:571] Iteration 33060, lr = 0.001 +I0616 07:53:44.396425 9857 solver.cpp:242] Iteration 33080, loss = 0.484191 +I0616 07:53:44.396466 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25539 (* 1 = 0.25539 loss) +I0616 07:53:44.396471 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206452 (* 1 = 0.206452 loss) +I0616 07:53:44.396476 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0653608 (* 1 = 0.0653608 loss) +I0616 07:53:44.396479 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0376041 (* 1 = 0.0376041 loss) +I0616 07:53:44.396483 9857 solver.cpp:571] Iteration 33080, lr = 0.001 +I0616 07:53:55.994606 9857 solver.cpp:242] Iteration 33100, loss = 0.721351 +I0616 07:53:55.994647 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143953 (* 1 = 0.143953 loss) +I0616 07:53:55.994652 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29466 (* 1 = 0.29466 loss) +I0616 07:53:55.994657 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.064063 (* 1 = 0.064063 loss) +I0616 07:53:55.994660 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0068272 (* 1 = 0.0068272 loss) +I0616 07:53:55.994664 9857 solver.cpp:571] Iteration 33100, lr = 0.001 +I0616 07:54:07.624058 9857 solver.cpp:242] Iteration 33120, loss = 1.20946 +I0616 07:54:07.624100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244413 (* 1 = 0.244413 loss) +I0616 07:54:07.624105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.348008 (* 1 = 0.348008 loss) +I0616 07:54:07.624109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0298179 (* 1 = 0.0298179 loss) +I0616 07:54:07.624114 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254513 (* 1 = 0.0254513 loss) +I0616 07:54:07.624117 9857 solver.cpp:571] Iteration 33120, lr = 0.001 +I0616 07:54:18.980818 9857 solver.cpp:242] Iteration 33140, loss = 0.806087 +I0616 07:54:18.980860 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.216128 (* 1 = 0.216128 loss) +I0616 07:54:18.980866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19398 (* 1 = 0.19398 loss) +I0616 07:54:18.980870 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150429 (* 1 = 0.150429 loss) +I0616 07:54:18.980875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.04202 (* 1 = 0.04202 loss) +I0616 07:54:18.980878 9857 solver.cpp:571] Iteration 33140, lr = 0.001 +I0616 07:54:30.258296 9857 solver.cpp:242] Iteration 33160, loss = 0.599112 +I0616 07:54:30.258337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103521 (* 1 = 0.103521 loss) +I0616 07:54:30.258343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208094 (* 1 = 0.208094 loss) +I0616 07:54:30.258347 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0206348 (* 1 = 0.0206348 loss) +I0616 07:54:30.258352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00880824 (* 1 = 0.00880824 loss) +I0616 07:54:30.258355 9857 solver.cpp:571] Iteration 33160, lr = 0.001 +I0616 07:54:41.828630 9857 solver.cpp:242] Iteration 33180, loss = 1.35017 +I0616 07:54:41.828670 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368991 (* 1 = 0.368991 loss) +I0616 07:54:41.828676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.818424 (* 1 = 0.818424 loss) +I0616 07:54:41.828680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.455625 (* 1 = 0.455625 loss) +I0616 07:54:41.828683 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.176041 (* 1 = 0.176041 loss) +I0616 07:54:41.828687 9857 solver.cpp:571] Iteration 33180, lr = 0.001 +speed: 0.631s / iter +I0616 07:54:53.740927 9857 solver.cpp:242] Iteration 33200, loss = 1.08738 +I0616 07:54:53.740970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.437958 (* 1 = 0.437958 loss) +I0616 07:54:53.740977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.583738 (* 1 = 0.583738 loss) +I0616 07:54:53.740980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.357295 (* 1 = 0.357295 loss) +I0616 07:54:53.740984 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102842 (* 1 = 0.102842 loss) +I0616 07:54:53.740988 9857 solver.cpp:571] Iteration 33200, lr = 0.001 +I0616 07:55:05.472533 9857 solver.cpp:242] Iteration 33220, loss = 0.484968 +I0616 07:55:05.472574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0662974 (* 1 = 0.0662974 loss) +I0616 07:55:05.472580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15456 (* 1 = 0.15456 loss) +I0616 07:55:05.472584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0180464 (* 1 = 0.0180464 loss) +I0616 07:55:05.472589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00981843 (* 1 = 0.00981843 loss) +I0616 07:55:05.472592 9857 solver.cpp:571] Iteration 33220, lr = 0.001 +I0616 07:55:17.003525 9857 solver.cpp:242] Iteration 33240, loss = 1.01307 +I0616 07:55:17.003566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121773 (* 1 = 0.121773 loss) +I0616 07:55:17.003571 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12944 (* 1 = 0.12944 loss) +I0616 07:55:17.003576 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0183605 (* 1 = 0.0183605 loss) +I0616 07:55:17.003579 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00022226 (* 1 = 0.00022226 loss) +I0616 07:55:17.003583 9857 solver.cpp:571] Iteration 33240, lr = 0.001 +I0616 07:55:28.586093 9857 solver.cpp:242] Iteration 33260, loss = 0.693486 +I0616 07:55:28.586135 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.330599 (* 1 = 0.330599 loss) +I0616 07:55:28.586141 9857 solver.cpp:258] Train net output #1: loss_cls = 0.43849 (* 1 = 0.43849 loss) +I0616 07:55:28.586145 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117308 (* 1 = 0.117308 loss) +I0616 07:55:28.586148 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240136 (* 1 = 0.0240136 loss) +I0616 07:55:28.586153 9857 solver.cpp:571] Iteration 33260, lr = 0.001 +I0616 07:55:40.080365 9857 solver.cpp:242] Iteration 33280, loss = 0.607315 +I0616 07:55:40.080406 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138693 (* 1 = 0.138693 loss) +I0616 07:55:40.080412 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190116 (* 1 = 0.190116 loss) +I0616 07:55:40.080416 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0469895 (* 1 = 0.0469895 loss) +I0616 07:55:40.080420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251609 (* 1 = 0.0251609 loss) +I0616 07:55:40.080425 9857 solver.cpp:571] Iteration 33280, lr = 0.001 +I0616 07:55:51.339298 9857 solver.cpp:242] Iteration 33300, loss = 0.372433 +I0616 07:55:51.339339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148956 (* 1 = 0.148956 loss) +I0616 07:55:51.339344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14806 (* 1 = 0.14806 loss) +I0616 07:55:51.339349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.087064 (* 1 = 0.087064 loss) +I0616 07:55:51.339352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00471676 (* 1 = 0.00471676 loss) +I0616 07:55:51.339355 9857 solver.cpp:571] Iteration 33300, lr = 0.001 +I0616 07:56:03.053302 9857 solver.cpp:242] Iteration 33320, loss = 1.20971 +I0616 07:56:03.053344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.380358 (* 1 = 0.380358 loss) +I0616 07:56:03.053349 9857 solver.cpp:258] Train net output #1: loss_cls = 0.773868 (* 1 = 0.773868 loss) +I0616 07:56:03.053352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.170042 (* 1 = 0.170042 loss) +I0616 07:56:03.053356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0490497 (* 1 = 0.0490497 loss) +I0616 07:56:03.053360 9857 solver.cpp:571] Iteration 33320, lr = 0.001 +I0616 07:56:14.485646 9857 solver.cpp:242] Iteration 33340, loss = 0.998339 +I0616 07:56:14.485687 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280976 (* 1 = 0.280976 loss) +I0616 07:56:14.485692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252413 (* 1 = 0.252413 loss) +I0616 07:56:14.485695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0600475 (* 1 = 0.0600475 loss) +I0616 07:56:14.485699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119001 (* 1 = 0.119001 loss) +I0616 07:56:14.485703 9857 solver.cpp:571] Iteration 33340, lr = 0.001 +I0616 07:56:26.019805 9857 solver.cpp:242] Iteration 33360, loss = 0.30225 +I0616 07:56:26.019847 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110567 (* 1 = 0.110567 loss) +I0616 07:56:26.019853 9857 solver.cpp:258] Train net output #1: loss_cls = 0.117714 (* 1 = 0.117714 loss) +I0616 07:56:26.019857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0559395 (* 1 = 0.0559395 loss) +I0616 07:56:26.019860 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0684411 (* 1 = 0.0684411 loss) +I0616 07:56:26.019865 9857 solver.cpp:571] Iteration 33360, lr = 0.001 +I0616 07:56:37.727011 9857 solver.cpp:242] Iteration 33380, loss = 0.592829 +I0616 07:56:37.727052 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304829 (* 1 = 0.304829 loss) +I0616 07:56:37.727058 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265643 (* 1 = 0.265643 loss) +I0616 07:56:37.727062 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142464 (* 1 = 0.142464 loss) +I0616 07:56:37.727066 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0190921 (* 1 = 0.0190921 loss) +I0616 07:56:37.727069 9857 solver.cpp:571] Iteration 33380, lr = 0.001 +speed: 0.630s / iter +I0616 07:56:49.270141 9857 solver.cpp:242] Iteration 33400, loss = 0.511993 +I0616 07:56:49.270165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0488866 (* 1 = 0.0488866 loss) +I0616 07:56:49.270170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123273 (* 1 = 0.123273 loss) +I0616 07:56:49.270174 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555249 (* 1 = 0.0555249 loss) +I0616 07:56:49.270179 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0441301 (* 1 = 0.0441301 loss) +I0616 07:56:49.270182 9857 solver.cpp:571] Iteration 33400, lr = 0.001 +I0616 07:57:00.853688 9857 solver.cpp:242] Iteration 33420, loss = 0.769817 +I0616 07:57:00.853729 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180853 (* 1 = 0.180853 loss) +I0616 07:57:00.853734 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118413 (* 1 = 0.118413 loss) +I0616 07:57:00.853739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00477656 (* 1 = 0.00477656 loss) +I0616 07:57:00.853742 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00402279 (* 1 = 0.00402279 loss) +I0616 07:57:00.853746 9857 solver.cpp:571] Iteration 33420, lr = 0.001 +I0616 07:57:12.460355 9857 solver.cpp:242] Iteration 33440, loss = 1.57095 +I0616 07:57:12.460397 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413845 (* 1 = 0.413845 loss) +I0616 07:57:12.460402 9857 solver.cpp:258] Train net output #1: loss_cls = 0.914554 (* 1 = 0.914554 loss) +I0616 07:57:12.460407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.312752 (* 1 = 0.312752 loss) +I0616 07:57:12.460409 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116519 (* 1 = 0.116519 loss) +I0616 07:57:12.460413 9857 solver.cpp:571] Iteration 33440, lr = 0.001 +I0616 07:57:24.271219 9857 solver.cpp:242] Iteration 33460, loss = 0.665545 +I0616 07:57:24.271261 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326021 (* 1 = 0.326021 loss) +I0616 07:57:24.271266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.469341 (* 1 = 0.469341 loss) +I0616 07:57:24.271270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0595051 (* 1 = 0.0595051 loss) +I0616 07:57:24.271275 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291603 (* 1 = 0.0291603 loss) +I0616 07:57:24.271278 9857 solver.cpp:571] Iteration 33460, lr = 0.001 +I0616 07:57:35.914273 9857 solver.cpp:242] Iteration 33480, loss = 1.16022 +I0616 07:57:35.914315 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38705 (* 1 = 0.38705 loss) +I0616 07:57:35.914320 9857 solver.cpp:258] Train net output #1: loss_cls = 0.54918 (* 1 = 0.54918 loss) +I0616 07:57:35.914325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142138 (* 1 = 0.142138 loss) +I0616 07:57:35.914330 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00428429 (* 1 = 0.00428429 loss) +I0616 07:57:35.914332 9857 solver.cpp:571] Iteration 33480, lr = 0.001 +I0616 07:57:47.644232 9857 solver.cpp:242] Iteration 33500, loss = 1.51697 +I0616 07:57:47.644274 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300387 (* 1 = 0.300387 loss) +I0616 07:57:47.644280 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562847 (* 1 = 0.562847 loss) +I0616 07:57:47.644284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.296173 (* 1 = 0.296173 loss) +I0616 07:57:47.644289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.161397 (* 1 = 0.161397 loss) +I0616 07:57:47.644291 9857 solver.cpp:571] Iteration 33500, lr = 0.001 +I0616 07:57:59.278242 9857 solver.cpp:242] Iteration 33520, loss = 0.792038 +I0616 07:57:59.278285 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.440413 (* 1 = 0.440413 loss) +I0616 07:57:59.278290 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459599 (* 1 = 0.459599 loss) +I0616 07:57:59.278293 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.229079 (* 1 = 0.229079 loss) +I0616 07:57:59.278297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0903418 (* 1 = 0.0903418 loss) +I0616 07:57:59.278301 9857 solver.cpp:571] Iteration 33520, lr = 0.001 +I0616 07:58:11.042630 9857 solver.cpp:242] Iteration 33540, loss = 1.17035 +I0616 07:58:11.042671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.508519 (* 1 = 0.508519 loss) +I0616 07:58:11.042677 9857 solver.cpp:258] Train net output #1: loss_cls = 0.489409 (* 1 = 0.489409 loss) +I0616 07:58:11.042681 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0706498 (* 1 = 0.0706498 loss) +I0616 07:58:11.042685 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360485 (* 1 = 0.0360485 loss) +I0616 07:58:11.042690 9857 solver.cpp:571] Iteration 33540, lr = 0.001 +I0616 07:58:22.547657 9857 solver.cpp:242] Iteration 33560, loss = 0.53056 +I0616 07:58:22.547701 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137663 (* 1 = 0.137663 loss) +I0616 07:58:22.547706 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208158 (* 1 = 0.208158 loss) +I0616 07:58:22.547711 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0273241 (* 1 = 0.0273241 loss) +I0616 07:58:22.547714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0064838 (* 1 = 0.0064838 loss) +I0616 07:58:22.547719 9857 solver.cpp:571] Iteration 33560, lr = 0.001 +I0616 07:58:34.051888 9857 solver.cpp:242] Iteration 33580, loss = 0.980566 +I0616 07:58:34.051928 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127518 (* 1 = 0.127518 loss) +I0616 07:58:34.051934 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17659 (* 1 = 0.17659 loss) +I0616 07:58:34.051937 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0551041 (* 1 = 0.0551041 loss) +I0616 07:58:34.051941 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0389734 (* 1 = 0.0389734 loss) +I0616 07:58:34.051945 9857 solver.cpp:571] Iteration 33580, lr = 0.001 +speed: 0.630s / iter +I0616 07:58:45.413873 9857 solver.cpp:242] Iteration 33600, loss = 0.636158 +I0616 07:58:45.413916 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204332 (* 1 = 0.204332 loss) +I0616 07:58:45.413921 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234005 (* 1 = 0.234005 loss) +I0616 07:58:45.413925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0707784 (* 1 = 0.0707784 loss) +I0616 07:58:45.413929 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00891639 (* 1 = 0.00891639 loss) +I0616 07:58:45.413933 9857 solver.cpp:571] Iteration 33600, lr = 0.001 +I0616 07:58:56.997905 9857 solver.cpp:242] Iteration 33620, loss = 0.906827 +I0616 07:58:56.997944 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304115 (* 1 = 0.304115 loss) +I0616 07:58:56.997951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.387407 (* 1 = 0.387407 loss) +I0616 07:58:56.997954 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.31354 (* 1 = 0.31354 loss) +I0616 07:58:56.997958 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.174313 (* 1 = 0.174313 loss) +I0616 07:58:56.997962 9857 solver.cpp:571] Iteration 33620, lr = 0.001 +I0616 07:59:08.464062 9857 solver.cpp:242] Iteration 33640, loss = 0.830452 +I0616 07:59:08.464105 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172486 (* 1 = 0.172486 loss) +I0616 07:59:08.464112 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388112 (* 1 = 0.388112 loss) +I0616 07:59:08.464115 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.362047 (* 1 = 0.362047 loss) +I0616 07:59:08.464120 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.543509 (* 1 = 0.543509 loss) +I0616 07:59:08.464124 9857 solver.cpp:571] Iteration 33640, lr = 0.001 +I0616 07:59:20.081315 9857 solver.cpp:242] Iteration 33660, loss = 0.95384 +I0616 07:59:20.081357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.564577 (* 1 = 0.564577 loss) +I0616 07:59:20.081362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.604946 (* 1 = 0.604946 loss) +I0616 07:59:20.081365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135843 (* 1 = 0.135843 loss) +I0616 07:59:20.081369 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0396983 (* 1 = 0.0396983 loss) +I0616 07:59:20.081373 9857 solver.cpp:571] Iteration 33660, lr = 0.001 +I0616 07:59:31.608239 9857 solver.cpp:242] Iteration 33680, loss = 0.585959 +I0616 07:59:31.608283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346379 (* 1 = 0.346379 loss) +I0616 07:59:31.608288 9857 solver.cpp:258] Train net output #1: loss_cls = 0.391761 (* 1 = 0.391761 loss) +I0616 07:59:31.608292 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0482548 (* 1 = 0.0482548 loss) +I0616 07:59:31.608295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0386983 (* 1 = 0.0386983 loss) +I0616 07:59:31.608299 9857 solver.cpp:571] Iteration 33680, lr = 0.001 +I0616 07:59:43.194092 9857 solver.cpp:242] Iteration 33700, loss = 0.697365 +I0616 07:59:43.194133 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178304 (* 1 = 0.178304 loss) +I0616 07:59:43.194138 9857 solver.cpp:258] Train net output #1: loss_cls = 0.377621 (* 1 = 0.377621 loss) +I0616 07:59:43.194142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.035549 (* 1 = 0.035549 loss) +I0616 07:59:43.194145 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175279 (* 1 = 0.0175279 loss) +I0616 07:59:43.194149 9857 solver.cpp:571] Iteration 33700, lr = 0.001 +I0616 07:59:54.668926 9857 solver.cpp:242] Iteration 33720, loss = 0.485017 +I0616 07:59:54.668967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0626411 (* 1 = 0.0626411 loss) +I0616 07:59:54.668973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399755 (* 1 = 0.399755 loss) +I0616 07:59:54.668977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0587265 (* 1 = 0.0587265 loss) +I0616 07:59:54.668982 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00191405 (* 1 = 0.00191405 loss) +I0616 07:59:54.668985 9857 solver.cpp:571] Iteration 33720, lr = 0.001 +I0616 08:00:06.301487 9857 solver.cpp:242] Iteration 33740, loss = 2.11028 +I0616 08:00:06.301529 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435477 (* 1 = 0.435477 loss) +I0616 08:00:06.301535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.754631 (* 1 = 0.754631 loss) +I0616 08:00:06.301539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.276704 (* 1 = 0.276704 loss) +I0616 08:00:06.301543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0450845 (* 1 = 0.0450845 loss) +I0616 08:00:06.301548 9857 solver.cpp:571] Iteration 33740, lr = 0.001 +I0616 08:00:18.003985 9857 solver.cpp:242] Iteration 33760, loss = 0.565567 +I0616 08:00:18.004027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157696 (* 1 = 0.157696 loss) +I0616 08:00:18.004032 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378388 (* 1 = 0.378388 loss) +I0616 08:00:18.004037 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12185 (* 1 = 0.12185 loss) +I0616 08:00:18.004041 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.178586 (* 1 = 0.178586 loss) +I0616 08:00:18.004045 9857 solver.cpp:571] Iteration 33760, lr = 0.001 +I0616 08:00:29.782843 9857 solver.cpp:242] Iteration 33780, loss = 0.868144 +I0616 08:00:29.782886 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214424 (* 1 = 0.214424 loss) +I0616 08:00:29.782891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372822 (* 1 = 0.372822 loss) +I0616 08:00:29.782896 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0811976 (* 1 = 0.0811976 loss) +I0616 08:00:29.782899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0458104 (* 1 = 0.0458104 loss) +I0616 08:00:29.782903 9857 solver.cpp:571] Iteration 33780, lr = 0.001 +speed: 0.630s / iter +I0616 08:00:41.580672 9857 solver.cpp:242] Iteration 33800, loss = 1.15976 +I0616 08:00:41.580715 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.289738 (* 1 = 0.289738 loss) +I0616 08:00:41.580720 9857 solver.cpp:258] Train net output #1: loss_cls = 0.633266 (* 1 = 0.633266 loss) +I0616 08:00:41.580725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0971365 (* 1 = 0.0971365 loss) +I0616 08:00:41.580729 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128284 (* 1 = 0.0128284 loss) +I0616 08:00:41.580732 9857 solver.cpp:571] Iteration 33800, lr = 0.001 +I0616 08:00:53.290683 9857 solver.cpp:242] Iteration 33820, loss = 1.0862 +I0616 08:00:53.290724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285894 (* 1 = 0.285894 loss) +I0616 08:00:53.290730 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402823 (* 1 = 0.402823 loss) +I0616 08:00:53.290735 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605046 (* 1 = 0.0605046 loss) +I0616 08:00:53.290738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140809 (* 1 = 0.0140809 loss) +I0616 08:00:53.290742 9857 solver.cpp:571] Iteration 33820, lr = 0.001 +I0616 08:01:04.975015 9857 solver.cpp:242] Iteration 33840, loss = 0.649194 +I0616 08:01:04.975057 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.243575 (* 1 = 0.243575 loss) +I0616 08:01:04.975064 9857 solver.cpp:258] Train net output #1: loss_cls = 0.529478 (* 1 = 0.529478 loss) +I0616 08:01:04.975067 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0928984 (* 1 = 0.0928984 loss) +I0616 08:01:04.975071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00201192 (* 1 = 0.00201192 loss) +I0616 08:01:04.975075 9857 solver.cpp:571] Iteration 33840, lr = 0.001 +I0616 08:01:16.394453 9857 solver.cpp:242] Iteration 33860, loss = 0.535367 +I0616 08:01:16.394495 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225181 (* 1 = 0.225181 loss) +I0616 08:01:16.394501 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201026 (* 1 = 0.201026 loss) +I0616 08:01:16.394506 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0342288 (* 1 = 0.0342288 loss) +I0616 08:01:16.394510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00311182 (* 1 = 0.00311182 loss) +I0616 08:01:16.394513 9857 solver.cpp:571] Iteration 33860, lr = 0.001 +I0616 08:01:27.841006 9857 solver.cpp:242] Iteration 33880, loss = 0.639394 +I0616 08:01:27.841049 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100147 (* 1 = 0.100147 loss) +I0616 08:01:27.841054 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177031 (* 1 = 0.177031 loss) +I0616 08:01:27.841059 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287224 (* 1 = 0.0287224 loss) +I0616 08:01:27.841063 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173336 (* 1 = 0.0173336 loss) +I0616 08:01:27.841066 9857 solver.cpp:571] Iteration 33880, lr = 0.001 +I0616 08:01:39.366662 9857 solver.cpp:242] Iteration 33900, loss = 0.583786 +I0616 08:01:39.366704 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266621 (* 1 = 0.266621 loss) +I0616 08:01:39.366710 9857 solver.cpp:258] Train net output #1: loss_cls = 0.330811 (* 1 = 0.330811 loss) +I0616 08:01:39.366714 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0730299 (* 1 = 0.0730299 loss) +I0616 08:01:39.366719 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275373 (* 1 = 0.0275373 loss) +I0616 08:01:39.366722 9857 solver.cpp:571] Iteration 33900, lr = 0.001 +I0616 08:01:50.863904 9857 solver.cpp:242] Iteration 33920, loss = 1.15283 +I0616 08:01:50.863945 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.485206 (* 1 = 0.485206 loss) +I0616 08:01:50.863951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.776045 (* 1 = 0.776045 loss) +I0616 08:01:50.863955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.299717 (* 1 = 0.299717 loss) +I0616 08:01:50.863960 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0624219 (* 1 = 0.0624219 loss) +I0616 08:01:50.863963 9857 solver.cpp:571] Iteration 33920, lr = 0.001 +I0616 08:02:02.494437 9857 solver.cpp:242] Iteration 33940, loss = 1.34139 +I0616 08:02:02.494479 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401593 (* 1 = 0.401593 loss) +I0616 08:02:02.494485 9857 solver.cpp:258] Train net output #1: loss_cls = 1.18225 (* 1 = 1.18225 loss) +I0616 08:02:02.494489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220704 (* 1 = 0.220704 loss) +I0616 08:02:02.494493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0500889 (* 1 = 0.0500889 loss) +I0616 08:02:02.494498 9857 solver.cpp:571] Iteration 33940, lr = 0.001 +I0616 08:02:14.122558 9857 solver.cpp:242] Iteration 33960, loss = 0.623348 +I0616 08:02:14.122599 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164021 (* 1 = 0.164021 loss) +I0616 08:02:14.122606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155689 (* 1 = 0.155689 loss) +I0616 08:02:14.122609 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0965468 (* 1 = 0.0965468 loss) +I0616 08:02:14.122613 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.216735 (* 1 = 0.216735 loss) +I0616 08:02:14.122617 9857 solver.cpp:571] Iteration 33960, lr = 0.001 +I0616 08:02:25.741245 9857 solver.cpp:242] Iteration 33980, loss = 0.387937 +I0616 08:02:25.741289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10912 (* 1 = 0.10912 loss) +I0616 08:02:25.741294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173015 (* 1 = 0.173015 loss) +I0616 08:02:25.741299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.072278 (* 1 = 0.072278 loss) +I0616 08:02:25.741302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107245 (* 1 = 0.0107245 loss) +I0616 08:02:25.741307 9857 solver.cpp:571] Iteration 33980, lr = 0.001 +speed: 0.629s / iter +I0616 08:02:37.263707 9857 solver.cpp:242] Iteration 34000, loss = 0.30826 +I0616 08:02:37.263748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178585 (* 1 = 0.178585 loss) +I0616 08:02:37.263754 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206615 (* 1 = 0.206615 loss) +I0616 08:02:37.263758 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0292989 (* 1 = 0.0292989 loss) +I0616 08:02:37.263762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00738837 (* 1 = 0.00738837 loss) +I0616 08:02:37.263767 9857 solver.cpp:571] Iteration 34000, lr = 0.001 +I0616 08:02:48.603322 9857 solver.cpp:242] Iteration 34020, loss = 0.75264 +I0616 08:02:48.603365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268761 (* 1 = 0.268761 loss) +I0616 08:02:48.603370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.444522 (* 1 = 0.444522 loss) +I0616 08:02:48.603375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126261 (* 1 = 0.126261 loss) +I0616 08:02:48.603379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0864019 (* 1 = 0.0864019 loss) +I0616 08:02:48.603382 9857 solver.cpp:571] Iteration 34020, lr = 0.001 +I0616 08:03:00.227815 9857 solver.cpp:242] Iteration 34040, loss = 0.919317 +I0616 08:03:00.227859 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185221 (* 1 = 0.185221 loss) +I0616 08:03:00.227864 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498975 (* 1 = 0.498975 loss) +I0616 08:03:00.227869 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.079414 (* 1 = 0.079414 loss) +I0616 08:03:00.227871 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230952 (* 1 = 0.0230952 loss) +I0616 08:03:00.227875 9857 solver.cpp:571] Iteration 34040, lr = 0.001 +I0616 08:03:11.489024 9857 solver.cpp:242] Iteration 34060, loss = 0.914328 +I0616 08:03:11.489068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253253 (* 1 = 0.253253 loss) +I0616 08:03:11.489073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.531935 (* 1 = 0.531935 loss) +I0616 08:03:11.489076 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.225217 (* 1 = 0.225217 loss) +I0616 08:03:11.489080 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.184277 (* 1 = 0.184277 loss) +I0616 08:03:11.489084 9857 solver.cpp:571] Iteration 34060, lr = 0.001 +I0616 08:03:23.208510 9857 solver.cpp:242] Iteration 34080, loss = 0.694283 +I0616 08:03:23.208552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247949 (* 1 = 0.247949 loss) +I0616 08:03:23.208559 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375823 (* 1 = 0.375823 loss) +I0616 08:03:23.208562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.288131 (* 1 = 0.288131 loss) +I0616 08:03:23.208566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0450657 (* 1 = 0.0450657 loss) +I0616 08:03:23.208570 9857 solver.cpp:571] Iteration 34080, lr = 0.001 +I0616 08:03:34.630396 9857 solver.cpp:242] Iteration 34100, loss = 0.58795 +I0616 08:03:34.630439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0689625 (* 1 = 0.0689625 loss) +I0616 08:03:34.630445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101446 (* 1 = 0.101446 loss) +I0616 08:03:34.630448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0379771 (* 1 = 0.0379771 loss) +I0616 08:03:34.630452 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140338 (* 1 = 0.0140338 loss) +I0616 08:03:34.630455 9857 solver.cpp:571] Iteration 34100, lr = 0.001 +I0616 08:03:46.134274 9857 solver.cpp:242] Iteration 34120, loss = 0.363482 +I0616 08:03:46.134317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0525763 (* 1 = 0.0525763 loss) +I0616 08:03:46.134322 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0862177 (* 1 = 0.0862177 loss) +I0616 08:03:46.134326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0243884 (* 1 = 0.0243884 loss) +I0616 08:03:46.134330 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230022 (* 1 = 0.0230022 loss) +I0616 08:03:46.134335 9857 solver.cpp:571] Iteration 34120, lr = 0.001 +I0616 08:03:57.653717 9857 solver.cpp:242] Iteration 34140, loss = 0.682518 +I0616 08:03:57.653760 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17765 (* 1 = 0.17765 loss) +I0616 08:03:57.653765 9857 solver.cpp:258] Train net output #1: loss_cls = 0.141102 (* 1 = 0.141102 loss) +I0616 08:03:57.653770 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0223898 (* 1 = 0.0223898 loss) +I0616 08:03:57.653774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0204503 (* 1 = 0.0204503 loss) +I0616 08:03:57.653777 9857 solver.cpp:571] Iteration 34140, lr = 0.001 +I0616 08:04:09.267782 9857 solver.cpp:242] Iteration 34160, loss = 0.721934 +I0616 08:04:09.267827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0402353 (* 1 = 0.0402353 loss) +I0616 08:04:09.267834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0924797 (* 1 = 0.0924797 loss) +I0616 08:04:09.267840 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.303413 (* 1 = 0.303413 loss) +I0616 08:04:09.267845 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.205751 (* 1 = 0.205751 loss) +I0616 08:04:09.267850 9857 solver.cpp:571] Iteration 34160, lr = 0.001 +I0616 08:04:20.886493 9857 solver.cpp:242] Iteration 34180, loss = 0.884517 +I0616 08:04:20.886535 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215954 (* 1 = 0.215954 loss) +I0616 08:04:20.886541 9857 solver.cpp:258] Train net output #1: loss_cls = 0.412132 (* 1 = 0.412132 loss) +I0616 08:04:20.886545 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145019 (* 1 = 0.145019 loss) +I0616 08:04:20.886549 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131529 (* 1 = 0.0131529 loss) +I0616 08:04:20.886554 9857 solver.cpp:571] Iteration 34180, lr = 0.001 +speed: 0.629s / iter +I0616 08:04:32.168943 9857 solver.cpp:242] Iteration 34200, loss = 0.516172 +I0616 08:04:32.168985 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109522 (* 1 = 0.109522 loss) +I0616 08:04:32.168992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213037 (* 1 = 0.213037 loss) +I0616 08:04:32.168995 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0531465 (* 1 = 0.0531465 loss) +I0616 08:04:32.168999 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00711054 (* 1 = 0.00711054 loss) +I0616 08:04:32.169003 9857 solver.cpp:571] Iteration 34200, lr = 0.001 +I0616 08:04:44.049841 9857 solver.cpp:242] Iteration 34220, loss = 0.595096 +I0616 08:04:44.049883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222698 (* 1 = 0.222698 loss) +I0616 08:04:44.049888 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338909 (* 1 = 0.338909 loss) +I0616 08:04:44.049893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0593984 (* 1 = 0.0593984 loss) +I0616 08:04:44.049897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116638 (* 1 = 0.0116638 loss) +I0616 08:04:44.049901 9857 solver.cpp:571] Iteration 34220, lr = 0.001 +I0616 08:04:55.732192 9857 solver.cpp:242] Iteration 34240, loss = 1.2496 +I0616 08:04:55.732234 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27675 (* 1 = 0.27675 loss) +I0616 08:04:55.732240 9857 solver.cpp:258] Train net output #1: loss_cls = 0.405032 (* 1 = 0.405032 loss) +I0616 08:04:55.732244 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0196525 (* 1 = 0.0196525 loss) +I0616 08:04:55.732249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.202445 (* 1 = 0.202445 loss) +I0616 08:04:55.732252 9857 solver.cpp:571] Iteration 34240, lr = 0.001 +I0616 08:05:07.425501 9857 solver.cpp:242] Iteration 34260, loss = 1.30641 +I0616 08:05:07.425544 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336037 (* 1 = 0.336037 loss) +I0616 08:05:07.425550 9857 solver.cpp:258] Train net output #1: loss_cls = 0.762442 (* 1 = 0.762442 loss) +I0616 08:05:07.425554 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0907863 (* 1 = 0.0907863 loss) +I0616 08:05:07.425559 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0792192 (* 1 = 0.0792192 loss) +I0616 08:05:07.425562 9857 solver.cpp:571] Iteration 34260, lr = 0.001 +I0616 08:05:19.031612 9857 solver.cpp:242] Iteration 34280, loss = 1.38961 +I0616 08:05:19.031656 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.524858 (* 1 = 0.524858 loss) +I0616 08:05:19.031661 9857 solver.cpp:258] Train net output #1: loss_cls = 0.62843 (* 1 = 0.62843 loss) +I0616 08:05:19.031666 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219023 (* 1 = 0.219023 loss) +I0616 08:05:19.031669 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0372457 (* 1 = 0.0372457 loss) +I0616 08:05:19.031673 9857 solver.cpp:571] Iteration 34280, lr = 0.001 +I0616 08:05:30.378125 9857 solver.cpp:242] Iteration 34300, loss = 0.453785 +I0616 08:05:30.378168 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182249 (* 1 = 0.182249 loss) +I0616 08:05:30.378173 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221845 (* 1 = 0.221845 loss) +I0616 08:05:30.378178 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0268617 (* 1 = 0.0268617 loss) +I0616 08:05:30.378182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283448 (* 1 = 0.0283448 loss) +I0616 08:05:30.378186 9857 solver.cpp:571] Iteration 34300, lr = 0.001 +I0616 08:05:41.745982 9857 solver.cpp:242] Iteration 34320, loss = 0.894421 +I0616 08:05:41.746023 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0902968 (* 1 = 0.0902968 loss) +I0616 08:05:41.746028 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196528 (* 1 = 0.196528 loss) +I0616 08:05:41.746032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0111533 (* 1 = 0.0111533 loss) +I0616 08:05:41.746037 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158744 (* 1 = 0.0158744 loss) +I0616 08:05:41.746040 9857 solver.cpp:571] Iteration 34320, lr = 0.001 +I0616 08:05:53.337929 9857 solver.cpp:242] Iteration 34340, loss = 0.942337 +I0616 08:05:53.337971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313484 (* 1 = 0.313484 loss) +I0616 08:05:53.337977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.747076 (* 1 = 0.747076 loss) +I0616 08:05:53.337981 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175076 (* 1 = 0.175076 loss) +I0616 08:05:53.337985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210415 (* 1 = 0.0210415 loss) +I0616 08:05:53.337988 9857 solver.cpp:571] Iteration 34340, lr = 0.001 +I0616 08:06:04.690351 9857 solver.cpp:242] Iteration 34360, loss = 0.51522 +I0616 08:06:04.690392 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310099 (* 1 = 0.310099 loss) +I0616 08:06:04.690397 9857 solver.cpp:258] Train net output #1: loss_cls = 0.293947 (* 1 = 0.293947 loss) +I0616 08:06:04.690402 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0510293 (* 1 = 0.0510293 loss) +I0616 08:06:04.690405 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161507 (* 1 = 0.0161507 loss) +I0616 08:06:04.690408 9857 solver.cpp:571] Iteration 34360, lr = 0.001 +I0616 08:06:16.170930 9857 solver.cpp:242] Iteration 34380, loss = 0.492847 +I0616 08:06:16.170972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.208712 (* 1 = 0.208712 loss) +I0616 08:06:16.170977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152315 (* 1 = 0.152315 loss) +I0616 08:06:16.170981 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374343 (* 1 = 0.0374343 loss) +I0616 08:06:16.170985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00507321 (* 1 = 0.00507321 loss) +I0616 08:06:16.170989 9857 solver.cpp:571] Iteration 34380, lr = 0.001 +speed: 0.629s / iter +I0616 08:06:27.685614 9857 solver.cpp:242] Iteration 34400, loss = 0.748323 +I0616 08:06:27.685657 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257168 (* 1 = 0.257168 loss) +I0616 08:06:27.685662 9857 solver.cpp:258] Train net output #1: loss_cls = 0.319652 (* 1 = 0.319652 loss) +I0616 08:06:27.685667 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386028 (* 1 = 0.0386028 loss) +I0616 08:06:27.685670 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173079 (* 1 = 0.0173079 loss) +I0616 08:06:27.685674 9857 solver.cpp:571] Iteration 34400, lr = 0.001 +I0616 08:06:39.213390 9857 solver.cpp:242] Iteration 34420, loss = 0.714668 +I0616 08:06:39.213433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0642432 (* 1 = 0.0642432 loss) +I0616 08:06:39.213438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121222 (* 1 = 0.121222 loss) +I0616 08:06:39.213443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.035685 (* 1 = 0.035685 loss) +I0616 08:06:39.213445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0244113 (* 1 = 0.0244113 loss) +I0616 08:06:39.213449 9857 solver.cpp:571] Iteration 34420, lr = 0.001 +I0616 08:06:50.885926 9857 solver.cpp:242] Iteration 34440, loss = 0.586032 +I0616 08:06:50.885967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0857011 (* 1 = 0.0857011 loss) +I0616 08:06:50.885973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119394 (* 1 = 0.119394 loss) +I0616 08:06:50.885977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.176699 (* 1 = 0.176699 loss) +I0616 08:06:50.885982 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0177611 (* 1 = 0.0177611 loss) +I0616 08:06:50.885985 9857 solver.cpp:571] Iteration 34440, lr = 0.001 +I0616 08:07:02.568156 9857 solver.cpp:242] Iteration 34460, loss = 0.782901 +I0616 08:07:02.568195 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384135 (* 1 = 0.384135 loss) +I0616 08:07:02.568202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176198 (* 1 = 0.176198 loss) +I0616 08:07:02.568205 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0799686 (* 1 = 0.0799686 loss) +I0616 08:07:02.568209 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.034773 (* 1 = 0.034773 loss) +I0616 08:07:02.568213 9857 solver.cpp:571] Iteration 34460, lr = 0.001 +I0616 08:07:14.163283 9857 solver.cpp:242] Iteration 34480, loss = 0.899966 +I0616 08:07:14.163326 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.49735 (* 1 = 0.49735 loss) +I0616 08:07:14.163331 9857 solver.cpp:258] Train net output #1: loss_cls = 0.66219 (* 1 = 0.66219 loss) +I0616 08:07:14.163336 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178785 (* 1 = 0.178785 loss) +I0616 08:07:14.163339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0185822 (* 1 = 0.0185822 loss) +I0616 08:07:14.163343 9857 solver.cpp:571] Iteration 34480, lr = 0.001 +I0616 08:07:25.737084 9857 solver.cpp:242] Iteration 34500, loss = 0.879897 +I0616 08:07:25.737126 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382339 (* 1 = 0.382339 loss) +I0616 08:07:25.737133 9857 solver.cpp:258] Train net output #1: loss_cls = 0.602522 (* 1 = 0.602522 loss) +I0616 08:07:25.737136 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0966423 (* 1 = 0.0966423 loss) +I0616 08:07:25.737140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.338942 (* 1 = 0.338942 loss) +I0616 08:07:25.737144 9857 solver.cpp:571] Iteration 34500, lr = 0.001 +I0616 08:07:37.559303 9857 solver.cpp:242] Iteration 34520, loss = 0.822283 +I0616 08:07:37.559342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312671 (* 1 = 0.312671 loss) +I0616 08:07:37.559348 9857 solver.cpp:258] Train net output #1: loss_cls = 0.436333 (* 1 = 0.436333 loss) +I0616 08:07:37.559352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0322726 (* 1 = 0.0322726 loss) +I0616 08:07:37.559356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00446298 (* 1 = 0.00446298 loss) +I0616 08:07:37.559360 9857 solver.cpp:571] Iteration 34520, lr = 0.001 +I0616 08:07:49.226115 9857 solver.cpp:242] Iteration 34540, loss = 0.35504 +I0616 08:07:49.226155 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138788 (* 1 = 0.138788 loss) +I0616 08:07:49.226161 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259324 (* 1 = 0.259324 loss) +I0616 08:07:49.226164 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0452544 (* 1 = 0.0452544 loss) +I0616 08:07:49.226168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116109 (* 1 = 0.0116109 loss) +I0616 08:07:49.226171 9857 solver.cpp:571] Iteration 34540, lr = 0.001 +I0616 08:08:00.840800 9857 solver.cpp:242] Iteration 34560, loss = 1.5764 +I0616 08:08:00.840843 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1923 (* 1 = 0.1923 loss) +I0616 08:08:00.840849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248336 (* 1 = 0.248336 loss) +I0616 08:08:00.840853 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.204195 (* 1 = 0.204195 loss) +I0616 08:08:00.840857 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0396875 (* 1 = 0.0396875 loss) +I0616 08:08:00.840862 9857 solver.cpp:571] Iteration 34560, lr = 0.001 +I0616 08:08:12.385097 9857 solver.cpp:242] Iteration 34580, loss = 0.824427 +I0616 08:08:12.385140 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202938 (* 1 = 0.202938 loss) +I0616 08:08:12.385146 9857 solver.cpp:258] Train net output #1: loss_cls = 0.606549 (* 1 = 0.606549 loss) +I0616 08:08:12.385150 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139337 (* 1 = 0.139337 loss) +I0616 08:08:12.385154 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0458766 (* 1 = 0.0458766 loss) +I0616 08:08:12.385159 9857 solver.cpp:571] Iteration 34580, lr = 0.001 +speed: 0.629s / iter +I0616 08:08:24.252790 9857 solver.cpp:242] Iteration 34600, loss = 0.99923 +I0616 08:08:24.252832 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140006 (* 1 = 0.140006 loss) +I0616 08:08:24.252838 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203635 (* 1 = 0.203635 loss) +I0616 08:08:24.252842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0570617 (* 1 = 0.0570617 loss) +I0616 08:08:24.252847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0456734 (* 1 = 0.0456734 loss) +I0616 08:08:24.252851 9857 solver.cpp:571] Iteration 34600, lr = 0.001 +I0616 08:08:35.799607 9857 solver.cpp:242] Iteration 34620, loss = 1.44094 +I0616 08:08:35.799649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.596012 (* 1 = 0.596012 loss) +I0616 08:08:35.799654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.869424 (* 1 = 0.869424 loss) +I0616 08:08:35.799659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0585363 (* 1 = 0.0585363 loss) +I0616 08:08:35.799661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0331992 (* 1 = 0.0331992 loss) +I0616 08:08:35.799665 9857 solver.cpp:571] Iteration 34620, lr = 0.001 +I0616 08:08:47.580122 9857 solver.cpp:242] Iteration 34640, loss = 0.342003 +I0616 08:08:47.580164 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176026 (* 1 = 0.176026 loss) +I0616 08:08:47.580170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18226 (* 1 = 0.18226 loss) +I0616 08:08:47.580174 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0384339 (* 1 = 0.0384339 loss) +I0616 08:08:47.580178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0148693 (* 1 = 0.0148693 loss) +I0616 08:08:47.580183 9857 solver.cpp:571] Iteration 34640, lr = 0.001 +I0616 08:08:59.314684 9857 solver.cpp:242] Iteration 34660, loss = 0.785781 +I0616 08:08:59.314728 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0728936 (* 1 = 0.0728936 loss) +I0616 08:08:59.314733 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145621 (* 1 = 0.145621 loss) +I0616 08:08:59.314738 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.189087 (* 1 = 0.189087 loss) +I0616 08:08:59.314741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.03757 (* 1 = 0.03757 loss) +I0616 08:08:59.314745 9857 solver.cpp:571] Iteration 34660, lr = 0.001 +I0616 08:09:10.890012 9857 solver.cpp:242] Iteration 34680, loss = 0.791976 +I0616 08:09:10.890053 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.361224 (* 1 = 0.361224 loss) +I0616 08:09:10.890074 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244487 (* 1 = 0.244487 loss) +I0616 08:09:10.890079 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0810889 (* 1 = 0.0810889 loss) +I0616 08:09:10.890081 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292295 (* 1 = 0.0292295 loss) +I0616 08:09:10.890085 9857 solver.cpp:571] Iteration 34680, lr = 0.001 +I0616 08:09:22.538403 9857 solver.cpp:242] Iteration 34700, loss = 0.330792 +I0616 08:09:22.538445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122435 (* 1 = 0.122435 loss) +I0616 08:09:22.538450 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144234 (* 1 = 0.144234 loss) +I0616 08:09:22.538455 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190501 (* 1 = 0.0190501 loss) +I0616 08:09:22.538458 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00168362 (* 1 = 0.00168362 loss) +I0616 08:09:22.538463 9857 solver.cpp:571] Iteration 34700, lr = 0.001 +I0616 08:09:34.214926 9857 solver.cpp:242] Iteration 34720, loss = 0.527048 +I0616 08:09:34.214968 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124178 (* 1 = 0.124178 loss) +I0616 08:09:34.214973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183444 (* 1 = 0.183444 loss) +I0616 08:09:34.214977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00760828 (* 1 = 0.00760828 loss) +I0616 08:09:34.214982 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00295866 (* 1 = 0.00295866 loss) +I0616 08:09:34.214985 9857 solver.cpp:571] Iteration 34720, lr = 0.001 +I0616 08:09:45.653862 9857 solver.cpp:242] Iteration 34740, loss = 0.829654 +I0616 08:09:45.653905 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197781 (* 1 = 0.197781 loss) +I0616 08:09:45.653910 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200317 (* 1 = 0.200317 loss) +I0616 08:09:45.653914 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0404625 (* 1 = 0.0404625 loss) +I0616 08:09:45.653919 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115973 (* 1 = 0.0115973 loss) +I0616 08:09:45.653925 9857 solver.cpp:571] Iteration 34740, lr = 0.001 +I0616 08:09:57.239004 9857 solver.cpp:242] Iteration 34760, loss = 0.249395 +I0616 08:09:57.239047 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0997448 (* 1 = 0.0997448 loss) +I0616 08:09:57.239053 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132633 (* 1 = 0.132633 loss) +I0616 08:09:57.239056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0130554 (* 1 = 0.0130554 loss) +I0616 08:09:57.239059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00843206 (* 1 = 0.00843206 loss) +I0616 08:09:57.239063 9857 solver.cpp:571] Iteration 34760, lr = 0.001 +I0616 08:10:08.711597 9857 solver.cpp:242] Iteration 34780, loss = 1.28216 +I0616 08:10:08.711639 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.469028 (* 1 = 0.469028 loss) +I0616 08:10:08.711644 9857 solver.cpp:258] Train net output #1: loss_cls = 0.673338 (* 1 = 0.673338 loss) +I0616 08:10:08.711649 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186449 (* 1 = 0.186449 loss) +I0616 08:10:08.711652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0501496 (* 1 = 0.0501496 loss) +I0616 08:10:08.711658 9857 solver.cpp:571] Iteration 34780, lr = 0.001 +speed: 0.628s / iter +I0616 08:10:20.250030 9857 solver.cpp:242] Iteration 34800, loss = 0.673365 +I0616 08:10:20.250071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302801 (* 1 = 0.302801 loss) +I0616 08:10:20.250077 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431424 (* 1 = 0.431424 loss) +I0616 08:10:20.250080 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151433 (* 1 = 0.151433 loss) +I0616 08:10:20.250084 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0681662 (* 1 = 0.0681662 loss) +I0616 08:10:20.250088 9857 solver.cpp:571] Iteration 34800, lr = 0.001 +I0616 08:10:31.780241 9857 solver.cpp:242] Iteration 34820, loss = 1.11408 +I0616 08:10:31.780282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311244 (* 1 = 0.311244 loss) +I0616 08:10:31.780287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.569738 (* 1 = 0.569738 loss) +I0616 08:10:31.780290 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22546 (* 1 = 0.22546 loss) +I0616 08:10:31.780294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0200154 (* 1 = 0.0200154 loss) +I0616 08:10:31.780298 9857 solver.cpp:571] Iteration 34820, lr = 0.001 +I0616 08:10:43.187458 9857 solver.cpp:242] Iteration 34840, loss = 1.54338 +I0616 08:10:43.187499 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.380493 (* 1 = 0.380493 loss) +I0616 08:10:43.187505 9857 solver.cpp:258] Train net output #1: loss_cls = 0.587092 (* 1 = 0.587092 loss) +I0616 08:10:43.187508 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.21723 (* 1 = 0.21723 loss) +I0616 08:10:43.187511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.499045 (* 1 = 0.499045 loss) +I0616 08:10:43.187515 9857 solver.cpp:571] Iteration 34840, lr = 0.001 +I0616 08:10:54.915182 9857 solver.cpp:242] Iteration 34860, loss = 0.724675 +I0616 08:10:54.915223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0865801 (* 1 = 0.0865801 loss) +I0616 08:10:54.915228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.2007 (* 1 = 0.2007 loss) +I0616 08:10:54.915232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0129178 (* 1 = 0.0129178 loss) +I0616 08:10:54.915236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.002301 (* 1 = 0.002301 loss) +I0616 08:10:54.915241 9857 solver.cpp:571] Iteration 34860, lr = 0.001 +I0616 08:11:06.467370 9857 solver.cpp:242] Iteration 34880, loss = 1.029 +I0616 08:11:06.467411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0617135 (* 1 = 0.0617135 loss) +I0616 08:11:06.467417 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154092 (* 1 = 0.154092 loss) +I0616 08:11:06.467420 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0469423 (* 1 = 0.0469423 loss) +I0616 08:11:06.467424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397975 (* 1 = 0.0397975 loss) +I0616 08:11:06.467428 9857 solver.cpp:571] Iteration 34880, lr = 0.001 +I0616 08:11:17.909682 9857 solver.cpp:242] Iteration 34900, loss = 0.692593 +I0616 08:11:17.909724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327107 (* 1 = 0.327107 loss) +I0616 08:11:17.909729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267708 (* 1 = 0.267708 loss) +I0616 08:11:17.909734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146344 (* 1 = 0.146344 loss) +I0616 08:11:17.909737 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251796 (* 1 = 0.0251796 loss) +I0616 08:11:17.909741 9857 solver.cpp:571] Iteration 34900, lr = 0.001 +I0616 08:11:29.636304 9857 solver.cpp:242] Iteration 34920, loss = 0.918271 +I0616 08:11:29.636346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337828 (* 1 = 0.337828 loss) +I0616 08:11:29.636351 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18808 (* 1 = 0.18808 loss) +I0616 08:11:29.636356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.06124 (* 1 = 0.06124 loss) +I0616 08:11:29.636359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0607392 (* 1 = 0.0607392 loss) +I0616 08:11:29.636364 9857 solver.cpp:571] Iteration 34920, lr = 0.001 +I0616 08:11:41.141402 9857 solver.cpp:242] Iteration 34940, loss = 1.78678 +I0616 08:11:41.141444 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435867 (* 1 = 0.435867 loss) +I0616 08:11:41.141451 9857 solver.cpp:258] Train net output #1: loss_cls = 1.33899 (* 1 = 1.33899 loss) +I0616 08:11:41.141456 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11074 (* 1 = 0.11074 loss) +I0616 08:11:41.141459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0354932 (* 1 = 0.0354932 loss) +I0616 08:11:41.141464 9857 solver.cpp:571] Iteration 34940, lr = 0.001 +I0616 08:11:52.421332 9857 solver.cpp:242] Iteration 34960, loss = 1.16526 +I0616 08:11:52.421375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311209 (* 1 = 0.311209 loss) +I0616 08:11:52.421380 9857 solver.cpp:258] Train net output #1: loss_cls = 0.915331 (* 1 = 0.915331 loss) +I0616 08:11:52.421385 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190656 (* 1 = 0.190656 loss) +I0616 08:11:52.421388 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0208291 (* 1 = 0.0208291 loss) +I0616 08:11:52.421392 9857 solver.cpp:571] Iteration 34960, lr = 0.001 +I0616 08:12:03.888923 9857 solver.cpp:242] Iteration 34980, loss = 0.7884 +I0616 08:12:03.888967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127548 (* 1 = 0.127548 loss) +I0616 08:12:03.888972 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272676 (* 1 = 0.272676 loss) +I0616 08:12:03.888978 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0414473 (* 1 = 0.0414473 loss) +I0616 08:12:03.888981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144374 (* 1 = 0.144374 loss) +I0616 08:12:03.888984 9857 solver.cpp:571] Iteration 34980, lr = 0.001 +speed: 0.628s / iter +I0616 08:12:15.423420 9857 solver.cpp:242] Iteration 35000, loss = 0.728962 +I0616 08:12:15.423462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132817 (* 1 = 0.132817 loss) +I0616 08:12:15.423468 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161691 (* 1 = 0.161691 loss) +I0616 08:12:15.423472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120876 (* 1 = 0.120876 loss) +I0616 08:12:15.423476 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0341409 (* 1 = 0.0341409 loss) +I0616 08:12:15.423480 9857 solver.cpp:571] Iteration 35000, lr = 0.001 +I0616 08:12:27.083503 9857 solver.cpp:242] Iteration 35020, loss = 1.28488 +I0616 08:12:27.083544 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315936 (* 1 = 0.315936 loss) +I0616 08:12:27.083550 9857 solver.cpp:258] Train net output #1: loss_cls = 0.675387 (* 1 = 0.675387 loss) +I0616 08:12:27.083554 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0928374 (* 1 = 0.0928374 loss) +I0616 08:12:27.083559 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028884 (* 1 = 0.028884 loss) +I0616 08:12:27.083562 9857 solver.cpp:571] Iteration 35020, lr = 0.001 +I0616 08:12:38.593917 9857 solver.cpp:242] Iteration 35040, loss = 0.772985 +I0616 08:12:38.593960 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112665 (* 1 = 0.112665 loss) +I0616 08:12:38.593964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280452 (* 1 = 0.280452 loss) +I0616 08:12:38.593968 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.213213 (* 1 = 0.213213 loss) +I0616 08:12:38.593972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00550847 (* 1 = 0.00550847 loss) +I0616 08:12:38.593976 9857 solver.cpp:571] Iteration 35040, lr = 0.001 +I0616 08:12:50.120594 9857 solver.cpp:242] Iteration 35060, loss = 0.504918 +I0616 08:12:50.120635 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278649 (* 1 = 0.278649 loss) +I0616 08:12:50.120640 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188819 (* 1 = 0.188819 loss) +I0616 08:12:50.120645 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0204159 (* 1 = 0.0204159 loss) +I0616 08:12:50.120648 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0594968 (* 1 = 0.0594968 loss) +I0616 08:12:50.120652 9857 solver.cpp:571] Iteration 35060, lr = 0.001 +I0616 08:13:01.825135 9857 solver.cpp:242] Iteration 35080, loss = 1.30011 +I0616 08:13:01.825176 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.387179 (* 1 = 0.387179 loss) +I0616 08:13:01.825182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.339525 (* 1 = 0.339525 loss) +I0616 08:13:01.825186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0293224 (* 1 = 0.0293224 loss) +I0616 08:13:01.825191 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00579653 (* 1 = 0.00579653 loss) +I0616 08:13:01.825194 9857 solver.cpp:571] Iteration 35080, lr = 0.001 +I0616 08:13:13.514741 9857 solver.cpp:242] Iteration 35100, loss = 1.19982 +I0616 08:13:13.514786 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111988 (* 1 = 0.111988 loss) +I0616 08:13:13.514792 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271962 (* 1 = 0.271962 loss) +I0616 08:13:13.514796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0203322 (* 1 = 0.0203322 loss) +I0616 08:13:13.514801 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0335838 (* 1 = 0.0335838 loss) +I0616 08:13:13.514804 9857 solver.cpp:571] Iteration 35100, lr = 0.001 +I0616 08:13:25.052362 9857 solver.cpp:242] Iteration 35120, loss = 1.30776 +I0616 08:13:25.052402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221973 (* 1 = 0.221973 loss) +I0616 08:13:25.052407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.807433 (* 1 = 0.807433 loss) +I0616 08:13:25.052412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0587736 (* 1 = 0.0587736 loss) +I0616 08:13:25.052415 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0433622 (* 1 = 0.0433622 loss) +I0616 08:13:25.052419 9857 solver.cpp:571] Iteration 35120, lr = 0.001 +I0616 08:13:36.566229 9857 solver.cpp:242] Iteration 35140, loss = 0.544908 +I0616 08:13:36.566270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0398644 (* 1 = 0.0398644 loss) +I0616 08:13:36.566277 9857 solver.cpp:258] Train net output #1: loss_cls = 0.273628 (* 1 = 0.273628 loss) +I0616 08:13:36.566280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169857 (* 1 = 0.169857 loss) +I0616 08:13:36.566283 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.048336 (* 1 = 0.048336 loss) +I0616 08:13:36.566287 9857 solver.cpp:571] Iteration 35140, lr = 0.001 +I0616 08:13:48.122140 9857 solver.cpp:242] Iteration 35160, loss = 0.517145 +I0616 08:13:48.122184 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254201 (* 1 = 0.254201 loss) +I0616 08:13:48.122189 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266413 (* 1 = 0.266413 loss) +I0616 08:13:48.122194 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0631355 (* 1 = 0.0631355 loss) +I0616 08:13:48.122198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0535516 (* 1 = 0.0535516 loss) +I0616 08:13:48.122205 9857 solver.cpp:571] Iteration 35160, lr = 0.001 +I0616 08:13:59.660406 9857 solver.cpp:242] Iteration 35180, loss = 0.984115 +I0616 08:13:59.660449 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311793 (* 1 = 0.311793 loss) +I0616 08:13:59.660454 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314963 (* 1 = 0.314963 loss) +I0616 08:13:59.660457 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126289 (* 1 = 0.126289 loss) +I0616 08:13:59.660461 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.443309 (* 1 = 0.443309 loss) +I0616 08:13:59.660465 9857 solver.cpp:571] Iteration 35180, lr = 0.001 +speed: 0.628s / iter +I0616 08:14:11.440706 9857 solver.cpp:242] Iteration 35200, loss = 1.39986 +I0616 08:14:11.440747 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329059 (* 1 = 0.329059 loss) +I0616 08:14:11.440752 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562916 (* 1 = 0.562916 loss) +I0616 08:14:11.440755 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157089 (* 1 = 0.157089 loss) +I0616 08:14:11.440759 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.213866 (* 1 = 0.213866 loss) +I0616 08:14:11.440762 9857 solver.cpp:571] Iteration 35200, lr = 0.001 +I0616 08:14:23.035331 9857 solver.cpp:242] Iteration 35220, loss = 1.00995 +I0616 08:14:23.035372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254927 (* 1 = 0.254927 loss) +I0616 08:14:23.035378 9857 solver.cpp:258] Train net output #1: loss_cls = 0.496571 (* 1 = 0.496571 loss) +I0616 08:14:23.035382 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144738 (* 1 = 0.144738 loss) +I0616 08:14:23.035387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029167 (* 1 = 0.029167 loss) +I0616 08:14:23.035390 9857 solver.cpp:571] Iteration 35220, lr = 0.001 +I0616 08:14:34.648501 9857 solver.cpp:242] Iteration 35240, loss = 0.800045 +I0616 08:14:34.648542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1713 (* 1 = 0.1713 loss) +I0616 08:14:34.648548 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514088 (* 1 = 0.514088 loss) +I0616 08:14:34.648552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15203 (* 1 = 0.15203 loss) +I0616 08:14:34.648556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00456014 (* 1 = 0.00456014 loss) +I0616 08:14:34.648561 9857 solver.cpp:571] Iteration 35240, lr = 0.001 +I0616 08:14:46.256471 9857 solver.cpp:242] Iteration 35260, loss = 2.01258 +I0616 08:14:46.256515 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396233 (* 1 = 0.396233 loss) +I0616 08:14:46.256520 9857 solver.cpp:258] Train net output #1: loss_cls = 0.699943 (* 1 = 0.699943 loss) +I0616 08:14:46.256525 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.424685 (* 1 = 0.424685 loss) +I0616 08:14:46.256527 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.362229 (* 1 = 0.362229 loss) +I0616 08:14:46.256533 9857 solver.cpp:571] Iteration 35260, lr = 0.001 +I0616 08:14:57.755363 9857 solver.cpp:242] Iteration 35280, loss = 0.552456 +I0616 08:14:57.755406 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307456 (* 1 = 0.307456 loss) +I0616 08:14:57.755411 9857 solver.cpp:258] Train net output #1: loss_cls = 0.291291 (* 1 = 0.291291 loss) +I0616 08:14:57.755415 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118182 (* 1 = 0.118182 loss) +I0616 08:14:57.755434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00575964 (* 1 = 0.00575964 loss) +I0616 08:14:57.755439 9857 solver.cpp:571] Iteration 35280, lr = 0.001 +I0616 08:15:09.349439 9857 solver.cpp:242] Iteration 35300, loss = 0.596182 +I0616 08:15:09.349481 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153717 (* 1 = 0.153717 loss) +I0616 08:15:09.349486 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185465 (* 1 = 0.185465 loss) +I0616 08:15:09.349490 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0942769 (* 1 = 0.0942769 loss) +I0616 08:15:09.349494 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189684 (* 1 = 0.0189684 loss) +I0616 08:15:09.349498 9857 solver.cpp:571] Iteration 35300, lr = 0.001 +I0616 08:15:20.984961 9857 solver.cpp:242] Iteration 35320, loss = 1.27575 +I0616 08:15:20.985002 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.246588 (* 1 = 0.246588 loss) +I0616 08:15:20.985008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.71587 (* 1 = 0.71587 loss) +I0616 08:15:20.985011 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140232 (* 1 = 0.140232 loss) +I0616 08:15:20.985016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0729089 (* 1 = 0.0729089 loss) +I0616 08:15:20.985019 9857 solver.cpp:571] Iteration 35320, lr = 0.001 +I0616 08:15:32.283895 9857 solver.cpp:242] Iteration 35340, loss = 0.63461 +I0616 08:15:32.283937 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196939 (* 1 = 0.196939 loss) +I0616 08:15:32.283943 9857 solver.cpp:258] Train net output #1: loss_cls = 0.447316 (* 1 = 0.447316 loss) +I0616 08:15:32.283947 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0891687 (* 1 = 0.0891687 loss) +I0616 08:15:32.283951 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0386164 (* 1 = 0.0386164 loss) +I0616 08:15:32.283954 9857 solver.cpp:571] Iteration 35340, lr = 0.001 +I0616 08:15:43.976989 9857 solver.cpp:242] Iteration 35360, loss = 1.08718 +I0616 08:15:43.977031 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.330042 (* 1 = 0.330042 loss) +I0616 08:15:43.977036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.653897 (* 1 = 0.653897 loss) +I0616 08:15:43.977041 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106763 (* 1 = 0.106763 loss) +I0616 08:15:43.977044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292926 (* 1 = 0.0292926 loss) +I0616 08:15:43.977048 9857 solver.cpp:571] Iteration 35360, lr = 0.001 +I0616 08:15:55.662037 9857 solver.cpp:242] Iteration 35380, loss = 0.510791 +I0616 08:15:55.662077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10065 (* 1 = 0.10065 loss) +I0616 08:15:55.662083 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181604 (* 1 = 0.181604 loss) +I0616 08:15:55.662087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00904064 (* 1 = 0.00904064 loss) +I0616 08:15:55.662091 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00179485 (* 1 = 0.00179485 loss) +I0616 08:15:55.662096 9857 solver.cpp:571] Iteration 35380, lr = 0.001 +speed: 0.627s / iter +I0616 08:16:07.215876 9857 solver.cpp:242] Iteration 35400, loss = 0.620751 +I0616 08:16:07.215919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392717 (* 1 = 0.392717 loss) +I0616 08:16:07.215924 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372608 (* 1 = 0.372608 loss) +I0616 08:16:07.215929 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172188 (* 1 = 0.172188 loss) +I0616 08:16:07.215931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0366311 (* 1 = 0.0366311 loss) +I0616 08:16:07.215935 9857 solver.cpp:571] Iteration 35400, lr = 0.001 +I0616 08:16:18.841605 9857 solver.cpp:242] Iteration 35420, loss = 0.520892 +I0616 08:16:18.841647 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0897413 (* 1 = 0.0897413 loss) +I0616 08:16:18.841653 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157944 (* 1 = 0.157944 loss) +I0616 08:16:18.841657 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125343 (* 1 = 0.125343 loss) +I0616 08:16:18.841661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00354934 (* 1 = 0.00354934 loss) +I0616 08:16:18.841665 9857 solver.cpp:571] Iteration 35420, lr = 0.001 +I0616 08:16:30.202790 9857 solver.cpp:242] Iteration 35440, loss = 1.05291 +I0616 08:16:30.202832 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0496355 (* 1 = 0.0496355 loss) +I0616 08:16:30.202854 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134679 (* 1 = 0.134679 loss) +I0616 08:16:30.202858 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0413721 (* 1 = 0.0413721 loss) +I0616 08:16:30.202877 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276529 (* 1 = 0.0276529 loss) +I0616 08:16:30.202880 9857 solver.cpp:571] Iteration 35440, lr = 0.001 +I0616 08:16:41.368837 9857 solver.cpp:242] Iteration 35460, loss = 0.590302 +I0616 08:16:41.368876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260581 (* 1 = 0.260581 loss) +I0616 08:16:41.368881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263322 (* 1 = 0.263322 loss) +I0616 08:16:41.368886 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0278452 (* 1 = 0.0278452 loss) +I0616 08:16:41.368890 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136876 (* 1 = 0.0136876 loss) +I0616 08:16:41.368893 9857 solver.cpp:571] Iteration 35460, lr = 0.001 +I0616 08:16:52.686210 9857 solver.cpp:242] Iteration 35480, loss = 0.459452 +I0616 08:16:52.686252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134194 (* 1 = 0.134194 loss) +I0616 08:16:52.686257 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266844 (* 1 = 0.266844 loss) +I0616 08:16:52.686261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197847 (* 1 = 0.197847 loss) +I0616 08:16:52.686265 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00741635 (* 1 = 0.00741635 loss) +I0616 08:16:52.686269 9857 solver.cpp:571] Iteration 35480, lr = 0.001 +I0616 08:17:04.277400 9857 solver.cpp:242] Iteration 35500, loss = 0.7717 +I0616 08:17:04.277441 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276579 (* 1 = 0.276579 loss) +I0616 08:17:04.277446 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315144 (* 1 = 0.315144 loss) +I0616 08:17:04.277451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134377 (* 1 = 0.134377 loss) +I0616 08:17:04.277456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0355321 (* 1 = 0.0355321 loss) +I0616 08:17:04.277462 9857 solver.cpp:571] Iteration 35500, lr = 0.001 +I0616 08:17:15.777503 9857 solver.cpp:242] Iteration 35520, loss = 0.478063 +I0616 08:17:15.777545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186107 (* 1 = 0.186107 loss) +I0616 08:17:15.777550 9857 solver.cpp:258] Train net output #1: loss_cls = 0.336303 (* 1 = 0.336303 loss) +I0616 08:17:15.777555 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.090296 (* 1 = 0.090296 loss) +I0616 08:17:15.777559 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.036248 (* 1 = 0.036248 loss) +I0616 08:17:15.777562 9857 solver.cpp:571] Iteration 35520, lr = 0.001 +I0616 08:17:27.180795 9857 solver.cpp:242] Iteration 35540, loss = 0.443635 +I0616 08:17:27.180836 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186066 (* 1 = 0.186066 loss) +I0616 08:17:27.180841 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288987 (* 1 = 0.288987 loss) +I0616 08:17:27.180846 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0431607 (* 1 = 0.0431607 loss) +I0616 08:17:27.180850 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0262507 (* 1 = 0.0262507 loss) +I0616 08:17:27.180853 9857 solver.cpp:571] Iteration 35540, lr = 0.001 +I0616 08:17:38.837621 9857 solver.cpp:242] Iteration 35560, loss = 1.02095 +I0616 08:17:38.837664 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299988 (* 1 = 0.299988 loss) +I0616 08:17:38.837671 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313481 (* 1 = 0.313481 loss) +I0616 08:17:38.837674 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130937 (* 1 = 0.130937 loss) +I0616 08:17:38.837677 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.179316 (* 1 = 0.179316 loss) +I0616 08:17:38.837682 9857 solver.cpp:571] Iteration 35560, lr = 0.001 +I0616 08:17:50.366180 9857 solver.cpp:242] Iteration 35580, loss = 1.22769 +I0616 08:17:50.366222 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112408 (* 1 = 0.112408 loss) +I0616 08:17:50.366228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130833 (* 1 = 0.130833 loss) +I0616 08:17:50.366232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0158628 (* 1 = 0.0158628 loss) +I0616 08:17:50.366235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00296259 (* 1 = 0.00296259 loss) +I0616 08:17:50.366240 9857 solver.cpp:571] Iteration 35580, lr = 0.001 +speed: 0.627s / iter +I0616 08:18:01.939728 9857 solver.cpp:242] Iteration 35600, loss = 0.738361 +I0616 08:18:01.939769 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32909 (* 1 = 0.32909 loss) +I0616 08:18:01.939775 9857 solver.cpp:258] Train net output #1: loss_cls = 0.347997 (* 1 = 0.347997 loss) +I0616 08:18:01.939779 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19871 (* 1 = 0.19871 loss) +I0616 08:18:01.939784 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126538 (* 1 = 0.126538 loss) +I0616 08:18:01.939787 9857 solver.cpp:571] Iteration 35600, lr = 0.001 +I0616 08:18:13.476131 9857 solver.cpp:242] Iteration 35620, loss = 1.25456 +I0616 08:18:13.476173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24622 (* 1 = 0.24622 loss) +I0616 08:18:13.476178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223399 (* 1 = 0.223399 loss) +I0616 08:18:13.476183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0435788 (* 1 = 0.0435788 loss) +I0616 08:18:13.476186 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0497677 (* 1 = 0.0497677 loss) +I0616 08:18:13.476191 9857 solver.cpp:571] Iteration 35620, lr = 0.001 +I0616 08:18:25.264442 9857 solver.cpp:242] Iteration 35640, loss = 0.699474 +I0616 08:18:25.264482 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250873 (* 1 = 0.250873 loss) +I0616 08:18:25.264488 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166609 (* 1 = 0.166609 loss) +I0616 08:18:25.264492 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0810048 (* 1 = 0.0810048 loss) +I0616 08:18:25.264497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120295 (* 1 = 0.0120295 loss) +I0616 08:18:25.264500 9857 solver.cpp:571] Iteration 35640, lr = 0.001 +I0616 08:18:36.930029 9857 solver.cpp:242] Iteration 35660, loss = 0.813784 +I0616 08:18:36.930071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115526 (* 1 = 0.115526 loss) +I0616 08:18:36.930078 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296649 (* 1 = 0.296649 loss) +I0616 08:18:36.930081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0659949 (* 1 = 0.0659949 loss) +I0616 08:18:36.930085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0452916 (* 1 = 0.0452916 loss) +I0616 08:18:36.930089 9857 solver.cpp:571] Iteration 35660, lr = 0.001 +I0616 08:18:48.516434 9857 solver.cpp:242] Iteration 35680, loss = 0.370412 +I0616 08:18:48.516477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0729537 (* 1 = 0.0729537 loss) +I0616 08:18:48.516484 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11099 (* 1 = 0.11099 loss) +I0616 08:18:48.516487 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.026503 (* 1 = 0.026503 loss) +I0616 08:18:48.516491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00848921 (* 1 = 0.00848921 loss) +I0616 08:18:48.516494 9857 solver.cpp:571] Iteration 35680, lr = 0.001 +I0616 08:19:00.037408 9857 solver.cpp:242] Iteration 35700, loss = 0.443988 +I0616 08:19:00.037451 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217931 (* 1 = 0.217931 loss) +I0616 08:19:00.037456 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196216 (* 1 = 0.196216 loss) +I0616 08:19:00.037461 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0523346 (* 1 = 0.0523346 loss) +I0616 08:19:00.037464 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00608955 (* 1 = 0.00608955 loss) +I0616 08:19:00.037468 9857 solver.cpp:571] Iteration 35700, lr = 0.001 +I0616 08:19:11.769235 9857 solver.cpp:242] Iteration 35720, loss = 0.737013 +I0616 08:19:11.769278 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122503 (* 1 = 0.122503 loss) +I0616 08:19:11.769284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23888 (* 1 = 0.23888 loss) +I0616 08:19:11.769287 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0361827 (* 1 = 0.0361827 loss) +I0616 08:19:11.769291 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00996743 (* 1 = 0.00996743 loss) +I0616 08:19:11.769295 9857 solver.cpp:571] Iteration 35720, lr = 0.001 +I0616 08:19:23.326536 9857 solver.cpp:242] Iteration 35740, loss = 1.21434 +I0616 08:19:23.326578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166175 (* 1 = 0.166175 loss) +I0616 08:19:23.326583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.325454 (* 1 = 0.325454 loss) +I0616 08:19:23.326588 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0564274 (* 1 = 0.0564274 loss) +I0616 08:19:23.326591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0284556 (* 1 = 0.0284556 loss) +I0616 08:19:23.326596 9857 solver.cpp:571] Iteration 35740, lr = 0.001 +I0616 08:19:34.953228 9857 solver.cpp:242] Iteration 35760, loss = 0.864313 +I0616 08:19:34.953270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159627 (* 1 = 0.159627 loss) +I0616 08:19:34.953276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354561 (* 1 = 0.354561 loss) +I0616 08:19:34.953280 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0745703 (* 1 = 0.0745703 loss) +I0616 08:19:34.953284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00429338 (* 1 = 0.00429338 loss) +I0616 08:19:34.953289 9857 solver.cpp:571] Iteration 35760, lr = 0.001 +I0616 08:19:46.524190 9857 solver.cpp:242] Iteration 35780, loss = 0.868505 +I0616 08:19:46.524232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436556 (* 1 = 0.436556 loss) +I0616 08:19:46.524237 9857 solver.cpp:258] Train net output #1: loss_cls = 0.841584 (* 1 = 0.841584 loss) +I0616 08:19:46.524241 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0326458 (* 1 = 0.0326458 loss) +I0616 08:19:46.524245 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223125 (* 1 = 0.0223125 loss) +I0616 08:19:46.524250 9857 solver.cpp:571] Iteration 35780, lr = 0.001 +speed: 0.627s / iter +I0616 08:19:57.883745 9857 solver.cpp:242] Iteration 35800, loss = 0.91079 +I0616 08:19:57.883788 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.475485 (* 1 = 0.475485 loss) +I0616 08:19:57.883795 9857 solver.cpp:258] Train net output #1: loss_cls = 0.510561 (* 1 = 0.510561 loss) +I0616 08:19:57.883798 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0484254 (* 1 = 0.0484254 loss) +I0616 08:19:57.883802 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0302425 (* 1 = 0.0302425 loss) +I0616 08:19:57.883806 9857 solver.cpp:571] Iteration 35800, lr = 0.001 +I0616 08:20:09.634804 9857 solver.cpp:242] Iteration 35820, loss = 1.51358 +I0616 08:20:09.634846 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38168 (* 1 = 0.38168 loss) +I0616 08:20:09.634851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45726 (* 1 = 0.45726 loss) +I0616 08:20:09.634856 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0803808 (* 1 = 0.0803808 loss) +I0616 08:20:09.634860 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0573577 (* 1 = 0.0573577 loss) +I0616 08:20:09.634865 9857 solver.cpp:571] Iteration 35820, lr = 0.001 +I0616 08:20:21.240244 9857 solver.cpp:242] Iteration 35840, loss = 0.93168 +I0616 08:20:21.240288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411662 (* 1 = 0.411662 loss) +I0616 08:20:21.240293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.477563 (* 1 = 0.477563 loss) +I0616 08:20:21.240298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125557 (* 1 = 0.125557 loss) +I0616 08:20:21.240301 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0990798 (* 1 = 0.0990798 loss) +I0616 08:20:21.240305 9857 solver.cpp:571] Iteration 35840, lr = 0.001 +I0616 08:20:32.742369 9857 solver.cpp:242] Iteration 35860, loss = 0.73074 +I0616 08:20:32.742411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0697003 (* 1 = 0.0697003 loss) +I0616 08:20:32.742418 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262223 (* 1 = 0.262223 loss) +I0616 08:20:32.742421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0434262 (* 1 = 0.0434262 loss) +I0616 08:20:32.742425 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00688963 (* 1 = 0.00688963 loss) +I0616 08:20:32.742429 9857 solver.cpp:571] Iteration 35860, lr = 0.001 +I0616 08:20:44.039119 9857 solver.cpp:242] Iteration 35880, loss = 0.777569 +I0616 08:20:44.039162 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172314 (* 1 = 0.172314 loss) +I0616 08:20:44.039168 9857 solver.cpp:258] Train net output #1: loss_cls = 0.762211 (* 1 = 0.762211 loss) +I0616 08:20:44.039172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0521175 (* 1 = 0.0521175 loss) +I0616 08:20:44.039176 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00813637 (* 1 = 0.00813637 loss) +I0616 08:20:44.039180 9857 solver.cpp:571] Iteration 35880, lr = 0.001 +I0616 08:20:55.775154 9857 solver.cpp:242] Iteration 35900, loss = 0.838674 +I0616 08:20:55.775197 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0445036 (* 1 = 0.0445036 loss) +I0616 08:20:55.775202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372745 (* 1 = 0.372745 loss) +I0616 08:20:55.775207 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0270219 (* 1 = 0.0270219 loss) +I0616 08:20:55.775210 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126054 (* 1 = 0.0126054 loss) +I0616 08:20:55.775214 9857 solver.cpp:571] Iteration 35900, lr = 0.001 +I0616 08:21:07.361348 9857 solver.cpp:242] Iteration 35920, loss = 0.50313 +I0616 08:21:07.361389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0862393 (* 1 = 0.0862393 loss) +I0616 08:21:07.361394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137159 (* 1 = 0.137159 loss) +I0616 08:21:07.361399 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0316159 (* 1 = 0.0316159 loss) +I0616 08:21:07.361402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127447 (* 1 = 0.0127447 loss) +I0616 08:21:07.361407 9857 solver.cpp:571] Iteration 35920, lr = 0.001 +I0616 08:21:19.002506 9857 solver.cpp:242] Iteration 35940, loss = 0.663334 +I0616 08:21:19.002547 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10532 (* 1 = 0.10532 loss) +I0616 08:21:19.002552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140327 (* 1 = 0.140327 loss) +I0616 08:21:19.002557 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0740915 (* 1 = 0.0740915 loss) +I0616 08:21:19.002560 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254024 (* 1 = 0.0254024 loss) +I0616 08:21:19.002564 9857 solver.cpp:571] Iteration 35940, lr = 0.001 +I0616 08:21:30.417744 9857 solver.cpp:242] Iteration 35960, loss = 1.98962 +I0616 08:21:30.417786 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.574018 (* 1 = 0.574018 loss) +I0616 08:21:30.417793 9857 solver.cpp:258] Train net output #1: loss_cls = 0.848328 (* 1 = 0.848328 loss) +I0616 08:21:30.417796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.31963 (* 1 = 0.31963 loss) +I0616 08:21:30.417800 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.317086 (* 1 = 0.317086 loss) +I0616 08:21:30.417804 9857 solver.cpp:571] Iteration 35960, lr = 0.001 +I0616 08:21:41.971609 9857 solver.cpp:242] Iteration 35980, loss = 0.578989 +I0616 08:21:41.971652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305245 (* 1 = 0.305245 loss) +I0616 08:21:41.971657 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298241 (* 1 = 0.298241 loss) +I0616 08:21:41.971660 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144382 (* 1 = 0.144382 loss) +I0616 08:21:41.971664 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0868021 (* 1 = 0.0868021 loss) +I0616 08:21:41.971668 9857 solver.cpp:571] Iteration 35980, lr = 0.001 +speed: 0.627s / iter +I0616 08:21:53.245203 9857 solver.cpp:242] Iteration 36000, loss = 0.958125 +I0616 08:21:53.245244 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260001 (* 1 = 0.260001 loss) +I0616 08:21:53.245250 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338181 (* 1 = 0.338181 loss) +I0616 08:21:53.245254 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.17615 (* 1 = 0.17615 loss) +I0616 08:21:53.245259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193777 (* 1 = 0.0193777 loss) +I0616 08:21:53.245261 9857 solver.cpp:571] Iteration 36000, lr = 0.001 +I0616 08:22:04.919109 9857 solver.cpp:242] Iteration 36020, loss = 1.22047 +I0616 08:22:04.919150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174448 (* 1 = 0.174448 loss) +I0616 08:22:04.919157 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335421 (* 1 = 0.335421 loss) +I0616 08:22:04.919160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11674 (* 1 = 0.11674 loss) +I0616 08:22:04.919163 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0426851 (* 1 = 0.0426851 loss) +I0616 08:22:04.919168 9857 solver.cpp:571] Iteration 36020, lr = 0.001 +I0616 08:22:16.433617 9857 solver.cpp:242] Iteration 36040, loss = 0.34914 +I0616 08:22:16.433660 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157607 (* 1 = 0.157607 loss) +I0616 08:22:16.433665 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134917 (* 1 = 0.134917 loss) +I0616 08:22:16.433670 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0412451 (* 1 = 0.0412451 loss) +I0616 08:22:16.433673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0256373 (* 1 = 0.0256373 loss) +I0616 08:22:16.433676 9857 solver.cpp:571] Iteration 36040, lr = 0.001 +I0616 08:22:27.926854 9857 solver.cpp:242] Iteration 36060, loss = 0.867113 +I0616 08:22:27.926898 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133851 (* 1 = 0.133851 loss) +I0616 08:22:27.926903 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22985 (* 1 = 0.22985 loss) +I0616 08:22:27.926908 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0472674 (* 1 = 0.0472674 loss) +I0616 08:22:27.926911 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00310671 (* 1 = 0.00310671 loss) +I0616 08:22:27.926915 9857 solver.cpp:571] Iteration 36060, lr = 0.001 +I0616 08:22:39.532075 9857 solver.cpp:242] Iteration 36080, loss = 1.14773 +I0616 08:22:39.532116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424316 (* 1 = 0.424316 loss) +I0616 08:22:39.532122 9857 solver.cpp:258] Train net output #1: loss_cls = 0.714824 (* 1 = 0.714824 loss) +I0616 08:22:39.532126 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112091 (* 1 = 0.112091 loss) +I0616 08:22:39.532130 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288294 (* 1 = 0.0288294 loss) +I0616 08:22:39.532135 9857 solver.cpp:571] Iteration 36080, lr = 0.001 +I0616 08:22:50.887194 9857 solver.cpp:242] Iteration 36100, loss = 0.926688 +I0616 08:22:50.887236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310705 (* 1 = 0.310705 loss) +I0616 08:22:50.887241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.34166 (* 1 = 0.34166 loss) +I0616 08:22:50.887245 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0869783 (* 1 = 0.0869783 loss) +I0616 08:22:50.887249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0195251 (* 1 = 0.0195251 loss) +I0616 08:22:50.887255 9857 solver.cpp:571] Iteration 36100, lr = 0.001 +I0616 08:23:02.430027 9857 solver.cpp:242] Iteration 36120, loss = 0.85244 +I0616 08:23:02.430068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196878 (* 1 = 0.196878 loss) +I0616 08:23:02.430074 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313324 (* 1 = 0.313324 loss) +I0616 08:23:02.430078 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0570551 (* 1 = 0.0570551 loss) +I0616 08:23:02.430083 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00215028 (* 1 = 0.00215028 loss) +I0616 08:23:02.430086 9857 solver.cpp:571] Iteration 36120, lr = 0.001 +I0616 08:23:14.072363 9857 solver.cpp:242] Iteration 36140, loss = 0.4132 +I0616 08:23:14.072404 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0920212 (* 1 = 0.0920212 loss) +I0616 08:23:14.072410 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0857932 (* 1 = 0.0857932 loss) +I0616 08:23:14.072415 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0245529 (* 1 = 0.0245529 loss) +I0616 08:23:14.072419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119716 (* 1 = 0.0119716 loss) +I0616 08:23:14.072425 9857 solver.cpp:571] Iteration 36140, lr = 0.001 +I0616 08:23:25.582667 9857 solver.cpp:242] Iteration 36160, loss = 1.25946 +I0616 08:23:25.582710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272973 (* 1 = 0.272973 loss) +I0616 08:23:25.582716 9857 solver.cpp:258] Train net output #1: loss_cls = 1.20438 (* 1 = 1.20438 loss) +I0616 08:23:25.582720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15553 (* 1 = 0.15553 loss) +I0616 08:23:25.582725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0418175 (* 1 = 0.0418175 loss) +I0616 08:23:25.582728 9857 solver.cpp:571] Iteration 36160, lr = 0.001 +I0616 08:23:37.159893 9857 solver.cpp:242] Iteration 36180, loss = 1.76992 +I0616 08:23:37.159935 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374218 (* 1 = 0.374218 loss) +I0616 08:23:37.159940 9857 solver.cpp:258] Train net output #1: loss_cls = 0.491201 (* 1 = 0.491201 loss) +I0616 08:23:37.159945 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0660914 (* 1 = 0.0660914 loss) +I0616 08:23:37.159948 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0322373 (* 1 = 0.0322373 loss) +I0616 08:23:37.159953 9857 solver.cpp:571] Iteration 36180, lr = 0.001 +speed: 0.626s / iter +I0616 08:23:48.702018 9857 solver.cpp:242] Iteration 36200, loss = 0.695268 +I0616 08:23:48.702059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0771379 (* 1 = 0.0771379 loss) +I0616 08:23:48.702064 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263194 (* 1 = 0.263194 loss) +I0616 08:23:48.702069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0487258 (* 1 = 0.0487258 loss) +I0616 08:23:48.702074 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239565 (* 1 = 0.0239565 loss) +I0616 08:23:48.702076 9857 solver.cpp:571] Iteration 36200, lr = 0.001 +I0616 08:24:00.490926 9857 solver.cpp:242] Iteration 36220, loss = 1.13971 +I0616 08:24:00.490967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309304 (* 1 = 0.309304 loss) +I0616 08:24:00.490973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.47251 (* 1 = 0.47251 loss) +I0616 08:24:00.490978 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0939004 (* 1 = 0.0939004 loss) +I0616 08:24:00.490981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0249762 (* 1 = 0.0249762 loss) +I0616 08:24:00.490985 9857 solver.cpp:571] Iteration 36220, lr = 0.001 +I0616 08:24:12.088831 9857 solver.cpp:242] Iteration 36240, loss = 0.716218 +I0616 08:24:12.088873 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.091017 (* 1 = 0.091017 loss) +I0616 08:24:12.088879 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151876 (* 1 = 0.151876 loss) +I0616 08:24:12.088883 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0312006 (* 1 = 0.0312006 loss) +I0616 08:24:12.088887 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00393077 (* 1 = 0.00393077 loss) +I0616 08:24:12.088891 9857 solver.cpp:571] Iteration 36240, lr = 0.001 +I0616 08:24:23.691175 9857 solver.cpp:242] Iteration 36260, loss = 0.866044 +I0616 08:24:23.691216 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.486342 (* 1 = 0.486342 loss) +I0616 08:24:23.691222 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45261 (* 1 = 0.45261 loss) +I0616 08:24:23.691226 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146257 (* 1 = 0.146257 loss) +I0616 08:24:23.691231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0546303 (* 1 = 0.0546303 loss) +I0616 08:24:23.691234 9857 solver.cpp:571] Iteration 36260, lr = 0.001 +I0616 08:24:35.324108 9857 solver.cpp:242] Iteration 36280, loss = 0.518974 +I0616 08:24:35.324151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133727 (* 1 = 0.133727 loss) +I0616 08:24:35.324156 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167523 (* 1 = 0.167523 loss) +I0616 08:24:35.324161 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123499 (* 1 = 0.123499 loss) +I0616 08:24:35.324164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102481 (* 1 = 0.0102481 loss) +I0616 08:24:35.324168 9857 solver.cpp:571] Iteration 36280, lr = 0.001 +I0616 08:24:47.062505 9857 solver.cpp:242] Iteration 36300, loss = 1.15144 +I0616 08:24:47.062542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.595166 (* 1 = 0.595166 loss) +I0616 08:24:47.062548 9857 solver.cpp:258] Train net output #1: loss_cls = 0.56035 (* 1 = 0.56035 loss) +I0616 08:24:47.062552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.177624 (* 1 = 0.177624 loss) +I0616 08:24:47.062556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0347034 (* 1 = 0.0347034 loss) +I0616 08:24:47.062559 9857 solver.cpp:571] Iteration 36300, lr = 0.001 +I0616 08:24:58.753144 9857 solver.cpp:242] Iteration 36320, loss = 0.543969 +I0616 08:24:58.753182 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.142475 (* 1 = 0.142475 loss) +I0616 08:24:58.753188 9857 solver.cpp:258] Train net output #1: loss_cls = 0.447872 (* 1 = 0.447872 loss) +I0616 08:24:58.753192 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.034348 (* 1 = 0.034348 loss) +I0616 08:24:58.753196 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00742118 (* 1 = 0.00742118 loss) +I0616 08:24:58.753199 9857 solver.cpp:571] Iteration 36320, lr = 0.001 +I0616 08:25:10.370026 9857 solver.cpp:242] Iteration 36340, loss = 1.10276 +I0616 08:25:10.370067 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367366 (* 1 = 0.367366 loss) +I0616 08:25:10.370086 9857 solver.cpp:258] Train net output #1: loss_cls = 0.801076 (* 1 = 0.801076 loss) +I0616 08:25:10.370091 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.272141 (* 1 = 0.272141 loss) +I0616 08:25:10.370095 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0815468 (* 1 = 0.0815468 loss) +I0616 08:25:10.370100 9857 solver.cpp:571] Iteration 36340, lr = 0.001 +I0616 08:25:21.799499 9857 solver.cpp:242] Iteration 36360, loss = 0.917746 +I0616 08:25:21.799540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322895 (* 1 = 0.322895 loss) +I0616 08:25:21.799546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.698258 (* 1 = 0.698258 loss) +I0616 08:25:21.799549 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0970728 (* 1 = 0.0970728 loss) +I0616 08:25:21.799553 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.048209 (* 1 = 0.048209 loss) +I0616 08:25:21.799557 9857 solver.cpp:571] Iteration 36360, lr = 0.001 +I0616 08:25:33.320269 9857 solver.cpp:242] Iteration 36380, loss = 1.10063 +I0616 08:25:33.320312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.404796 (* 1 = 0.404796 loss) +I0616 08:25:33.320319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.639125 (* 1 = 0.639125 loss) +I0616 08:25:33.320323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.329926 (* 1 = 0.329926 loss) +I0616 08:25:33.320327 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0370145 (* 1 = 0.0370145 loss) +I0616 08:25:33.320332 9857 solver.cpp:571] Iteration 36380, lr = 0.001 +speed: 0.626s / iter +I0616 08:25:44.729655 9857 solver.cpp:242] Iteration 36400, loss = 0.89009 +I0616 08:25:44.729698 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133047 (* 1 = 0.133047 loss) +I0616 08:25:44.729703 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28345 (* 1 = 0.28345 loss) +I0616 08:25:44.729707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0473183 (* 1 = 0.0473183 loss) +I0616 08:25:44.729712 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147575 (* 1 = 0.0147575 loss) +I0616 08:25:44.729715 9857 solver.cpp:571] Iteration 36400, lr = 0.001 +I0616 08:25:56.396283 9857 solver.cpp:242] Iteration 36420, loss = 0.887275 +I0616 08:25:56.396325 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341593 (* 1 = 0.341593 loss) +I0616 08:25:56.396332 9857 solver.cpp:258] Train net output #1: loss_cls = 0.799934 (* 1 = 0.799934 loss) +I0616 08:25:56.396335 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133182 (* 1 = 0.133182 loss) +I0616 08:25:56.396339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0686264 (* 1 = 0.0686264 loss) +I0616 08:25:56.396342 9857 solver.cpp:571] Iteration 36420, lr = 0.001 +I0616 08:26:07.864488 9857 solver.cpp:242] Iteration 36440, loss = 0.380309 +I0616 08:26:07.864531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164044 (* 1 = 0.164044 loss) +I0616 08:26:07.864537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134468 (* 1 = 0.134468 loss) +I0616 08:26:07.864540 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126096 (* 1 = 0.126096 loss) +I0616 08:26:07.864544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0444602 (* 1 = 0.0444602 loss) +I0616 08:26:07.864548 9857 solver.cpp:571] Iteration 36440, lr = 0.001 +I0616 08:26:19.420198 9857 solver.cpp:242] Iteration 36460, loss = 0.501426 +I0616 08:26:19.420240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0895931 (* 1 = 0.0895931 loss) +I0616 08:26:19.420246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0665857 (* 1 = 0.0665857 loss) +I0616 08:26:19.420250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0278364 (* 1 = 0.0278364 loss) +I0616 08:26:19.420253 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0243048 (* 1 = 0.0243048 loss) +I0616 08:26:19.420258 9857 solver.cpp:571] Iteration 36460, lr = 0.001 +I0616 08:26:31.121647 9857 solver.cpp:242] Iteration 36480, loss = 0.939967 +I0616 08:26:31.121690 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154764 (* 1 = 0.154764 loss) +I0616 08:26:31.121695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279144 (* 1 = 0.279144 loss) +I0616 08:26:31.121700 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.132104 (* 1 = 0.132104 loss) +I0616 08:26:31.121704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00267731 (* 1 = 0.00267731 loss) +I0616 08:26:31.121708 9857 solver.cpp:571] Iteration 36480, lr = 0.001 +I0616 08:26:42.620721 9857 solver.cpp:242] Iteration 36500, loss = 0.607865 +I0616 08:26:42.620764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184499 (* 1 = 0.184499 loss) +I0616 08:26:42.620769 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498987 (* 1 = 0.498987 loss) +I0616 08:26:42.620774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0291274 (* 1 = 0.0291274 loss) +I0616 08:26:42.620776 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0626187 (* 1 = 0.0626187 loss) +I0616 08:26:42.620780 9857 solver.cpp:571] Iteration 36500, lr = 0.001 +I0616 08:26:54.531545 9857 solver.cpp:242] Iteration 36520, loss = 0.583844 +I0616 08:26:54.531586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299801 (* 1 = 0.299801 loss) +I0616 08:26:54.531592 9857 solver.cpp:258] Train net output #1: loss_cls = 0.574064 (* 1 = 0.574064 loss) +I0616 08:26:54.531597 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0651022 (* 1 = 0.0651022 loss) +I0616 08:26:54.531601 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234185 (* 1 = 0.0234185 loss) +I0616 08:26:54.531620 9857 solver.cpp:571] Iteration 36520, lr = 0.001 +I0616 08:27:06.228149 9857 solver.cpp:242] Iteration 36540, loss = 0.944328 +I0616 08:27:06.228193 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218037 (* 1 = 0.218037 loss) +I0616 08:27:06.228199 9857 solver.cpp:258] Train net output #1: loss_cls = 0.660311 (* 1 = 0.660311 loss) +I0616 08:27:06.228202 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130691 (* 1 = 0.130691 loss) +I0616 08:27:06.228205 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0503481 (* 1 = 0.0503481 loss) +I0616 08:27:06.228210 9857 solver.cpp:571] Iteration 36540, lr = 0.001 +I0616 08:27:17.554837 9857 solver.cpp:242] Iteration 36560, loss = 1.5555 +I0616 08:27:17.554878 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267824 (* 1 = 0.267824 loss) +I0616 08:27:17.554883 9857 solver.cpp:258] Train net output #1: loss_cls = 0.854512 (* 1 = 0.854512 loss) +I0616 08:27:17.554888 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20891 (* 1 = 0.20891 loss) +I0616 08:27:17.554891 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104508 (* 1 = 0.104508 loss) +I0616 08:27:17.554895 9857 solver.cpp:571] Iteration 36560, lr = 0.001 +I0616 08:27:29.181020 9857 solver.cpp:242] Iteration 36580, loss = 0.917268 +I0616 08:27:29.181061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221014 (* 1 = 0.221014 loss) +I0616 08:27:29.181066 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375848 (* 1 = 0.375848 loss) +I0616 08:27:29.181071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0538966 (* 1 = 0.0538966 loss) +I0616 08:27:29.181074 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0177147 (* 1 = 0.0177147 loss) +I0616 08:27:29.181078 9857 solver.cpp:571] Iteration 36580, lr = 0.001 +speed: 0.626s / iter +I0616 08:27:40.923316 9857 solver.cpp:242] Iteration 36600, loss = 0.925207 +I0616 08:27:40.923357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.31738 (* 1 = 0.31738 loss) +I0616 08:27:40.923362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.422143 (* 1 = 0.422143 loss) +I0616 08:27:40.923367 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0534792 (* 1 = 0.0534792 loss) +I0616 08:27:40.923370 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0814746 (* 1 = 0.0814746 loss) +I0616 08:27:40.923374 9857 solver.cpp:571] Iteration 36600, lr = 0.001 +I0616 08:27:52.376528 9857 solver.cpp:242] Iteration 36620, loss = 1.05267 +I0616 08:27:52.376569 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360161 (* 1 = 0.360161 loss) +I0616 08:27:52.376575 9857 solver.cpp:258] Train net output #1: loss_cls = 0.568343 (* 1 = 0.568343 loss) +I0616 08:27:52.376579 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.213721 (* 1 = 0.213721 loss) +I0616 08:27:52.376583 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0478491 (* 1 = 0.0478491 loss) +I0616 08:27:52.376587 9857 solver.cpp:571] Iteration 36620, lr = 0.001 +I0616 08:28:03.901933 9857 solver.cpp:242] Iteration 36640, loss = 0.748477 +I0616 08:28:03.901974 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138837 (* 1 = 0.138837 loss) +I0616 08:28:03.901980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.299859 (* 1 = 0.299859 loss) +I0616 08:28:03.901985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220661 (* 1 = 0.220661 loss) +I0616 08:28:03.901989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00190298 (* 1 = 0.00190298 loss) +I0616 08:28:03.901993 9857 solver.cpp:571] Iteration 36640, lr = 0.001 +I0616 08:28:15.285511 9857 solver.cpp:242] Iteration 36660, loss = 0.368948 +I0616 08:28:15.285553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0626792 (* 1 = 0.0626792 loss) +I0616 08:28:15.285558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0978168 (* 1 = 0.0978168 loss) +I0616 08:28:15.285562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0524981 (* 1 = 0.0524981 loss) +I0616 08:28:15.285567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223149 (* 1 = 0.0223149 loss) +I0616 08:28:15.285570 9857 solver.cpp:571] Iteration 36660, lr = 0.001 +I0616 08:28:26.686652 9857 solver.cpp:242] Iteration 36680, loss = 0.454085 +I0616 08:28:26.686694 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0757706 (* 1 = 0.0757706 loss) +I0616 08:28:26.686700 9857 solver.cpp:258] Train net output #1: loss_cls = 0.408953 (* 1 = 0.408953 loss) +I0616 08:28:26.686704 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0695205 (* 1 = 0.0695205 loss) +I0616 08:28:26.686708 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0166994 (* 1 = 0.0166994 loss) +I0616 08:28:26.686712 9857 solver.cpp:571] Iteration 36680, lr = 0.001 +I0616 08:28:38.181802 9857 solver.cpp:242] Iteration 36700, loss = 0.967143 +I0616 08:28:38.181845 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275488 (* 1 = 0.275488 loss) +I0616 08:28:38.181851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20567 (* 1 = 0.20567 loss) +I0616 08:28:38.181855 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0572685 (* 1 = 0.0572685 loss) +I0616 08:28:38.181859 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0750364 (* 1 = 0.0750364 loss) +I0616 08:28:38.181864 9857 solver.cpp:571] Iteration 36700, lr = 0.001 +I0616 08:28:49.761051 9857 solver.cpp:242] Iteration 36720, loss = 0.573009 +I0616 08:28:49.761090 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1194 (* 1 = 0.1194 loss) +I0616 08:28:49.761096 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131915 (* 1 = 0.131915 loss) +I0616 08:28:49.761099 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104731 (* 1 = 0.0104731 loss) +I0616 08:28:49.761103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227122 (* 1 = 0.0227122 loss) +I0616 08:28:49.761107 9857 solver.cpp:571] Iteration 36720, lr = 0.001 +I0616 08:29:01.069591 9857 solver.cpp:242] Iteration 36740, loss = 0.61996 +I0616 08:29:01.069630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113364 (* 1 = 0.113364 loss) +I0616 08:29:01.069636 9857 solver.cpp:258] Train net output #1: loss_cls = 0.364654 (* 1 = 0.364654 loss) +I0616 08:29:01.069640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0369139 (* 1 = 0.0369139 loss) +I0616 08:29:01.069644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308106 (* 1 = 0.0308106 loss) +I0616 08:29:01.069648 9857 solver.cpp:571] Iteration 36740, lr = 0.001 +I0616 08:29:12.505326 9857 solver.cpp:242] Iteration 36760, loss = 0.423691 +I0616 08:29:12.505367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.078695 (* 1 = 0.078695 loss) +I0616 08:29:12.505373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181627 (* 1 = 0.181627 loss) +I0616 08:29:12.505378 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.02757 (* 1 = 0.02757 loss) +I0616 08:29:12.505383 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138027 (* 1 = 0.0138027 loss) +I0616 08:29:12.505386 9857 solver.cpp:571] Iteration 36760, lr = 0.001 +I0616 08:29:24.125644 9857 solver.cpp:242] Iteration 36780, loss = 0.431712 +I0616 08:29:24.125685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147448 (* 1 = 0.147448 loss) +I0616 08:29:24.125691 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145689 (* 1 = 0.145689 loss) +I0616 08:29:24.125695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0193987 (* 1 = 0.0193987 loss) +I0616 08:29:24.125700 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028599 (* 1 = 0.028599 loss) +I0616 08:29:24.125706 9857 solver.cpp:571] Iteration 36780, lr = 0.001 +speed: 0.625s / iter +I0616 08:29:35.521668 9857 solver.cpp:242] Iteration 36800, loss = 1.12883 +I0616 08:29:35.521710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175637 (* 1 = 0.175637 loss) +I0616 08:29:35.521716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203311 (* 1 = 0.203311 loss) +I0616 08:29:35.521721 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0936146 (* 1 = 0.0936146 loss) +I0616 08:29:35.521724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0543519 (* 1 = 0.0543519 loss) +I0616 08:29:35.521729 9857 solver.cpp:571] Iteration 36800, lr = 0.001 +I0616 08:29:47.208658 9857 solver.cpp:242] Iteration 36820, loss = 0.916187 +I0616 08:29:47.208701 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182351 (* 1 = 0.182351 loss) +I0616 08:29:47.208706 9857 solver.cpp:258] Train net output #1: loss_cls = 0.479826 (* 1 = 0.479826 loss) +I0616 08:29:47.208711 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0273951 (* 1 = 0.0273951 loss) +I0616 08:29:47.208714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0154775 (* 1 = 0.0154775 loss) +I0616 08:29:47.208719 9857 solver.cpp:571] Iteration 36820, lr = 0.001 +I0616 08:29:58.709894 9857 solver.cpp:242] Iteration 36840, loss = 0.427741 +I0616 08:29:58.709935 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101699 (* 1 = 0.101699 loss) +I0616 08:29:58.709941 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172491 (* 1 = 0.172491 loss) +I0616 08:29:58.709945 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0714807 (* 1 = 0.0714807 loss) +I0616 08:29:58.709949 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015449 (* 1 = 0.015449 loss) +I0616 08:29:58.709954 9857 solver.cpp:571] Iteration 36840, lr = 0.001 +I0616 08:30:10.155220 9857 solver.cpp:242] Iteration 36860, loss = 0.597573 +I0616 08:30:10.155262 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189176 (* 1 = 0.189176 loss) +I0616 08:30:10.155268 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203679 (* 1 = 0.203679 loss) +I0616 08:30:10.155272 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0359736 (* 1 = 0.0359736 loss) +I0616 08:30:10.155277 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00666951 (* 1 = 0.00666951 loss) +I0616 08:30:10.155279 9857 solver.cpp:571] Iteration 36860, lr = 0.001 +I0616 08:30:21.671222 9857 solver.cpp:242] Iteration 36880, loss = 0.515337 +I0616 08:30:21.671264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.142451 (* 1 = 0.142451 loss) +I0616 08:30:21.671269 9857 solver.cpp:258] Train net output #1: loss_cls = 0.452374 (* 1 = 0.452374 loss) +I0616 08:30:21.671273 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152708 (* 1 = 0.152708 loss) +I0616 08:30:21.671277 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0241809 (* 1 = 0.0241809 loss) +I0616 08:30:21.671281 9857 solver.cpp:571] Iteration 36880, lr = 0.001 +I0616 08:30:33.098949 9857 solver.cpp:242] Iteration 36900, loss = 0.910718 +I0616 08:30:33.098992 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.245545 (* 1 = 0.245545 loss) +I0616 08:30:33.098997 9857 solver.cpp:258] Train net output #1: loss_cls = 0.284871 (* 1 = 0.284871 loss) +I0616 08:30:33.099002 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0677165 (* 1 = 0.0677165 loss) +I0616 08:30:33.099005 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0510796 (* 1 = 0.0510796 loss) +I0616 08:30:33.099009 9857 solver.cpp:571] Iteration 36900, lr = 0.001 +I0616 08:30:44.523674 9857 solver.cpp:242] Iteration 36920, loss = 1.14078 +I0616 08:30:44.523715 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.500432 (* 1 = 0.500432 loss) +I0616 08:30:44.523721 9857 solver.cpp:258] Train net output #1: loss_cls = 1.336 (* 1 = 1.336 loss) +I0616 08:30:44.523725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0128832 (* 1 = 0.0128832 loss) +I0616 08:30:44.523730 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180856 (* 1 = 0.0180856 loss) +I0616 08:30:44.523733 9857 solver.cpp:571] Iteration 36920, lr = 0.001 +I0616 08:30:56.085085 9857 solver.cpp:242] Iteration 36940, loss = 1.02167 +I0616 08:30:56.085127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.450421 (* 1 = 0.450421 loss) +I0616 08:30:56.085132 9857 solver.cpp:258] Train net output #1: loss_cls = 0.645457 (* 1 = 0.645457 loss) +I0616 08:30:56.085136 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.218002 (* 1 = 0.218002 loss) +I0616 08:30:56.085140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.254751 (* 1 = 0.254751 loss) +I0616 08:30:56.085144 9857 solver.cpp:571] Iteration 36940, lr = 0.001 +I0616 08:31:07.464373 9857 solver.cpp:242] Iteration 36960, loss = 0.509853 +I0616 08:31:07.464416 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171589 (* 1 = 0.171589 loss) +I0616 08:31:07.464422 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169518 (* 1 = 0.169518 loss) +I0616 08:31:07.464426 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00937705 (* 1 = 0.00937705 loss) +I0616 08:31:07.464431 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01466 (* 1 = 0.01466 loss) +I0616 08:31:07.464434 9857 solver.cpp:571] Iteration 36960, lr = 0.001 +I0616 08:31:19.033372 9857 solver.cpp:242] Iteration 36980, loss = 0.861799 +I0616 08:31:19.033414 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331878 (* 1 = 0.331878 loss) +I0616 08:31:19.033419 9857 solver.cpp:258] Train net output #1: loss_cls = 0.50107 (* 1 = 0.50107 loss) +I0616 08:31:19.033423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.217373 (* 1 = 0.217373 loss) +I0616 08:31:19.033427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0731044 (* 1 = 0.0731044 loss) +I0616 08:31:19.033432 9857 solver.cpp:571] Iteration 36980, lr = 0.001 +speed: 0.625s / iter +I0616 08:31:30.433418 9857 solver.cpp:242] Iteration 37000, loss = 0.612412 +I0616 08:31:30.433460 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351731 (* 1 = 0.351731 loss) +I0616 08:31:30.433466 9857 solver.cpp:258] Train net output #1: loss_cls = 0.466685 (* 1 = 0.466685 loss) +I0616 08:31:30.433470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.053448 (* 1 = 0.053448 loss) +I0616 08:31:30.433475 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0536607 (* 1 = 0.0536607 loss) +I0616 08:31:30.433477 9857 solver.cpp:571] Iteration 37000, lr = 0.001 +I0616 08:31:41.742230 9857 solver.cpp:242] Iteration 37020, loss = 0.776081 +I0616 08:31:41.742272 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303977 (* 1 = 0.303977 loss) +I0616 08:31:41.742277 9857 solver.cpp:258] Train net output #1: loss_cls = 0.439542 (* 1 = 0.439542 loss) +I0616 08:31:41.742281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104391 (* 1 = 0.104391 loss) +I0616 08:31:41.742285 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397551 (* 1 = 0.0397551 loss) +I0616 08:31:41.742290 9857 solver.cpp:571] Iteration 37020, lr = 0.001 +I0616 08:31:53.319277 9857 solver.cpp:242] Iteration 37040, loss = 1.02108 +I0616 08:31:53.319319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424248 (* 1 = 0.424248 loss) +I0616 08:31:53.319324 9857 solver.cpp:258] Train net output #1: loss_cls = 0.490275 (* 1 = 0.490275 loss) +I0616 08:31:53.319329 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186904 (* 1 = 0.186904 loss) +I0616 08:31:53.319331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.223905 (* 1 = 0.223905 loss) +I0616 08:31:53.319335 9857 solver.cpp:571] Iteration 37040, lr = 0.001 +I0616 08:32:04.930821 9857 solver.cpp:242] Iteration 37060, loss = 0.626037 +I0616 08:32:04.930862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237374 (* 1 = 0.237374 loss) +I0616 08:32:04.930867 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294436 (* 1 = 0.294436 loss) +I0616 08:32:04.930872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0562052 (* 1 = 0.0562052 loss) +I0616 08:32:04.930876 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129041 (* 1 = 0.0129041 loss) +I0616 08:32:04.930879 9857 solver.cpp:571] Iteration 37060, lr = 0.001 +I0616 08:32:16.577488 9857 solver.cpp:242] Iteration 37080, loss = 0.742958 +I0616 08:32:16.577529 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3305 (* 1 = 0.3305 loss) +I0616 08:32:16.577535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281454 (* 1 = 0.281454 loss) +I0616 08:32:16.577539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0687311 (* 1 = 0.0687311 loss) +I0616 08:32:16.577543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182822 (* 1 = 0.0182822 loss) +I0616 08:32:16.577548 9857 solver.cpp:571] Iteration 37080, lr = 0.001 +I0616 08:32:28.016892 9857 solver.cpp:242] Iteration 37100, loss = 0.526209 +I0616 08:32:28.016933 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229157 (* 1 = 0.229157 loss) +I0616 08:32:28.016939 9857 solver.cpp:258] Train net output #1: loss_cls = 0.373296 (* 1 = 0.373296 loss) +I0616 08:32:28.016943 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0604469 (* 1 = 0.0604469 loss) +I0616 08:32:28.016947 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0502093 (* 1 = 0.0502093 loss) +I0616 08:32:28.016950 9857 solver.cpp:571] Iteration 37100, lr = 0.001 +I0616 08:32:39.892499 9857 solver.cpp:242] Iteration 37120, loss = 0.724677 +I0616 08:32:39.892540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160556 (* 1 = 0.160556 loss) +I0616 08:32:39.892545 9857 solver.cpp:258] Train net output #1: loss_cls = 0.373325 (* 1 = 0.373325 loss) +I0616 08:32:39.892549 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10972 (* 1 = 0.10972 loss) +I0616 08:32:39.892554 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10849 (* 1 = 0.10849 loss) +I0616 08:32:39.892559 9857 solver.cpp:571] Iteration 37120, lr = 0.001 +I0616 08:32:51.549793 9857 solver.cpp:242] Iteration 37140, loss = 0.849175 +I0616 08:32:51.549834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.42673 (* 1 = 0.42673 loss) +I0616 08:32:51.549840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.463302 (* 1 = 0.463302 loss) +I0616 08:32:51.549844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106895 (* 1 = 0.106895 loss) +I0616 08:32:51.549849 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198456 (* 1 = 0.0198456 loss) +I0616 08:32:51.549852 9857 solver.cpp:571] Iteration 37140, lr = 0.001 +I0616 08:33:03.129838 9857 solver.cpp:242] Iteration 37160, loss = 1.23553 +I0616 08:33:03.129880 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45046 (* 1 = 0.45046 loss) +I0616 08:33:03.129885 9857 solver.cpp:258] Train net output #1: loss_cls = 0.533334 (* 1 = 0.533334 loss) +I0616 08:33:03.129890 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149639 (* 1 = 0.149639 loss) +I0616 08:33:03.129894 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122999 (* 1 = 0.0122999 loss) +I0616 08:33:03.129897 9857 solver.cpp:571] Iteration 37160, lr = 0.001 +I0616 08:33:14.645119 9857 solver.cpp:242] Iteration 37180, loss = 0.213486 +I0616 08:33:14.645161 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101712 (* 1 = 0.101712 loss) +I0616 08:33:14.645167 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0880287 (* 1 = 0.0880287 loss) +I0616 08:33:14.645171 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0241743 (* 1 = 0.0241743 loss) +I0616 08:33:14.645175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00613025 (* 1 = 0.00613025 loss) +I0616 08:33:14.645179 9857 solver.cpp:571] Iteration 37180, lr = 0.001 +speed: 0.625s / iter +I0616 08:33:26.195343 9857 solver.cpp:242] Iteration 37200, loss = 0.782088 +I0616 08:33:26.195385 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358633 (* 1 = 0.358633 loss) +I0616 08:33:26.195390 9857 solver.cpp:258] Train net output #1: loss_cls = 0.656481 (* 1 = 0.656481 loss) +I0616 08:33:26.195394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157119 (* 1 = 0.157119 loss) +I0616 08:33:26.195399 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0521071 (* 1 = 0.0521071 loss) +I0616 08:33:26.195401 9857 solver.cpp:571] Iteration 37200, lr = 0.001 +I0616 08:33:37.604400 9857 solver.cpp:242] Iteration 37220, loss = 0.916604 +I0616 08:33:37.604442 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275846 (* 1 = 0.275846 loss) +I0616 08:33:37.604447 9857 solver.cpp:258] Train net output #1: loss_cls = 0.746103 (* 1 = 0.746103 loss) +I0616 08:33:37.604451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.170471 (* 1 = 0.170471 loss) +I0616 08:33:37.604455 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0501527 (* 1 = 0.0501527 loss) +I0616 08:33:37.604460 9857 solver.cpp:571] Iteration 37220, lr = 0.001 +I0616 08:33:49.104420 9857 solver.cpp:242] Iteration 37240, loss = 0.385891 +I0616 08:33:49.104462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265312 (* 1 = 0.265312 loss) +I0616 08:33:49.104467 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145195 (* 1 = 0.145195 loss) +I0616 08:33:49.104472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.069892 (* 1 = 0.069892 loss) +I0616 08:33:49.104476 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00886504 (* 1 = 0.00886504 loss) +I0616 08:33:49.104480 9857 solver.cpp:571] Iteration 37240, lr = 0.001 +I0616 08:34:00.811523 9857 solver.cpp:242] Iteration 37260, loss = 0.331708 +I0616 08:34:00.811566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0720995 (* 1 = 0.0720995 loss) +I0616 08:34:00.811573 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262578 (* 1 = 0.262578 loss) +I0616 08:34:00.811576 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0181725 (* 1 = 0.0181725 loss) +I0616 08:34:00.811579 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0587823 (* 1 = 0.0587823 loss) +I0616 08:34:00.811583 9857 solver.cpp:571] Iteration 37260, lr = 0.001 +I0616 08:34:12.480080 9857 solver.cpp:242] Iteration 37280, loss = 0.382419 +I0616 08:34:12.480121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101132 (* 1 = 0.101132 loss) +I0616 08:34:12.480128 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124601 (* 1 = 0.124601 loss) +I0616 08:34:12.480131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160705 (* 1 = 0.160705 loss) +I0616 08:34:12.480135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00482775 (* 1 = 0.00482775 loss) +I0616 08:34:12.480139 9857 solver.cpp:571] Iteration 37280, lr = 0.001 +I0616 08:34:23.795747 9857 solver.cpp:242] Iteration 37300, loss = 0.919849 +I0616 08:34:23.795788 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108495 (* 1 = 0.108495 loss) +I0616 08:34:23.795794 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135441 (* 1 = 0.135441 loss) +I0616 08:34:23.795797 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0712351 (* 1 = 0.0712351 loss) +I0616 08:34:23.795800 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127366 (* 1 = 0.0127366 loss) +I0616 08:34:23.795804 9857 solver.cpp:571] Iteration 37300, lr = 0.001 +I0616 08:34:35.458531 9857 solver.cpp:242] Iteration 37320, loss = 0.595961 +I0616 08:34:35.458575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0895749 (* 1 = 0.0895749 loss) +I0616 08:34:35.458580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0653039 (* 1 = 0.0653039 loss) +I0616 08:34:35.458583 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0496993 (* 1 = 0.0496993 loss) +I0616 08:34:35.458587 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00189007 (* 1 = 0.00189007 loss) +I0616 08:34:35.458591 9857 solver.cpp:571] Iteration 37320, lr = 0.001 +I0616 08:34:46.864897 9857 solver.cpp:242] Iteration 37340, loss = 1.12649 +I0616 08:34:46.864940 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338227 (* 1 = 0.338227 loss) +I0616 08:34:46.864946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266336 (* 1 = 0.266336 loss) +I0616 08:34:46.864950 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.051211 (* 1 = 0.051211 loss) +I0616 08:34:46.864954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220229 (* 1 = 0.0220229 loss) +I0616 08:34:46.864959 9857 solver.cpp:571] Iteration 37340, lr = 0.001 +I0616 08:34:58.442394 9857 solver.cpp:242] Iteration 37360, loss = 0.540507 +I0616 08:34:58.442436 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134972 (* 1 = 0.134972 loss) +I0616 08:34:58.442441 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140759 (* 1 = 0.140759 loss) +I0616 08:34:58.442446 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0495974 (* 1 = 0.0495974 loss) +I0616 08:34:58.442451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00728036 (* 1 = 0.00728036 loss) +I0616 08:34:58.442453 9857 solver.cpp:571] Iteration 37360, lr = 0.001 +I0616 08:35:10.414196 9857 solver.cpp:242] Iteration 37380, loss = 1.18788 +I0616 08:35:10.414239 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0818015 (* 1 = 0.0818015 loss) +I0616 08:35:10.414245 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0920063 (* 1 = 0.0920063 loss) +I0616 08:35:10.414249 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0252994 (* 1 = 0.0252994 loss) +I0616 08:35:10.414253 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00462063 (* 1 = 0.00462063 loss) +I0616 08:35:10.414258 9857 solver.cpp:571] Iteration 37380, lr = 0.001 +speed: 0.625s / iter +I0616 08:35:22.183637 9857 solver.cpp:242] Iteration 37400, loss = 0.651078 +I0616 08:35:22.183678 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272479 (* 1 = 0.272479 loss) +I0616 08:35:22.183683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342255 (* 1 = 0.342255 loss) +I0616 08:35:22.183688 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230382 (* 1 = 0.0230382 loss) +I0616 08:35:22.183691 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325759 (* 1 = 0.0325759 loss) +I0616 08:35:22.183696 9857 solver.cpp:571] Iteration 37400, lr = 0.001 +I0616 08:35:34.079391 9857 solver.cpp:242] Iteration 37420, loss = 0.513707 +I0616 08:35:34.079432 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167849 (* 1 = 0.167849 loss) +I0616 08:35:34.079437 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204548 (* 1 = 0.204548 loss) +I0616 08:35:34.079442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0910664 (* 1 = 0.0910664 loss) +I0616 08:35:34.079447 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0595887 (* 1 = 0.0595887 loss) +I0616 08:35:34.079452 9857 solver.cpp:571] Iteration 37420, lr = 0.001 +I0616 08:35:45.657582 9857 solver.cpp:242] Iteration 37440, loss = 0.511373 +I0616 08:35:45.657625 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212223 (* 1 = 0.212223 loss) +I0616 08:35:45.657631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231313 (* 1 = 0.231313 loss) +I0616 08:35:45.657636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0368655 (* 1 = 0.0368655 loss) +I0616 08:35:45.657639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209616 (* 1 = 0.0209616 loss) +I0616 08:35:45.657644 9857 solver.cpp:571] Iteration 37440, lr = 0.001 +I0616 08:35:57.284428 9857 solver.cpp:242] Iteration 37460, loss = 0.560026 +I0616 08:35:57.284472 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0562816 (* 1 = 0.0562816 loss) +I0616 08:35:57.284478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186411 (* 1 = 0.186411 loss) +I0616 08:35:57.284483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0178908 (* 1 = 0.0178908 loss) +I0616 08:35:57.284487 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143024 (* 1 = 0.0143024 loss) +I0616 08:35:57.284490 9857 solver.cpp:571] Iteration 37460, lr = 0.001 +I0616 08:36:08.943933 9857 solver.cpp:242] Iteration 37480, loss = 0.966354 +I0616 08:36:08.943975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272452 (* 1 = 0.272452 loss) +I0616 08:36:08.943981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.58109 (* 1 = 0.58109 loss) +I0616 08:36:08.943985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0632283 (* 1 = 0.0632283 loss) +I0616 08:36:08.943989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0406995 (* 1 = 0.0406995 loss) +I0616 08:36:08.943994 9857 solver.cpp:571] Iteration 37480, lr = 0.001 +I0616 08:36:20.690029 9857 solver.cpp:242] Iteration 37500, loss = 0.738047 +I0616 08:36:20.690071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368842 (* 1 = 0.368842 loss) +I0616 08:36:20.690078 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363385 (* 1 = 0.363385 loss) +I0616 08:36:20.690081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0902647 (* 1 = 0.0902647 loss) +I0616 08:36:20.690085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0387823 (* 1 = 0.0387823 loss) +I0616 08:36:20.690088 9857 solver.cpp:571] Iteration 37500, lr = 0.001 +I0616 08:36:32.426833 9857 solver.cpp:242] Iteration 37520, loss = 0.283263 +I0616 08:36:32.426875 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0924323 (* 1 = 0.0924323 loss) +I0616 08:36:32.426882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110715 (* 1 = 0.110715 loss) +I0616 08:36:32.426887 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0313873 (* 1 = 0.0313873 loss) +I0616 08:36:32.426890 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00433016 (* 1 = 0.00433016 loss) +I0616 08:36:32.426894 9857 solver.cpp:571] Iteration 37520, lr = 0.001 +I0616 08:36:44.013918 9857 solver.cpp:242] Iteration 37540, loss = 0.455891 +I0616 08:36:44.013959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110745 (* 1 = 0.110745 loss) +I0616 08:36:44.013965 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340792 (* 1 = 0.340792 loss) +I0616 08:36:44.013969 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0678637 (* 1 = 0.0678637 loss) +I0616 08:36:44.013973 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0693825 (* 1 = 0.0693825 loss) +I0616 08:36:44.013978 9857 solver.cpp:571] Iteration 37540, lr = 0.001 +I0616 08:36:55.395542 9857 solver.cpp:242] Iteration 37560, loss = 0.900165 +I0616 08:36:55.395586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320343 (* 1 = 0.320343 loss) +I0616 08:36:55.395591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.656284 (* 1 = 0.656284 loss) +I0616 08:36:55.395594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186492 (* 1 = 0.186492 loss) +I0616 08:36:55.395598 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.416309 (* 1 = 0.416309 loss) +I0616 08:36:55.395602 9857 solver.cpp:571] Iteration 37560, lr = 0.001 +I0616 08:37:07.106955 9857 solver.cpp:242] Iteration 37580, loss = 1.3533 +I0616 08:37:07.106997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264041 (* 1 = 0.264041 loss) +I0616 08:37:07.107002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.44945 (* 1 = 0.44945 loss) +I0616 08:37:07.107007 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120836 (* 1 = 0.120836 loss) +I0616 08:37:07.107009 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.28065 (* 1 = 1.28065 loss) +I0616 08:37:07.107013 9857 solver.cpp:571] Iteration 37580, lr = 0.001 +speed: 0.625s / iter +I0616 08:37:18.834803 9857 solver.cpp:242] Iteration 37600, loss = 0.786248 +I0616 08:37:18.834843 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343798 (* 1 = 0.343798 loss) +I0616 08:37:18.834849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.547967 (* 1 = 0.547967 loss) +I0616 08:37:18.834853 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.243375 (* 1 = 0.243375 loss) +I0616 08:37:18.834857 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0977732 (* 1 = 0.0977732 loss) +I0616 08:37:18.834861 9857 solver.cpp:571] Iteration 37600, lr = 0.001 +I0616 08:37:30.310938 9857 solver.cpp:242] Iteration 37620, loss = 0.472757 +I0616 08:37:30.310981 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172038 (* 1 = 0.172038 loss) +I0616 08:37:30.310986 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250067 (* 1 = 0.250067 loss) +I0616 08:37:30.310989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.03557 (* 1 = 0.03557 loss) +I0616 08:37:30.310994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000841373 (* 1 = 0.000841373 loss) +I0616 08:37:30.311000 9857 solver.cpp:571] Iteration 37620, lr = 0.001 +I0616 08:37:41.904398 9857 solver.cpp:242] Iteration 37640, loss = 0.656193 +I0616 08:37:41.904438 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406025 (* 1 = 0.406025 loss) +I0616 08:37:41.904444 9857 solver.cpp:258] Train net output #1: loss_cls = 0.361581 (* 1 = 0.361581 loss) +I0616 08:37:41.904448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193482 (* 1 = 0.193482 loss) +I0616 08:37:41.904453 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0250848 (* 1 = 0.0250848 loss) +I0616 08:37:41.904456 9857 solver.cpp:571] Iteration 37640, lr = 0.001 +I0616 08:37:53.700536 9857 solver.cpp:242] Iteration 37660, loss = 1.19709 +I0616 08:37:53.700578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350541 (* 1 = 0.350541 loss) +I0616 08:37:53.700583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.462702 (* 1 = 0.462702 loss) +I0616 08:37:53.700588 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.2784 (* 1 = 0.2784 loss) +I0616 08:37:53.700592 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136934 (* 1 = 0.136934 loss) +I0616 08:37:53.700595 9857 solver.cpp:571] Iteration 37660, lr = 0.001 +I0616 08:38:05.372679 9857 solver.cpp:242] Iteration 37680, loss = 0.379044 +I0616 08:38:05.372721 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.091965 (* 1 = 0.091965 loss) +I0616 08:38:05.372726 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230821 (* 1 = 0.230821 loss) +I0616 08:38:05.372730 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0734459 (* 1 = 0.0734459 loss) +I0616 08:38:05.372735 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0460745 (* 1 = 0.0460745 loss) +I0616 08:38:05.372738 9857 solver.cpp:571] Iteration 37680, lr = 0.001 +I0616 08:38:16.612344 9857 solver.cpp:242] Iteration 37700, loss = 0.656983 +I0616 08:38:16.612386 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384379 (* 1 = 0.384379 loss) +I0616 08:38:16.612392 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498236 (* 1 = 0.498236 loss) +I0616 08:38:16.612396 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10228 (* 1 = 0.10228 loss) +I0616 08:38:16.612401 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308822 (* 1 = 0.0308822 loss) +I0616 08:38:16.612404 9857 solver.cpp:571] Iteration 37700, lr = 0.001 +I0616 08:38:28.031080 9857 solver.cpp:242] Iteration 37720, loss = 0.746208 +I0616 08:38:28.031121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281072 (* 1 = 0.281072 loss) +I0616 08:38:28.031126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.311723 (* 1 = 0.311723 loss) +I0616 08:38:28.031131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0717945 (* 1 = 0.0717945 loss) +I0616 08:38:28.031134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0330486 (* 1 = 0.0330486 loss) +I0616 08:38:28.031138 9857 solver.cpp:571] Iteration 37720, lr = 0.001 +I0616 08:38:39.873144 9857 solver.cpp:242] Iteration 37740, loss = 0.635656 +I0616 08:38:39.873186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266006 (* 1 = 0.266006 loss) +I0616 08:38:39.873193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363083 (* 1 = 0.363083 loss) +I0616 08:38:39.873196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111077 (* 1 = 0.111077 loss) +I0616 08:38:39.873199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.281635 (* 1 = 0.281635 loss) +I0616 08:38:39.873203 9857 solver.cpp:571] Iteration 37740, lr = 0.001 +I0616 08:38:51.384600 9857 solver.cpp:242] Iteration 37760, loss = 0.726337 +I0616 08:38:51.384642 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244967 (* 1 = 0.244967 loss) +I0616 08:38:51.384649 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234238 (* 1 = 0.234238 loss) +I0616 08:38:51.384652 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1034 (* 1 = 0.1034 loss) +I0616 08:38:51.384656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.13534 (* 1 = 0.13534 loss) +I0616 08:38:51.384660 9857 solver.cpp:571] Iteration 37760, lr = 0.001 +I0616 08:39:02.694291 9857 solver.cpp:242] Iteration 37780, loss = 1.05681 +I0616 08:39:02.694334 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249316 (* 1 = 0.249316 loss) +I0616 08:39:02.694339 9857 solver.cpp:258] Train net output #1: loss_cls = 0.493754 (* 1 = 0.493754 loss) +I0616 08:39:02.694344 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0990379 (* 1 = 0.0990379 loss) +I0616 08:39:02.694349 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0409862 (* 1 = 0.0409862 loss) +I0616 08:39:02.694351 9857 solver.cpp:571] Iteration 37780, lr = 0.001 +speed: 0.624s / iter +I0616 08:39:14.301512 9857 solver.cpp:242] Iteration 37800, loss = 0.928421 +I0616 08:39:14.301555 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306994 (* 1 = 0.306994 loss) +I0616 08:39:14.301560 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459337 (* 1 = 0.459337 loss) +I0616 08:39:14.301564 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130751 (* 1 = 0.130751 loss) +I0616 08:39:14.301568 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156672 (* 1 = 0.156672 loss) +I0616 08:39:14.301573 9857 solver.cpp:571] Iteration 37800, lr = 0.001 +I0616 08:39:25.664021 9857 solver.cpp:242] Iteration 37820, loss = 0.579384 +I0616 08:39:25.664062 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182925 (* 1 = 0.182925 loss) +I0616 08:39:25.664067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.258853 (* 1 = 0.258853 loss) +I0616 08:39:25.664072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143387 (* 1 = 0.143387 loss) +I0616 08:39:25.664075 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030683 (* 1 = 0.030683 loss) +I0616 08:39:25.664079 9857 solver.cpp:571] Iteration 37820, lr = 0.001 +I0616 08:39:37.108860 9857 solver.cpp:242] Iteration 37840, loss = 0.493445 +I0616 08:39:37.108903 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.142056 (* 1 = 0.142056 loss) +I0616 08:39:37.108908 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181176 (* 1 = 0.181176 loss) +I0616 08:39:37.108912 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0282212 (* 1 = 0.0282212 loss) +I0616 08:39:37.108916 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168733 (* 1 = 0.0168733 loss) +I0616 08:39:37.108921 9857 solver.cpp:571] Iteration 37840, lr = 0.001 +I0616 08:39:48.437489 9857 solver.cpp:242] Iteration 37860, loss = 0.533324 +I0616 08:39:48.437528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193676 (* 1 = 0.193676 loss) +I0616 08:39:48.437536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.330674 (* 1 = 0.330674 loss) +I0616 08:39:48.437539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0992874 (* 1 = 0.0992874 loss) +I0616 08:39:48.437543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.026898 (* 1 = 0.026898 loss) +I0616 08:39:48.437547 9857 solver.cpp:571] Iteration 37860, lr = 0.001 +I0616 08:39:59.977785 9857 solver.cpp:242] Iteration 37880, loss = 1.23098 +I0616 08:39:59.977828 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.397074 (* 1 = 0.397074 loss) +I0616 08:39:59.977834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.569572 (* 1 = 0.569572 loss) +I0616 08:39:59.977838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294629 (* 1 = 0.294629 loss) +I0616 08:39:59.977843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.46986 (* 1 = 0.46986 loss) +I0616 08:39:59.977846 9857 solver.cpp:571] Iteration 37880, lr = 0.001 +I0616 08:40:11.635159 9857 solver.cpp:242] Iteration 37900, loss = 0.948302 +I0616 08:40:11.635203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.273409 (* 1 = 0.273409 loss) +I0616 08:40:11.635210 9857 solver.cpp:258] Train net output #1: loss_cls = 0.385041 (* 1 = 0.385041 loss) +I0616 08:40:11.635213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0366266 (* 1 = 0.0366266 loss) +I0616 08:40:11.635217 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.039884 (* 1 = 0.039884 loss) +I0616 08:40:11.635221 9857 solver.cpp:571] Iteration 37900, lr = 0.001 +I0616 08:40:23.300544 9857 solver.cpp:242] Iteration 37920, loss = 0.498533 +I0616 08:40:23.300585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141701 (* 1 = 0.141701 loss) +I0616 08:40:23.300591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345383 (* 1 = 0.345383 loss) +I0616 08:40:23.300596 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0778728 (* 1 = 0.0778728 loss) +I0616 08:40:23.300600 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00452968 (* 1 = 0.00452968 loss) +I0616 08:40:23.300603 9857 solver.cpp:571] Iteration 37920, lr = 0.001 +I0616 08:40:34.812623 9857 solver.cpp:242] Iteration 37940, loss = 1.28906 +I0616 08:40:34.812660 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255081 (* 1 = 0.255081 loss) +I0616 08:40:34.812667 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328664 (* 1 = 0.328664 loss) +I0616 08:40:34.812671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0897082 (* 1 = 0.0897082 loss) +I0616 08:40:34.812675 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0287863 (* 1 = 0.0287863 loss) +I0616 08:40:34.812680 9857 solver.cpp:571] Iteration 37940, lr = 0.001 +I0616 08:40:46.179934 9857 solver.cpp:242] Iteration 37960, loss = 1.21967 +I0616 08:40:46.179975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293905 (* 1 = 0.293905 loss) +I0616 08:40:46.179980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.356281 (* 1 = 0.356281 loss) +I0616 08:40:46.179985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124773 (* 1 = 0.124773 loss) +I0616 08:40:46.179988 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.988083 (* 1 = 0.988083 loss) +I0616 08:40:46.179992 9857 solver.cpp:571] Iteration 37960, lr = 0.001 +I0616 08:40:58.082482 9857 solver.cpp:242] Iteration 37980, loss = 0.462595 +I0616 08:40:58.082525 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123397 (* 1 = 0.123397 loss) +I0616 08:40:58.082530 9857 solver.cpp:258] Train net output #1: loss_cls = 0.444257 (* 1 = 0.444257 loss) +I0616 08:40:58.082535 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100298 (* 1 = 0.100298 loss) +I0616 08:40:58.082540 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00270627 (* 1 = 0.00270627 loss) +I0616 08:40:58.082543 9857 solver.cpp:571] Iteration 37980, lr = 0.001 +speed: 0.624s / iter +I0616 08:41:09.900048 9857 solver.cpp:242] Iteration 38000, loss = 1.05187 +I0616 08:41:09.900089 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396364 (* 1 = 0.396364 loss) +I0616 08:41:09.900094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338333 (* 1 = 0.338333 loss) +I0616 08:41:09.900097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.389878 (* 1 = 0.389878 loss) +I0616 08:41:09.900101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.627144 (* 1 = 0.627144 loss) +I0616 08:41:09.900105 9857 solver.cpp:571] Iteration 38000, lr = 0.001 +I0616 08:41:21.447305 9857 solver.cpp:242] Iteration 38020, loss = 0.62796 +I0616 08:41:21.447350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134124 (* 1 = 0.134124 loss) +I0616 08:41:21.447355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148774 (* 1 = 0.148774 loss) +I0616 08:41:21.447358 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0338724 (* 1 = 0.0338724 loss) +I0616 08:41:21.447362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103361 (* 1 = 0.0103361 loss) +I0616 08:41:21.447366 9857 solver.cpp:571] Iteration 38020, lr = 0.001 +I0616 08:41:33.183722 9857 solver.cpp:242] Iteration 38040, loss = 0.854143 +I0616 08:41:33.183764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0912362 (* 1 = 0.0912362 loss) +I0616 08:41:33.183770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266173 (* 1 = 0.266173 loss) +I0616 08:41:33.183774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0583475 (* 1 = 0.0583475 loss) +I0616 08:41:33.183779 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00338335 (* 1 = 0.00338335 loss) +I0616 08:41:33.183782 9857 solver.cpp:571] Iteration 38040, lr = 0.001 +I0616 08:41:44.540623 9857 solver.cpp:242] Iteration 38060, loss = 1.0708 +I0616 08:41:44.540664 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38369 (* 1 = 0.38369 loss) +I0616 08:41:44.540670 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283386 (* 1 = 0.283386 loss) +I0616 08:41:44.540674 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175801 (* 1 = 0.175801 loss) +I0616 08:41:44.540678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.146368 (* 1 = 0.146368 loss) +I0616 08:41:44.540681 9857 solver.cpp:571] Iteration 38060, lr = 0.001 +I0616 08:41:56.063719 9857 solver.cpp:242] Iteration 38080, loss = 0.516677 +I0616 08:41:56.063761 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241183 (* 1 = 0.241183 loss) +I0616 08:41:56.063766 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276525 (* 1 = 0.276525 loss) +I0616 08:41:56.063771 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0535947 (* 1 = 0.0535947 loss) +I0616 08:41:56.063774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.026483 (* 1 = 0.026483 loss) +I0616 08:41:56.063778 9857 solver.cpp:571] Iteration 38080, lr = 0.001 +I0616 08:42:07.681648 9857 solver.cpp:242] Iteration 38100, loss = 0.786366 +I0616 08:42:07.681689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290417 (* 1 = 0.290417 loss) +I0616 08:42:07.681694 9857 solver.cpp:258] Train net output #1: loss_cls = 0.44268 (* 1 = 0.44268 loss) +I0616 08:42:07.681699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.27779 (* 1 = 0.27779 loss) +I0616 08:42:07.681704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202805 (* 1 = 0.0202805 loss) +I0616 08:42:07.681707 9857 solver.cpp:571] Iteration 38100, lr = 0.001 +I0616 08:42:19.416728 9857 solver.cpp:242] Iteration 38120, loss = 0.874431 +I0616 08:42:19.416770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17241 (* 1 = 0.17241 loss) +I0616 08:42:19.416775 9857 solver.cpp:258] Train net output #1: loss_cls = 0.339577 (* 1 = 0.339577 loss) +I0616 08:42:19.416780 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0410804 (* 1 = 0.0410804 loss) +I0616 08:42:19.416784 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114731 (* 1 = 0.0114731 loss) +I0616 08:42:19.416787 9857 solver.cpp:571] Iteration 38120, lr = 0.001 +I0616 08:42:30.898331 9857 solver.cpp:242] Iteration 38140, loss = 1.43151 +I0616 08:42:30.898375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.522126 (* 1 = 0.522126 loss) +I0616 08:42:30.898380 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418906 (* 1 = 0.418906 loss) +I0616 08:42:30.898383 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0845449 (* 1 = 0.0845449 loss) +I0616 08:42:30.898387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0465232 (* 1 = 0.0465232 loss) +I0616 08:42:30.898391 9857 solver.cpp:571] Iteration 38140, lr = 0.001 +I0616 08:42:42.486361 9857 solver.cpp:242] Iteration 38160, loss = 0.993006 +I0616 08:42:42.486402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313564 (* 1 = 0.313564 loss) +I0616 08:42:42.486408 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191635 (* 1 = 0.191635 loss) +I0616 08:42:42.486413 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0459431 (* 1 = 0.0459431 loss) +I0616 08:42:42.486416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115875 (* 1 = 0.0115875 loss) +I0616 08:42:42.486420 9857 solver.cpp:571] Iteration 38160, lr = 0.001 +I0616 08:42:54.114673 9857 solver.cpp:242] Iteration 38180, loss = 1.22278 +I0616 08:42:54.114713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291023 (* 1 = 0.291023 loss) +I0616 08:42:54.114719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.906101 (* 1 = 0.906101 loss) +I0616 08:42:54.114723 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161218 (* 1 = 0.161218 loss) +I0616 08:42:54.114727 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0395393 (* 1 = 0.0395393 loss) +I0616 08:42:54.114732 9857 solver.cpp:571] Iteration 38180, lr = 0.001 +speed: 0.624s / iter +I0616 08:43:05.819356 9857 solver.cpp:242] Iteration 38200, loss = 1.4376 +I0616 08:43:05.819399 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297114 (* 1 = 0.297114 loss) +I0616 08:43:05.819406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.320586 (* 1 = 0.320586 loss) +I0616 08:43:05.819423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130579 (* 1 = 0.130579 loss) +I0616 08:43:05.819428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0380515 (* 1 = 0.0380515 loss) +I0616 08:43:05.819447 9857 solver.cpp:571] Iteration 38200, lr = 0.001 +I0616 08:43:17.651412 9857 solver.cpp:242] Iteration 38220, loss = 0.752167 +I0616 08:43:17.651454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265663 (* 1 = 0.265663 loss) +I0616 08:43:17.651460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202996 (* 1 = 0.202996 loss) +I0616 08:43:17.651464 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.030456 (* 1 = 0.030456 loss) +I0616 08:43:17.651468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119288 (* 1 = 0.0119288 loss) +I0616 08:43:17.651471 9857 solver.cpp:571] Iteration 38220, lr = 0.001 +I0616 08:43:29.280813 9857 solver.cpp:242] Iteration 38240, loss = 0.627005 +I0616 08:43:29.280855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153051 (* 1 = 0.153051 loss) +I0616 08:43:29.280861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214567 (* 1 = 0.214567 loss) +I0616 08:43:29.280866 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00825818 (* 1 = 0.00825818 loss) +I0616 08:43:29.280870 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00754939 (* 1 = 0.00754939 loss) +I0616 08:43:29.280874 9857 solver.cpp:571] Iteration 38240, lr = 0.001 +I0616 08:43:40.746414 9857 solver.cpp:242] Iteration 38260, loss = 1.4004 +I0616 08:43:40.746454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.547642 (* 1 = 0.547642 loss) +I0616 08:43:40.746460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.461802 (* 1 = 0.461802 loss) +I0616 08:43:40.746464 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.243259 (* 1 = 0.243259 loss) +I0616 08:43:40.746469 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0321425 (* 1 = 0.0321425 loss) +I0616 08:43:40.746472 9857 solver.cpp:571] Iteration 38260, lr = 0.001 +I0616 08:43:52.503785 9857 solver.cpp:242] Iteration 38280, loss = 0.671223 +I0616 08:43:52.503828 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117163 (* 1 = 0.117163 loss) +I0616 08:43:52.503834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.546911 (* 1 = 0.546911 loss) +I0616 08:43:52.503837 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0313203 (* 1 = 0.0313203 loss) +I0616 08:43:52.503841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0908862 (* 1 = 0.0908862 loss) +I0616 08:43:52.503845 9857 solver.cpp:571] Iteration 38280, lr = 0.001 +I0616 08:44:03.705633 9857 solver.cpp:242] Iteration 38300, loss = 0.554723 +I0616 08:44:03.705675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181001 (* 1 = 0.181001 loss) +I0616 08:44:03.705680 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18243 (* 1 = 0.18243 loss) +I0616 08:44:03.705684 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165876 (* 1 = 0.165876 loss) +I0616 08:44:03.705688 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0274876 (* 1 = 0.0274876 loss) +I0616 08:44:03.705693 9857 solver.cpp:571] Iteration 38300, lr = 0.001 +I0616 08:44:15.058166 9857 solver.cpp:242] Iteration 38320, loss = 1.02119 +I0616 08:44:15.058207 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267102 (* 1 = 0.267102 loss) +I0616 08:44:15.058212 9857 solver.cpp:258] Train net output #1: loss_cls = 0.521896 (* 1 = 0.521896 loss) +I0616 08:44:15.058217 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117223 (* 1 = 0.117223 loss) +I0616 08:44:15.058220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0678884 (* 1 = 0.0678884 loss) +I0616 08:44:15.058224 9857 solver.cpp:571] Iteration 38320, lr = 0.001 +I0616 08:44:26.539623 9857 solver.cpp:242] Iteration 38340, loss = 1.32455 +I0616 08:44:26.539665 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.446743 (* 1 = 0.446743 loss) +I0616 08:44:26.539671 9857 solver.cpp:258] Train net output #1: loss_cls = 0.830549 (* 1 = 0.830549 loss) +I0616 08:44:26.539675 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.199035 (* 1 = 0.199035 loss) +I0616 08:44:26.539680 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129266 (* 1 = 0.129266 loss) +I0616 08:44:26.539682 9857 solver.cpp:571] Iteration 38340, lr = 0.001 +I0616 08:44:37.825827 9857 solver.cpp:242] Iteration 38360, loss = 0.815487 +I0616 08:44:37.825870 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316025 (* 1 = 0.316025 loss) +I0616 08:44:37.825875 9857 solver.cpp:258] Train net output #1: loss_cls = 0.362296 (* 1 = 0.362296 loss) +I0616 08:44:37.825881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0670676 (* 1 = 0.0670676 loss) +I0616 08:44:37.825883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0434371 (* 1 = 0.0434371 loss) +I0616 08:44:37.825887 9857 solver.cpp:571] Iteration 38360, lr = 0.001 +I0616 08:44:49.334844 9857 solver.cpp:242] Iteration 38380, loss = 0.592987 +I0616 08:44:49.334883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.248452 (* 1 = 0.248452 loss) +I0616 08:44:49.334889 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321837 (* 1 = 0.321837 loss) +I0616 08:44:49.334893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163678 (* 1 = 0.163678 loss) +I0616 08:44:49.334897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0321786 (* 1 = 0.0321786 loss) +I0616 08:44:49.334900 9857 solver.cpp:571] Iteration 38380, lr = 0.001 +speed: 0.624s / iter +I0616 08:45:00.851589 9857 solver.cpp:242] Iteration 38400, loss = 0.603099 +I0616 08:45:00.851631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140739 (* 1 = 0.140739 loss) +I0616 08:45:00.851637 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210308 (* 1 = 0.210308 loss) +I0616 08:45:00.851641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0620915 (* 1 = 0.0620915 loss) +I0616 08:45:00.851645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00425751 (* 1 = 0.00425751 loss) +I0616 08:45:00.851649 9857 solver.cpp:571] Iteration 38400, lr = 0.001 +I0616 08:45:12.508040 9857 solver.cpp:242] Iteration 38420, loss = 0.894341 +I0616 08:45:12.508082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0718859 (* 1 = 0.0718859 loss) +I0616 08:45:12.508088 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207991 (* 1 = 0.207991 loss) +I0616 08:45:12.508092 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0691174 (* 1 = 0.0691174 loss) +I0616 08:45:12.508095 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00877107 (* 1 = 0.00877107 loss) +I0616 08:45:12.508100 9857 solver.cpp:571] Iteration 38420, lr = 0.001 +I0616 08:45:23.856775 9857 solver.cpp:242] Iteration 38440, loss = 1.47918 +I0616 08:45:23.856818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.410768 (* 1 = 0.410768 loss) +I0616 08:45:23.856823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.920316 (* 1 = 0.920316 loss) +I0616 08:45:23.856828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.283524 (* 1 = 0.283524 loss) +I0616 08:45:23.856832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.672441 (* 1 = 0.672441 loss) +I0616 08:45:23.856835 9857 solver.cpp:571] Iteration 38440, lr = 0.001 +I0616 08:45:35.428238 9857 solver.cpp:242] Iteration 38460, loss = 0.295534 +I0616 08:45:35.428282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161065 (* 1 = 0.161065 loss) +I0616 08:45:35.428287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200032 (* 1 = 0.200032 loss) +I0616 08:45:35.428290 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141977 (* 1 = 0.0141977 loss) +I0616 08:45:35.428294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00402322 (* 1 = 0.00402322 loss) +I0616 08:45:35.428298 9857 solver.cpp:571] Iteration 38460, lr = 0.001 +I0616 08:45:46.915854 9857 solver.cpp:242] Iteration 38480, loss = 0.701964 +I0616 08:45:46.915894 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199208 (* 1 = 0.199208 loss) +I0616 08:45:46.915899 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351382 (* 1 = 0.351382 loss) +I0616 08:45:46.915917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0416837 (* 1 = 0.0416837 loss) +I0616 08:45:46.915921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0730281 (* 1 = 0.0730281 loss) +I0616 08:45:46.915925 9857 solver.cpp:571] Iteration 38480, lr = 0.001 +I0616 08:45:58.337784 9857 solver.cpp:242] Iteration 38500, loss = 0.530058 +I0616 08:45:58.337827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.385271 (* 1 = 0.385271 loss) +I0616 08:45:58.337833 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257554 (* 1 = 0.257554 loss) +I0616 08:45:58.337837 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0681665 (* 1 = 0.0681665 loss) +I0616 08:45:58.337841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0380232 (* 1 = 0.0380232 loss) +I0616 08:45:58.337846 9857 solver.cpp:571] Iteration 38500, lr = 0.001 +I0616 08:46:09.859555 9857 solver.cpp:242] Iteration 38520, loss = 1.08677 +I0616 08:46:09.859597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124244 (* 1 = 0.124244 loss) +I0616 08:46:09.859603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.468278 (* 1 = 0.468278 loss) +I0616 08:46:09.859607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0401582 (* 1 = 0.0401582 loss) +I0616 08:46:09.859611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00692041 (* 1 = 0.00692041 loss) +I0616 08:46:09.859614 9857 solver.cpp:571] Iteration 38520, lr = 0.001 +I0616 08:46:21.234707 9857 solver.cpp:242] Iteration 38540, loss = 0.618595 +I0616 08:46:21.234750 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145062 (* 1 = 0.145062 loss) +I0616 08:46:21.234755 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131604 (* 1 = 0.131604 loss) +I0616 08:46:21.234762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212428 (* 1 = 0.0212428 loss) +I0616 08:46:21.234766 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0501008 (* 1 = 0.0501008 loss) +I0616 08:46:21.234769 9857 solver.cpp:571] Iteration 38540, lr = 0.001 +I0616 08:46:32.669859 9857 solver.cpp:242] Iteration 38560, loss = 0.453582 +I0616 08:46:32.669903 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174034 (* 1 = 0.174034 loss) +I0616 08:46:32.669909 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0986177 (* 1 = 0.0986177 loss) +I0616 08:46:32.669912 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00391348 (* 1 = 0.00391348 loss) +I0616 08:46:32.669915 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00431547 (* 1 = 0.00431547 loss) +I0616 08:46:32.669919 9857 solver.cpp:571] Iteration 38560, lr = 0.001 +I0616 08:46:44.310004 9857 solver.cpp:242] Iteration 38580, loss = 1.08619 +I0616 08:46:44.310046 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.456551 (* 1 = 0.456551 loss) +I0616 08:46:44.310052 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350389 (* 1 = 0.350389 loss) +I0616 08:46:44.310056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0464804 (* 1 = 0.0464804 loss) +I0616 08:46:44.310060 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0343704 (* 1 = 0.0343704 loss) +I0616 08:46:44.310065 9857 solver.cpp:571] Iteration 38580, lr = 0.001 +speed: 0.623s / iter +I0616 08:46:55.805799 9857 solver.cpp:242] Iteration 38600, loss = 0.637324 +I0616 08:46:55.805840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120518 (* 1 = 0.120518 loss) +I0616 08:46:55.805845 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233631 (* 1 = 0.233631 loss) +I0616 08:46:55.805850 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0313534 (* 1 = 0.0313534 loss) +I0616 08:46:55.805853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00722874 (* 1 = 0.00722874 loss) +I0616 08:46:55.805857 9857 solver.cpp:571] Iteration 38600, lr = 0.001 +I0616 08:47:07.213620 9857 solver.cpp:242] Iteration 38620, loss = 0.623133 +I0616 08:47:07.213662 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178862 (* 1 = 0.178862 loss) +I0616 08:47:07.213667 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301391 (* 1 = 0.301391 loss) +I0616 08:47:07.213671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136553 (* 1 = 0.136553 loss) +I0616 08:47:07.213675 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390889 (* 1 = 0.0390889 loss) +I0616 08:47:07.213678 9857 solver.cpp:571] Iteration 38620, lr = 0.001 +I0616 08:47:18.699501 9857 solver.cpp:242] Iteration 38640, loss = 0.540892 +I0616 08:47:18.699542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0746502 (* 1 = 0.0746502 loss) +I0616 08:47:18.699548 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181977 (* 1 = 0.181977 loss) +I0616 08:47:18.699553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0470281 (* 1 = 0.0470281 loss) +I0616 08:47:18.699555 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0486277 (* 1 = 0.0486277 loss) +I0616 08:47:18.699559 9857 solver.cpp:571] Iteration 38640, lr = 0.001 +I0616 08:47:29.890002 9857 solver.cpp:242] Iteration 38660, loss = 1.07266 +I0616 08:47:29.890040 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345491 (* 1 = 0.345491 loss) +I0616 08:47:29.890046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.487072 (* 1 = 0.487072 loss) +I0616 08:47:29.890049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144136 (* 1 = 0.144136 loss) +I0616 08:47:29.890053 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0348811 (* 1 = 0.0348811 loss) +I0616 08:47:29.890058 9857 solver.cpp:571] Iteration 38660, lr = 0.001 +I0616 08:47:41.399518 9857 solver.cpp:242] Iteration 38680, loss = 0.423302 +I0616 08:47:41.399560 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11766 (* 1 = 0.11766 loss) +I0616 08:47:41.399566 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192127 (* 1 = 0.192127 loss) +I0616 08:47:41.399570 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00778065 (* 1 = 0.00778065 loss) +I0616 08:47:41.399574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0525866 (* 1 = 0.0525866 loss) +I0616 08:47:41.399579 9857 solver.cpp:571] Iteration 38680, lr = 0.001 +I0616 08:47:52.883273 9857 solver.cpp:242] Iteration 38700, loss = 0.375596 +I0616 08:47:52.883313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111826 (* 1 = 0.111826 loss) +I0616 08:47:52.883319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144962 (* 1 = 0.144962 loss) +I0616 08:47:52.883323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.066887 (* 1 = 0.066887 loss) +I0616 08:47:52.883327 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132538 (* 1 = 0.0132538 loss) +I0616 08:47:52.883332 9857 solver.cpp:571] Iteration 38700, lr = 0.001 +I0616 08:48:04.505645 9857 solver.cpp:242] Iteration 38720, loss = 0.680298 +I0616 08:48:04.505688 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260811 (* 1 = 0.260811 loss) +I0616 08:48:04.505695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276557 (* 1 = 0.276557 loss) +I0616 08:48:04.505699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.189282 (* 1 = 0.189282 loss) +I0616 08:48:04.505703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0345313 (* 1 = 0.0345313 loss) +I0616 08:48:04.505707 9857 solver.cpp:571] Iteration 38720, lr = 0.001 +I0616 08:48:15.944993 9857 solver.cpp:242] Iteration 38740, loss = 1.03803 +I0616 08:48:15.945035 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386306 (* 1 = 0.386306 loss) +I0616 08:48:15.945042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.51683 (* 1 = 0.51683 loss) +I0616 08:48:15.945046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.281743 (* 1 = 0.281743 loss) +I0616 08:48:15.945050 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.057444 (* 1 = 0.057444 loss) +I0616 08:48:15.945055 9857 solver.cpp:571] Iteration 38740, lr = 0.001 +I0616 08:48:27.527248 9857 solver.cpp:242] Iteration 38760, loss = 0.754526 +I0616 08:48:27.527290 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.40787 (* 1 = 0.40787 loss) +I0616 08:48:27.527297 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262909 (* 1 = 0.262909 loss) +I0616 08:48:27.527300 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103299 (* 1 = 0.103299 loss) +I0616 08:48:27.527304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0471635 (* 1 = 0.0471635 loss) +I0616 08:48:27.527307 9857 solver.cpp:571] Iteration 38760, lr = 0.001 +I0616 08:48:39.091357 9857 solver.cpp:242] Iteration 38780, loss = 0.541869 +I0616 08:48:39.091401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159514 (* 1 = 0.159514 loss) +I0616 08:48:39.091406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281088 (* 1 = 0.281088 loss) +I0616 08:48:39.091410 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.16728 (* 1 = 0.16728 loss) +I0616 08:48:39.091414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0271076 (* 1 = 0.0271076 loss) +I0616 08:48:39.091418 9857 solver.cpp:571] Iteration 38780, lr = 0.001 +speed: 0.623s / iter +I0616 08:48:50.479452 9857 solver.cpp:242] Iteration 38800, loss = 0.433337 +I0616 08:48:50.479491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0792094 (* 1 = 0.0792094 loss) +I0616 08:48:50.479511 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140622 (* 1 = 0.140622 loss) +I0616 08:48:50.479516 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.013916 (* 1 = 0.013916 loss) +I0616 08:48:50.479518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.268843 (* 1 = 0.268843 loss) +I0616 08:48:50.479522 9857 solver.cpp:571] Iteration 38800, lr = 0.001 +I0616 08:49:02.000211 9857 solver.cpp:242] Iteration 38820, loss = 0.456302 +I0616 08:49:02.000253 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193301 (* 1 = 0.193301 loss) +I0616 08:49:02.000258 9857 solver.cpp:258] Train net output #1: loss_cls = 0.408576 (* 1 = 0.408576 loss) +I0616 08:49:02.000262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00896296 (* 1 = 0.00896296 loss) +I0616 08:49:02.000267 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0287874 (* 1 = 0.0287874 loss) +I0616 08:49:02.000270 9857 solver.cpp:571] Iteration 38820, lr = 0.001 +I0616 08:49:13.482707 9857 solver.cpp:242] Iteration 38840, loss = 1.20704 +I0616 08:49:13.482748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354374 (* 1 = 0.354374 loss) +I0616 08:49:13.482753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.62043 (* 1 = 0.62043 loss) +I0616 08:49:13.482761 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0562305 (* 1 = 0.0562305 loss) +I0616 08:49:13.482765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0805305 (* 1 = 0.0805305 loss) +I0616 08:49:13.482769 9857 solver.cpp:571] Iteration 38840, lr = 0.001 +I0616 08:49:25.110190 9857 solver.cpp:242] Iteration 38860, loss = 1.57947 +I0616 08:49:25.110231 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384571 (* 1 = 0.384571 loss) +I0616 08:49:25.110236 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303346 (* 1 = 0.303346 loss) +I0616 08:49:25.110241 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142899 (* 1 = 0.142899 loss) +I0616 08:49:25.110244 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0750125 (* 1 = 0.0750125 loss) +I0616 08:49:25.110249 9857 solver.cpp:571] Iteration 38860, lr = 0.001 +I0616 08:49:36.463474 9857 solver.cpp:242] Iteration 38880, loss = 1.68982 +I0616 08:49:36.463515 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.422338 (* 1 = 0.422338 loss) +I0616 08:49:36.463521 9857 solver.cpp:258] Train net output #1: loss_cls = 0.93171 (* 1 = 0.93171 loss) +I0616 08:49:36.463524 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.28721 (* 1 = 0.28721 loss) +I0616 08:49:36.463528 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110797 (* 1 = 0.110797 loss) +I0616 08:49:36.463534 9857 solver.cpp:571] Iteration 38880, lr = 0.001 +I0616 08:49:48.089803 9857 solver.cpp:242] Iteration 38900, loss = 0.630767 +I0616 08:49:48.089845 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144058 (* 1 = 0.144058 loss) +I0616 08:49:48.089851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287772 (* 1 = 0.287772 loss) +I0616 08:49:48.089855 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194235 (* 1 = 0.194235 loss) +I0616 08:49:48.089859 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00339539 (* 1 = 0.00339539 loss) +I0616 08:49:48.089862 9857 solver.cpp:571] Iteration 38900, lr = 0.001 +I0616 08:49:59.721304 9857 solver.cpp:242] Iteration 38920, loss = 0.970421 +I0616 08:49:59.721346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10363 (* 1 = 0.10363 loss) +I0616 08:49:59.721354 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206099 (* 1 = 0.206099 loss) +I0616 08:49:59.721357 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110167 (* 1 = 0.110167 loss) +I0616 08:49:59.721361 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.048989 (* 1 = 0.048989 loss) +I0616 08:49:59.721364 9857 solver.cpp:571] Iteration 38920, lr = 0.001 +I0616 08:50:11.366485 9857 solver.cpp:242] Iteration 38940, loss = 0.536664 +I0616 08:50:11.366513 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118933 (* 1 = 0.118933 loss) +I0616 08:50:11.366519 9857 solver.cpp:258] Train net output #1: loss_cls = 0.198478 (* 1 = 0.198478 loss) +I0616 08:50:11.366523 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0368285 (* 1 = 0.0368285 loss) +I0616 08:50:11.366529 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140607 (* 1 = 0.0140607 loss) +I0616 08:50:11.366534 9857 solver.cpp:571] Iteration 38940, lr = 0.001 +I0616 08:50:22.656874 9857 solver.cpp:242] Iteration 38960, loss = 0.749936 +I0616 08:50:22.656918 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173095 (* 1 = 0.173095 loss) +I0616 08:50:22.656924 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294168 (* 1 = 0.294168 loss) +I0616 08:50:22.656927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0248146 (* 1 = 0.0248146 loss) +I0616 08:50:22.656931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00974318 (* 1 = 0.00974318 loss) +I0616 08:50:22.656934 9857 solver.cpp:571] Iteration 38960, lr = 0.001 +I0616 08:50:34.325644 9857 solver.cpp:242] Iteration 38980, loss = 0.271925 +I0616 08:50:34.325686 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0482253 (* 1 = 0.0482253 loss) +I0616 08:50:34.325692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137133 (* 1 = 0.137133 loss) +I0616 08:50:34.325696 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0203243 (* 1 = 0.0203243 loss) +I0616 08:50:34.325700 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00217786 (* 1 = 0.00217786 loss) +I0616 08:50:34.325703 9857 solver.cpp:571] Iteration 38980, lr = 0.001 +speed: 0.623s / iter +I0616 08:50:45.831732 9857 solver.cpp:242] Iteration 39000, loss = 0.496131 +I0616 08:50:45.831775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272901 (* 1 = 0.272901 loss) +I0616 08:50:45.831781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260036 (* 1 = 0.260036 loss) +I0616 08:50:45.831785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0984133 (* 1 = 0.0984133 loss) +I0616 08:50:45.831789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00959832 (* 1 = 0.00959832 loss) +I0616 08:50:45.831792 9857 solver.cpp:571] Iteration 39000, lr = 0.001 +I0616 08:50:57.360410 9857 solver.cpp:242] Iteration 39020, loss = 1.34028 +I0616 08:50:57.360450 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.370533 (* 1 = 0.370533 loss) +I0616 08:50:57.360456 9857 solver.cpp:258] Train net output #1: loss_cls = 0.764239 (* 1 = 0.764239 loss) +I0616 08:50:57.360460 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.347557 (* 1 = 0.347557 loss) +I0616 08:50:57.360463 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.32377 (* 1 = 0.32377 loss) +I0616 08:50:57.360467 9857 solver.cpp:571] Iteration 39020, lr = 0.001 +I0616 08:51:09.284823 9857 solver.cpp:242] Iteration 39040, loss = 0.784384 +I0616 08:51:09.284863 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.471512 (* 1 = 0.471512 loss) +I0616 08:51:09.284869 9857 solver.cpp:258] Train net output #1: loss_cls = 0.629335 (* 1 = 0.629335 loss) +I0616 08:51:09.284873 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0619943 (* 1 = 0.0619943 loss) +I0616 08:51:09.284878 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0753938 (* 1 = 0.0753938 loss) +I0616 08:51:09.284881 9857 solver.cpp:571] Iteration 39040, lr = 0.001 +I0616 08:51:20.876835 9857 solver.cpp:242] Iteration 39060, loss = 0.541656 +I0616 08:51:20.876878 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11031 (* 1 = 0.11031 loss) +I0616 08:51:20.876883 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163762 (* 1 = 0.163762 loss) +I0616 08:51:20.876888 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0394887 (* 1 = 0.0394887 loss) +I0616 08:51:20.876891 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00976764 (* 1 = 0.00976764 loss) +I0616 08:51:20.876895 9857 solver.cpp:571] Iteration 39060, lr = 0.001 +I0616 08:51:32.501526 9857 solver.cpp:242] Iteration 39080, loss = 0.444524 +I0616 08:51:32.501569 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.240024 (* 1 = 0.240024 loss) +I0616 08:51:32.501574 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213805 (* 1 = 0.213805 loss) +I0616 08:51:32.501579 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0422873 (* 1 = 0.0422873 loss) +I0616 08:51:32.501582 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397851 (* 1 = 0.0397851 loss) +I0616 08:51:32.501586 9857 solver.cpp:571] Iteration 39080, lr = 0.001 +I0616 08:51:43.950359 9857 solver.cpp:242] Iteration 39100, loss = 1.12405 +I0616 08:51:43.950402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200128 (* 1 = 0.200128 loss) +I0616 08:51:43.950407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.624659 (* 1 = 0.624659 loss) +I0616 08:51:43.950410 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.233166 (* 1 = 0.233166 loss) +I0616 08:51:43.950414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0072272 (* 1 = 0.0072272 loss) +I0616 08:51:43.950418 9857 solver.cpp:571] Iteration 39100, lr = 0.001 +I0616 08:51:55.740790 9857 solver.cpp:242] Iteration 39120, loss = 0.4439 +I0616 08:51:55.740833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244132 (* 1 = 0.244132 loss) +I0616 08:51:55.740839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230786 (* 1 = 0.230786 loss) +I0616 08:51:55.740842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058346 (* 1 = 0.058346 loss) +I0616 08:51:55.740846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0200758 (* 1 = 0.0200758 loss) +I0616 08:51:55.740850 9857 solver.cpp:571] Iteration 39120, lr = 0.001 +I0616 08:52:06.988808 9857 solver.cpp:242] Iteration 39140, loss = 0.714501 +I0616 08:52:06.988850 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376433 (* 1 = 0.376433 loss) +I0616 08:52:06.988855 9857 solver.cpp:258] Train net output #1: loss_cls = 0.464226 (* 1 = 0.464226 loss) +I0616 08:52:06.988859 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.208765 (* 1 = 0.208765 loss) +I0616 08:52:06.988863 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.152232 (* 1 = 0.152232 loss) +I0616 08:52:06.988868 9857 solver.cpp:571] Iteration 39140, lr = 0.001 +I0616 08:52:18.599849 9857 solver.cpp:242] Iteration 39160, loss = 0.663884 +I0616 08:52:18.599879 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207086 (* 1 = 0.207086 loss) +I0616 08:52:18.599886 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146603 (* 1 = 0.146603 loss) +I0616 08:52:18.599892 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0224233 (* 1 = 0.0224233 loss) +I0616 08:52:18.599900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00426197 (* 1 = 0.00426197 loss) +I0616 08:52:18.599908 9857 solver.cpp:571] Iteration 39160, lr = 0.001 +I0616 08:52:30.397308 9857 solver.cpp:242] Iteration 39180, loss = 1.06721 +I0616 08:52:30.397336 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351683 (* 1 = 0.351683 loss) +I0616 08:52:30.397344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.870025 (* 1 = 0.870025 loss) +I0616 08:52:30.397351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.218076 (* 1 = 0.218076 loss) +I0616 08:52:30.397356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0554565 (* 1 = 0.0554565 loss) +I0616 08:52:30.397364 9857 solver.cpp:571] Iteration 39180, lr = 0.001 +speed: 0.623s / iter +I0616 08:52:41.885757 9857 solver.cpp:242] Iteration 39200, loss = 0.838687 +I0616 08:52:41.885787 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116286 (* 1 = 0.116286 loss) +I0616 08:52:41.885794 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249468 (* 1 = 0.249468 loss) +I0616 08:52:41.885799 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0899114 (* 1 = 0.0899114 loss) +I0616 08:52:41.885805 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0267062 (* 1 = 0.0267062 loss) +I0616 08:52:41.885812 9857 solver.cpp:571] Iteration 39200, lr = 0.001 +I0616 08:52:53.449558 9857 solver.cpp:242] Iteration 39220, loss = 0.925877 +I0616 08:52:53.449587 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.31411 (* 1 = 0.31411 loss) +I0616 08:52:53.449594 9857 solver.cpp:258] Train net output #1: loss_cls = 0.656332 (* 1 = 0.656332 loss) +I0616 08:52:53.449600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.262906 (* 1 = 0.262906 loss) +I0616 08:52:53.449606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152215 (* 1 = 0.0152215 loss) +I0616 08:52:53.449615 9857 solver.cpp:571] Iteration 39220, lr = 0.001 +I0616 08:53:05.122396 9857 solver.cpp:242] Iteration 39240, loss = 0.963382 +I0616 08:53:05.122426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21086 (* 1 = 0.21086 loss) +I0616 08:53:05.122432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1516 (* 1 = 0.1516 loss) +I0616 08:53:05.122438 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143331 (* 1 = 0.143331 loss) +I0616 08:53:05.122444 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00140469 (* 1 = 0.00140469 loss) +I0616 08:53:05.122452 9857 solver.cpp:571] Iteration 39240, lr = 0.001 +I0616 08:53:16.747035 9857 solver.cpp:242] Iteration 39260, loss = 0.413927 +I0616 08:53:16.747066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0804828 (* 1 = 0.0804828 loss) +I0616 08:53:16.747073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147555 (* 1 = 0.147555 loss) +I0616 08:53:16.747079 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0647133 (* 1 = 0.0647133 loss) +I0616 08:53:16.747086 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00344191 (* 1 = 0.00344191 loss) +I0616 08:53:16.747092 9857 solver.cpp:571] Iteration 39260, lr = 0.001 +I0616 08:53:28.177636 9857 solver.cpp:242] Iteration 39280, loss = 0.831681 +I0616 08:53:28.177666 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315041 (* 1 = 0.315041 loss) +I0616 08:53:28.177675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.475778 (* 1 = 0.475778 loss) +I0616 08:53:28.177680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161138 (* 1 = 0.161138 loss) +I0616 08:53:28.177686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.45154 (* 1 = 0.45154 loss) +I0616 08:53:28.177693 9857 solver.cpp:571] Iteration 39280, lr = 0.001 +I0616 08:53:39.707185 9857 solver.cpp:242] Iteration 39300, loss = 0.690495 +I0616 08:53:39.707212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309357 (* 1 = 0.309357 loss) +I0616 08:53:39.707221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.428419 (* 1 = 0.428419 loss) +I0616 08:53:39.707226 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102443 (* 1 = 0.102443 loss) +I0616 08:53:39.707233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0726262 (* 1 = 0.0726262 loss) +I0616 08:53:39.707240 9857 solver.cpp:571] Iteration 39300, lr = 0.001 +I0616 08:53:51.250321 9857 solver.cpp:242] Iteration 39320, loss = 1.15324 +I0616 08:53:51.250351 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.383816 (* 1 = 0.383816 loss) +I0616 08:53:51.250358 9857 solver.cpp:258] Train net output #1: loss_cls = 0.534776 (* 1 = 0.534776 loss) +I0616 08:53:51.250365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162287 (* 1 = 0.162287 loss) +I0616 08:53:51.250370 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.071157 (* 1 = 0.071157 loss) +I0616 08:53:51.250376 9857 solver.cpp:571] Iteration 39320, lr = 0.001 +I0616 08:54:03.198509 9857 solver.cpp:242] Iteration 39340, loss = 0.638478 +I0616 08:54:03.198552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.087688 (* 1 = 0.087688 loss) +I0616 08:54:03.198559 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221049 (* 1 = 0.221049 loss) +I0616 08:54:03.198562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0120791 (* 1 = 0.0120791 loss) +I0616 08:54:03.198566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222308 (* 1 = 0.0222308 loss) +I0616 08:54:03.198570 9857 solver.cpp:571] Iteration 39340, lr = 0.001 +I0616 08:54:14.691460 9857 solver.cpp:242] Iteration 39360, loss = 0.619071 +I0616 08:54:14.691503 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.289602 (* 1 = 0.289602 loss) +I0616 08:54:14.691507 9857 solver.cpp:258] Train net output #1: loss_cls = 0.467979 (* 1 = 0.467979 loss) +I0616 08:54:14.691511 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.180231 (* 1 = 0.180231 loss) +I0616 08:54:14.691515 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323779 (* 1 = 0.0323779 loss) +I0616 08:54:14.691519 9857 solver.cpp:571] Iteration 39360, lr = 0.001 +I0616 08:54:26.196723 9857 solver.cpp:242] Iteration 39380, loss = 0.878597 +I0616 08:54:26.196766 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310685 (* 1 = 0.310685 loss) +I0616 08:54:26.196771 9857 solver.cpp:258] Train net output #1: loss_cls = 0.600266 (* 1 = 0.600266 loss) +I0616 08:54:26.196775 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158098 (* 1 = 0.158098 loss) +I0616 08:54:26.196779 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.283509 (* 1 = 0.283509 loss) +I0616 08:54:26.196784 9857 solver.cpp:571] Iteration 39380, lr = 0.001 +speed: 0.622s / iter +I0616 08:54:37.733793 9857 solver.cpp:242] Iteration 39400, loss = 0.521695 +I0616 08:54:37.733835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275691 (* 1 = 0.275691 loss) +I0616 08:54:37.733841 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335889 (* 1 = 0.335889 loss) +I0616 08:54:37.733845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0111554 (* 1 = 0.0111554 loss) +I0616 08:54:37.733850 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255357 (* 1 = 0.0255357 loss) +I0616 08:54:37.733853 9857 solver.cpp:571] Iteration 39400, lr = 0.001 +I0616 08:54:49.265449 9857 solver.cpp:242] Iteration 39420, loss = 0.661141 +I0616 08:54:49.265491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100231 (* 1 = 0.100231 loss) +I0616 08:54:49.265496 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206876 (* 1 = 0.206876 loss) +I0616 08:54:49.265501 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0423173 (* 1 = 0.0423173 loss) +I0616 08:54:49.265504 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164581 (* 1 = 0.0164581 loss) +I0616 08:54:49.265507 9857 solver.cpp:571] Iteration 39420, lr = 0.001 +I0616 08:55:00.642464 9857 solver.cpp:242] Iteration 39440, loss = 0.553637 +I0616 08:55:00.642506 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151511 (* 1 = 0.151511 loss) +I0616 08:55:00.642511 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287125 (* 1 = 0.287125 loss) +I0616 08:55:00.642515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104939 (* 1 = 0.104939 loss) +I0616 08:55:00.642519 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0155136 (* 1 = 0.0155136 loss) +I0616 08:55:00.642524 9857 solver.cpp:571] Iteration 39440, lr = 0.001 +I0616 08:55:12.445101 9857 solver.cpp:242] Iteration 39460, loss = 0.311146 +I0616 08:55:12.445144 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136103 (* 1 = 0.136103 loss) +I0616 08:55:12.445150 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162758 (* 1 = 0.162758 loss) +I0616 08:55:12.445154 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116196 (* 1 = 0.116196 loss) +I0616 08:55:12.445158 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102392 (* 1 = 0.0102392 loss) +I0616 08:55:12.445163 9857 solver.cpp:571] Iteration 39460, lr = 0.001 +I0616 08:55:24.177122 9857 solver.cpp:242] Iteration 39480, loss = 0.265487 +I0616 08:55:24.177165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0697249 (* 1 = 0.0697249 loss) +I0616 08:55:24.177170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167784 (* 1 = 0.167784 loss) +I0616 08:55:24.177173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0244384 (* 1 = 0.0244384 loss) +I0616 08:55:24.177177 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0399644 (* 1 = 0.0399644 loss) +I0616 08:55:24.177181 9857 solver.cpp:571] Iteration 39480, lr = 0.001 +I0616 08:55:35.788974 9857 solver.cpp:242] Iteration 39500, loss = 0.975796 +I0616 08:55:35.789016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.430503 (* 1 = 0.430503 loss) +I0616 08:55:35.789021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.474266 (* 1 = 0.474266 loss) +I0616 08:55:35.789026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0650423 (* 1 = 0.0650423 loss) +I0616 08:55:35.789028 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0535937 (* 1 = 0.0535937 loss) +I0616 08:55:35.789032 9857 solver.cpp:571] Iteration 39500, lr = 0.001 +I0616 08:55:47.234318 9857 solver.cpp:242] Iteration 39520, loss = 0.981271 +I0616 08:55:47.234359 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2514 (* 1 = 0.2514 loss) +I0616 08:55:47.234364 9857 solver.cpp:258] Train net output #1: loss_cls = 0.601407 (* 1 = 0.601407 loss) +I0616 08:55:47.234369 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.402393 (* 1 = 0.402393 loss) +I0616 08:55:47.234372 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.464627 (* 1 = 0.464627 loss) +I0616 08:55:47.234375 9857 solver.cpp:571] Iteration 39520, lr = 0.001 +I0616 08:55:58.568066 9857 solver.cpp:242] Iteration 39540, loss = 0.404438 +I0616 08:55:58.568109 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305741 (* 1 = 0.305741 loss) +I0616 08:55:58.568114 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172379 (* 1 = 0.172379 loss) +I0616 08:55:58.568120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230207 (* 1 = 0.0230207 loss) +I0616 08:55:58.568122 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00620221 (* 1 = 0.00620221 loss) +I0616 08:55:58.568126 9857 solver.cpp:571] Iteration 39540, lr = 0.001 +I0616 08:56:10.164039 9857 solver.cpp:242] Iteration 39560, loss = 0.772373 +I0616 08:56:10.164082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.473491 (* 1 = 0.473491 loss) +I0616 08:56:10.164086 9857 solver.cpp:258] Train net output #1: loss_cls = 0.474389 (* 1 = 0.474389 loss) +I0616 08:56:10.164090 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.282696 (* 1 = 0.282696 loss) +I0616 08:56:10.164094 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0880074 (* 1 = 0.0880074 loss) +I0616 08:56:10.164098 9857 solver.cpp:571] Iteration 39560, lr = 0.001 +I0616 08:56:21.749389 9857 solver.cpp:242] Iteration 39580, loss = 0.747167 +I0616 08:56:21.749433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.393 (* 1 = 0.393 loss) +I0616 08:56:21.749438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.455622 (* 1 = 0.455622 loss) +I0616 08:56:21.749442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0750175 (* 1 = 0.0750175 loss) +I0616 08:56:21.749446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162042 (* 1 = 0.0162042 loss) +I0616 08:56:21.749449 9857 solver.cpp:571] Iteration 39580, lr = 0.001 +speed: 0.622s / iter +I0616 08:56:33.141592 9857 solver.cpp:242] Iteration 39600, loss = 0.509086 +I0616 08:56:33.141634 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193554 (* 1 = 0.193554 loss) +I0616 08:56:33.141640 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306363 (* 1 = 0.306363 loss) +I0616 08:56:33.141644 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0816327 (* 1 = 0.0816327 loss) +I0616 08:56:33.141649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231181 (* 1 = 0.0231181 loss) +I0616 08:56:33.141652 9857 solver.cpp:571] Iteration 39600, lr = 0.001 +I0616 08:56:44.844015 9857 solver.cpp:242] Iteration 39620, loss = 0.536816 +I0616 08:56:44.844058 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128485 (* 1 = 0.128485 loss) +I0616 08:56:44.844063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232462 (* 1 = 0.232462 loss) +I0616 08:56:44.844068 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.06806 (* 1 = 0.06806 loss) +I0616 08:56:44.844071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273934 (* 1 = 0.0273934 loss) +I0616 08:56:44.844075 9857 solver.cpp:571] Iteration 39620, lr = 0.001 +I0616 08:56:56.264610 9857 solver.cpp:242] Iteration 39640, loss = 1.02285 +I0616 08:56:56.264652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316917 (* 1 = 0.316917 loss) +I0616 08:56:56.264657 9857 solver.cpp:258] Train net output #1: loss_cls = 0.772842 (* 1 = 0.772842 loss) +I0616 08:56:56.264662 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160154 (* 1 = 0.160154 loss) +I0616 08:56:56.264664 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0609992 (* 1 = 0.0609992 loss) +I0616 08:56:56.264668 9857 solver.cpp:571] Iteration 39640, lr = 0.001 +I0616 08:57:07.704682 9857 solver.cpp:242] Iteration 39660, loss = 0.405971 +I0616 08:57:07.704725 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118799 (* 1 = 0.118799 loss) +I0616 08:57:07.704730 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294405 (* 1 = 0.294405 loss) +I0616 08:57:07.704735 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.022355 (* 1 = 0.022355 loss) +I0616 08:57:07.704738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00351261 (* 1 = 0.00351261 loss) +I0616 08:57:07.704742 9857 solver.cpp:571] Iteration 39660, lr = 0.001 +I0616 08:57:19.133183 9857 solver.cpp:242] Iteration 39680, loss = 0.675658 +I0616 08:57:19.133224 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205958 (* 1 = 0.205958 loss) +I0616 08:57:19.133230 9857 solver.cpp:258] Train net output #1: loss_cls = 0.54448 (* 1 = 0.54448 loss) +I0616 08:57:19.133234 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129188 (* 1 = 0.129188 loss) +I0616 08:57:19.133239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0408303 (* 1 = 0.0408303 loss) +I0616 08:57:19.133242 9857 solver.cpp:571] Iteration 39680, lr = 0.001 +I0616 08:57:30.799648 9857 solver.cpp:242] Iteration 39700, loss = 0.567676 +I0616 08:57:30.799688 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0819379 (* 1 = 0.0819379 loss) +I0616 08:57:30.799695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300256 (* 1 = 0.300256 loss) +I0616 08:57:30.799698 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0724875 (* 1 = 0.0724875 loss) +I0616 08:57:30.799701 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029081 (* 1 = 0.029081 loss) +I0616 08:57:30.799705 9857 solver.cpp:571] Iteration 39700, lr = 0.001 +I0616 08:57:42.351285 9857 solver.cpp:242] Iteration 39720, loss = 0.638644 +I0616 08:57:42.351325 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163599 (* 1 = 0.163599 loss) +I0616 08:57:42.351331 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102543 (* 1 = 0.102543 loss) +I0616 08:57:42.351336 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246738 (* 1 = 0.246738 loss) +I0616 08:57:42.351339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.453678 (* 1 = 0.453678 loss) +I0616 08:57:42.351343 9857 solver.cpp:571] Iteration 39720, lr = 0.001 +I0616 08:57:54.206158 9857 solver.cpp:242] Iteration 39740, loss = 0.392058 +I0616 08:57:54.206199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144572 (* 1 = 0.144572 loss) +I0616 08:57:54.206204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183448 (* 1 = 0.183448 loss) +I0616 08:57:54.206209 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.013239 (* 1 = 0.013239 loss) +I0616 08:57:54.206212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00535638 (* 1 = 0.00535638 loss) +I0616 08:57:54.206217 9857 solver.cpp:571] Iteration 39740, lr = 0.001 +I0616 08:58:05.824216 9857 solver.cpp:242] Iteration 39760, loss = 0.484832 +I0616 08:58:05.824256 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28326 (* 1 = 0.28326 loss) +I0616 08:58:05.824262 9857 solver.cpp:258] Train net output #1: loss_cls = 0.369956 (* 1 = 0.369956 loss) +I0616 08:58:05.824266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0292557 (* 1 = 0.0292557 loss) +I0616 08:58:05.824270 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0548392 (* 1 = 0.0548392 loss) +I0616 08:58:05.824275 9857 solver.cpp:571] Iteration 39760, lr = 0.001 +I0616 08:58:17.392834 9857 solver.cpp:242] Iteration 39780, loss = 1.13687 +I0616 08:58:17.392876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.459467 (* 1 = 0.459467 loss) +I0616 08:58:17.392882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.443029 (* 1 = 0.443029 loss) +I0616 08:58:17.392886 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.232898 (* 1 = 0.232898 loss) +I0616 08:58:17.392890 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126539 (* 1 = 0.126539 loss) +I0616 08:58:17.392894 9857 solver.cpp:571] Iteration 39780, lr = 0.001 +speed: 0.622s / iter +I0616 08:58:29.217605 9857 solver.cpp:242] Iteration 39800, loss = 0.522088 +I0616 08:58:29.217648 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122795 (* 1 = 0.122795 loss) +I0616 08:58:29.217654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149925 (* 1 = 0.149925 loss) +I0616 08:58:29.217658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124491 (* 1 = 0.124491 loss) +I0616 08:58:29.217661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308016 (* 1 = 0.0308016 loss) +I0616 08:58:29.217665 9857 solver.cpp:571] Iteration 39800, lr = 0.001 +I0616 08:58:40.811090 9857 solver.cpp:242] Iteration 39820, loss = 0.930701 +I0616 08:58:40.811132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272057 (* 1 = 0.272057 loss) +I0616 08:58:40.811137 9857 solver.cpp:258] Train net output #1: loss_cls = 0.52359 (* 1 = 0.52359 loss) +I0616 08:58:40.811142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0350823 (* 1 = 0.0350823 loss) +I0616 08:58:40.811146 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125092 (* 1 = 0.0125092 loss) +I0616 08:58:40.811149 9857 solver.cpp:571] Iteration 39820, lr = 0.001 +I0616 08:58:52.388758 9857 solver.cpp:242] Iteration 39840, loss = 1.17583 +I0616 08:58:52.388802 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382115 (* 1 = 0.382115 loss) +I0616 08:58:52.388806 9857 solver.cpp:258] Train net output #1: loss_cls = 0.773119 (* 1 = 0.773119 loss) +I0616 08:58:52.388810 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164235 (* 1 = 0.164235 loss) +I0616 08:58:52.388814 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173425 (* 1 = 0.0173425 loss) +I0616 08:58:52.388818 9857 solver.cpp:571] Iteration 39840, lr = 0.001 +I0616 08:59:04.080463 9857 solver.cpp:242] Iteration 39860, loss = 0.764432 +I0616 08:59:04.080505 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.450659 (* 1 = 0.450659 loss) +I0616 08:59:04.080510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.355961 (* 1 = 0.355961 loss) +I0616 08:59:04.080514 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.080453 (* 1 = 0.080453 loss) +I0616 08:59:04.080518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.106258 (* 1 = 0.106258 loss) +I0616 08:59:04.080523 9857 solver.cpp:571] Iteration 39860, lr = 0.001 +I0616 08:59:15.554921 9857 solver.cpp:242] Iteration 39880, loss = 0.797656 +I0616 08:59:15.554963 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191915 (* 1 = 0.191915 loss) +I0616 08:59:15.554968 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289172 (* 1 = 0.289172 loss) +I0616 08:59:15.554973 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15357 (* 1 = 0.15357 loss) +I0616 08:59:15.554976 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0740322 (* 1 = 0.0740322 loss) +I0616 08:59:15.554980 9857 solver.cpp:571] Iteration 39880, lr = 0.001 +I0616 08:59:27.085271 9857 solver.cpp:242] Iteration 39900, loss = 0.954427 +I0616 08:59:27.085314 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147242 (* 1 = 0.147242 loss) +I0616 08:59:27.085319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.779137 (* 1 = 0.779137 loss) +I0616 08:59:27.085322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14028 (* 1 = 0.14028 loss) +I0616 08:59:27.085326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00772623 (* 1 = 0.00772623 loss) +I0616 08:59:27.085330 9857 solver.cpp:571] Iteration 39900, lr = 0.001 +I0616 08:59:38.652246 9857 solver.cpp:242] Iteration 39920, loss = 0.793935 +I0616 08:59:38.652289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0660595 (* 1 = 0.0660595 loss) +I0616 08:59:38.652294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316117 (* 1 = 0.316117 loss) +I0616 08:59:38.652298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.036126 (* 1 = 0.036126 loss) +I0616 08:59:38.652302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00491712 (* 1 = 0.00491712 loss) +I0616 08:59:38.652307 9857 solver.cpp:571] Iteration 39920, lr = 0.001 +I0616 08:59:50.095414 9857 solver.cpp:242] Iteration 39940, loss = 0.666292 +I0616 08:59:50.095453 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107652 (* 1 = 0.107652 loss) +I0616 08:59:50.095459 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0894816 (* 1 = 0.0894816 loss) +I0616 08:59:50.095463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0151907 (* 1 = 0.0151907 loss) +I0616 08:59:50.095466 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00226947 (* 1 = 0.00226947 loss) +I0616 08:59:50.095470 9857 solver.cpp:571] Iteration 39940, lr = 0.001 +I0616 09:00:01.618538 9857 solver.cpp:242] Iteration 39960, loss = 0.774888 +I0616 09:00:01.618578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324826 (* 1 = 0.324826 loss) +I0616 09:00:01.618583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.331003 (* 1 = 0.331003 loss) +I0616 09:00:01.618587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.176914 (* 1 = 0.176914 loss) +I0616 09:00:01.618592 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0726506 (* 1 = 0.0726506 loss) +I0616 09:00:01.618595 9857 solver.cpp:571] Iteration 39960, lr = 0.001 +I0616 09:00:13.028368 9857 solver.cpp:242] Iteration 39980, loss = 0.925342 +I0616 09:00:13.028398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409114 (* 1 = 0.409114 loss) +I0616 09:00:13.028405 9857 solver.cpp:258] Train net output #1: loss_cls = 0.68178 (* 1 = 0.68178 loss) +I0616 09:00:13.028411 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.279773 (* 1 = 0.279773 loss) +I0616 09:00:13.028419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.177051 (* 1 = 0.177051 loss) +I0616 09:00:13.028424 9857 solver.cpp:571] Iteration 39980, lr = 0.001 +speed: 0.622s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_40000.caffemodel +I0616 09:00:26.232972 9857 solver.cpp:242] Iteration 40000, loss = 1.13355 +I0616 09:00:26.233003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127382 (* 1 = 0.127382 loss) +I0616 09:00:26.233011 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326064 (* 1 = 0.326064 loss) +I0616 09:00:26.233016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0831939 (* 1 = 0.0831939 loss) +I0616 09:00:26.233022 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222902 (* 1 = 0.0222902 loss) +I0616 09:00:26.233027 9857 solver.cpp:571] Iteration 40000, lr = 0.001 +I0616 09:00:37.799545 9857 solver.cpp:242] Iteration 40020, loss = 0.853945 +I0616 09:00:37.799574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281305 (* 1 = 0.281305 loss) +I0616 09:00:37.799582 9857 solver.cpp:258] Train net output #1: loss_cls = 0.3323 (* 1 = 0.3323 loss) +I0616 09:00:37.799588 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0909465 (* 1 = 0.0909465 loss) +I0616 09:00:37.799594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0920079 (* 1 = 0.0920079 loss) +I0616 09:00:37.799603 9857 solver.cpp:571] Iteration 40020, lr = 0.001 +I0616 09:00:49.091363 9857 solver.cpp:242] Iteration 40040, loss = 0.480298 +I0616 09:00:49.091393 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32094 (* 1 = 0.32094 loss) +I0616 09:00:49.091401 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326357 (* 1 = 0.326357 loss) +I0616 09:00:49.091408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0445516 (* 1 = 0.0445516 loss) +I0616 09:00:49.091413 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0482803 (* 1 = 0.0482803 loss) +I0616 09:00:49.091423 9857 solver.cpp:571] Iteration 40040, lr = 0.001 +I0616 09:01:00.514794 9857 solver.cpp:242] Iteration 40060, loss = 0.335721 +I0616 09:01:00.514823 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145705 (* 1 = 0.145705 loss) +I0616 09:01:00.514832 9857 solver.cpp:258] Train net output #1: loss_cls = 0.085932 (* 1 = 0.085932 loss) +I0616 09:01:00.514838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0323962 (* 1 = 0.0323962 loss) +I0616 09:01:00.514844 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0559767 (* 1 = 0.0559767 loss) +I0616 09:01:00.514855 9857 solver.cpp:571] Iteration 40060, lr = 0.001 +I0616 09:01:12.038830 9857 solver.cpp:242] Iteration 40080, loss = 0.930085 +I0616 09:01:12.038858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.40206 (* 1 = 0.40206 loss) +I0616 09:01:12.038866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340838 (* 1 = 0.340838 loss) +I0616 09:01:12.038872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0551032 (* 1 = 0.0551032 loss) +I0616 09:01:12.038878 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115089 (* 1 = 0.0115089 loss) +I0616 09:01:12.038884 9857 solver.cpp:571] Iteration 40080, lr = 0.001 +I0616 09:01:23.977979 9857 solver.cpp:242] Iteration 40100, loss = 0.835745 +I0616 09:01:23.978010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297627 (* 1 = 0.297627 loss) +I0616 09:01:23.978018 9857 solver.cpp:258] Train net output #1: loss_cls = 0.57587 (* 1 = 0.57587 loss) +I0616 09:01:23.978024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.185948 (* 1 = 0.185948 loss) +I0616 09:01:23.978029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0549655 (* 1 = 0.0549655 loss) +I0616 09:01:23.978037 9857 solver.cpp:571] Iteration 40100, lr = 0.001 +I0616 09:01:35.568282 9857 solver.cpp:242] Iteration 40120, loss = 0.839044 +I0616 09:01:35.568312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193311 (* 1 = 0.193311 loss) +I0616 09:01:35.568320 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268884 (* 1 = 0.268884 loss) +I0616 09:01:35.568325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11149 (* 1 = 0.11149 loss) +I0616 09:01:35.568331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0351156 (* 1 = 0.0351156 loss) +I0616 09:01:35.568337 9857 solver.cpp:571] Iteration 40120, lr = 0.001 +I0616 09:01:47.001256 9857 solver.cpp:242] Iteration 40140, loss = 0.792202 +I0616 09:01:47.001286 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181303 (* 1 = 0.181303 loss) +I0616 09:01:47.001293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.348292 (* 1 = 0.348292 loss) +I0616 09:01:47.001301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0268658 (* 1 = 0.0268658 loss) +I0616 09:01:47.001307 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0178278 (* 1 = 0.0178278 loss) +I0616 09:01:47.001312 9857 solver.cpp:571] Iteration 40140, lr = 0.001 +I0616 09:01:58.696282 9857 solver.cpp:242] Iteration 40160, loss = 0.379729 +I0616 09:01:58.696311 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140174 (* 1 = 0.140174 loss) +I0616 09:01:58.696318 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209889 (* 1 = 0.209889 loss) +I0616 09:01:58.696326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0142238 (* 1 = 0.0142238 loss) +I0616 09:01:58.696332 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215185 (* 1 = 0.0215185 loss) +I0616 09:01:58.696339 9857 solver.cpp:571] Iteration 40160, lr = 0.001 +I0616 09:02:10.237623 9857 solver.cpp:242] Iteration 40180, loss = 0.64224 +I0616 09:02:10.237653 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163733 (* 1 = 0.163733 loss) +I0616 09:02:10.237660 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381112 (* 1 = 0.381112 loss) +I0616 09:02:10.237666 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153233 (* 1 = 0.153233 loss) +I0616 09:02:10.237673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144312 (* 1 = 0.144312 loss) +I0616 09:02:10.237681 9857 solver.cpp:571] Iteration 40180, lr = 0.001 +speed: 0.621s / iter +I0616 09:02:21.888231 9857 solver.cpp:242] Iteration 40200, loss = 0.742168 +I0616 09:02:21.888259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180199 (* 1 = 0.180199 loss) +I0616 09:02:21.888267 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32615 (* 1 = 0.32615 loss) +I0616 09:02:21.888273 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0460984 (* 1 = 0.0460984 loss) +I0616 09:02:21.888278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103515 (* 1 = 0.103515 loss) +I0616 09:02:21.888288 9857 solver.cpp:571] Iteration 40200, lr = 0.001 +I0616 09:02:33.185562 9857 solver.cpp:242] Iteration 40220, loss = 0.387415 +I0616 09:02:33.185591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106596 (* 1 = 0.106596 loss) +I0616 09:02:33.185600 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0971761 (* 1 = 0.0971761 loss) +I0616 09:02:33.185606 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0515309 (* 1 = 0.0515309 loss) +I0616 09:02:33.185611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00519682 (* 1 = 0.00519682 loss) +I0616 09:02:33.185618 9857 solver.cpp:571] Iteration 40220, lr = 0.001 +I0616 09:02:44.813710 9857 solver.cpp:242] Iteration 40240, loss = 1.1568 +I0616 09:02:44.813738 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270234 (* 1 = 0.270234 loss) +I0616 09:02:44.813746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.541121 (* 1 = 0.541121 loss) +I0616 09:02:44.813752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100446 (* 1 = 0.100446 loss) +I0616 09:02:44.813757 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.124433 (* 1 = 0.124433 loss) +I0616 09:02:44.813763 9857 solver.cpp:571] Iteration 40240, lr = 0.001 +I0616 09:02:56.338482 9857 solver.cpp:242] Iteration 40260, loss = 0.762471 +I0616 09:02:56.338511 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265165 (* 1 = 0.265165 loss) +I0616 09:02:56.338518 9857 solver.cpp:258] Train net output #1: loss_cls = 0.546497 (* 1 = 0.546497 loss) +I0616 09:02:56.338524 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186349 (* 1 = 0.186349 loss) +I0616 09:02:56.338531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033486 (* 1 = 0.033486 loss) +I0616 09:02:56.338536 9857 solver.cpp:571] Iteration 40260, lr = 0.001 +I0616 09:03:07.694802 9857 solver.cpp:242] Iteration 40280, loss = 1.05654 +I0616 09:03:07.694833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.391199 (* 1 = 0.391199 loss) +I0616 09:03:07.694840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.933378 (* 1 = 0.933378 loss) +I0616 09:03:07.694847 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0956697 (* 1 = 0.0956697 loss) +I0616 09:03:07.694854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0590979 (* 1 = 0.0590979 loss) +I0616 09:03:07.694859 9857 solver.cpp:571] Iteration 40280, lr = 0.001 +I0616 09:03:19.075495 9857 solver.cpp:242] Iteration 40300, loss = 0.84399 +I0616 09:03:19.075523 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107428 (* 1 = 0.107428 loss) +I0616 09:03:19.075531 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129877 (* 1 = 0.129877 loss) +I0616 09:03:19.075551 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0264454 (* 1 = 0.0264454 loss) +I0616 09:03:19.075557 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0434972 (* 1 = 0.0434972 loss) +I0616 09:03:19.075564 9857 solver.cpp:571] Iteration 40300, lr = 0.001 +I0616 09:03:30.519525 9857 solver.cpp:242] Iteration 40320, loss = 0.492547 +I0616 09:03:30.519554 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104135 (* 1 = 0.104135 loss) +I0616 09:03:30.519562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255332 (* 1 = 0.255332 loss) +I0616 09:03:30.519567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0707223 (* 1 = 0.0707223 loss) +I0616 09:03:30.519574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0755816 (* 1 = 0.0755816 loss) +I0616 09:03:30.519582 9857 solver.cpp:571] Iteration 40320, lr = 0.001 +I0616 09:03:42.430613 9857 solver.cpp:242] Iteration 40340, loss = 0.457618 +I0616 09:03:42.430641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247442 (* 1 = 0.247442 loss) +I0616 09:03:42.430649 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177695 (* 1 = 0.177695 loss) +I0616 09:03:42.430655 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0213734 (* 1 = 0.0213734 loss) +I0616 09:03:42.430661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0618323 (* 1 = 0.0618323 loss) +I0616 09:03:42.430668 9857 solver.cpp:571] Iteration 40340, lr = 0.001 +I0616 09:03:53.957761 9857 solver.cpp:242] Iteration 40360, loss = 0.741619 +I0616 09:03:53.957788 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297194 (* 1 = 0.297194 loss) +I0616 09:03:53.957795 9857 solver.cpp:258] Train net output #1: loss_cls = 0.526906 (* 1 = 0.526906 loss) +I0616 09:03:53.957801 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.237056 (* 1 = 0.237056 loss) +I0616 09:03:53.957808 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104592 (* 1 = 0.104592 loss) +I0616 09:03:53.957814 9857 solver.cpp:571] Iteration 40360, lr = 0.001 +I0616 09:04:05.330776 9857 solver.cpp:242] Iteration 40380, loss = 0.439176 +I0616 09:04:05.330818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11891 (* 1 = 0.11891 loss) +I0616 09:04:05.330824 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208827 (* 1 = 0.208827 loss) +I0616 09:04:05.330828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0358747 (* 1 = 0.0358747 loss) +I0616 09:04:05.330832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0070383 (* 1 = 0.0070383 loss) +I0616 09:04:05.330835 9857 solver.cpp:571] Iteration 40380, lr = 0.001 +speed: 0.621s / iter +I0616 09:04:16.617800 9857 solver.cpp:242] Iteration 40400, loss = 0.81029 +I0616 09:04:16.617842 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350007 (* 1 = 0.350007 loss) +I0616 09:04:16.617848 9857 solver.cpp:258] Train net output #1: loss_cls = 0.697276 (* 1 = 0.697276 loss) +I0616 09:04:16.617852 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0733353 (* 1 = 0.0733353 loss) +I0616 09:04:16.617856 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.142674 (* 1 = 0.142674 loss) +I0616 09:04:16.617861 9857 solver.cpp:571] Iteration 40400, lr = 0.001 +I0616 09:04:28.099846 9857 solver.cpp:242] Iteration 40420, loss = 1.27366 +I0616 09:04:28.099891 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.499514 (* 1 = 0.499514 loss) +I0616 09:04:28.099897 9857 solver.cpp:258] Train net output #1: loss_cls = 0.702847 (* 1 = 0.702847 loss) +I0616 09:04:28.099901 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0415686 (* 1 = 0.0415686 loss) +I0616 09:04:28.099905 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0475405 (* 1 = 0.0475405 loss) +I0616 09:04:28.099910 9857 solver.cpp:571] Iteration 40420, lr = 0.001 +I0616 09:04:39.753948 9857 solver.cpp:242] Iteration 40440, loss = 0.66904 +I0616 09:04:39.753989 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310876 (* 1 = 0.310876 loss) +I0616 09:04:39.753995 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263867 (* 1 = 0.263867 loss) +I0616 09:04:39.753999 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0528784 (* 1 = 0.0528784 loss) +I0616 09:04:39.754004 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0317234 (* 1 = 0.0317234 loss) +I0616 09:04:39.754006 9857 solver.cpp:571] Iteration 40440, lr = 0.001 +I0616 09:04:51.449738 9857 solver.cpp:242] Iteration 40460, loss = 0.514367 +I0616 09:04:51.449779 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104925 (* 1 = 0.104925 loss) +I0616 09:04:51.449784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1218 (* 1 = 0.1218 loss) +I0616 09:04:51.449789 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0526123 (* 1 = 0.0526123 loss) +I0616 09:04:51.449791 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00812577 (* 1 = 0.00812577 loss) +I0616 09:04:51.449795 9857 solver.cpp:571] Iteration 40460, lr = 0.001 +I0616 09:05:02.973922 9857 solver.cpp:242] Iteration 40480, loss = 1.15727 +I0616 09:05:02.973964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221394 (* 1 = 0.221394 loss) +I0616 09:05:02.973969 9857 solver.cpp:258] Train net output #1: loss_cls = 0.389595 (* 1 = 0.389595 loss) +I0616 09:05:02.973974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0727308 (* 1 = 0.0727308 loss) +I0616 09:05:02.973978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205531 (* 1 = 0.0205531 loss) +I0616 09:05:02.973981 9857 solver.cpp:571] Iteration 40480, lr = 0.001 +I0616 09:05:14.706193 9857 solver.cpp:242] Iteration 40500, loss = 0.9214 +I0616 09:05:14.706236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0820057 (* 1 = 0.0820057 loss) +I0616 09:05:14.706241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269922 (* 1 = 0.269922 loss) +I0616 09:05:14.706246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0249375 (* 1 = 0.0249375 loss) +I0616 09:05:14.706249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00218749 (* 1 = 0.00218749 loss) +I0616 09:05:14.706254 9857 solver.cpp:571] Iteration 40500, lr = 0.001 +I0616 09:05:26.295855 9857 solver.cpp:242] Iteration 40520, loss = 1.60455 +I0616 09:05:26.295895 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.617466 (* 1 = 0.617466 loss) +I0616 09:05:26.295900 9857 solver.cpp:258] Train net output #1: loss_cls = 0.52178 (* 1 = 0.52178 loss) +I0616 09:05:26.295904 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.244874 (* 1 = 0.244874 loss) +I0616 09:05:26.295908 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0595156 (* 1 = 0.0595156 loss) +I0616 09:05:26.295912 9857 solver.cpp:571] Iteration 40520, lr = 0.001 +I0616 09:05:37.704473 9857 solver.cpp:242] Iteration 40540, loss = 1.08476 +I0616 09:05:37.704516 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488792 (* 1 = 0.488792 loss) +I0616 09:05:37.704522 9857 solver.cpp:258] Train net output #1: loss_cls = 1.10095 (* 1 = 1.10095 loss) +I0616 09:05:37.704526 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0782878 (* 1 = 0.0782878 loss) +I0616 09:05:37.704530 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231704 (* 1 = 0.0231704 loss) +I0616 09:05:37.704533 9857 solver.cpp:571] Iteration 40540, lr = 0.001 +I0616 09:05:49.462726 9857 solver.cpp:242] Iteration 40560, loss = 0.462056 +I0616 09:05:49.462774 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123039 (* 1 = 0.123039 loss) +I0616 09:05:49.462779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259286 (* 1 = 0.259286 loss) +I0616 09:05:49.462785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0307324 (* 1 = 0.0307324 loss) +I0616 09:05:49.462787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.037791 (* 1 = 0.037791 loss) +I0616 09:05:49.462791 9857 solver.cpp:571] Iteration 40560, lr = 0.001 +I0616 09:06:01.175863 9857 solver.cpp:242] Iteration 40580, loss = 0.921189 +I0616 09:06:01.175904 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0718103 (* 1 = 0.0718103 loss) +I0616 09:06:01.175910 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0824668 (* 1 = 0.0824668 loss) +I0616 09:06:01.175915 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274523 (* 1 = 0.0274523 loss) +I0616 09:06:01.175917 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00658458 (* 1 = 0.00658458 loss) +I0616 09:06:01.175921 9857 solver.cpp:571] Iteration 40580, lr = 0.001 +speed: 0.621s / iter +I0616 09:06:12.608213 9857 solver.cpp:242] Iteration 40600, loss = 0.831117 +I0616 09:06:12.608255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0939412 (* 1 = 0.0939412 loss) +I0616 09:06:12.608260 9857 solver.cpp:258] Train net output #1: loss_cls = 0.334992 (* 1 = 0.334992 loss) +I0616 09:06:12.608264 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.31942 (* 1 = 0.31942 loss) +I0616 09:06:12.608268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00378762 (* 1 = 0.00378762 loss) +I0616 09:06:12.608273 9857 solver.cpp:571] Iteration 40600, lr = 0.001 +I0616 09:06:24.041016 9857 solver.cpp:242] Iteration 40620, loss = 0.68858 +I0616 09:06:24.041059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282002 (* 1 = 0.282002 loss) +I0616 09:06:24.041064 9857 solver.cpp:258] Train net output #1: loss_cls = 0.346077 (* 1 = 0.346077 loss) +I0616 09:06:24.041069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105241 (* 1 = 0.105241 loss) +I0616 09:06:24.041072 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127933 (* 1 = 0.0127933 loss) +I0616 09:06:24.041076 9857 solver.cpp:571] Iteration 40620, lr = 0.001 +I0616 09:06:35.687594 9857 solver.cpp:242] Iteration 40640, loss = 0.875726 +I0616 09:06:35.687636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334391 (* 1 = 0.334391 loss) +I0616 09:06:35.687643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.668956 (* 1 = 0.668956 loss) +I0616 09:06:35.687646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13948 (* 1 = 0.13948 loss) +I0616 09:06:35.687650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0632632 (* 1 = 0.0632632 loss) +I0616 09:06:35.687654 9857 solver.cpp:571] Iteration 40640, lr = 0.001 +I0616 09:06:47.334120 9857 solver.cpp:242] Iteration 40660, loss = 0.57636 +I0616 09:06:47.334167 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305567 (* 1 = 0.305567 loss) +I0616 09:06:47.334175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317872 (* 1 = 0.317872 loss) +I0616 09:06:47.334182 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.181732 (* 1 = 0.181732 loss) +I0616 09:06:47.334189 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0146347 (* 1 = 0.0146347 loss) +I0616 09:06:47.334197 9857 solver.cpp:571] Iteration 40660, lr = 0.001 +I0616 09:06:59.028952 9857 solver.cpp:242] Iteration 40680, loss = 0.686556 +I0616 09:06:59.028993 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173747 (* 1 = 0.173747 loss) +I0616 09:06:59.029000 9857 solver.cpp:258] Train net output #1: loss_cls = 0.319437 (* 1 = 0.319437 loss) +I0616 09:06:59.029003 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0544021 (* 1 = 0.0544021 loss) +I0616 09:06:59.029007 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251092 (* 1 = 0.0251092 loss) +I0616 09:06:59.029011 9857 solver.cpp:571] Iteration 40680, lr = 0.001 +I0616 09:07:10.710363 9857 solver.cpp:242] Iteration 40700, loss = 0.436914 +I0616 09:07:10.710407 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138686 (* 1 = 0.138686 loss) +I0616 09:07:10.710412 9857 solver.cpp:258] Train net output #1: loss_cls = 0.423653 (* 1 = 0.423653 loss) +I0616 09:07:10.710417 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0803979 (* 1 = 0.0803979 loss) +I0616 09:07:10.710420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033955 (* 1 = 0.033955 loss) +I0616 09:07:10.710423 9857 solver.cpp:571] Iteration 40700, lr = 0.001 +I0616 09:07:22.207212 9857 solver.cpp:242] Iteration 40720, loss = 1.2313 +I0616 09:07:22.207252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.526981 (* 1 = 0.526981 loss) +I0616 09:07:22.207258 9857 solver.cpp:258] Train net output #1: loss_cls = 0.569379 (* 1 = 0.569379 loss) +I0616 09:07:22.207262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.322 (* 1 = 0.322 loss) +I0616 09:07:22.207267 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.572066 (* 1 = 0.572066 loss) +I0616 09:07:22.207269 9857 solver.cpp:571] Iteration 40720, lr = 0.001 +I0616 09:07:33.735129 9857 solver.cpp:242] Iteration 40740, loss = 0.959648 +I0616 09:07:33.735172 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279627 (* 1 = 0.279627 loss) +I0616 09:07:33.735177 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27682 (* 1 = 0.27682 loss) +I0616 09:07:33.735182 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0681285 (* 1 = 0.0681285 loss) +I0616 09:07:33.735185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128692 (* 1 = 0.0128692 loss) +I0616 09:07:33.735189 9857 solver.cpp:571] Iteration 40740, lr = 0.001 +I0616 09:07:45.144567 9857 solver.cpp:242] Iteration 40760, loss = 0.475029 +I0616 09:07:45.144608 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167649 (* 1 = 0.167649 loss) +I0616 09:07:45.144613 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250569 (* 1 = 0.250569 loss) +I0616 09:07:45.144618 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107564 (* 1 = 0.107564 loss) +I0616 09:07:45.144621 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00120066 (* 1 = 0.00120066 loss) +I0616 09:07:45.144625 9857 solver.cpp:571] Iteration 40760, lr = 0.001 +I0616 09:07:56.672538 9857 solver.cpp:242] Iteration 40780, loss = 0.498539 +I0616 09:07:56.672592 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17106 (* 1 = 0.17106 loss) +I0616 09:07:56.672597 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149693 (* 1 = 0.149693 loss) +I0616 09:07:56.672602 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139544 (* 1 = 0.139544 loss) +I0616 09:07:56.672605 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0185661 (* 1 = 0.0185661 loss) +I0616 09:07:56.672610 9857 solver.cpp:571] Iteration 40780, lr = 0.001 +speed: 0.621s / iter +I0616 09:08:08.150557 9857 solver.cpp:242] Iteration 40800, loss = 1.00715 +I0616 09:08:08.150599 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197293 (* 1 = 0.197293 loss) +I0616 09:08:08.150604 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278994 (* 1 = 0.278994 loss) +I0616 09:08:08.150609 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0754552 (* 1 = 0.0754552 loss) +I0616 09:08:08.150612 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0853174 (* 1 = 0.0853174 loss) +I0616 09:08:08.150615 9857 solver.cpp:571] Iteration 40800, lr = 0.001 +I0616 09:08:19.793380 9857 solver.cpp:242] Iteration 40820, loss = 0.917906 +I0616 09:08:19.793422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149238 (* 1 = 0.149238 loss) +I0616 09:08:19.793428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.461052 (* 1 = 0.461052 loss) +I0616 09:08:19.793432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0313665 (* 1 = 0.0313665 loss) +I0616 09:08:19.793437 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00845505 (* 1 = 0.00845505 loss) +I0616 09:08:19.793440 9857 solver.cpp:571] Iteration 40820, lr = 0.001 +I0616 09:08:31.713435 9857 solver.cpp:242] Iteration 40840, loss = 0.413568 +I0616 09:08:31.713479 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0797776 (* 1 = 0.0797776 loss) +I0616 09:08:31.713484 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190045 (* 1 = 0.190045 loss) +I0616 09:08:31.713487 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172017 (* 1 = 0.172017 loss) +I0616 09:08:31.713491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114591 (* 1 = 0.0114591 loss) +I0616 09:08:31.713495 9857 solver.cpp:571] Iteration 40840, lr = 0.001 +I0616 09:08:43.084393 9857 solver.cpp:242] Iteration 40860, loss = 0.459469 +I0616 09:08:43.084437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.216881 (* 1 = 0.216881 loss) +I0616 09:08:43.084442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27647 (* 1 = 0.27647 loss) +I0616 09:08:43.084446 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0473526 (* 1 = 0.0473526 loss) +I0616 09:08:43.084450 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00420473 (* 1 = 0.00420473 loss) +I0616 09:08:43.084455 9857 solver.cpp:571] Iteration 40860, lr = 0.001 +I0616 09:08:54.787156 9857 solver.cpp:242] Iteration 40880, loss = 0.484478 +I0616 09:08:54.787199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220599 (* 1 = 0.220599 loss) +I0616 09:08:54.787204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252619 (* 1 = 0.252619 loss) +I0616 09:08:54.787209 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0530408 (* 1 = 0.0530408 loss) +I0616 09:08:54.787212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00834243 (* 1 = 0.00834243 loss) +I0616 09:08:54.787216 9857 solver.cpp:571] Iteration 40880, lr = 0.001 +I0616 09:09:06.654477 9857 solver.cpp:242] Iteration 40900, loss = 0.486441 +I0616 09:09:06.654520 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131092 (* 1 = 0.131092 loss) +I0616 09:09:06.654525 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310968 (* 1 = 0.310968 loss) +I0616 09:09:06.654528 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0208064 (* 1 = 0.0208064 loss) +I0616 09:09:06.654532 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210794 (* 1 = 0.0210794 loss) +I0616 09:09:06.654536 9857 solver.cpp:571] Iteration 40900, lr = 0.001 +I0616 09:09:18.337574 9857 solver.cpp:242] Iteration 40920, loss = 0.373964 +I0616 09:09:18.337616 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121188 (* 1 = 0.121188 loss) +I0616 09:09:18.337621 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248115 (* 1 = 0.248115 loss) +I0616 09:09:18.337625 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0690827 (* 1 = 0.0690827 loss) +I0616 09:09:18.337630 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029376 (* 1 = 0.029376 loss) +I0616 09:09:18.337633 9857 solver.cpp:571] Iteration 40920, lr = 0.001 +I0616 09:09:29.938616 9857 solver.cpp:242] Iteration 40940, loss = 1.11909 +I0616 09:09:29.938659 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.487911 (* 1 = 0.487911 loss) +I0616 09:09:29.938664 9857 solver.cpp:258] Train net output #1: loss_cls = 0.737839 (* 1 = 0.737839 loss) +I0616 09:09:29.938669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183143 (* 1 = 0.183143 loss) +I0616 09:09:29.938673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0548899 (* 1 = 0.0548899 loss) +I0616 09:09:29.938676 9857 solver.cpp:571] Iteration 40940, lr = 0.001 +I0616 09:09:41.498760 9857 solver.cpp:242] Iteration 40960, loss = 1.02814 +I0616 09:09:41.498803 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120309 (* 1 = 0.120309 loss) +I0616 09:09:41.498809 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298923 (* 1 = 0.298923 loss) +I0616 09:09:41.498813 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178764 (* 1 = 0.178764 loss) +I0616 09:09:41.498817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119345 (* 1 = 0.0119345 loss) +I0616 09:09:41.498821 9857 solver.cpp:571] Iteration 40960, lr = 0.001 +I0616 09:09:52.976440 9857 solver.cpp:242] Iteration 40980, loss = 0.61562 +I0616 09:09:52.976483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215829 (* 1 = 0.215829 loss) +I0616 09:09:52.976488 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289743 (* 1 = 0.289743 loss) +I0616 09:09:52.976493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101849 (* 1 = 0.101849 loss) +I0616 09:09:52.976497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00133052 (* 1 = 0.00133052 loss) +I0616 09:09:52.976501 9857 solver.cpp:571] Iteration 40980, lr = 0.001 +speed: 0.621s / iter +I0616 09:10:04.557858 9857 solver.cpp:242] Iteration 41000, loss = 0.663761 +I0616 09:10:04.557900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285314 (* 1 = 0.285314 loss) +I0616 09:10:04.557906 9857 solver.cpp:258] Train net output #1: loss_cls = 0.476944 (* 1 = 0.476944 loss) +I0616 09:10:04.557910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0507354 (* 1 = 0.0507354 loss) +I0616 09:10:04.557914 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0769399 (* 1 = 0.0769399 loss) +I0616 09:10:04.557919 9857 solver.cpp:571] Iteration 41000, lr = 0.001 +I0616 09:10:16.004076 9857 solver.cpp:242] Iteration 41020, loss = 0.500439 +I0616 09:10:16.004115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0677529 (* 1 = 0.0677529 loss) +I0616 09:10:16.004120 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151523 (* 1 = 0.151523 loss) +I0616 09:10:16.004125 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0926434 (* 1 = 0.0926434 loss) +I0616 09:10:16.004128 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139143 (* 1 = 0.0139143 loss) +I0616 09:10:16.004132 9857 solver.cpp:571] Iteration 41020, lr = 0.001 +I0616 09:10:27.642670 9857 solver.cpp:242] Iteration 41040, loss = 0.792873 +I0616 09:10:27.642709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220988 (* 1 = 0.220988 loss) +I0616 09:10:27.642715 9857 solver.cpp:258] Train net output #1: loss_cls = 0.45487 (* 1 = 0.45487 loss) +I0616 09:10:27.642719 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168835 (* 1 = 0.168835 loss) +I0616 09:10:27.642724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0470261 (* 1 = 0.0470261 loss) +I0616 09:10:27.642726 9857 solver.cpp:571] Iteration 41040, lr = 0.001 +I0616 09:10:39.365732 9857 solver.cpp:242] Iteration 41060, loss = 0.687782 +I0616 09:10:39.365775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138815 (* 1 = 0.138815 loss) +I0616 09:10:39.365780 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345898 (* 1 = 0.345898 loss) +I0616 09:10:39.365785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0407876 (* 1 = 0.0407876 loss) +I0616 09:10:39.365788 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0801061 (* 1 = 0.0801061 loss) +I0616 09:10:39.365792 9857 solver.cpp:571] Iteration 41060, lr = 0.001 +I0616 09:10:50.697453 9857 solver.cpp:242] Iteration 41080, loss = 0.514216 +I0616 09:10:50.697496 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247712 (* 1 = 0.247712 loss) +I0616 09:10:50.697502 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182365 (* 1 = 0.182365 loss) +I0616 09:10:50.697506 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.029196 (* 1 = 0.029196 loss) +I0616 09:10:50.697510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.016233 (* 1 = 0.016233 loss) +I0616 09:10:50.697515 9857 solver.cpp:571] Iteration 41080, lr = 0.001 +I0616 09:11:02.220242 9857 solver.cpp:242] Iteration 41100, loss = 1.21918 +I0616 09:11:02.220284 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262273 (* 1 = 0.262273 loss) +I0616 09:11:02.220290 9857 solver.cpp:258] Train net output #1: loss_cls = 0.661438 (* 1 = 0.661438 loss) +I0616 09:11:02.220294 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.448104 (* 1 = 0.448104 loss) +I0616 09:11:02.220299 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.456391 (* 1 = 0.456391 loss) +I0616 09:11:02.220302 9857 solver.cpp:571] Iteration 41100, lr = 0.001 +I0616 09:11:13.707101 9857 solver.cpp:242] Iteration 41120, loss = 0.63298 +I0616 09:11:13.707144 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211931 (* 1 = 0.211931 loss) +I0616 09:11:13.707149 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375987 (* 1 = 0.375987 loss) +I0616 09:11:13.707154 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102251 (* 1 = 0.102251 loss) +I0616 09:11:13.707156 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.201552 (* 1 = 0.201552 loss) +I0616 09:11:13.707160 9857 solver.cpp:571] Iteration 41120, lr = 0.001 +I0616 09:11:25.126457 9857 solver.cpp:242] Iteration 41140, loss = 1.92744 +I0616 09:11:25.126499 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298748 (* 1 = 0.298748 loss) +I0616 09:11:25.126505 9857 solver.cpp:258] Train net output #1: loss_cls = 0.610759 (* 1 = 0.610759 loss) +I0616 09:11:25.126509 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.231699 (* 1 = 0.231699 loss) +I0616 09:11:25.126513 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0523029 (* 1 = 0.0523029 loss) +I0616 09:11:25.126518 9857 solver.cpp:571] Iteration 41140, lr = 0.001 +I0616 09:11:36.491283 9857 solver.cpp:242] Iteration 41160, loss = 0.296829 +I0616 09:11:36.491325 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109037 (* 1 = 0.109037 loss) +I0616 09:11:36.491331 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148045 (* 1 = 0.148045 loss) +I0616 09:11:36.491335 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0217681 (* 1 = 0.0217681 loss) +I0616 09:11:36.491339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0204796 (* 1 = 0.0204796 loss) +I0616 09:11:36.491343 9857 solver.cpp:571] Iteration 41160, lr = 0.001 +I0616 09:11:48.090136 9857 solver.cpp:242] Iteration 41180, loss = 0.879973 +I0616 09:11:48.090178 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132766 (* 1 = 0.132766 loss) +I0616 09:11:48.090183 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140374 (* 1 = 0.140374 loss) +I0616 09:11:48.090188 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.05827 (* 1 = 0.05827 loss) +I0616 09:11:48.090190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0403023 (* 1 = 0.0403023 loss) +I0616 09:11:48.090194 9857 solver.cpp:571] Iteration 41180, lr = 0.001 +speed: 0.620s / iter +I0616 09:11:59.739055 9857 solver.cpp:242] Iteration 41200, loss = 0.35602 +I0616 09:11:59.739097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0856009 (* 1 = 0.0856009 loss) +I0616 09:11:59.739102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184319 (* 1 = 0.184319 loss) +I0616 09:11:59.739106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0387799 (* 1 = 0.0387799 loss) +I0616 09:11:59.739110 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0304945 (* 1 = 0.0304945 loss) +I0616 09:11:59.739114 9857 solver.cpp:571] Iteration 41200, lr = 0.001 +I0616 09:12:11.154469 9857 solver.cpp:242] Iteration 41220, loss = 0.622378 +I0616 09:12:11.154512 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167861 (* 1 = 0.167861 loss) +I0616 09:12:11.154517 9857 solver.cpp:258] Train net output #1: loss_cls = 0.540961 (* 1 = 0.540961 loss) +I0616 09:12:11.154521 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0250944 (* 1 = 0.0250944 loss) +I0616 09:12:11.154525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221426 (* 1 = 0.0221426 loss) +I0616 09:12:11.154530 9857 solver.cpp:571] Iteration 41220, lr = 0.001 +I0616 09:12:22.848603 9857 solver.cpp:242] Iteration 41240, loss = 0.437989 +I0616 09:12:22.848645 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147623 (* 1 = 0.147623 loss) +I0616 09:12:22.848650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159837 (* 1 = 0.159837 loss) +I0616 09:12:22.848654 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15745 (* 1 = 0.15745 loss) +I0616 09:12:22.848659 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00722111 (* 1 = 0.00722111 loss) +I0616 09:12:22.848662 9857 solver.cpp:571] Iteration 41240, lr = 0.001 +I0616 09:12:34.385877 9857 solver.cpp:242] Iteration 41260, loss = 0.260717 +I0616 09:12:34.385920 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0436509 (* 1 = 0.0436509 loss) +I0616 09:12:34.385924 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121956 (* 1 = 0.121956 loss) +I0616 09:12:34.385928 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176216 (* 1 = 0.0176216 loss) +I0616 09:12:34.385932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235752 (* 1 = 0.0235752 loss) +I0616 09:12:34.385936 9857 solver.cpp:571] Iteration 41260, lr = 0.001 +I0616 09:12:45.768180 9857 solver.cpp:242] Iteration 41280, loss = 0.561829 +I0616 09:12:45.768223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0933085 (* 1 = 0.0933085 loss) +I0616 09:12:45.768227 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269098 (* 1 = 0.269098 loss) +I0616 09:12:45.768231 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0148719 (* 1 = 0.0148719 loss) +I0616 09:12:45.768235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00935093 (* 1 = 0.00935093 loss) +I0616 09:12:45.768239 9857 solver.cpp:571] Iteration 41280, lr = 0.001 +I0616 09:12:57.517604 9857 solver.cpp:242] Iteration 41300, loss = 1.56157 +I0616 09:12:57.517645 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.410723 (* 1 = 0.410723 loss) +I0616 09:12:57.517650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.985395 (* 1 = 0.985395 loss) +I0616 09:12:57.517655 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.718316 (* 1 = 0.718316 loss) +I0616 09:12:57.517658 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.142155 (* 1 = 0.142155 loss) +I0616 09:12:57.517663 9857 solver.cpp:571] Iteration 41300, lr = 0.001 +I0616 09:13:09.095991 9857 solver.cpp:242] Iteration 41320, loss = 0.488734 +I0616 09:13:09.096034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280626 (* 1 = 0.280626 loss) +I0616 09:13:09.096038 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250472 (* 1 = 0.250472 loss) +I0616 09:13:09.096042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0795202 (* 1 = 0.0795202 loss) +I0616 09:13:09.096046 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168543 (* 1 = 0.0168543 loss) +I0616 09:13:09.096050 9857 solver.cpp:571] Iteration 41320, lr = 0.001 +I0616 09:13:20.547593 9857 solver.cpp:242] Iteration 41340, loss = 0.670873 +I0616 09:13:20.547636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181954 (* 1 = 0.181954 loss) +I0616 09:13:20.547641 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185075 (* 1 = 0.185075 loss) +I0616 09:13:20.547646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119758 (* 1 = 0.0119758 loss) +I0616 09:13:20.547649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00696601 (* 1 = 0.00696601 loss) +I0616 09:13:20.547653 9857 solver.cpp:571] Iteration 41340, lr = 0.001 +I0616 09:13:31.938488 9857 solver.cpp:242] Iteration 41360, loss = 0.544875 +I0616 09:13:31.938530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.404569 (* 1 = 0.404569 loss) +I0616 09:13:31.938535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255545 (* 1 = 0.255545 loss) +I0616 09:13:31.938540 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0403 (* 1 = 0.0403 loss) +I0616 09:13:31.938544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239124 (* 1 = 0.0239124 loss) +I0616 09:13:31.938547 9857 solver.cpp:571] Iteration 41360, lr = 0.001 +I0616 09:13:43.565071 9857 solver.cpp:242] Iteration 41380, loss = 0.858495 +I0616 09:13:43.565115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406602 (* 1 = 0.406602 loss) +I0616 09:13:43.565120 9857 solver.cpp:258] Train net output #1: loss_cls = 0.469944 (* 1 = 0.469944 loss) +I0616 09:13:43.565124 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.232189 (* 1 = 0.232189 loss) +I0616 09:13:43.565129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.282888 (* 1 = 0.282888 loss) +I0616 09:13:43.565131 9857 solver.cpp:571] Iteration 41380, lr = 0.001 +speed: 0.620s / iter +I0616 09:13:54.970958 9857 solver.cpp:242] Iteration 41400, loss = 0.786487 +I0616 09:13:54.971000 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.325262 (* 1 = 0.325262 loss) +I0616 09:13:54.971005 9857 solver.cpp:258] Train net output #1: loss_cls = 0.311774 (* 1 = 0.311774 loss) +I0616 09:13:54.971010 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0827111 (* 1 = 0.0827111 loss) +I0616 09:13:54.971014 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0370553 (* 1 = 0.0370553 loss) +I0616 09:13:54.971017 9857 solver.cpp:571] Iteration 41400, lr = 0.001 +I0616 09:14:06.639219 9857 solver.cpp:242] Iteration 41420, loss = 0.251463 +I0616 09:14:06.639259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130819 (* 1 = 0.130819 loss) +I0616 09:14:06.639266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123111 (* 1 = 0.123111 loss) +I0616 09:14:06.639269 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114365 (* 1 = 0.0114365 loss) +I0616 09:14:06.639273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00906429 (* 1 = 0.00906429 loss) +I0616 09:14:06.639276 9857 solver.cpp:571] Iteration 41420, lr = 0.001 +I0616 09:14:18.187185 9857 solver.cpp:242] Iteration 41440, loss = 0.728439 +I0616 09:14:18.187225 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3798 (* 1 = 0.3798 loss) +I0616 09:14:18.187232 9857 solver.cpp:258] Train net output #1: loss_cls = 0.645255 (* 1 = 0.645255 loss) +I0616 09:14:18.187235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.045387 (* 1 = 0.045387 loss) +I0616 09:14:18.187239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0315802 (* 1 = 0.0315802 loss) +I0616 09:14:18.187242 9857 solver.cpp:571] Iteration 41440, lr = 0.001 +I0616 09:14:29.872736 9857 solver.cpp:242] Iteration 41460, loss = 0.772167 +I0616 09:14:29.872778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353701 (* 1 = 0.353701 loss) +I0616 09:14:29.872784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.386193 (* 1 = 0.386193 loss) +I0616 09:14:29.872788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115193 (* 1 = 0.115193 loss) +I0616 09:14:29.872792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0265343 (* 1 = 0.0265343 loss) +I0616 09:14:29.872795 9857 solver.cpp:571] Iteration 41460, lr = 0.001 +I0616 09:14:41.412293 9857 solver.cpp:242] Iteration 41480, loss = 0.617534 +I0616 09:14:41.412335 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346479 (* 1 = 0.346479 loss) +I0616 09:14:41.412340 9857 solver.cpp:258] Train net output #1: loss_cls = 0.532939 (* 1 = 0.532939 loss) +I0616 09:14:41.412344 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12788 (* 1 = 0.12788 loss) +I0616 09:14:41.412348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0503103 (* 1 = 0.0503103 loss) +I0616 09:14:41.412353 9857 solver.cpp:571] Iteration 41480, lr = 0.001 +I0616 09:14:52.935109 9857 solver.cpp:242] Iteration 41500, loss = 0.794298 +I0616 09:14:52.935151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.38575 (* 1 = 0.38575 loss) +I0616 09:14:52.935156 9857 solver.cpp:258] Train net output #1: loss_cls = 0.613391 (* 1 = 0.613391 loss) +I0616 09:14:52.935160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0484899 (* 1 = 0.0484899 loss) +I0616 09:14:52.935164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00709628 (* 1 = 0.00709628 loss) +I0616 09:14:52.935168 9857 solver.cpp:571] Iteration 41500, lr = 0.001 +I0616 09:15:04.337241 9857 solver.cpp:242] Iteration 41520, loss = 0.643509 +I0616 09:15:04.337282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163997 (* 1 = 0.163997 loss) +I0616 09:15:04.337287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.297158 (* 1 = 0.297158 loss) +I0616 09:15:04.337291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0987976 (* 1 = 0.0987976 loss) +I0616 09:15:04.337296 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00178144 (* 1 = 0.00178144 loss) +I0616 09:15:04.337299 9857 solver.cpp:571] Iteration 41520, lr = 0.001 +I0616 09:15:15.897792 9857 solver.cpp:242] Iteration 41540, loss = 0.731975 +I0616 09:15:15.897835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159922 (* 1 = 0.159922 loss) +I0616 09:15:15.897840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132169 (* 1 = 0.132169 loss) +I0616 09:15:15.897845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0504135 (* 1 = 0.0504135 loss) +I0616 09:15:15.897848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140229 (* 1 = 0.0140229 loss) +I0616 09:15:15.897852 9857 solver.cpp:571] Iteration 41540, lr = 0.001 +I0616 09:15:27.519335 9857 solver.cpp:242] Iteration 41560, loss = 0.359331 +I0616 09:15:27.519377 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.098517 (* 1 = 0.098517 loss) +I0616 09:15:27.519383 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118076 (* 1 = 0.118076 loss) +I0616 09:15:27.519387 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0523067 (* 1 = 0.0523067 loss) +I0616 09:15:27.519392 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101876 (* 1 = 0.0101876 loss) +I0616 09:15:27.519394 9857 solver.cpp:571] Iteration 41560, lr = 0.001 +I0616 09:15:38.941872 9857 solver.cpp:242] Iteration 41580, loss = 0.764961 +I0616 09:15:38.941915 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0808926 (* 1 = 0.0808926 loss) +I0616 09:15:38.941920 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0739345 (* 1 = 0.0739345 loss) +I0616 09:15:38.941925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00302728 (* 1 = 0.00302728 loss) +I0616 09:15:38.941927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133667 (* 1 = 0.0133667 loss) +I0616 09:15:38.941931 9857 solver.cpp:571] Iteration 41580, lr = 0.001 +speed: 0.620s / iter +I0616 09:15:50.367791 9857 solver.cpp:242] Iteration 41600, loss = 1.1639 +I0616 09:15:50.367816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306483 (* 1 = 0.306483 loss) +I0616 09:15:50.367822 9857 solver.cpp:258] Train net output #1: loss_cls = 0.664164 (* 1 = 0.664164 loss) +I0616 09:15:50.367826 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107382 (* 1 = 0.107382 loss) +I0616 09:15:50.367830 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0503794 (* 1 = 0.0503794 loss) +I0616 09:15:50.367836 9857 solver.cpp:571] Iteration 41600, lr = 0.001 +I0616 09:16:01.853623 9857 solver.cpp:242] Iteration 41620, loss = 1.01245 +I0616 09:16:01.853667 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.295217 (* 1 = 0.295217 loss) +I0616 09:16:01.853672 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271971 (* 1 = 0.271971 loss) +I0616 09:16:01.853677 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.192101 (* 1 = 0.192101 loss) +I0616 09:16:01.853679 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.560355 (* 1 = 0.560355 loss) +I0616 09:16:01.853683 9857 solver.cpp:571] Iteration 41620, lr = 0.001 +I0616 09:16:13.502709 9857 solver.cpp:242] Iteration 41640, loss = 0.695258 +I0616 09:16:13.502751 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366977 (* 1 = 0.366977 loss) +I0616 09:16:13.502759 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343078 (* 1 = 0.343078 loss) +I0616 09:16:13.502764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.211413 (* 1 = 0.211413 loss) +I0616 09:16:13.502768 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0977595 (* 1 = 0.0977595 loss) +I0616 09:16:13.502773 9857 solver.cpp:571] Iteration 41640, lr = 0.001 +I0616 09:16:25.306922 9857 solver.cpp:242] Iteration 41660, loss = 0.783375 +I0616 09:16:25.306964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148435 (* 1 = 0.148435 loss) +I0616 09:16:25.306970 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343933 (* 1 = 0.343933 loss) +I0616 09:16:25.306973 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183877 (* 1 = 0.183877 loss) +I0616 09:16:25.306977 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142484 (* 1 = 0.0142484 loss) +I0616 09:16:25.306982 9857 solver.cpp:571] Iteration 41660, lr = 0.001 +I0616 09:16:36.864852 9857 solver.cpp:242] Iteration 41680, loss = 0.787354 +I0616 09:16:36.864893 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274996 (* 1 = 0.274996 loss) +I0616 09:16:36.864899 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231147 (* 1 = 0.231147 loss) +I0616 09:16:36.864903 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0743502 (* 1 = 0.0743502 loss) +I0616 09:16:36.864907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404666 (* 1 = 0.0404666 loss) +I0616 09:16:36.864912 9857 solver.cpp:571] Iteration 41680, lr = 0.001 +I0616 09:16:48.472476 9857 solver.cpp:242] Iteration 41700, loss = 0.751454 +I0616 09:16:48.472514 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19158 (* 1 = 0.19158 loss) +I0616 09:16:48.472520 9857 solver.cpp:258] Train net output #1: loss_cls = 0.649018 (* 1 = 0.649018 loss) +I0616 09:16:48.472524 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114668 (* 1 = 0.114668 loss) +I0616 09:16:48.472528 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226317 (* 1 = 0.0226317 loss) +I0616 09:16:48.472532 9857 solver.cpp:571] Iteration 41700, lr = 0.001 +I0616 09:17:00.047215 9857 solver.cpp:242] Iteration 41720, loss = 1.15019 +I0616 09:17:00.047255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.427123 (* 1 = 0.427123 loss) +I0616 09:17:00.047261 9857 solver.cpp:258] Train net output #1: loss_cls = 1.06569 (* 1 = 1.06569 loss) +I0616 09:17:00.047266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.281921 (* 1 = 0.281921 loss) +I0616 09:17:00.047268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137036 (* 1 = 0.137036 loss) +I0616 09:17:00.047272 9857 solver.cpp:571] Iteration 41720, lr = 0.001 +I0616 09:17:11.648098 9857 solver.cpp:242] Iteration 41740, loss = 0.91376 +I0616 09:17:11.648140 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160441 (* 1 = 0.160441 loss) +I0616 09:17:11.648145 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200923 (* 1 = 0.200923 loss) +I0616 09:17:11.648150 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106401 (* 1 = 0.106401 loss) +I0616 09:17:11.648154 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00728703 (* 1 = 0.00728703 loss) +I0616 09:17:11.648157 9857 solver.cpp:571] Iteration 41740, lr = 0.001 +I0616 09:17:23.401372 9857 solver.cpp:242] Iteration 41760, loss = 0.507112 +I0616 09:17:23.401414 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0670265 (* 1 = 0.0670265 loss) +I0616 09:17:23.401420 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145617 (* 1 = 0.145617 loss) +I0616 09:17:23.401424 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209522 (* 1 = 0.0209522 loss) +I0616 09:17:23.401427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00073491 (* 1 = 0.00073491 loss) +I0616 09:17:23.401432 9857 solver.cpp:571] Iteration 41760, lr = 0.001 +I0616 09:17:35.129907 9857 solver.cpp:242] Iteration 41780, loss = 0.581602 +I0616 09:17:35.129948 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231816 (* 1 = 0.231816 loss) +I0616 09:17:35.129953 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404966 (* 1 = 0.404966 loss) +I0616 09:17:35.129957 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0807899 (* 1 = 0.0807899 loss) +I0616 09:17:35.129961 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00661432 (* 1 = 0.00661432 loss) +I0616 09:17:35.129966 9857 solver.cpp:571] Iteration 41780, lr = 0.001 +speed: 0.620s / iter +I0616 09:17:46.861469 9857 solver.cpp:242] Iteration 41800, loss = 0.816773 +I0616 09:17:46.861510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249666 (* 1 = 0.249666 loss) +I0616 09:17:46.861515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280092 (* 1 = 0.280092 loss) +I0616 09:17:46.861520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106099 (* 1 = 0.106099 loss) +I0616 09:17:46.861522 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0354784 (* 1 = 0.0354784 loss) +I0616 09:17:46.861527 9857 solver.cpp:571] Iteration 41800, lr = 0.001 +I0616 09:17:58.333325 9857 solver.cpp:242] Iteration 41820, loss = 0.707578 +I0616 09:17:58.333367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13341 (* 1 = 0.13341 loss) +I0616 09:17:58.333374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245081 (* 1 = 0.245081 loss) +I0616 09:17:58.333377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0852815 (* 1 = 0.0852815 loss) +I0616 09:17:58.333381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144477 (* 1 = 0.0144477 loss) +I0616 09:17:58.333385 9857 solver.cpp:571] Iteration 41820, lr = 0.001 +I0616 09:18:09.889555 9857 solver.cpp:242] Iteration 41840, loss = 0.944778 +I0616 09:18:09.889597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368416 (* 1 = 0.368416 loss) +I0616 09:18:09.889603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.746391 (* 1 = 0.746391 loss) +I0616 09:18:09.889607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.276845 (* 1 = 0.276845 loss) +I0616 09:18:09.889611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.196876 (* 1 = 0.196876 loss) +I0616 09:18:09.889614 9857 solver.cpp:571] Iteration 41840, lr = 0.001 +I0616 09:18:21.455504 9857 solver.cpp:242] Iteration 41860, loss = 0.871063 +I0616 09:18:21.455545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346895 (* 1 = 0.346895 loss) +I0616 09:18:21.455551 9857 solver.cpp:258] Train net output #1: loss_cls = 0.547001 (* 1 = 0.547001 loss) +I0616 09:18:21.455555 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.27842 (* 1 = 0.27842 loss) +I0616 09:18:21.455559 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.017211 (* 1 = 0.017211 loss) +I0616 09:18:21.455564 9857 solver.cpp:571] Iteration 41860, lr = 0.001 +I0616 09:18:33.247465 9857 solver.cpp:242] Iteration 41880, loss = 1.62763 +I0616 09:18:33.247508 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.506056 (* 1 = 0.506056 loss) +I0616 09:18:33.247512 9857 solver.cpp:258] Train net output #1: loss_cls = 1.3217 (* 1 = 1.3217 loss) +I0616 09:18:33.247516 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.309604 (* 1 = 0.309604 loss) +I0616 09:18:33.247520 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.225815 (* 1 = 0.225815 loss) +I0616 09:18:33.247524 9857 solver.cpp:571] Iteration 41880, lr = 0.001 +I0616 09:18:45.004210 9857 solver.cpp:242] Iteration 41900, loss = 0.495238 +I0616 09:18:45.004251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0657004 (* 1 = 0.0657004 loss) +I0616 09:18:45.004258 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242245 (* 1 = 0.242245 loss) +I0616 09:18:45.004262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0429573 (* 1 = 0.0429573 loss) +I0616 09:18:45.004266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.183075 (* 1 = 0.183075 loss) +I0616 09:18:45.004269 9857 solver.cpp:571] Iteration 41900, lr = 0.001 +I0616 09:18:56.439846 9857 solver.cpp:242] Iteration 41920, loss = 1.0687 +I0616 09:18:56.439888 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.50818 (* 1 = 0.50818 loss) +I0616 09:18:56.439894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.636498 (* 1 = 0.636498 loss) +I0616 09:18:56.439898 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0894627 (* 1 = 0.0894627 loss) +I0616 09:18:56.439903 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0430925 (* 1 = 0.0430925 loss) +I0616 09:18:56.439906 9857 solver.cpp:571] Iteration 41920, lr = 0.001 +I0616 09:19:08.081879 9857 solver.cpp:242] Iteration 41940, loss = 0.986377 +I0616 09:19:08.081923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424724 (* 1 = 0.424724 loss) +I0616 09:19:08.081928 9857 solver.cpp:258] Train net output #1: loss_cls = 0.379823 (* 1 = 0.379823 loss) +I0616 09:19:08.081931 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133633 (* 1 = 0.133633 loss) +I0616 09:19:08.081935 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0297204 (* 1 = 0.0297204 loss) +I0616 09:19:08.081939 9857 solver.cpp:571] Iteration 41940, lr = 0.001 +I0616 09:19:19.782418 9857 solver.cpp:242] Iteration 41960, loss = 1.75015 +I0616 09:19:19.782461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.566491 (* 1 = 0.566491 loss) +I0616 09:19:19.782466 9857 solver.cpp:258] Train net output #1: loss_cls = 1.6463 (* 1 = 1.6463 loss) +I0616 09:19:19.782471 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.212783 (* 1 = 0.212783 loss) +I0616 09:19:19.782474 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137983 (* 1 = 0.137983 loss) +I0616 09:19:19.782479 9857 solver.cpp:571] Iteration 41960, lr = 0.001 +I0616 09:19:31.336596 9857 solver.cpp:242] Iteration 41980, loss = 0.753696 +I0616 09:19:31.336638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228557 (* 1 = 0.228557 loss) +I0616 09:19:31.336643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167759 (* 1 = 0.167759 loss) +I0616 09:19:31.336648 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274152 (* 1 = 0.0274152 loss) +I0616 09:19:31.336652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00963015 (* 1 = 0.00963015 loss) +I0616 09:19:31.336655 9857 solver.cpp:571] Iteration 41980, lr = 0.001 +speed: 0.620s / iter +I0616 09:19:42.628450 9857 solver.cpp:242] Iteration 42000, loss = 0.962276 +I0616 09:19:42.628491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258894 (* 1 = 0.258894 loss) +I0616 09:19:42.628497 9857 solver.cpp:258] Train net output #1: loss_cls = 0.318657 (* 1 = 0.318657 loss) +I0616 09:19:42.628501 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0671155 (* 1 = 0.0671155 loss) +I0616 09:19:42.628505 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0356356 (* 1 = 0.0356356 loss) +I0616 09:19:42.628509 9857 solver.cpp:571] Iteration 42000, lr = 0.001 +I0616 09:19:54.123092 9857 solver.cpp:242] Iteration 42020, loss = 0.944603 +I0616 09:19:54.123132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267432 (* 1 = 0.267432 loss) +I0616 09:19:54.123137 9857 solver.cpp:258] Train net output #1: loss_cls = 0.339562 (* 1 = 0.339562 loss) +I0616 09:19:54.123142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112536 (* 1 = 0.112536 loss) +I0616 09:19:54.123145 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0500497 (* 1 = 0.0500497 loss) +I0616 09:19:54.123149 9857 solver.cpp:571] Iteration 42020, lr = 0.001 +I0616 09:20:05.530349 9857 solver.cpp:242] Iteration 42040, loss = 0.598348 +I0616 09:20:05.530375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333775 (* 1 = 0.333775 loss) +I0616 09:20:05.530395 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267424 (* 1 = 0.267424 loss) +I0616 09:20:05.530400 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.198358 (* 1 = 0.198358 loss) +I0616 09:20:05.530403 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0468723 (* 1 = 0.0468723 loss) +I0616 09:20:05.530407 9857 solver.cpp:571] Iteration 42040, lr = 0.001 +I0616 09:20:16.855909 9857 solver.cpp:242] Iteration 42060, loss = 0.350004 +I0616 09:20:16.855952 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103355 (* 1 = 0.103355 loss) +I0616 09:20:16.855957 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137634 (* 1 = 0.137634 loss) +I0616 09:20:16.855962 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0200028 (* 1 = 0.0200028 loss) +I0616 09:20:16.855965 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00252854 (* 1 = 0.00252854 loss) +I0616 09:20:16.855968 9857 solver.cpp:571] Iteration 42060, lr = 0.001 +I0616 09:20:28.638658 9857 solver.cpp:242] Iteration 42080, loss = 1.04878 +I0616 09:20:28.638700 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324395 (* 1 = 0.324395 loss) +I0616 09:20:28.638705 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514379 (* 1 = 0.514379 loss) +I0616 09:20:28.638710 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.3006 (* 1 = 0.3006 loss) +I0616 09:20:28.638713 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.33289 (* 1 = 0.33289 loss) +I0616 09:20:28.638717 9857 solver.cpp:571] Iteration 42080, lr = 0.001 +I0616 09:20:40.463014 9857 solver.cpp:242] Iteration 42100, loss = 0.740474 +I0616 09:20:40.463057 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287484 (* 1 = 0.287484 loss) +I0616 09:20:40.463062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358828 (* 1 = 0.358828 loss) +I0616 09:20:40.463065 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190624 (* 1 = 0.190624 loss) +I0616 09:20:40.463069 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.051647 (* 1 = 0.051647 loss) +I0616 09:20:40.463073 9857 solver.cpp:571] Iteration 42100, lr = 0.001 +I0616 09:20:51.862601 9857 solver.cpp:242] Iteration 42120, loss = 0.419545 +I0616 09:20:51.862643 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173625 (* 1 = 0.173625 loss) +I0616 09:20:51.862648 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185615 (* 1 = 0.185615 loss) +I0616 09:20:51.862653 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0457358 (* 1 = 0.0457358 loss) +I0616 09:20:51.862656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00523394 (* 1 = 0.00523394 loss) +I0616 09:20:51.862660 9857 solver.cpp:571] Iteration 42120, lr = 0.001 +I0616 09:21:03.355731 9857 solver.cpp:242] Iteration 42140, loss = 0.540919 +I0616 09:21:03.355773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453302 (* 1 = 0.453302 loss) +I0616 09:21:03.355779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351967 (* 1 = 0.351967 loss) +I0616 09:21:03.355783 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0360991 (* 1 = 0.0360991 loss) +I0616 09:21:03.355787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145665 (* 1 = 0.0145665 loss) +I0616 09:21:03.355790 9857 solver.cpp:571] Iteration 42140, lr = 0.001 +I0616 09:21:14.813199 9857 solver.cpp:242] Iteration 42160, loss = 1.00894 +I0616 09:21:14.813243 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104346 (* 1 = 0.104346 loss) +I0616 09:21:14.813248 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150489 (* 1 = 0.150489 loss) +I0616 09:21:14.813252 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395831 (* 1 = 0.0395831 loss) +I0616 09:21:14.813256 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111613 (* 1 = 0.0111613 loss) +I0616 09:21:14.813259 9857 solver.cpp:571] Iteration 42160, lr = 0.001 +I0616 09:21:26.451393 9857 solver.cpp:242] Iteration 42180, loss = 0.661041 +I0616 09:21:26.451436 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.330265 (* 1 = 0.330265 loss) +I0616 09:21:26.451441 9857 solver.cpp:258] Train net output #1: loss_cls = 0.36349 (* 1 = 0.36349 loss) +I0616 09:21:26.451444 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.30799 (* 1 = 0.30799 loss) +I0616 09:21:26.451448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.084547 (* 1 = 0.084547 loss) +I0616 09:21:26.451452 9857 solver.cpp:571] Iteration 42180, lr = 0.001 +speed: 0.619s / iter +I0616 09:21:38.164326 9857 solver.cpp:242] Iteration 42200, loss = 1.01292 +I0616 09:21:38.164369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141002 (* 1 = 0.141002 loss) +I0616 09:21:38.164376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270058 (* 1 = 0.270058 loss) +I0616 09:21:38.164379 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0758408 (* 1 = 0.0758408 loss) +I0616 09:21:38.164383 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173476 (* 1 = 0.0173476 loss) +I0616 09:21:38.164387 9857 solver.cpp:571] Iteration 42200, lr = 0.001 +I0616 09:21:49.776716 9857 solver.cpp:242] Iteration 42220, loss = 0.468043 +I0616 09:21:49.776753 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130399 (* 1 = 0.130399 loss) +I0616 09:21:49.776759 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13812 (* 1 = 0.13812 loss) +I0616 09:21:49.776763 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127081 (* 1 = 0.127081 loss) +I0616 09:21:49.776767 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00984081 (* 1 = 0.00984081 loss) +I0616 09:21:49.776772 9857 solver.cpp:571] Iteration 42220, lr = 0.001 +I0616 09:22:01.353904 9857 solver.cpp:242] Iteration 42240, loss = 0.761079 +I0616 09:22:01.353945 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.476668 (* 1 = 0.476668 loss) +I0616 09:22:01.353950 9857 solver.cpp:258] Train net output #1: loss_cls = 0.494468 (* 1 = 0.494468 loss) +I0616 09:22:01.353955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.250133 (* 1 = 0.250133 loss) +I0616 09:22:01.353958 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0592489 (* 1 = 0.0592489 loss) +I0616 09:22:01.353962 9857 solver.cpp:571] Iteration 42240, lr = 0.001 +I0616 09:22:12.869834 9857 solver.cpp:242] Iteration 42260, loss = 0.568545 +I0616 09:22:12.869877 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352432 (* 1 = 0.352432 loss) +I0616 09:22:12.869882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32811 (* 1 = 0.32811 loss) +I0616 09:22:12.869886 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0422642 (* 1 = 0.0422642 loss) +I0616 09:22:12.869890 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0835374 (* 1 = 0.0835374 loss) +I0616 09:22:12.869894 9857 solver.cpp:571] Iteration 42260, lr = 0.001 +I0616 09:22:24.615183 9857 solver.cpp:242] Iteration 42280, loss = 0.710261 +I0616 09:22:24.615226 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25233 (* 1 = 0.25233 loss) +I0616 09:22:24.615231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.336867 (* 1 = 0.336867 loss) +I0616 09:22:24.615236 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117918 (* 1 = 0.117918 loss) +I0616 09:22:24.615238 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103081 (* 1 = 0.0103081 loss) +I0616 09:22:24.615242 9857 solver.cpp:571] Iteration 42280, lr = 0.001 +I0616 09:22:36.234246 9857 solver.cpp:242] Iteration 42300, loss = 1.04352 +I0616 09:22:36.234288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107464 (* 1 = 0.107464 loss) +I0616 09:22:36.234294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.385439 (* 1 = 0.385439 loss) +I0616 09:22:36.234298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0113518 (* 1 = 0.0113518 loss) +I0616 09:22:36.234302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00168006 (* 1 = 0.00168006 loss) +I0616 09:22:36.234305 9857 solver.cpp:571] Iteration 42300, lr = 0.001 +I0616 09:22:47.935776 9857 solver.cpp:242] Iteration 42320, loss = 0.502206 +I0616 09:22:47.935818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191691 (* 1 = 0.191691 loss) +I0616 09:22:47.935823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203697 (* 1 = 0.203697 loss) +I0616 09:22:47.935827 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172183 (* 1 = 0.172183 loss) +I0616 09:22:47.935832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0522537 (* 1 = 0.0522537 loss) +I0616 09:22:47.935835 9857 solver.cpp:571] Iteration 42320, lr = 0.001 +I0616 09:22:59.439496 9857 solver.cpp:242] Iteration 42340, loss = 0.852565 +I0616 09:22:59.439538 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178947 (* 1 = 0.178947 loss) +I0616 09:22:59.439543 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314466 (* 1 = 0.314466 loss) +I0616 09:22:59.439548 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105539 (* 1 = 0.105539 loss) +I0616 09:22:59.439550 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0298223 (* 1 = 0.0298223 loss) +I0616 09:22:59.439554 9857 solver.cpp:571] Iteration 42340, lr = 0.001 +I0616 09:23:10.699389 9857 solver.cpp:242] Iteration 42360, loss = 0.608088 +I0616 09:23:10.699432 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0871531 (* 1 = 0.0871531 loss) +I0616 09:23:10.699440 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162494 (* 1 = 0.162494 loss) +I0616 09:23:10.699443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0153133 (* 1 = 0.0153133 loss) +I0616 09:23:10.699446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00510496 (* 1 = 0.00510496 loss) +I0616 09:23:10.699450 9857 solver.cpp:571] Iteration 42360, lr = 0.001 +I0616 09:23:22.094544 9857 solver.cpp:242] Iteration 42380, loss = 0.344189 +I0616 09:23:22.094586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165898 (* 1 = 0.165898 loss) +I0616 09:23:22.094591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135732 (* 1 = 0.135732 loss) +I0616 09:23:22.094596 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0486053 (* 1 = 0.0486053 loss) +I0616 09:23:22.094600 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00396931 (* 1 = 0.00396931 loss) +I0616 09:23:22.094604 9857 solver.cpp:571] Iteration 42380, lr = 0.001 +speed: 0.619s / iter +I0616 09:23:33.505143 9857 solver.cpp:242] Iteration 42400, loss = 0.354504 +I0616 09:23:33.505187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212424 (* 1 = 0.212424 loss) +I0616 09:23:33.505192 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130479 (* 1 = 0.130479 loss) +I0616 09:23:33.505195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00582201 (* 1 = 0.00582201 loss) +I0616 09:23:33.505199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135531 (* 1 = 0.0135531 loss) +I0616 09:23:33.505203 9857 solver.cpp:571] Iteration 42400, lr = 0.001 +I0616 09:23:45.225996 9857 solver.cpp:242] Iteration 42420, loss = 0.759241 +I0616 09:23:45.226037 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258326 (* 1 = 0.258326 loss) +I0616 09:23:45.226042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.290463 (* 1 = 0.290463 loss) +I0616 09:23:45.226047 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0467094 (* 1 = 0.0467094 loss) +I0616 09:23:45.226050 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00765087 (* 1 = 0.00765087 loss) +I0616 09:23:45.226054 9857 solver.cpp:571] Iteration 42420, lr = 0.001 +I0616 09:23:56.911559 9857 solver.cpp:242] Iteration 42440, loss = 0.358867 +I0616 09:23:56.911602 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122034 (* 1 = 0.122034 loss) +I0616 09:23:56.911607 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151705 (* 1 = 0.151705 loss) +I0616 09:23:56.911612 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0111469 (* 1 = 0.0111469 loss) +I0616 09:23:56.911614 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.018556 (* 1 = 0.018556 loss) +I0616 09:23:56.911618 9857 solver.cpp:571] Iteration 42440, lr = 0.001 +I0616 09:24:08.434207 9857 solver.cpp:242] Iteration 42460, loss = 0.355658 +I0616 09:24:08.434250 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115564 (* 1 = 0.115564 loss) +I0616 09:24:08.434257 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170761 (* 1 = 0.170761 loss) +I0616 09:24:08.434260 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.019973 (* 1 = 0.019973 loss) +I0616 09:24:08.434264 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107798 (* 1 = 0.0107798 loss) +I0616 09:24:08.434268 9857 solver.cpp:571] Iteration 42460, lr = 0.001 +I0616 09:24:20.083020 9857 solver.cpp:242] Iteration 42480, loss = 0.730218 +I0616 09:24:20.083061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0497293 (* 1 = 0.0497293 loss) +I0616 09:24:20.083067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153365 (* 1 = 0.153365 loss) +I0616 09:24:20.083071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222322 (* 1 = 0.0222322 loss) +I0616 09:24:20.083076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00916795 (* 1 = 0.00916795 loss) +I0616 09:24:20.083078 9857 solver.cpp:571] Iteration 42480, lr = 0.001 +I0616 09:24:31.514986 9857 solver.cpp:242] Iteration 42500, loss = 1.23445 +I0616 09:24:31.515028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382681 (* 1 = 0.382681 loss) +I0616 09:24:31.515034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.727998 (* 1 = 0.727998 loss) +I0616 09:24:31.515038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160548 (* 1 = 0.160548 loss) +I0616 09:24:31.515043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0258575 (* 1 = 0.0258575 loss) +I0616 09:24:31.515048 9857 solver.cpp:571] Iteration 42500, lr = 0.001 +I0616 09:24:42.867437 9857 solver.cpp:242] Iteration 42520, loss = 1.48278 +I0616 09:24:42.867480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0742492 (* 1 = 0.0742492 loss) +I0616 09:24:42.867486 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378524 (* 1 = 0.378524 loss) +I0616 09:24:42.867491 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12394 (* 1 = 0.12394 loss) +I0616 09:24:42.867494 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0256312 (* 1 = 0.0256312 loss) +I0616 09:24:42.867498 9857 solver.cpp:571] Iteration 42520, lr = 0.001 +I0616 09:24:54.530026 9857 solver.cpp:242] Iteration 42540, loss = 0.850194 +I0616 09:24:54.530069 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269896 (* 1 = 0.269896 loss) +I0616 09:24:54.530074 9857 solver.cpp:258] Train net output #1: loss_cls = 0.258515 (* 1 = 0.258515 loss) +I0616 09:24:54.530079 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0912356 (* 1 = 0.0912356 loss) +I0616 09:24:54.530082 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0481397 (* 1 = 0.0481397 loss) +I0616 09:24:54.530087 9857 solver.cpp:571] Iteration 42540, lr = 0.001 +I0616 09:25:06.037237 9857 solver.cpp:242] Iteration 42560, loss = 0.894902 +I0616 09:25:06.037279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310922 (* 1 = 0.310922 loss) +I0616 09:25:06.037286 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384918 (* 1 = 0.384918 loss) +I0616 09:25:06.037289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.302537 (* 1 = 0.302537 loss) +I0616 09:25:06.037293 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.19655 (* 1 = 0.19655 loss) +I0616 09:25:06.037297 9857 solver.cpp:571] Iteration 42560, lr = 0.001 +I0616 09:25:17.703488 9857 solver.cpp:242] Iteration 42580, loss = 0.539889 +I0616 09:25:17.703532 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221819 (* 1 = 0.221819 loss) +I0616 09:25:17.703537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161205 (* 1 = 0.161205 loss) +I0616 09:25:17.703541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0926935 (* 1 = 0.0926935 loss) +I0616 09:25:17.703546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0453561 (* 1 = 0.0453561 loss) +I0616 09:25:17.703549 9857 solver.cpp:571] Iteration 42580, lr = 0.001 +speed: 0.619s / iter +I0616 09:25:29.385330 9857 solver.cpp:242] Iteration 42600, loss = 1.23989 +I0616 09:25:29.385372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220326 (* 1 = 0.220326 loss) +I0616 09:25:29.385377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136659 (* 1 = 0.136659 loss) +I0616 09:25:29.385381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0316836 (* 1 = 0.0316836 loss) +I0616 09:25:29.385385 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390047 (* 1 = 0.0390047 loss) +I0616 09:25:29.385390 9857 solver.cpp:571] Iteration 42600, lr = 0.001 +I0616 09:25:40.917364 9857 solver.cpp:242] Iteration 42620, loss = 0.552529 +I0616 09:25:40.917389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0463722 (* 1 = 0.0463722 loss) +I0616 09:25:40.917394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189826 (* 1 = 0.189826 loss) +I0616 09:25:40.917399 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249189 (* 1 = 0.249189 loss) +I0616 09:25:40.917402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0354971 (* 1 = 0.0354971 loss) +I0616 09:25:40.917408 9857 solver.cpp:571] Iteration 42620, lr = 0.001 +I0616 09:25:52.125273 9857 solver.cpp:242] Iteration 42640, loss = 0.495574 +I0616 09:25:52.125315 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155973 (* 1 = 0.155973 loss) +I0616 09:25:52.125321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125512 (* 1 = 0.125512 loss) +I0616 09:25:52.125325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0599005 (* 1 = 0.0599005 loss) +I0616 09:25:52.125329 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247269 (* 1 = 0.0247269 loss) +I0616 09:25:52.125332 9857 solver.cpp:571] Iteration 42640, lr = 0.001 +I0616 09:26:03.378048 9857 solver.cpp:242] Iteration 42660, loss = 0.681714 +I0616 09:26:03.378092 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.468508 (* 1 = 0.468508 loss) +I0616 09:26:03.378098 9857 solver.cpp:258] Train net output #1: loss_cls = 0.483392 (* 1 = 0.483392 loss) +I0616 09:26:03.378101 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126591 (* 1 = 0.126591 loss) +I0616 09:26:03.378105 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0272592 (* 1 = 0.0272592 loss) +I0616 09:26:03.378109 9857 solver.cpp:571] Iteration 42660, lr = 0.001 +I0616 09:26:14.882820 9857 solver.cpp:242] Iteration 42680, loss = 0.837201 +I0616 09:26:14.882864 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376041 (* 1 = 0.376041 loss) +I0616 09:26:14.882869 9857 solver.cpp:258] Train net output #1: loss_cls = 0.650095 (* 1 = 0.650095 loss) +I0616 09:26:14.882874 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.319262 (* 1 = 0.319262 loss) +I0616 09:26:14.882877 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0621129 (* 1 = 0.0621129 loss) +I0616 09:26:14.882881 9857 solver.cpp:571] Iteration 42680, lr = 0.001 +I0616 09:26:26.189792 9857 solver.cpp:242] Iteration 42700, loss = 0.865993 +I0616 09:26:26.189836 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287145 (* 1 = 0.287145 loss) +I0616 09:26:26.189842 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360605 (* 1 = 0.360605 loss) +I0616 09:26:26.189846 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0196705 (* 1 = 0.0196705 loss) +I0616 09:26:26.189851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397046 (* 1 = 0.0397046 loss) +I0616 09:26:26.189857 9857 solver.cpp:571] Iteration 42700, lr = 0.001 +I0616 09:26:37.899574 9857 solver.cpp:242] Iteration 42720, loss = 0.49531 +I0616 09:26:37.899615 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182006 (* 1 = 0.182006 loss) +I0616 09:26:37.899619 9857 solver.cpp:258] Train net output #1: loss_cls = 0.353196 (* 1 = 0.353196 loss) +I0616 09:26:37.899624 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0150577 (* 1 = 0.0150577 loss) +I0616 09:26:37.899627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00720054 (* 1 = 0.00720054 loss) +I0616 09:26:37.899631 9857 solver.cpp:571] Iteration 42720, lr = 0.001 +I0616 09:26:49.482311 9857 solver.cpp:242] Iteration 42740, loss = 1.07486 +I0616 09:26:49.482352 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181568 (* 1 = 0.181568 loss) +I0616 09:26:49.482357 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250871 (* 1 = 0.250871 loss) +I0616 09:26:49.482362 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0463077 (* 1 = 0.0463077 loss) +I0616 09:26:49.482365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139523 (* 1 = 0.0139523 loss) +I0616 09:26:49.482369 9857 solver.cpp:571] Iteration 42740, lr = 0.001 +I0616 09:27:00.990830 9857 solver.cpp:242] Iteration 42760, loss = 0.817969 +I0616 09:27:00.990870 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29465 (* 1 = 0.29465 loss) +I0616 09:27:00.990876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.397242 (* 1 = 0.397242 loss) +I0616 09:27:00.990880 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206436 (* 1 = 0.206436 loss) +I0616 09:27:00.990885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0904485 (* 1 = 0.0904485 loss) +I0616 09:27:00.990888 9857 solver.cpp:571] Iteration 42760, lr = 0.001 +I0616 09:27:12.488415 9857 solver.cpp:242] Iteration 42780, loss = 0.834307 +I0616 09:27:12.488456 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266762 (* 1 = 0.266762 loss) +I0616 09:27:12.488461 9857 solver.cpp:258] Train net output #1: loss_cls = 0.524674 (* 1 = 0.524674 loss) +I0616 09:27:12.488466 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.170521 (* 1 = 0.170521 loss) +I0616 09:27:12.488469 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.058089 (* 1 = 0.058089 loss) +I0616 09:27:12.488473 9857 solver.cpp:571] Iteration 42780, lr = 0.001 +speed: 0.619s / iter +I0616 09:27:23.996119 9857 solver.cpp:242] Iteration 42800, loss = 0.675891 +I0616 09:27:23.996160 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408771 (* 1 = 0.408771 loss) +I0616 09:27:23.996166 9857 solver.cpp:258] Train net output #1: loss_cls = 0.520035 (* 1 = 0.520035 loss) +I0616 09:27:23.996170 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0894766 (* 1 = 0.0894766 loss) +I0616 09:27:23.996175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140651 (* 1 = 0.0140651 loss) +I0616 09:27:23.996177 9857 solver.cpp:571] Iteration 42800, lr = 0.001 +I0616 09:27:35.338080 9857 solver.cpp:242] Iteration 42820, loss = 1.09249 +I0616 09:27:35.338121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304553 (* 1 = 0.304553 loss) +I0616 09:27:35.338127 9857 solver.cpp:258] Train net output #1: loss_cls = 0.557494 (* 1 = 0.557494 loss) +I0616 09:27:35.338131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.262974 (* 1 = 0.262974 loss) +I0616 09:27:35.338135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.170043 (* 1 = 0.170043 loss) +I0616 09:27:35.338138 9857 solver.cpp:571] Iteration 42820, lr = 0.001 +I0616 09:27:46.830148 9857 solver.cpp:242] Iteration 42840, loss = 1.37362 +I0616 09:27:46.830190 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304403 (* 1 = 0.304403 loss) +I0616 09:27:46.830195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.458846 (* 1 = 0.458846 loss) +I0616 09:27:46.830199 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.320147 (* 1 = 0.320147 loss) +I0616 09:27:46.830204 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.359615 (* 1 = 0.359615 loss) +I0616 09:27:46.830207 9857 solver.cpp:571] Iteration 42840, lr = 0.001 +I0616 09:27:58.448750 9857 solver.cpp:242] Iteration 42860, loss = 0.980325 +I0616 09:27:58.448789 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202791 (* 1 = 0.202791 loss) +I0616 09:27:58.448796 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431004 (* 1 = 0.431004 loss) +I0616 09:27:58.448799 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190968 (* 1 = 0.0190968 loss) +I0616 09:27:58.448803 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00555 (* 1 = 0.00555 loss) +I0616 09:27:58.448807 9857 solver.cpp:571] Iteration 42860, lr = 0.001 +I0616 09:28:09.850911 9857 solver.cpp:242] Iteration 42880, loss = 0.497803 +I0616 09:28:09.850955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0779212 (* 1 = 0.0779212 loss) +I0616 09:28:09.850960 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160104 (* 1 = 0.160104 loss) +I0616 09:28:09.850965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0159827 (* 1 = 0.0159827 loss) +I0616 09:28:09.850967 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00573783 (* 1 = 0.00573783 loss) +I0616 09:28:09.850971 9857 solver.cpp:571] Iteration 42880, lr = 0.001 +I0616 09:28:21.231868 9857 solver.cpp:242] Iteration 42900, loss = 0.417704 +I0616 09:28:21.231911 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131829 (* 1 = 0.131829 loss) +I0616 09:28:21.231916 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145127 (* 1 = 0.145127 loss) +I0616 09:28:21.231921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0450768 (* 1 = 0.0450768 loss) +I0616 09:28:21.231925 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.050665 (* 1 = 0.050665 loss) +I0616 09:28:21.231928 9857 solver.cpp:571] Iteration 42900, lr = 0.001 +I0616 09:28:33.072784 9857 solver.cpp:242] Iteration 42920, loss = 0.829294 +I0616 09:28:33.072827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309481 (* 1 = 0.309481 loss) +I0616 09:28:33.072832 9857 solver.cpp:258] Train net output #1: loss_cls = 0.377872 (* 1 = 0.377872 loss) +I0616 09:28:33.072837 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0378693 (* 1 = 0.0378693 loss) +I0616 09:28:33.072841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0270562 (* 1 = 0.0270562 loss) +I0616 09:28:33.072845 9857 solver.cpp:571] Iteration 42920, lr = 0.001 +I0616 09:28:44.266304 9857 solver.cpp:242] Iteration 42940, loss = 0.57433 +I0616 09:28:44.266347 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148755 (* 1 = 0.148755 loss) +I0616 09:28:44.266352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206209 (* 1 = 0.206209 loss) +I0616 09:28:44.266356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169376 (* 1 = 0.169376 loss) +I0616 09:28:44.266360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0196484 (* 1 = 0.0196484 loss) +I0616 09:28:44.266365 9857 solver.cpp:571] Iteration 42940, lr = 0.001 +I0616 09:28:55.756371 9857 solver.cpp:242] Iteration 42960, loss = 0.437654 +I0616 09:28:55.756413 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281558 (* 1 = 0.281558 loss) +I0616 09:28:55.756419 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243305 (* 1 = 0.243305 loss) +I0616 09:28:55.756423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0753274 (* 1 = 0.0753274 loss) +I0616 09:28:55.756427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0459577 (* 1 = 0.0459577 loss) +I0616 09:28:55.756430 9857 solver.cpp:571] Iteration 42960, lr = 0.001 +I0616 09:29:07.516820 9857 solver.cpp:242] Iteration 42980, loss = 1.05756 +I0616 09:29:07.516862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254138 (* 1 = 0.254138 loss) +I0616 09:29:07.516868 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216539 (* 1 = 0.216539 loss) +I0616 09:29:07.516872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136948 (* 1 = 0.136948 loss) +I0616 09:29:07.516875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.113935 (* 1 = 0.113935 loss) +I0616 09:29:07.516880 9857 solver.cpp:571] Iteration 42980, lr = 0.001 +speed: 0.619s / iter +I0616 09:29:19.106137 9857 solver.cpp:242] Iteration 43000, loss = 1.02067 +I0616 09:29:19.106178 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436697 (* 1 = 0.436697 loss) +I0616 09:29:19.106184 9857 solver.cpp:258] Train net output #1: loss_cls = 0.407985 (* 1 = 0.407985 loss) +I0616 09:29:19.106187 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.29074 (* 1 = 0.29074 loss) +I0616 09:29:19.106191 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0722265 (* 1 = 0.0722265 loss) +I0616 09:29:19.106195 9857 solver.cpp:571] Iteration 43000, lr = 0.001 +I0616 09:29:30.525357 9857 solver.cpp:242] Iteration 43020, loss = 0.553227 +I0616 09:29:30.525400 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215694 (* 1 = 0.215694 loss) +I0616 09:29:30.525406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21384 (* 1 = 0.21384 loss) +I0616 09:29:30.525410 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0134806 (* 1 = 0.0134806 loss) +I0616 09:29:30.525413 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0381327 (* 1 = 0.0381327 loss) +I0616 09:29:30.525418 9857 solver.cpp:571] Iteration 43020, lr = 0.001 +I0616 09:29:42.036844 9857 solver.cpp:242] Iteration 43040, loss = 0.968027 +I0616 09:29:42.036885 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290826 (* 1 = 0.290826 loss) +I0616 09:29:42.036890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.768308 (* 1 = 0.768308 loss) +I0616 09:29:42.036895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.266978 (* 1 = 0.266978 loss) +I0616 09:29:42.036900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0676293 (* 1 = 0.0676293 loss) +I0616 09:29:42.036903 9857 solver.cpp:571] Iteration 43040, lr = 0.001 +I0616 09:29:53.306015 9857 solver.cpp:242] Iteration 43060, loss = 0.884434 +I0616 09:29:53.306057 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181109 (* 1 = 0.181109 loss) +I0616 09:29:53.306063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187028 (* 1 = 0.187028 loss) +I0616 09:29:53.306067 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0867411 (* 1 = 0.0867411 loss) +I0616 09:29:53.306071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0077862 (* 1 = 0.0077862 loss) +I0616 09:29:53.306074 9857 solver.cpp:571] Iteration 43060, lr = 0.001 +I0616 09:30:04.834643 9857 solver.cpp:242] Iteration 43080, loss = 0.735158 +I0616 09:30:04.834686 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276469 (* 1 = 0.276469 loss) +I0616 09:30:04.834692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300817 (* 1 = 0.300817 loss) +I0616 09:30:04.834695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0712782 (* 1 = 0.0712782 loss) +I0616 09:30:04.834698 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0312247 (* 1 = 0.0312247 loss) +I0616 09:30:04.834702 9857 solver.cpp:571] Iteration 43080, lr = 0.001 +I0616 09:30:16.658373 9857 solver.cpp:242] Iteration 43100, loss = 0.931459 +I0616 09:30:16.658416 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17203 (* 1 = 0.17203 loss) +I0616 09:30:16.658421 9857 solver.cpp:258] Train net output #1: loss_cls = 0.537148 (* 1 = 0.537148 loss) +I0616 09:30:16.658426 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0218889 (* 1 = 0.0218889 loss) +I0616 09:30:16.658428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0324094 (* 1 = 0.0324094 loss) +I0616 09:30:16.658432 9857 solver.cpp:571] Iteration 43100, lr = 0.001 +I0616 09:30:28.332062 9857 solver.cpp:242] Iteration 43120, loss = 0.632449 +I0616 09:30:28.332104 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224429 (* 1 = 0.224429 loss) +I0616 09:30:28.332110 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301353 (* 1 = 0.301353 loss) +I0616 09:30:28.332114 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0705773 (* 1 = 0.0705773 loss) +I0616 09:30:28.332118 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0477749 (* 1 = 0.0477749 loss) +I0616 09:30:28.332123 9857 solver.cpp:571] Iteration 43120, lr = 0.001 +I0616 09:30:40.037055 9857 solver.cpp:242] Iteration 43140, loss = 0.458522 +I0616 09:30:40.037097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283318 (* 1 = 0.283318 loss) +I0616 09:30:40.037103 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161236 (* 1 = 0.161236 loss) +I0616 09:30:40.037107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537026 (* 1 = 0.0537026 loss) +I0616 09:30:40.037111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00447803 (* 1 = 0.00447803 loss) +I0616 09:30:40.037117 9857 solver.cpp:571] Iteration 43140, lr = 0.001 +I0616 09:30:51.360239 9857 solver.cpp:242] Iteration 43160, loss = 1.37636 +I0616 09:30:51.360282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.463819 (* 1 = 0.463819 loss) +I0616 09:30:51.360287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.584216 (* 1 = 0.584216 loss) +I0616 09:30:51.360291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.579308 (* 1 = 0.579308 loss) +I0616 09:30:51.360296 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.691603 (* 1 = 0.691603 loss) +I0616 09:30:51.360299 9857 solver.cpp:571] Iteration 43160, lr = 0.001 +I0616 09:31:02.907901 9857 solver.cpp:242] Iteration 43180, loss = 0.951228 +I0616 09:31:02.907943 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316867 (* 1 = 0.316867 loss) +I0616 09:31:02.907948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.427385 (* 1 = 0.427385 loss) +I0616 09:31:02.907951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15585 (* 1 = 0.15585 loss) +I0616 09:31:02.907955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0343047 (* 1 = 0.0343047 loss) +I0616 09:31:02.907959 9857 solver.cpp:571] Iteration 43180, lr = 0.001 +speed: 0.618s / iter +I0616 09:31:14.377818 9857 solver.cpp:242] Iteration 43200, loss = 0.325893 +I0616 09:31:14.377861 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144592 (* 1 = 0.144592 loss) +I0616 09:31:14.377866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1748 (* 1 = 0.1748 loss) +I0616 09:31:14.377871 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.043436 (* 1 = 0.043436 loss) +I0616 09:31:14.377873 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0166592 (* 1 = 0.0166592 loss) +I0616 09:31:14.377877 9857 solver.cpp:571] Iteration 43200, lr = 0.001 +I0616 09:31:25.923974 9857 solver.cpp:242] Iteration 43220, loss = 0.728623 +I0616 09:31:25.924015 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186507 (* 1 = 0.186507 loss) +I0616 09:31:25.924020 9857 solver.cpp:258] Train net output #1: loss_cls = 0.560784 (* 1 = 0.560784 loss) +I0616 09:31:25.924023 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0630891 (* 1 = 0.0630891 loss) +I0616 09:31:25.924027 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147045 (* 1 = 0.0147045 loss) +I0616 09:31:25.924031 9857 solver.cpp:571] Iteration 43220, lr = 0.001 +I0616 09:31:37.506144 9857 solver.cpp:242] Iteration 43240, loss = 0.45378 +I0616 09:31:37.506186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177323 (* 1 = 0.177323 loss) +I0616 09:31:37.506191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16341 (* 1 = 0.16341 loss) +I0616 09:31:37.506196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058204 (* 1 = 0.058204 loss) +I0616 09:31:37.506199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00274517 (* 1 = 0.00274517 loss) +I0616 09:31:37.506203 9857 solver.cpp:571] Iteration 43240, lr = 0.001 +I0616 09:31:48.953117 9857 solver.cpp:242] Iteration 43260, loss = 0.976997 +I0616 09:31:48.953160 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13041 (* 1 = 0.13041 loss) +I0616 09:31:48.953166 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250968 (* 1 = 0.250968 loss) +I0616 09:31:48.953169 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0705789 (* 1 = 0.0705789 loss) +I0616 09:31:48.953173 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104572 (* 1 = 0.0104572 loss) +I0616 09:31:48.953177 9857 solver.cpp:571] Iteration 43260, lr = 0.001 +I0616 09:32:00.320749 9857 solver.cpp:242] Iteration 43280, loss = 0.700372 +I0616 09:32:00.320791 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16277 (* 1 = 0.16277 loss) +I0616 09:32:00.320796 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251325 (* 1 = 0.251325 loss) +I0616 09:32:00.320801 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.237171 (* 1 = 0.237171 loss) +I0616 09:32:00.320804 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714305 (* 1 = 0.0714305 loss) +I0616 09:32:00.320808 9857 solver.cpp:571] Iteration 43280, lr = 0.001 +I0616 09:32:11.572268 9857 solver.cpp:242] Iteration 43300, loss = 0.436802 +I0616 09:32:11.572309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0582105 (* 1 = 0.0582105 loss) +I0616 09:32:11.572314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169572 (* 1 = 0.169572 loss) +I0616 09:32:11.572319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0665116 (* 1 = 0.0665116 loss) +I0616 09:32:11.572322 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00237349 (* 1 = 0.00237349 loss) +I0616 09:32:11.572326 9857 solver.cpp:571] Iteration 43300, lr = 0.001 +I0616 09:32:23.489467 9857 solver.cpp:242] Iteration 43320, loss = 0.625985 +I0616 09:32:23.489506 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288337 (* 1 = 0.288337 loss) +I0616 09:32:23.489512 9857 solver.cpp:258] Train net output #1: loss_cls = 0.356709 (* 1 = 0.356709 loss) +I0616 09:32:23.489516 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.239919 (* 1 = 0.239919 loss) +I0616 09:32:23.489521 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0513283 (* 1 = 0.0513283 loss) +I0616 09:32:23.489524 9857 solver.cpp:571] Iteration 43320, lr = 0.001 +I0616 09:32:35.047571 9857 solver.cpp:242] Iteration 43340, loss = 0.716882 +I0616 09:32:35.047610 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151291 (* 1 = 0.151291 loss) +I0616 09:32:35.047616 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335227 (* 1 = 0.335227 loss) +I0616 09:32:35.047619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0366991 (* 1 = 0.0366991 loss) +I0616 09:32:35.047623 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00238927 (* 1 = 0.00238927 loss) +I0616 09:32:35.047627 9857 solver.cpp:571] Iteration 43340, lr = 0.001 +I0616 09:32:46.745980 9857 solver.cpp:242] Iteration 43360, loss = 0.790448 +I0616 09:32:46.746021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316725 (* 1 = 0.316725 loss) +I0616 09:32:46.746026 9857 solver.cpp:258] Train net output #1: loss_cls = 0.566612 (* 1 = 0.566612 loss) +I0616 09:32:46.746031 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0896101 (* 1 = 0.0896101 loss) +I0616 09:32:46.746033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128447 (* 1 = 0.128447 loss) +I0616 09:32:46.746037 9857 solver.cpp:571] Iteration 43360, lr = 0.001 +I0616 09:32:58.105448 9857 solver.cpp:242] Iteration 43380, loss = 0.861512 +I0616 09:32:58.105490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166662 (* 1 = 0.166662 loss) +I0616 09:32:58.105496 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17085 (* 1 = 0.17085 loss) +I0616 09:32:58.105500 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0924552 (* 1 = 0.0924552 loss) +I0616 09:32:58.105504 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.004333 (* 1 = 0.004333 loss) +I0616 09:32:58.105509 9857 solver.cpp:571] Iteration 43380, lr = 0.001 +speed: 0.618s / iter +I0616 09:33:09.689251 9857 solver.cpp:242] Iteration 43400, loss = 0.949588 +I0616 09:33:09.689293 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151709 (* 1 = 0.151709 loss) +I0616 09:33:09.689299 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197068 (* 1 = 0.197068 loss) +I0616 09:33:09.689303 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0379412 (* 1 = 0.0379412 loss) +I0616 09:33:09.689306 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150657 (* 1 = 0.0150657 loss) +I0616 09:33:09.689311 9857 solver.cpp:571] Iteration 43400, lr = 0.001 +I0616 09:33:21.521054 9857 solver.cpp:242] Iteration 43420, loss = 0.623532 +I0616 09:33:21.521096 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0759555 (* 1 = 0.0759555 loss) +I0616 09:33:21.521102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129198 (* 1 = 0.129198 loss) +I0616 09:33:21.521106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0219322 (* 1 = 0.0219322 loss) +I0616 09:33:21.521111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123346 (* 1 = 0.0123346 loss) +I0616 09:33:21.521114 9857 solver.cpp:571] Iteration 43420, lr = 0.001 +I0616 09:33:33.258170 9857 solver.cpp:242] Iteration 43440, loss = 0.928434 +I0616 09:33:33.258213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199472 (* 1 = 0.199472 loss) +I0616 09:33:33.258218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.725617 (* 1 = 0.725617 loss) +I0616 09:33:33.258221 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0708848 (* 1 = 0.0708848 loss) +I0616 09:33:33.258225 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180196 (* 1 = 0.0180196 loss) +I0616 09:33:33.258229 9857 solver.cpp:571] Iteration 43440, lr = 0.001 +I0616 09:33:44.761775 9857 solver.cpp:242] Iteration 43460, loss = 0.671159 +I0616 09:33:44.761816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145177 (* 1 = 0.145177 loss) +I0616 09:33:44.761821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196583 (* 1 = 0.196583 loss) +I0616 09:33:44.761826 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0578718 (* 1 = 0.0578718 loss) +I0616 09:33:44.761829 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0206309 (* 1 = 0.0206309 loss) +I0616 09:33:44.761833 9857 solver.cpp:571] Iteration 43460, lr = 0.001 +I0616 09:33:56.272346 9857 solver.cpp:242] Iteration 43480, loss = 0.905228 +I0616 09:33:56.272387 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271815 (* 1 = 0.271815 loss) +I0616 09:33:56.272393 9857 solver.cpp:258] Train net output #1: loss_cls = 0.677283 (* 1 = 0.677283 loss) +I0616 09:33:56.272397 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.33232 (* 1 = 0.33232 loss) +I0616 09:33:56.272402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.160159 (* 1 = 0.160159 loss) +I0616 09:33:56.272405 9857 solver.cpp:571] Iteration 43480, lr = 0.001 +I0616 09:34:08.146682 9857 solver.cpp:242] Iteration 43500, loss = 1.03744 +I0616 09:34:08.146724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411374 (* 1 = 0.411374 loss) +I0616 09:34:08.146729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313744 (* 1 = 0.313744 loss) +I0616 09:34:08.146733 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167182 (* 1 = 0.167182 loss) +I0616 09:34:08.146738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.197632 (* 1 = 0.197632 loss) +I0616 09:34:08.146741 9857 solver.cpp:571] Iteration 43500, lr = 0.001 +I0616 09:34:19.719068 9857 solver.cpp:242] Iteration 43520, loss = 1.09532 +I0616 09:34:19.719108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.348377 (* 1 = 0.348377 loss) +I0616 09:34:19.719113 9857 solver.cpp:258] Train net output #1: loss_cls = 0.472742 (* 1 = 0.472742 loss) +I0616 09:34:19.719118 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107111 (* 1 = 0.107111 loss) +I0616 09:34:19.719121 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.682035 (* 1 = 0.682035 loss) +I0616 09:34:19.719125 9857 solver.cpp:571] Iteration 43520, lr = 0.001 +I0616 09:34:31.211293 9857 solver.cpp:242] Iteration 43540, loss = 0.478511 +I0616 09:34:31.211334 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.119657 (* 1 = 0.119657 loss) +I0616 09:34:31.211339 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158676 (* 1 = 0.158676 loss) +I0616 09:34:31.211344 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139653 (* 1 = 0.139653 loss) +I0616 09:34:31.211347 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254944 (* 1 = 0.0254944 loss) +I0616 09:34:31.211351 9857 solver.cpp:571] Iteration 43540, lr = 0.001 +I0616 09:34:42.978358 9857 solver.cpp:242] Iteration 43560, loss = 1.59566 +I0616 09:34:42.978399 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.465811 (* 1 = 0.465811 loss) +I0616 09:34:42.978405 9857 solver.cpp:258] Train net output #1: loss_cls = 1.12764 (* 1 = 1.12764 loss) +I0616 09:34:42.978410 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.278067 (* 1 = 0.278067 loss) +I0616 09:34:42.978412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.106867 (* 1 = 0.106867 loss) +I0616 09:34:42.978416 9857 solver.cpp:571] Iteration 43560, lr = 0.001 +I0616 09:34:54.326611 9857 solver.cpp:242] Iteration 43580, loss = 1.17062 +I0616 09:34:54.326653 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374542 (* 1 = 0.374542 loss) +I0616 09:34:54.326658 9857 solver.cpp:258] Train net output #1: loss_cls = 0.73459 (* 1 = 0.73459 loss) +I0616 09:34:54.326663 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168564 (* 1 = 0.168564 loss) +I0616 09:34:54.326665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.045661 (* 1 = 0.045661 loss) +I0616 09:34:54.326669 9857 solver.cpp:571] Iteration 43580, lr = 0.001 +speed: 0.618s / iter +I0616 09:35:05.589826 9857 solver.cpp:242] Iteration 43600, loss = 0.725783 +I0616 09:35:05.589869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178479 (* 1 = 0.178479 loss) +I0616 09:35:05.589874 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283682 (* 1 = 0.283682 loss) +I0616 09:35:05.589879 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0999847 (* 1 = 0.0999847 loss) +I0616 09:35:05.589881 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0644606 (* 1 = 0.0644606 loss) +I0616 09:35:05.589885 9857 solver.cpp:571] Iteration 43600, lr = 0.001 +I0616 09:35:16.990671 9857 solver.cpp:242] Iteration 43620, loss = 0.661261 +I0616 09:35:16.990713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290309 (* 1 = 0.290309 loss) +I0616 09:35:16.990718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31852 (* 1 = 0.31852 loss) +I0616 09:35:16.990722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0746618 (* 1 = 0.0746618 loss) +I0616 09:35:16.990726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0429979 (* 1 = 0.0429979 loss) +I0616 09:35:16.990731 9857 solver.cpp:571] Iteration 43620, lr = 0.001 +I0616 09:35:28.573724 9857 solver.cpp:242] Iteration 43640, loss = 0.843262 +I0616 09:35:28.573767 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.417944 (* 1 = 0.417944 loss) +I0616 09:35:28.573772 9857 solver.cpp:258] Train net output #1: loss_cls = 0.533006 (* 1 = 0.533006 loss) +I0616 09:35:28.573777 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.240866 (* 1 = 0.240866 loss) +I0616 09:35:28.573781 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0655306 (* 1 = 0.0655306 loss) +I0616 09:35:28.573786 9857 solver.cpp:571] Iteration 43640, lr = 0.001 +I0616 09:35:40.141939 9857 solver.cpp:242] Iteration 43660, loss = 1.70678 +I0616 09:35:40.141980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447516 (* 1 = 0.447516 loss) +I0616 09:35:40.141986 9857 solver.cpp:258] Train net output #1: loss_cls = 0.929776 (* 1 = 0.929776 loss) +I0616 09:35:40.141990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.432138 (* 1 = 0.432138 loss) +I0616 09:35:40.141993 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.197593 (* 1 = 0.197593 loss) +I0616 09:35:40.141998 9857 solver.cpp:571] Iteration 43660, lr = 0.001 +I0616 09:35:51.566143 9857 solver.cpp:242] Iteration 43680, loss = 0.5723 +I0616 09:35:51.566185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0870848 (* 1 = 0.0870848 loss) +I0616 09:35:51.566191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18071 (* 1 = 0.18071 loss) +I0616 09:35:51.566195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176843 (* 1 = 0.0176843 loss) +I0616 09:35:51.566200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00334286 (* 1 = 0.00334286 loss) +I0616 09:35:51.566203 9857 solver.cpp:571] Iteration 43680, lr = 0.001 +I0616 09:36:03.300117 9857 solver.cpp:242] Iteration 43700, loss = 0.68612 +I0616 09:36:03.300159 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162589 (* 1 = 0.162589 loss) +I0616 09:36:03.300164 9857 solver.cpp:258] Train net output #1: loss_cls = 0.411462 (* 1 = 0.411462 loss) +I0616 09:36:03.300169 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0885891 (* 1 = 0.0885891 loss) +I0616 09:36:03.300173 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00437691 (* 1 = 0.00437691 loss) +I0616 09:36:03.300176 9857 solver.cpp:571] Iteration 43700, lr = 0.001 +I0616 09:36:14.762850 9857 solver.cpp:242] Iteration 43720, loss = 0.912041 +I0616 09:36:14.762892 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250628 (* 1 = 0.250628 loss) +I0616 09:36:14.762898 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399401 (* 1 = 0.399401 loss) +I0616 09:36:14.762902 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0337982 (* 1 = 0.0337982 loss) +I0616 09:36:14.762907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126638 (* 1 = 0.0126638 loss) +I0616 09:36:14.762910 9857 solver.cpp:571] Iteration 43720, lr = 0.001 +I0616 09:36:26.409800 9857 solver.cpp:242] Iteration 43740, loss = 0.514966 +I0616 09:36:26.409840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130181 (* 1 = 0.130181 loss) +I0616 09:36:26.409847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28851 (* 1 = 0.28851 loss) +I0616 09:36:26.409850 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0810654 (* 1 = 0.0810654 loss) +I0616 09:36:26.409854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0572236 (* 1 = 0.0572236 loss) +I0616 09:36:26.409858 9857 solver.cpp:571] Iteration 43740, lr = 0.001 +I0616 09:36:37.829519 9857 solver.cpp:242] Iteration 43760, loss = 0.326659 +I0616 09:36:37.829562 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121278 (* 1 = 0.121278 loss) +I0616 09:36:37.829568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102216 (* 1 = 0.102216 loss) +I0616 09:36:37.829572 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00483966 (* 1 = 0.00483966 loss) +I0616 09:36:37.829576 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0341079 (* 1 = 0.0341079 loss) +I0616 09:36:37.829579 9857 solver.cpp:571] Iteration 43760, lr = 0.001 +I0616 09:36:49.373222 9857 solver.cpp:242] Iteration 43780, loss = 1.11727 +I0616 09:36:49.373265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.465522 (* 1 = 0.465522 loss) +I0616 09:36:49.373270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.63747 (* 1 = 0.63747 loss) +I0616 09:36:49.373273 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0720482 (* 1 = 0.0720482 loss) +I0616 09:36:49.373277 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167542 (* 1 = 0.0167542 loss) +I0616 09:36:49.373281 9857 solver.cpp:571] Iteration 43780, lr = 0.001 +speed: 0.618s / iter +I0616 09:37:00.784852 9857 solver.cpp:242] Iteration 43800, loss = 0.840469 +I0616 09:37:00.784893 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307872 (* 1 = 0.307872 loss) +I0616 09:37:00.784898 9857 solver.cpp:258] Train net output #1: loss_cls = 0.682208 (* 1 = 0.682208 loss) +I0616 09:37:00.784903 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153947 (* 1 = 0.153947 loss) +I0616 09:37:00.784906 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.05926 (* 1 = 0.05926 loss) +I0616 09:37:00.784910 9857 solver.cpp:571] Iteration 43800, lr = 0.001 +I0616 09:37:12.271176 9857 solver.cpp:242] Iteration 43820, loss = 0.80489 +I0616 09:37:12.271219 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338921 (* 1 = 0.338921 loss) +I0616 09:37:12.271224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.495523 (* 1 = 0.495523 loss) +I0616 09:37:12.271227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104434 (* 1 = 0.104434 loss) +I0616 09:37:12.271231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0137722 (* 1 = 0.0137722 loss) +I0616 09:37:12.271235 9857 solver.cpp:571] Iteration 43820, lr = 0.001 +I0616 09:37:23.826540 9857 solver.cpp:242] Iteration 43840, loss = 0.549114 +I0616 09:37:23.826581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141765 (* 1 = 0.141765 loss) +I0616 09:37:23.826586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189534 (* 1 = 0.189534 loss) +I0616 09:37:23.826591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0674826 (* 1 = 0.0674826 loss) +I0616 09:37:23.826594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.178961 (* 1 = 0.178961 loss) +I0616 09:37:23.826598 9857 solver.cpp:571] Iteration 43840, lr = 0.001 +I0616 09:37:35.482164 9857 solver.cpp:242] Iteration 43860, loss = 0.735153 +I0616 09:37:35.482204 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311423 (* 1 = 0.311423 loss) +I0616 09:37:35.482210 9857 solver.cpp:258] Train net output #1: loss_cls = 0.36703 (* 1 = 0.36703 loss) +I0616 09:37:35.482214 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129707 (* 1 = 0.129707 loss) +I0616 09:37:35.482218 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251001 (* 1 = 0.0251001 loss) +I0616 09:37:35.482221 9857 solver.cpp:571] Iteration 43860, lr = 0.001 +I0616 09:37:46.961771 9857 solver.cpp:242] Iteration 43880, loss = 0.72819 +I0616 09:37:46.961812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.410354 (* 1 = 0.410354 loss) +I0616 09:37:46.961818 9857 solver.cpp:258] Train net output #1: loss_cls = 0.39559 (* 1 = 0.39559 loss) +I0616 09:37:46.961822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0768989 (* 1 = 0.0768989 loss) +I0616 09:37:46.961827 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0268312 (* 1 = 0.0268312 loss) +I0616 09:37:46.961829 9857 solver.cpp:571] Iteration 43880, lr = 0.001 +I0616 09:37:58.360040 9857 solver.cpp:242] Iteration 43900, loss = 1.53224 +I0616 09:37:58.360082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.454944 (* 1 = 0.454944 loss) +I0616 09:37:58.360088 9857 solver.cpp:258] Train net output #1: loss_cls = 0.599872 (* 1 = 0.599872 loss) +I0616 09:37:58.360092 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.432624 (* 1 = 0.432624 loss) +I0616 09:37:58.360096 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136888 (* 1 = 0.136888 loss) +I0616 09:37:58.360100 9857 solver.cpp:571] Iteration 43900, lr = 0.001 +I0616 09:38:09.845304 9857 solver.cpp:242] Iteration 43920, loss = 1.25234 +I0616 09:38:09.845346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.407916 (* 1 = 0.407916 loss) +I0616 09:38:09.845352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.767908 (* 1 = 0.767908 loss) +I0616 09:38:09.845356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10146 (* 1 = 0.10146 loss) +I0616 09:38:09.845360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167283 (* 1 = 0.0167283 loss) +I0616 09:38:09.845365 9857 solver.cpp:571] Iteration 43920, lr = 0.001 +I0616 09:38:21.442432 9857 solver.cpp:242] Iteration 43940, loss = 0.68471 +I0616 09:38:21.442474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193804 (* 1 = 0.193804 loss) +I0616 09:38:21.442479 9857 solver.cpp:258] Train net output #1: loss_cls = 0.647337 (* 1 = 0.647337 loss) +I0616 09:38:21.442483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0913481 (* 1 = 0.0913481 loss) +I0616 09:38:21.442488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00844748 (* 1 = 0.00844748 loss) +I0616 09:38:21.442492 9857 solver.cpp:571] Iteration 43940, lr = 0.001 +I0616 09:38:32.825258 9857 solver.cpp:242] Iteration 43960, loss = 0.685075 +I0616 09:38:32.825299 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286257 (* 1 = 0.286257 loss) +I0616 09:38:32.825305 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245935 (* 1 = 0.245935 loss) +I0616 09:38:32.825309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141767 (* 1 = 0.141767 loss) +I0616 09:38:32.825312 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158081 (* 1 = 0.0158081 loss) +I0616 09:38:32.825316 9857 solver.cpp:571] Iteration 43960, lr = 0.001 +I0616 09:38:44.276417 9857 solver.cpp:242] Iteration 43980, loss = 1.16668 +I0616 09:38:44.276459 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247024 (* 1 = 0.247024 loss) +I0616 09:38:44.276465 9857 solver.cpp:258] Train net output #1: loss_cls = 0.453378 (* 1 = 0.453378 loss) +I0616 09:38:44.276469 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.342284 (* 1 = 0.342284 loss) +I0616 09:38:44.276473 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.485025 (* 1 = 0.485025 loss) +I0616 09:38:44.276476 9857 solver.cpp:571] Iteration 43980, lr = 0.001 +speed: 0.618s / iter +I0616 09:38:55.806632 9857 solver.cpp:242] Iteration 44000, loss = 0.900897 +I0616 09:38:55.806673 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191733 (* 1 = 0.191733 loss) +I0616 09:38:55.806679 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292429 (* 1 = 0.292429 loss) +I0616 09:38:55.806684 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0164779 (* 1 = 0.0164779 loss) +I0616 09:38:55.806686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104862 (* 1 = 0.0104862 loss) +I0616 09:38:55.806690 9857 solver.cpp:571] Iteration 44000, lr = 0.001 +I0616 09:39:07.545801 9857 solver.cpp:242] Iteration 44020, loss = 0.706839 +I0616 09:39:07.545843 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0429035 (* 1 = 0.0429035 loss) +I0616 09:39:07.545848 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250398 (* 1 = 0.250398 loss) +I0616 09:39:07.545852 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287482 (* 1 = 0.0287482 loss) +I0616 09:39:07.545856 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00456392 (* 1 = 0.00456392 loss) +I0616 09:39:07.545861 9857 solver.cpp:571] Iteration 44020, lr = 0.001 +I0616 09:39:19.215191 9857 solver.cpp:242] Iteration 44040, loss = 0.527177 +I0616 09:39:19.215232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291436 (* 1 = 0.291436 loss) +I0616 09:39:19.215237 9857 solver.cpp:258] Train net output #1: loss_cls = 0.198256 (* 1 = 0.198256 loss) +I0616 09:39:19.215242 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0499954 (* 1 = 0.0499954 loss) +I0616 09:39:19.215246 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00492442 (* 1 = 0.00492442 loss) +I0616 09:39:19.215250 9857 solver.cpp:571] Iteration 44040, lr = 0.001 +I0616 09:39:30.675923 9857 solver.cpp:242] Iteration 44060, loss = 0.460964 +I0616 09:39:30.675963 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0993164 (* 1 = 0.0993164 loss) +I0616 09:39:30.675969 9857 solver.cpp:258] Train net output #1: loss_cls = 0.379958 (* 1 = 0.379958 loss) +I0616 09:39:30.675973 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0251109 (* 1 = 0.0251109 loss) +I0616 09:39:30.675977 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102385 (* 1 = 0.0102385 loss) +I0616 09:39:30.675981 9857 solver.cpp:571] Iteration 44060, lr = 0.001 +I0616 09:39:42.361063 9857 solver.cpp:242] Iteration 44080, loss = 0.801122 +I0616 09:39:42.361104 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.431306 (* 1 = 0.431306 loss) +I0616 09:39:42.361109 9857 solver.cpp:258] Train net output #1: loss_cls = 0.532899 (* 1 = 0.532899 loss) +I0616 09:39:42.361114 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188865 (* 1 = 0.188865 loss) +I0616 09:39:42.361119 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.067081 (* 1 = 0.067081 loss) +I0616 09:39:42.361121 9857 solver.cpp:571] Iteration 44080, lr = 0.001 +I0616 09:39:53.931059 9857 solver.cpp:242] Iteration 44100, loss = 0.962237 +I0616 09:39:53.931102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.061052 (* 1 = 0.061052 loss) +I0616 09:39:53.931107 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203926 (* 1 = 0.203926 loss) +I0616 09:39:53.931112 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133909 (* 1 = 0.133909 loss) +I0616 09:39:53.931114 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167802 (* 1 = 0.0167802 loss) +I0616 09:39:53.931118 9857 solver.cpp:571] Iteration 44100, lr = 0.001 +I0616 09:40:05.204219 9857 solver.cpp:242] Iteration 44120, loss = 0.398647 +I0616 09:40:05.204260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155455 (* 1 = 0.155455 loss) +I0616 09:40:05.204267 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345924 (* 1 = 0.345924 loss) +I0616 09:40:05.204270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0619349 (* 1 = 0.0619349 loss) +I0616 09:40:05.204274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0377293 (* 1 = 0.0377293 loss) +I0616 09:40:05.204278 9857 solver.cpp:571] Iteration 44120, lr = 0.001 +I0616 09:40:16.809011 9857 solver.cpp:242] Iteration 44140, loss = 0.654816 +I0616 09:40:16.809056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.418098 (* 1 = 0.418098 loss) +I0616 09:40:16.809061 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248602 (* 1 = 0.248602 loss) +I0616 09:40:16.809064 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0564405 (* 1 = 0.0564405 loss) +I0616 09:40:16.809068 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00751421 (* 1 = 0.00751421 loss) +I0616 09:40:16.809072 9857 solver.cpp:571] Iteration 44140, lr = 0.001 +I0616 09:40:28.502384 9857 solver.cpp:242] Iteration 44160, loss = 1.20756 +I0616 09:40:28.502426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241689 (* 1 = 0.241689 loss) +I0616 09:40:28.502432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201644 (* 1 = 0.201644 loss) +I0616 09:40:28.502436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102295 (* 1 = 0.102295 loss) +I0616 09:40:28.502440 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253486 (* 1 = 0.0253486 loss) +I0616 09:40:28.502444 9857 solver.cpp:571] Iteration 44160, lr = 0.001 +I0616 09:40:39.888700 9857 solver.cpp:242] Iteration 44180, loss = 1.06314 +I0616 09:40:39.888742 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251539 (* 1 = 0.251539 loss) +I0616 09:40:39.888748 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270407 (* 1 = 0.270407 loss) +I0616 09:40:39.888752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0781465 (* 1 = 0.0781465 loss) +I0616 09:40:39.888756 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319553 (* 1 = 0.0319553 loss) +I0616 09:40:39.888759 9857 solver.cpp:571] Iteration 44180, lr = 0.001 +speed: 0.617s / iter +I0616 09:40:51.211477 9857 solver.cpp:242] Iteration 44200, loss = 1.06912 +I0616 09:40:51.211519 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.547257 (* 1 = 0.547257 loss) +I0616 09:40:51.211524 9857 solver.cpp:258] Train net output #1: loss_cls = 0.826463 (* 1 = 0.826463 loss) +I0616 09:40:51.211529 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.35646 (* 1 = 0.35646 loss) +I0616 09:40:51.211531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0458656 (* 1 = 0.0458656 loss) +I0616 09:40:51.211536 9857 solver.cpp:571] Iteration 44200, lr = 0.001 +I0616 09:41:02.960932 9857 solver.cpp:242] Iteration 44220, loss = 1.2128 +I0616 09:41:02.960973 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.398391 (* 1 = 0.398391 loss) +I0616 09:41:02.960979 9857 solver.cpp:258] Train net output #1: loss_cls = 0.626734 (* 1 = 0.626734 loss) +I0616 09:41:02.960983 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.383716 (* 1 = 0.383716 loss) +I0616 09:41:02.960988 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0785934 (* 1 = 0.0785934 loss) +I0616 09:41:02.960990 9857 solver.cpp:571] Iteration 44220, lr = 0.001 +I0616 09:41:14.631325 9857 solver.cpp:242] Iteration 44240, loss = 0.721846 +I0616 09:41:14.631362 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255467 (* 1 = 0.255467 loss) +I0616 09:41:14.631368 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306386 (* 1 = 0.306386 loss) +I0616 09:41:14.631372 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122067 (* 1 = 0.122067 loss) +I0616 09:41:14.631376 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0594187 (* 1 = 0.0594187 loss) +I0616 09:41:14.631381 9857 solver.cpp:571] Iteration 44240, lr = 0.001 +I0616 09:41:25.950462 9857 solver.cpp:242] Iteration 44260, loss = 0.602346 +I0616 09:41:25.950505 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363447 (* 1 = 0.363447 loss) +I0616 09:41:25.950510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243128 (* 1 = 0.243128 loss) +I0616 09:41:25.950515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0826805 (* 1 = 0.0826805 loss) +I0616 09:41:25.950518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163308 (* 1 = 0.0163308 loss) +I0616 09:41:25.950521 9857 solver.cpp:571] Iteration 44260, lr = 0.001 +I0616 09:41:37.571182 9857 solver.cpp:242] Iteration 44280, loss = 1.00563 +I0616 09:41:37.571224 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103602 (* 1 = 0.103602 loss) +I0616 09:41:37.571230 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134351 (* 1 = 0.134351 loss) +I0616 09:41:37.571234 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0295513 (* 1 = 0.0295513 loss) +I0616 09:41:37.571238 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00372407 (* 1 = 0.00372407 loss) +I0616 09:41:37.571241 9857 solver.cpp:571] Iteration 44280, lr = 0.001 +I0616 09:41:49.366330 9857 solver.cpp:242] Iteration 44300, loss = 0.775085 +I0616 09:41:49.366374 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285961 (* 1 = 0.285961 loss) +I0616 09:41:49.366379 9857 solver.cpp:258] Train net output #1: loss_cls = 0.362287 (* 1 = 0.362287 loss) +I0616 09:41:49.366384 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.233471 (* 1 = 0.233471 loss) +I0616 09:41:49.366387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.240348 (* 1 = 0.240348 loss) +I0616 09:41:49.366391 9857 solver.cpp:571] Iteration 44300, lr = 0.001 +I0616 09:42:00.828498 9857 solver.cpp:242] Iteration 44320, loss = 0.951717 +I0616 09:42:00.828542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359371 (* 1 = 0.359371 loss) +I0616 09:42:00.828547 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401985 (* 1 = 0.401985 loss) +I0616 09:42:00.828552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235974 (* 1 = 0.235974 loss) +I0616 09:42:00.828555 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.44217 (* 1 = 0.44217 loss) +I0616 09:42:00.828559 9857 solver.cpp:571] Iteration 44320, lr = 0.001 +I0616 09:42:12.382273 9857 solver.cpp:242] Iteration 44340, loss = 0.867914 +I0616 09:42:12.382316 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287006 (* 1 = 0.287006 loss) +I0616 09:42:12.382321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432567 (* 1 = 0.432567 loss) +I0616 09:42:12.382326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.156309 (* 1 = 0.156309 loss) +I0616 09:42:12.382329 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254616 (* 1 = 0.0254616 loss) +I0616 09:42:12.382333 9857 solver.cpp:571] Iteration 44340, lr = 0.001 +I0616 09:42:23.658764 9857 solver.cpp:242] Iteration 44360, loss = 0.490382 +I0616 09:42:23.658807 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177815 (* 1 = 0.177815 loss) +I0616 09:42:23.658812 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283986 (* 1 = 0.283986 loss) +I0616 09:42:23.658817 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0973268 (* 1 = 0.0973268 loss) +I0616 09:42:23.658820 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0454261 (* 1 = 0.0454261 loss) +I0616 09:42:23.658824 9857 solver.cpp:571] Iteration 44360, lr = 0.001 +I0616 09:42:35.167186 9857 solver.cpp:242] Iteration 44380, loss = 0.558704 +I0616 09:42:35.167225 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206101 (* 1 = 0.206101 loss) +I0616 09:42:35.167230 9857 solver.cpp:258] Train net output #1: loss_cls = 0.220515 (* 1 = 0.220515 loss) +I0616 09:42:35.167234 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106028 (* 1 = 0.106028 loss) +I0616 09:42:35.167238 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00555577 (* 1 = 0.00555577 loss) +I0616 09:42:35.167243 9857 solver.cpp:571] Iteration 44380, lr = 0.001 +speed: 0.617s / iter +I0616 09:42:46.789352 9857 solver.cpp:242] Iteration 44400, loss = 1.0659 +I0616 09:42:46.789394 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0999023 (* 1 = 0.0999023 loss) +I0616 09:42:46.789400 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231923 (* 1 = 0.231923 loss) +I0616 09:42:46.789404 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.222236 (* 1 = 0.222236 loss) +I0616 09:42:46.789408 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00200706 (* 1 = 0.00200706 loss) +I0616 09:42:46.789412 9857 solver.cpp:571] Iteration 44400, lr = 0.001 +I0616 09:42:58.112414 9857 solver.cpp:242] Iteration 44420, loss = 1.16637 +I0616 09:42:58.112457 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.414348 (* 1 = 0.414348 loss) +I0616 09:42:58.112463 9857 solver.cpp:258] Train net output #1: loss_cls = 0.688553 (* 1 = 0.688553 loss) +I0616 09:42:58.112468 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.413292 (* 1 = 0.413292 loss) +I0616 09:42:58.112470 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.641608 (* 1 = 0.641608 loss) +I0616 09:42:58.112474 9857 solver.cpp:571] Iteration 44420, lr = 0.001 +I0616 09:43:09.677000 9857 solver.cpp:242] Iteration 44440, loss = 0.459275 +I0616 09:43:09.677039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151535 (* 1 = 0.151535 loss) +I0616 09:43:09.677045 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195599 (* 1 = 0.195599 loss) +I0616 09:43:09.677049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0622282 (* 1 = 0.0622282 loss) +I0616 09:43:09.677053 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0090378 (* 1 = 0.0090378 loss) +I0616 09:43:09.677057 9857 solver.cpp:571] Iteration 44440, lr = 0.001 +I0616 09:43:21.524806 9857 solver.cpp:242] Iteration 44460, loss = 1.3193 +I0616 09:43:21.524847 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.507754 (* 1 = 0.507754 loss) +I0616 09:43:21.524853 9857 solver.cpp:258] Train net output #1: loss_cls = 0.95628 (* 1 = 0.95628 loss) +I0616 09:43:21.524857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.308167 (* 1 = 0.308167 loss) +I0616 09:43:21.524862 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291736 (* 1 = 0.0291736 loss) +I0616 09:43:21.524864 9857 solver.cpp:571] Iteration 44460, lr = 0.001 +I0616 09:43:32.822837 9857 solver.cpp:242] Iteration 44480, loss = 0.690033 +I0616 09:43:32.822880 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230694 (* 1 = 0.230694 loss) +I0616 09:43:32.822885 9857 solver.cpp:258] Train net output #1: loss_cls = 0.565904 (* 1 = 0.565904 loss) +I0616 09:43:32.822890 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605565 (* 1 = 0.0605565 loss) +I0616 09:43:32.822893 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00911085 (* 1 = 0.00911085 loss) +I0616 09:43:32.822896 9857 solver.cpp:571] Iteration 44480, lr = 0.001 +I0616 09:43:44.456046 9857 solver.cpp:242] Iteration 44500, loss = 0.469771 +I0616 09:43:44.456089 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1087 (* 1 = 0.1087 loss) +I0616 09:43:44.456094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321463 (* 1 = 0.321463 loss) +I0616 09:43:44.456099 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.049402 (* 1 = 0.049402 loss) +I0616 09:43:44.456101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.019339 (* 1 = 0.019339 loss) +I0616 09:43:44.456105 9857 solver.cpp:571] Iteration 44500, lr = 0.001 +I0616 09:43:56.128036 9857 solver.cpp:242] Iteration 44520, loss = 0.574901 +I0616 09:43:56.128077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292568 (* 1 = 0.292568 loss) +I0616 09:43:56.128082 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326981 (* 1 = 0.326981 loss) +I0616 09:43:56.128085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0661211 (* 1 = 0.0661211 loss) +I0616 09:43:56.128089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0480616 (* 1 = 0.0480616 loss) +I0616 09:43:56.128093 9857 solver.cpp:571] Iteration 44520, lr = 0.001 +I0616 09:44:07.657325 9857 solver.cpp:242] Iteration 44540, loss = 0.919425 +I0616 09:44:07.657366 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186052 (* 1 = 0.186052 loss) +I0616 09:44:07.657372 9857 solver.cpp:258] Train net output #1: loss_cls = 0.365166 (* 1 = 0.365166 loss) +I0616 09:44:07.657377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605838 (* 1 = 0.0605838 loss) +I0616 09:44:07.657379 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337151 (* 1 = 0.0337151 loss) +I0616 09:44:07.657383 9857 solver.cpp:571] Iteration 44540, lr = 0.001 +I0616 09:44:19.423548 9857 solver.cpp:242] Iteration 44560, loss = 0.677751 +I0616 09:44:19.423590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159739 (* 1 = 0.159739 loss) +I0616 09:44:19.423595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163955 (* 1 = 0.163955 loss) +I0616 09:44:19.423599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0186975 (* 1 = 0.0186975 loss) +I0616 09:44:19.423604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145963 (* 1 = 0.0145963 loss) +I0616 09:44:19.423607 9857 solver.cpp:571] Iteration 44560, lr = 0.001 +I0616 09:44:31.006242 9857 solver.cpp:242] Iteration 44580, loss = 1.17864 +I0616 09:44:31.006283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281421 (* 1 = 0.281421 loss) +I0616 09:44:31.006289 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210207 (* 1 = 0.210207 loss) +I0616 09:44:31.006294 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0872576 (* 1 = 0.0872576 loss) +I0616 09:44:31.006297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.157676 (* 1 = 0.157676 loss) +I0616 09:44:31.006315 9857 solver.cpp:571] Iteration 44580, lr = 0.001 +speed: 0.617s / iter +I0616 09:44:42.804522 9857 solver.cpp:242] Iteration 44600, loss = 1.36345 +I0616 09:44:42.804563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.485204 (* 1 = 0.485204 loss) +I0616 09:44:42.804568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.526092 (* 1 = 0.526092 loss) +I0616 09:44:42.804571 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.433363 (* 1 = 0.433363 loss) +I0616 09:44:42.804575 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0627617 (* 1 = 0.0627617 loss) +I0616 09:44:42.804579 9857 solver.cpp:571] Iteration 44600, lr = 0.001 +I0616 09:44:54.342981 9857 solver.cpp:242] Iteration 44620, loss = 0.767878 +I0616 09:44:54.343024 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332601 (* 1 = 0.332601 loss) +I0616 09:44:54.343029 9857 solver.cpp:258] Train net output #1: loss_cls = 0.591966 (* 1 = 0.591966 loss) +I0616 09:44:54.343034 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0437851 (* 1 = 0.0437851 loss) +I0616 09:44:54.343037 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0300496 (* 1 = 0.0300496 loss) +I0616 09:44:54.343041 9857 solver.cpp:571] Iteration 44620, lr = 0.001 +I0616 09:45:06.106055 9857 solver.cpp:242] Iteration 44640, loss = 1.00132 +I0616 09:45:06.106096 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.455188 (* 1 = 0.455188 loss) +I0616 09:45:06.106102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.892145 (* 1 = 0.892145 loss) +I0616 09:45:06.106106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159874 (* 1 = 0.159874 loss) +I0616 09:45:06.106111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0775829 (* 1 = 0.0775829 loss) +I0616 09:45:06.106113 9857 solver.cpp:571] Iteration 44640, lr = 0.001 +I0616 09:45:17.559960 9857 solver.cpp:242] Iteration 44660, loss = 0.411976 +I0616 09:45:17.560003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203174 (* 1 = 0.203174 loss) +I0616 09:45:17.560008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262849 (* 1 = 0.262849 loss) +I0616 09:45:17.560011 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0545653 (* 1 = 0.0545653 loss) +I0616 09:45:17.560015 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313383 (* 1 = 0.0313383 loss) +I0616 09:45:17.560019 9857 solver.cpp:571] Iteration 44660, lr = 0.001 +I0616 09:45:29.182610 9857 solver.cpp:242] Iteration 44680, loss = 1.10618 +I0616 09:45:29.182652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233245 (* 1 = 0.233245 loss) +I0616 09:45:29.182657 9857 solver.cpp:258] Train net output #1: loss_cls = 0.859357 (* 1 = 0.859357 loss) +I0616 09:45:29.182662 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107615 (* 1 = 0.107615 loss) +I0616 09:45:29.182665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220039 (* 1 = 0.0220039 loss) +I0616 09:45:29.182669 9857 solver.cpp:571] Iteration 44680, lr = 0.001 +I0616 09:45:40.812721 9857 solver.cpp:242] Iteration 44700, loss = 0.613916 +I0616 09:45:40.812764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20694 (* 1 = 0.20694 loss) +I0616 09:45:40.812770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431664 (* 1 = 0.431664 loss) +I0616 09:45:40.812774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0552452 (* 1 = 0.0552452 loss) +I0616 09:45:40.812778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00870719 (* 1 = 0.00870719 loss) +I0616 09:45:40.812783 9857 solver.cpp:571] Iteration 44700, lr = 0.001 +I0616 09:45:52.489074 9857 solver.cpp:242] Iteration 44720, loss = 0.800211 +I0616 09:45:52.489115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235054 (* 1 = 0.235054 loss) +I0616 09:45:52.489121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287324 (* 1 = 0.287324 loss) +I0616 09:45:52.489125 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.075352 (* 1 = 0.075352 loss) +I0616 09:45:52.489130 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0531913 (* 1 = 0.0531913 loss) +I0616 09:45:52.489132 9857 solver.cpp:571] Iteration 44720, lr = 0.001 +I0616 09:46:03.970485 9857 solver.cpp:242] Iteration 44740, loss = 0.984099 +I0616 09:46:03.970527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.415332 (* 1 = 0.415332 loss) +I0616 09:46:03.970533 9857 solver.cpp:258] Train net output #1: loss_cls = 0.882513 (* 1 = 0.882513 loss) +I0616 09:46:03.970537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193895 (* 1 = 0.193895 loss) +I0616 09:46:03.970541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360395 (* 1 = 0.0360395 loss) +I0616 09:46:03.970544 9857 solver.cpp:571] Iteration 44740, lr = 0.001 +I0616 09:46:15.462251 9857 solver.cpp:242] Iteration 44760, loss = 0.556017 +I0616 09:46:15.462293 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0319119 (* 1 = 0.0319119 loss) +I0616 09:46:15.462299 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107737 (* 1 = 0.107737 loss) +I0616 09:46:15.462303 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0530226 (* 1 = 0.0530226 loss) +I0616 09:46:15.462307 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00546652 (* 1 = 0.00546652 loss) +I0616 09:46:15.462311 9857 solver.cpp:571] Iteration 44760, lr = 0.001 +I0616 09:46:26.998529 9857 solver.cpp:242] Iteration 44780, loss = 0.545662 +I0616 09:46:26.998571 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244996 (* 1 = 0.244996 loss) +I0616 09:46:26.998576 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294445 (* 1 = 0.294445 loss) +I0616 09:46:26.998581 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0489401 (* 1 = 0.0489401 loss) +I0616 09:46:26.998585 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0472274 (* 1 = 0.0472274 loss) +I0616 09:46:26.998589 9857 solver.cpp:571] Iteration 44780, lr = 0.001 +speed: 0.617s / iter +I0616 09:46:38.632369 9857 solver.cpp:242] Iteration 44800, loss = 1.12233 +I0616 09:46:38.632411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350821 (* 1 = 0.350821 loss) +I0616 09:46:38.632416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.478991 (* 1 = 0.478991 loss) +I0616 09:46:38.632421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0558007 (* 1 = 0.0558007 loss) +I0616 09:46:38.632424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112786 (* 1 = 0.0112786 loss) +I0616 09:46:38.632428 9857 solver.cpp:571] Iteration 44800, lr = 0.001 +I0616 09:46:50.105121 9857 solver.cpp:242] Iteration 44820, loss = 0.422826 +I0616 09:46:50.105159 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158364 (* 1 = 0.158364 loss) +I0616 09:46:50.105165 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150258 (* 1 = 0.150258 loss) +I0616 09:46:50.105170 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0437714 (* 1 = 0.0437714 loss) +I0616 09:46:50.105173 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00317436 (* 1 = 0.00317436 loss) +I0616 09:46:50.105177 9857 solver.cpp:571] Iteration 44820, lr = 0.001 +I0616 09:47:01.456636 9857 solver.cpp:242] Iteration 44840, loss = 1.02573 +I0616 09:47:01.456678 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293999 (* 1 = 0.293999 loss) +I0616 09:47:01.456683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.447378 (* 1 = 0.447378 loss) +I0616 09:47:01.456687 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294051 (* 1 = 0.294051 loss) +I0616 09:47:01.456691 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.223025 (* 1 = 0.223025 loss) +I0616 09:47:01.456696 9857 solver.cpp:571] Iteration 44840, lr = 0.001 +I0616 09:47:13.051434 9857 solver.cpp:242] Iteration 44860, loss = 0.583674 +I0616 09:47:13.051475 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132528 (* 1 = 0.132528 loss) +I0616 09:47:13.051481 9857 solver.cpp:258] Train net output #1: loss_cls = 0.329503 (* 1 = 0.329503 loss) +I0616 09:47:13.051484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.24217 (* 1 = 0.24217 loss) +I0616 09:47:13.051488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130125 (* 1 = 0.0130125 loss) +I0616 09:47:13.051492 9857 solver.cpp:571] Iteration 44860, lr = 0.001 +I0616 09:47:24.587100 9857 solver.cpp:242] Iteration 44880, loss = 0.570564 +I0616 09:47:24.587141 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254003 (* 1 = 0.254003 loss) +I0616 09:47:24.587146 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210657 (* 1 = 0.210657 loss) +I0616 09:47:24.587151 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0493743 (* 1 = 0.0493743 loss) +I0616 09:47:24.587154 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0342263 (* 1 = 0.0342263 loss) +I0616 09:47:24.587158 9857 solver.cpp:571] Iteration 44880, lr = 0.001 +I0616 09:47:36.228157 9857 solver.cpp:242] Iteration 44900, loss = 0.483617 +I0616 09:47:36.228199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13496 (* 1 = 0.13496 loss) +I0616 09:47:36.228204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137029 (* 1 = 0.137029 loss) +I0616 09:47:36.228207 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228519 (* 1 = 0.0228519 loss) +I0616 09:47:36.228211 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0352422 (* 1 = 0.0352422 loss) +I0616 09:47:36.228215 9857 solver.cpp:571] Iteration 44900, lr = 0.001 +I0616 09:47:47.654225 9857 solver.cpp:242] Iteration 44920, loss = 0.478598 +I0616 09:47:47.654268 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0976646 (* 1 = 0.0976646 loss) +I0616 09:47:47.654273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127652 (* 1 = 0.127652 loss) +I0616 09:47:47.654278 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0138945 (* 1 = 0.0138945 loss) +I0616 09:47:47.654281 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00876301 (* 1 = 0.00876301 loss) +I0616 09:47:47.654285 9857 solver.cpp:571] Iteration 44920, lr = 0.001 +I0616 09:47:59.154969 9857 solver.cpp:242] Iteration 44940, loss = 0.887675 +I0616 09:47:59.155014 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0625805 (* 1 = 0.0625805 loss) +I0616 09:47:59.155019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162824 (* 1 = 0.162824 loss) +I0616 09:47:59.155024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119113 (* 1 = 0.119113 loss) +I0616 09:47:59.155026 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0463742 (* 1 = 0.0463742 loss) +I0616 09:47:59.155030 9857 solver.cpp:571] Iteration 44940, lr = 0.001 +I0616 09:48:10.869204 9857 solver.cpp:242] Iteration 44960, loss = 0.627239 +I0616 09:48:10.869246 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193368 (* 1 = 0.193368 loss) +I0616 09:48:10.869252 9857 solver.cpp:258] Train net output #1: loss_cls = 0.376509 (* 1 = 0.376509 loss) +I0616 09:48:10.869256 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.018401 (* 1 = 0.018401 loss) +I0616 09:48:10.869261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140154 (* 1 = 0.0140154 loss) +I0616 09:48:10.869264 9857 solver.cpp:571] Iteration 44960, lr = 0.001 +I0616 09:48:22.286399 9857 solver.cpp:242] Iteration 44980, loss = 1.07057 +I0616 09:48:22.286442 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.509314 (* 1 = 0.509314 loss) +I0616 09:48:22.286448 9857 solver.cpp:258] Train net output #1: loss_cls = 0.976856 (* 1 = 0.976856 loss) +I0616 09:48:22.286451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169542 (* 1 = 0.169542 loss) +I0616 09:48:22.286455 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0401983 (* 1 = 0.0401983 loss) +I0616 09:48:22.286459 9857 solver.cpp:571] Iteration 44980, lr = 0.001 +speed: 0.617s / iter +I0616 09:48:33.862555 9857 solver.cpp:242] Iteration 45000, loss = 1.49712 +I0616 09:48:33.862597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255604 (* 1 = 0.255604 loss) +I0616 09:48:33.862603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315856 (* 1 = 0.315856 loss) +I0616 09:48:33.862607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.34068 (* 1 = 0.34068 loss) +I0616 09:48:33.862610 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0383285 (* 1 = 0.0383285 loss) +I0616 09:48:33.862614 9857 solver.cpp:571] Iteration 45000, lr = 0.001 +I0616 09:48:45.521225 9857 solver.cpp:242] Iteration 45020, loss = 1.21813 +I0616 09:48:45.521267 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.444297 (* 1 = 0.444297 loss) +I0616 09:48:45.521273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.909263 (* 1 = 0.909263 loss) +I0616 09:48:45.521277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.272182 (* 1 = 0.272182 loss) +I0616 09:48:45.521281 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162999 (* 1 = 0.162999 loss) +I0616 09:48:45.521284 9857 solver.cpp:571] Iteration 45020, lr = 0.001 +I0616 09:48:56.806205 9857 solver.cpp:242] Iteration 45040, loss = 0.372885 +I0616 09:48:56.806247 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0839852 (* 1 = 0.0839852 loss) +I0616 09:48:56.806252 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0748778 (* 1 = 0.0748778 loss) +I0616 09:48:56.806257 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163197 (* 1 = 0.0163197 loss) +I0616 09:48:56.806260 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0018164 (* 1 = 0.0018164 loss) +I0616 09:48:56.806264 9857 solver.cpp:571] Iteration 45040, lr = 0.001 +I0616 09:49:08.437095 9857 solver.cpp:242] Iteration 45060, loss = 0.436264 +I0616 09:49:08.437137 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165251 (* 1 = 0.165251 loss) +I0616 09:49:08.437144 9857 solver.cpp:258] Train net output #1: loss_cls = 0.273647 (* 1 = 0.273647 loss) +I0616 09:49:08.437147 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205732 (* 1 = 0.205732 loss) +I0616 09:49:08.437150 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0363395 (* 1 = 0.0363395 loss) +I0616 09:49:08.437155 9857 solver.cpp:571] Iteration 45060, lr = 0.001 +I0616 09:49:20.009356 9857 solver.cpp:242] Iteration 45080, loss = 1.16378 +I0616 09:49:20.009398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413645 (* 1 = 0.413645 loss) +I0616 09:49:20.009403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.557024 (* 1 = 0.557024 loss) +I0616 09:49:20.009407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224258 (* 1 = 0.224258 loss) +I0616 09:49:20.009412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111432 (* 1 = 0.111432 loss) +I0616 09:49:20.009415 9857 solver.cpp:571] Iteration 45080, lr = 0.001 +I0616 09:49:31.569104 9857 solver.cpp:242] Iteration 45100, loss = 0.446306 +I0616 09:49:31.569147 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135415 (* 1 = 0.135415 loss) +I0616 09:49:31.569152 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17976 (* 1 = 0.17976 loss) +I0616 09:49:31.569156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0575296 (* 1 = 0.0575296 loss) +I0616 09:49:31.569160 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225766 (* 1 = 0.0225766 loss) +I0616 09:49:31.569164 9857 solver.cpp:571] Iteration 45100, lr = 0.001 +I0616 09:49:43.362224 9857 solver.cpp:242] Iteration 45120, loss = 0.624013 +I0616 09:49:43.362267 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171249 (* 1 = 0.171249 loss) +I0616 09:49:43.362272 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29331 (* 1 = 0.29331 loss) +I0616 09:49:43.362277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0852846 (* 1 = 0.0852846 loss) +I0616 09:49:43.362280 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0322757 (* 1 = 0.0322757 loss) +I0616 09:49:43.362284 9857 solver.cpp:571] Iteration 45120, lr = 0.001 +I0616 09:49:54.988524 9857 solver.cpp:242] Iteration 45140, loss = 1.15467 +I0616 09:49:54.988566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285224 (* 1 = 0.285224 loss) +I0616 09:49:54.988571 9857 solver.cpp:258] Train net output #1: loss_cls = 0.526764 (* 1 = 0.526764 loss) +I0616 09:49:54.988575 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.355552 (* 1 = 0.355552 loss) +I0616 09:49:54.988579 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.604298 (* 1 = 0.604298 loss) +I0616 09:49:54.988582 9857 solver.cpp:571] Iteration 45140, lr = 0.001 +I0616 09:50:06.632138 9857 solver.cpp:242] Iteration 45160, loss = 0.735215 +I0616 09:50:06.632179 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286163 (* 1 = 0.286163 loss) +I0616 09:50:06.632184 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358498 (* 1 = 0.358498 loss) +I0616 09:50:06.632189 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0236715 (* 1 = 0.0236715 loss) +I0616 09:50:06.632192 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0617923 (* 1 = 0.0617923 loss) +I0616 09:50:06.632196 9857 solver.cpp:571] Iteration 45160, lr = 0.001 +I0616 09:50:18.133185 9857 solver.cpp:242] Iteration 45180, loss = 1.11039 +I0616 09:50:18.133229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.455282 (* 1 = 0.455282 loss) +I0616 09:50:18.133234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.810665 (* 1 = 0.810665 loss) +I0616 09:50:18.133237 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0451579 (* 1 = 0.0451579 loss) +I0616 09:50:18.133241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235586 (* 1 = 0.0235586 loss) +I0616 09:50:18.133245 9857 solver.cpp:571] Iteration 45180, lr = 0.001 +speed: 0.617s / iter +I0616 09:50:29.714875 9857 solver.cpp:242] Iteration 45200, loss = 0.263359 +I0616 09:50:29.714917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114436 (* 1 = 0.114436 loss) +I0616 09:50:29.714923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0995666 (* 1 = 0.0995666 loss) +I0616 09:50:29.714927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0534018 (* 1 = 0.0534018 loss) +I0616 09:50:29.714931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119793 (* 1 = 0.0119793 loss) +I0616 09:50:29.714934 9857 solver.cpp:571] Iteration 45200, lr = 0.001 +I0616 09:50:41.051712 9857 solver.cpp:242] Iteration 45220, loss = 1.38226 +I0616 09:50:41.051753 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343974 (* 1 = 0.343974 loss) +I0616 09:50:41.051759 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38191 (* 1 = 0.38191 loss) +I0616 09:50:41.051764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103685 (* 1 = 0.103685 loss) +I0616 09:50:41.051767 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0488575 (* 1 = 0.0488575 loss) +I0616 09:50:41.051771 9857 solver.cpp:571] Iteration 45220, lr = 0.001 +I0616 09:50:52.643127 9857 solver.cpp:242] Iteration 45240, loss = 1.38629 +I0616 09:50:52.643169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21213 (* 1 = 0.21213 loss) +I0616 09:50:52.643175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.37776 (* 1 = 0.37776 loss) +I0616 09:50:52.643179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0091527 (* 1 = 0.0091527 loss) +I0616 09:50:52.643183 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.034641 (* 1 = 0.034641 loss) +I0616 09:50:52.643187 9857 solver.cpp:571] Iteration 45240, lr = 0.001 +I0616 09:51:04.234324 9857 solver.cpp:242] Iteration 45260, loss = 1.30671 +I0616 09:51:04.234367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292266 (* 1 = 0.292266 loss) +I0616 09:51:04.234372 9857 solver.cpp:258] Train net output #1: loss_cls = 0.597183 (* 1 = 0.597183 loss) +I0616 09:51:04.234376 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.289618 (* 1 = 0.289618 loss) +I0616 09:51:04.234380 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.627847 (* 1 = 0.627847 loss) +I0616 09:51:04.234383 9857 solver.cpp:571] Iteration 45260, lr = 0.001 +I0616 09:51:15.793880 9857 solver.cpp:242] Iteration 45280, loss = 0.922193 +I0616 09:51:15.793923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.474433 (* 1 = 0.474433 loss) +I0616 09:51:15.793929 9857 solver.cpp:258] Train net output #1: loss_cls = 0.621777 (* 1 = 0.621777 loss) +I0616 09:51:15.793932 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.255303 (* 1 = 0.255303 loss) +I0616 09:51:15.793936 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.267571 (* 1 = 0.267571 loss) +I0616 09:51:15.793941 9857 solver.cpp:571] Iteration 45280, lr = 0.001 +I0616 09:51:27.480022 9857 solver.cpp:242] Iteration 45300, loss = 1.06433 +I0616 09:51:27.480062 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24559 (* 1 = 0.24559 loss) +I0616 09:51:27.480068 9857 solver.cpp:258] Train net output #1: loss_cls = 0.577235 (* 1 = 0.577235 loss) +I0616 09:51:27.480072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227878 (* 1 = 0.227878 loss) +I0616 09:51:27.480077 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0477821 (* 1 = 0.0477821 loss) +I0616 09:51:27.480080 9857 solver.cpp:571] Iteration 45300, lr = 0.001 +I0616 09:51:38.945792 9857 solver.cpp:242] Iteration 45320, loss = 0.351874 +I0616 09:51:38.945837 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0689453 (* 1 = 0.0689453 loss) +I0616 09:51:38.945842 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184759 (* 1 = 0.184759 loss) +I0616 09:51:38.945845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0472759 (* 1 = 0.0472759 loss) +I0616 09:51:38.945849 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286218 (* 1 = 0.0286218 loss) +I0616 09:51:38.945853 9857 solver.cpp:571] Iteration 45320, lr = 0.001 +I0616 09:51:50.357520 9857 solver.cpp:242] Iteration 45340, loss = 0.828471 +I0616 09:51:50.357563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.465547 (* 1 = 0.465547 loss) +I0616 09:51:50.357568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.441846 (* 1 = 0.441846 loss) +I0616 09:51:50.357571 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165335 (* 1 = 0.165335 loss) +I0616 09:51:50.357575 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0491849 (* 1 = 0.0491849 loss) +I0616 09:51:50.357579 9857 solver.cpp:571] Iteration 45340, lr = 0.001 +I0616 09:52:01.768242 9857 solver.cpp:242] Iteration 45360, loss = 0.503988 +I0616 09:52:01.768285 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0747387 (* 1 = 0.0747387 loss) +I0616 09:52:01.768290 9857 solver.cpp:258] Train net output #1: loss_cls = 0.445192 (* 1 = 0.445192 loss) +I0616 09:52:01.768295 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0455772 (* 1 = 0.0455772 loss) +I0616 09:52:01.768297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251441 (* 1 = 0.0251441 loss) +I0616 09:52:01.768301 9857 solver.cpp:571] Iteration 45360, lr = 0.001 +I0616 09:52:13.512784 9857 solver.cpp:242] Iteration 45380, loss = 0.598468 +I0616 09:52:13.512827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233082 (* 1 = 0.233082 loss) +I0616 09:52:13.512832 9857 solver.cpp:258] Train net output #1: loss_cls = 0.193948 (* 1 = 0.193948 loss) +I0616 09:52:13.512836 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108479 (* 1 = 0.108479 loss) +I0616 09:52:13.512840 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306022 (* 1 = 0.0306022 loss) +I0616 09:52:13.512845 9857 solver.cpp:571] Iteration 45380, lr = 0.001 +speed: 0.616s / iter +I0616 09:52:24.965674 9857 solver.cpp:242] Iteration 45400, loss = 0.964592 +I0616 09:52:24.965716 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346446 (* 1 = 0.346446 loss) +I0616 09:52:24.965721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381016 (* 1 = 0.381016 loss) +I0616 09:52:24.965725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.173994 (* 1 = 0.173994 loss) +I0616 09:52:24.965729 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0563746 (* 1 = 0.0563746 loss) +I0616 09:52:24.965734 9857 solver.cpp:571] Iteration 45400, lr = 0.001 +I0616 09:52:36.580484 9857 solver.cpp:242] Iteration 45420, loss = 0.98701 +I0616 09:52:36.580528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373734 (* 1 = 0.373734 loss) +I0616 09:52:36.580533 9857 solver.cpp:258] Train net output #1: loss_cls = 0.702295 (* 1 = 0.702295 loss) +I0616 09:52:36.580538 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0990406 (* 1 = 0.0990406 loss) +I0616 09:52:36.580541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0645987 (* 1 = 0.0645987 loss) +I0616 09:52:36.580545 9857 solver.cpp:571] Iteration 45420, lr = 0.001 +I0616 09:52:48.106796 9857 solver.cpp:242] Iteration 45440, loss = 0.568162 +I0616 09:52:48.106840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.248534 (* 1 = 0.248534 loss) +I0616 09:52:48.106845 9857 solver.cpp:258] Train net output #1: loss_cls = 0.241851 (* 1 = 0.241851 loss) +I0616 09:52:48.106849 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.040244 (* 1 = 0.040244 loss) +I0616 09:52:48.106853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0236262 (* 1 = 0.0236262 loss) +I0616 09:52:48.106858 9857 solver.cpp:571] Iteration 45440, lr = 0.001 +I0616 09:52:59.821676 9857 solver.cpp:242] Iteration 45460, loss = 0.971552 +I0616 09:52:59.821717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.546059 (* 1 = 0.546059 loss) +I0616 09:52:59.821722 9857 solver.cpp:258] Train net output #1: loss_cls = 0.618025 (* 1 = 0.618025 loss) +I0616 09:52:59.821727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.17369 (* 1 = 0.17369 loss) +I0616 09:52:59.821730 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199924 (* 1 = 0.0199924 loss) +I0616 09:52:59.821734 9857 solver.cpp:571] Iteration 45460, lr = 0.001 +I0616 09:53:11.378962 9857 solver.cpp:242] Iteration 45480, loss = 1.52333 +I0616 09:53:11.379004 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45324 (* 1 = 0.45324 loss) +I0616 09:53:11.379009 9857 solver.cpp:258] Train net output #1: loss_cls = 0.60858 (* 1 = 0.60858 loss) +I0616 09:53:11.379012 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101552 (* 1 = 0.101552 loss) +I0616 09:53:11.379016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119899 (* 1 = 0.119899 loss) +I0616 09:53:11.379020 9857 solver.cpp:571] Iteration 45480, lr = 0.001 +I0616 09:53:23.043622 9857 solver.cpp:242] Iteration 45500, loss = 0.311783 +I0616 09:53:23.043664 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12595 (* 1 = 0.12595 loss) +I0616 09:53:23.043670 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125151 (* 1 = 0.125151 loss) +I0616 09:53:23.043675 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222994 (* 1 = 0.0222994 loss) +I0616 09:53:23.043678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150938 (* 1 = 0.0150938 loss) +I0616 09:53:23.043683 9857 solver.cpp:571] Iteration 45500, lr = 0.001 +I0616 09:53:34.620571 9857 solver.cpp:242] Iteration 45520, loss = 0.394331 +I0616 09:53:34.620612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0757958 (* 1 = 0.0757958 loss) +I0616 09:53:34.620618 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184021 (* 1 = 0.184021 loss) +I0616 09:53:34.620622 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0407134 (* 1 = 0.0407134 loss) +I0616 09:53:34.620626 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.024226 (* 1 = 0.024226 loss) +I0616 09:53:34.620630 9857 solver.cpp:571] Iteration 45520, lr = 0.001 +I0616 09:53:46.102288 9857 solver.cpp:242] Iteration 45540, loss = 0.56855 +I0616 09:53:46.102330 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13845 (* 1 = 0.13845 loss) +I0616 09:53:46.102335 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183475 (* 1 = 0.183475 loss) +I0616 09:53:46.102339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0779536 (* 1 = 0.0779536 loss) +I0616 09:53:46.102344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.313061 (* 1 = 0.313061 loss) +I0616 09:53:46.102349 9857 solver.cpp:571] Iteration 45540, lr = 0.001 +I0616 09:53:58.052839 9857 solver.cpp:242] Iteration 45560, loss = 0.815176 +I0616 09:53:58.052881 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265233 (* 1 = 0.265233 loss) +I0616 09:53:58.052886 9857 solver.cpp:258] Train net output #1: loss_cls = 0.636063 (* 1 = 0.636063 loss) +I0616 09:53:58.052889 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186049 (* 1 = 0.186049 loss) +I0616 09:53:58.052893 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.125352 (* 1 = 0.125352 loss) +I0616 09:53:58.052896 9857 solver.cpp:571] Iteration 45560, lr = 0.001 +I0616 09:54:09.592162 9857 solver.cpp:242] Iteration 45580, loss = 1.17994 +I0616 09:54:09.592205 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.387927 (* 1 = 0.387927 loss) +I0616 09:54:09.592211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.770435 (* 1 = 0.770435 loss) +I0616 09:54:09.592214 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0476676 (* 1 = 0.0476676 loss) +I0616 09:54:09.592218 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0486777 (* 1 = 0.0486777 loss) +I0616 09:54:09.592221 9857 solver.cpp:571] Iteration 45580, lr = 0.001 +speed: 0.616s / iter +I0616 09:54:21.343268 9857 solver.cpp:242] Iteration 45600, loss = 0.601509 +I0616 09:54:21.343312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274448 (* 1 = 0.274448 loss) +I0616 09:54:21.343317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371053 (* 1 = 0.371053 loss) +I0616 09:54:21.343322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0715151 (* 1 = 0.0715151 loss) +I0616 09:54:21.343324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254212 (* 1 = 0.0254212 loss) +I0616 09:54:21.343328 9857 solver.cpp:571] Iteration 45600, lr = 0.001 +I0616 09:54:32.973067 9857 solver.cpp:242] Iteration 45620, loss = 0.471467 +I0616 09:54:32.973107 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.084037 (* 1 = 0.084037 loss) +I0616 09:54:32.973114 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250659 (* 1 = 0.250659 loss) +I0616 09:54:32.973117 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15942 (* 1 = 0.15942 loss) +I0616 09:54:32.973120 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.155063 (* 1 = 0.155063 loss) +I0616 09:54:32.973125 9857 solver.cpp:571] Iteration 45620, lr = 0.001 +I0616 09:54:44.722923 9857 solver.cpp:242] Iteration 45640, loss = 0.52018 +I0616 09:54:44.722965 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266681 (* 1 = 0.266681 loss) +I0616 09:54:44.722970 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333284 (* 1 = 0.333284 loss) +I0616 09:54:44.722975 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0687137 (* 1 = 0.0687137 loss) +I0616 09:54:44.722978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0282287 (* 1 = 0.0282287 loss) +I0616 09:54:44.722982 9857 solver.cpp:571] Iteration 45640, lr = 0.001 +I0616 09:54:56.210196 9857 solver.cpp:242] Iteration 45660, loss = 0.553174 +I0616 09:54:56.210238 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244415 (* 1 = 0.244415 loss) +I0616 09:54:56.210243 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174093 (* 1 = 0.174093 loss) +I0616 09:54:56.210248 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112006 (* 1 = 0.0112006 loss) +I0616 09:54:56.210252 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227354 (* 1 = 0.0227354 loss) +I0616 09:54:56.210255 9857 solver.cpp:571] Iteration 45660, lr = 0.001 +I0616 09:55:07.779383 9857 solver.cpp:242] Iteration 45680, loss = 0.441493 +I0616 09:55:07.779425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158446 (* 1 = 0.158446 loss) +I0616 09:55:07.779431 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182918 (* 1 = 0.182918 loss) +I0616 09:55:07.779435 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0588079 (* 1 = 0.0588079 loss) +I0616 09:55:07.779439 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0236694 (* 1 = 0.0236694 loss) +I0616 09:55:07.779443 9857 solver.cpp:571] Iteration 45680, lr = 0.001 +I0616 09:55:19.606894 9857 solver.cpp:242] Iteration 45700, loss = 0.74205 +I0616 09:55:19.606938 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.407288 (* 1 = 0.407288 loss) +I0616 09:55:19.606943 9857 solver.cpp:258] Train net output #1: loss_cls = 0.324248 (* 1 = 0.324248 loss) +I0616 09:55:19.606947 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102312 (* 1 = 0.102312 loss) +I0616 09:55:19.606951 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.039319 (* 1 = 0.039319 loss) +I0616 09:55:19.606956 9857 solver.cpp:571] Iteration 45700, lr = 0.001 +I0616 09:55:31.198529 9857 solver.cpp:242] Iteration 45720, loss = 0.536833 +I0616 09:55:31.198570 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158364 (* 1 = 0.158364 loss) +I0616 09:55:31.198576 9857 solver.cpp:258] Train net output #1: loss_cls = 0.540961 (* 1 = 0.540961 loss) +I0616 09:55:31.198580 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0669943 (* 1 = 0.0669943 loss) +I0616 09:55:31.198585 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216788 (* 1 = 0.0216788 loss) +I0616 09:55:31.198588 9857 solver.cpp:571] Iteration 45720, lr = 0.001 +I0616 09:55:42.928308 9857 solver.cpp:242] Iteration 45740, loss = 0.783932 +I0616 09:55:42.928350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214096 (* 1 = 0.214096 loss) +I0616 09:55:42.928355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250421 (* 1 = 0.250421 loss) +I0616 09:55:42.928359 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186075 (* 1 = 0.186075 loss) +I0616 09:55:42.928364 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0442173 (* 1 = 0.0442173 loss) +I0616 09:55:42.928367 9857 solver.cpp:571] Iteration 45740, lr = 0.001 +I0616 09:55:54.337914 9857 solver.cpp:242] Iteration 45760, loss = 0.51692 +I0616 09:55:54.337957 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131424 (* 1 = 0.131424 loss) +I0616 09:55:54.337965 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204659 (* 1 = 0.204659 loss) +I0616 09:55:54.337968 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0121267 (* 1 = 0.0121267 loss) +I0616 09:55:54.337972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00873436 (* 1 = 0.00873436 loss) +I0616 09:55:54.337976 9857 solver.cpp:571] Iteration 45760, lr = 0.001 +I0616 09:56:06.000079 9857 solver.cpp:242] Iteration 45780, loss = 0.308145 +I0616 09:56:06.000119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0790407 (* 1 = 0.0790407 loss) +I0616 09:56:06.000125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121926 (* 1 = 0.121926 loss) +I0616 09:56:06.000129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107489 (* 1 = 0.107489 loss) +I0616 09:56:06.000133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.057023 (* 1 = 0.057023 loss) +I0616 09:56:06.000136 9857 solver.cpp:571] Iteration 45780, lr = 0.001 +speed: 0.616s / iter +I0616 09:56:17.587786 9857 solver.cpp:242] Iteration 45800, loss = 0.674646 +I0616 09:56:17.587827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218124 (* 1 = 0.218124 loss) +I0616 09:56:17.587833 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253396 (* 1 = 0.253396 loss) +I0616 09:56:17.587837 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.310098 (* 1 = 0.310098 loss) +I0616 09:56:17.587841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153083 (* 1 = 0.0153083 loss) +I0616 09:56:17.587844 9857 solver.cpp:571] Iteration 45800, lr = 0.001 +I0616 09:56:29.188412 9857 solver.cpp:242] Iteration 45820, loss = 1.30183 +I0616 09:56:29.188452 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.277814 (* 1 = 0.277814 loss) +I0616 09:56:29.188457 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392231 (* 1 = 0.392231 loss) +I0616 09:56:29.188462 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249726 (* 1 = 0.249726 loss) +I0616 09:56:29.188465 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.240009 (* 1 = 0.240009 loss) +I0616 09:56:29.188468 9857 solver.cpp:571] Iteration 45820, lr = 0.001 +I0616 09:56:40.582145 9857 solver.cpp:242] Iteration 45840, loss = 1.08398 +I0616 09:56:40.582187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.615717 (* 1 = 0.615717 loss) +I0616 09:56:40.582193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.689897 (* 1 = 0.689897 loss) +I0616 09:56:40.582197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135732 (* 1 = 0.135732 loss) +I0616 09:56:40.582201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0687526 (* 1 = 0.0687526 loss) +I0616 09:56:40.582206 9857 solver.cpp:571] Iteration 45840, lr = 0.001 +I0616 09:56:52.106592 9857 solver.cpp:242] Iteration 45860, loss = 0.473489 +I0616 09:56:52.106633 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172257 (* 1 = 0.172257 loss) +I0616 09:56:52.106639 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0817086 (* 1 = 0.0817086 loss) +I0616 09:56:52.106643 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0221041 (* 1 = 0.0221041 loss) +I0616 09:56:52.106647 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239376 (* 1 = 0.0239376 loss) +I0616 09:56:52.106652 9857 solver.cpp:571] Iteration 45860, lr = 0.001 +I0616 09:57:03.761528 9857 solver.cpp:242] Iteration 45880, loss = 1.47824 +I0616 09:57:03.761571 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399645 (* 1 = 0.399645 loss) +I0616 09:57:03.761577 9857 solver.cpp:258] Train net output #1: loss_cls = 0.396501 (* 1 = 0.396501 loss) +I0616 09:57:03.761581 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.189369 (* 1 = 0.189369 loss) +I0616 09:57:03.761585 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0432504 (* 1 = 0.0432504 loss) +I0616 09:57:03.761589 9857 solver.cpp:571] Iteration 45880, lr = 0.001 +I0616 09:57:15.163271 9857 solver.cpp:242] Iteration 45900, loss = 0.801838 +I0616 09:57:15.163312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.34966 (* 1 = 0.34966 loss) +I0616 09:57:15.163318 9857 solver.cpp:258] Train net output #1: loss_cls = 0.476273 (* 1 = 0.476273 loss) +I0616 09:57:15.163322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160762 (* 1 = 0.160762 loss) +I0616 09:57:15.163326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.170519 (* 1 = 0.170519 loss) +I0616 09:57:15.163329 9857 solver.cpp:571] Iteration 45900, lr = 0.001 +I0616 09:57:26.761270 9857 solver.cpp:242] Iteration 45920, loss = 0.680712 +I0616 09:57:26.761312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0443591 (* 1 = 0.0443591 loss) +I0616 09:57:26.761317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166656 (* 1 = 0.166656 loss) +I0616 09:57:26.761322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129697 (* 1 = 0.129697 loss) +I0616 09:57:26.761324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.125752 (* 1 = 0.125752 loss) +I0616 09:57:26.761328 9857 solver.cpp:571] Iteration 45920, lr = 0.001 +I0616 09:57:38.237887 9857 solver.cpp:242] Iteration 45940, loss = 0.517315 +I0616 09:57:38.237931 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180667 (* 1 = 0.180667 loss) +I0616 09:57:38.237936 9857 solver.cpp:258] Train net output #1: loss_cls = 0.510639 (* 1 = 0.510639 loss) +I0616 09:57:38.237939 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0285118 (* 1 = 0.0285118 loss) +I0616 09:57:38.237943 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0488374 (* 1 = 0.0488374 loss) +I0616 09:57:38.237947 9857 solver.cpp:571] Iteration 45940, lr = 0.001 +I0616 09:57:49.578218 9857 solver.cpp:242] Iteration 45960, loss = 0.779511 +I0616 09:57:49.578261 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230896 (* 1 = 0.230896 loss) +I0616 09:57:49.578266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204824 (* 1 = 0.204824 loss) +I0616 09:57:49.578270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0379962 (* 1 = 0.0379962 loss) +I0616 09:57:49.578274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.023371 (* 1 = 0.023371 loss) +I0616 09:57:49.578279 9857 solver.cpp:571] Iteration 45960, lr = 0.001 +I0616 09:58:01.177968 9857 solver.cpp:242] Iteration 45980, loss = 0.538554 +I0616 09:58:01.178010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1882 (* 1 = 0.1882 loss) +I0616 09:58:01.178016 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322361 (* 1 = 0.322361 loss) +I0616 09:58:01.178020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19829 (* 1 = 0.19829 loss) +I0616 09:58:01.178023 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0196531 (* 1 = 0.0196531 loss) +I0616 09:58:01.178027 9857 solver.cpp:571] Iteration 45980, lr = 0.001 +speed: 0.616s / iter +I0616 09:58:12.783474 9857 solver.cpp:242] Iteration 46000, loss = 0.72808 +I0616 09:58:12.783515 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135072 (* 1 = 0.135072 loss) +I0616 09:58:12.783521 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360943 (* 1 = 0.360943 loss) +I0616 09:58:12.783525 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235385 (* 1 = 0.235385 loss) +I0616 09:58:12.783529 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000978861 (* 1 = 0.000978861 loss) +I0616 09:58:12.783534 9857 solver.cpp:571] Iteration 46000, lr = 0.001 +I0616 09:58:24.377271 9857 solver.cpp:242] Iteration 46020, loss = 0.83261 +I0616 09:58:24.377313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287483 (* 1 = 0.287483 loss) +I0616 09:58:24.377320 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431391 (* 1 = 0.431391 loss) +I0616 09:58:24.377323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106355 (* 1 = 0.106355 loss) +I0616 09:58:24.377327 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00972025 (* 1 = 0.00972025 loss) +I0616 09:58:24.377331 9857 solver.cpp:571] Iteration 46020, lr = 0.001 +I0616 09:58:35.932790 9857 solver.cpp:242] Iteration 46040, loss = 0.679869 +I0616 09:58:35.932833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300727 (* 1 = 0.300727 loss) +I0616 09:58:35.932840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19794 (* 1 = 0.19794 loss) +I0616 09:58:35.932844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0600901 (* 1 = 0.0600901 loss) +I0616 09:58:35.932848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0406515 (* 1 = 0.0406515 loss) +I0616 09:58:35.932852 9857 solver.cpp:571] Iteration 46040, lr = 0.001 +I0616 09:58:47.362480 9857 solver.cpp:242] Iteration 46060, loss = 0.912565 +I0616 09:58:47.362524 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218413 (* 1 = 0.218413 loss) +I0616 09:58:47.362529 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134369 (* 1 = 0.134369 loss) +I0616 09:58:47.362532 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0456769 (* 1 = 0.0456769 loss) +I0616 09:58:47.362536 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0599782 (* 1 = 0.0599782 loss) +I0616 09:58:47.362540 9857 solver.cpp:571] Iteration 46060, lr = 0.001 +I0616 09:58:58.869804 9857 solver.cpp:242] Iteration 46080, loss = 0.753661 +I0616 09:58:58.869845 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353514 (* 1 = 0.353514 loss) +I0616 09:58:58.869850 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32449 (* 1 = 0.32449 loss) +I0616 09:58:58.869854 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.087736 (* 1 = 0.087736 loss) +I0616 09:58:58.869858 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0381117 (* 1 = 0.0381117 loss) +I0616 09:58:58.869863 9857 solver.cpp:571] Iteration 46080, lr = 0.001 +I0616 09:59:10.729845 9857 solver.cpp:242] Iteration 46100, loss = 0.439982 +I0616 09:59:10.729887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150833 (* 1 = 0.150833 loss) +I0616 09:59:10.729892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343948 (* 1 = 0.343948 loss) +I0616 09:59:10.729897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0291779 (* 1 = 0.0291779 loss) +I0616 09:59:10.729900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0555185 (* 1 = 0.0555185 loss) +I0616 09:59:10.729904 9857 solver.cpp:571] Iteration 46100, lr = 0.001 +I0616 09:59:22.287297 9857 solver.cpp:242] Iteration 46120, loss = 1.15153 +I0616 09:59:22.287339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299507 (* 1 = 0.299507 loss) +I0616 09:59:22.287345 9857 solver.cpp:258] Train net output #1: loss_cls = 0.462047 (* 1 = 0.462047 loss) +I0616 09:59:22.287349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.243895 (* 1 = 0.243895 loss) +I0616 09:59:22.287353 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0933193 (* 1 = 0.0933193 loss) +I0616 09:59:22.287358 9857 solver.cpp:571] Iteration 46120, lr = 0.001 +I0616 09:59:33.774521 9857 solver.cpp:242] Iteration 46140, loss = 1.2237 +I0616 09:59:33.774564 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110542 (* 1 = 0.110542 loss) +I0616 09:59:33.774569 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286357 (* 1 = 0.286357 loss) +I0616 09:59:33.774574 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.274142 (* 1 = 0.274142 loss) +I0616 09:59:33.774577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.383737 (* 1 = 0.383737 loss) +I0616 09:59:33.774581 9857 solver.cpp:571] Iteration 46140, lr = 0.001 +I0616 09:59:45.190238 9857 solver.cpp:242] Iteration 46160, loss = 0.666319 +I0616 09:59:45.190279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201773 (* 1 = 0.201773 loss) +I0616 09:59:45.190284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150468 (* 1 = 0.150468 loss) +I0616 09:59:45.190289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00443629 (* 1 = 0.00443629 loss) +I0616 09:59:45.190292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0213973 (* 1 = 0.0213973 loss) +I0616 09:59:45.190296 9857 solver.cpp:571] Iteration 46160, lr = 0.001 +I0616 09:59:56.650866 9857 solver.cpp:242] Iteration 46180, loss = 0.519485 +I0616 09:59:56.650907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129921 (* 1 = 0.129921 loss) +I0616 09:59:56.650913 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223279 (* 1 = 0.223279 loss) +I0616 09:59:56.650918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00565771 (* 1 = 0.00565771 loss) +I0616 09:59:56.650921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136585 (* 1 = 0.0136585 loss) +I0616 09:59:56.650925 9857 solver.cpp:571] Iteration 46180, lr = 0.001 +speed: 0.616s / iter +I0616 10:00:08.658834 9857 solver.cpp:242] Iteration 46200, loss = 0.649035 +I0616 10:00:08.658879 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16529 (* 1 = 0.16529 loss) +I0616 10:00:08.658884 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333565 (* 1 = 0.333565 loss) +I0616 10:00:08.658887 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0575656 (* 1 = 0.0575656 loss) +I0616 10:00:08.658891 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0298349 (* 1 = 0.0298349 loss) +I0616 10:00:08.658895 9857 solver.cpp:571] Iteration 46200, lr = 0.001 +I0616 10:00:20.289245 9857 solver.cpp:242] Iteration 46220, loss = 0.32496 +I0616 10:00:20.289286 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0602694 (* 1 = 0.0602694 loss) +I0616 10:00:20.289291 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156198 (* 1 = 0.156198 loss) +I0616 10:00:20.289295 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0834409 (* 1 = 0.0834409 loss) +I0616 10:00:20.289299 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0303679 (* 1 = 0.0303679 loss) +I0616 10:00:20.289304 9857 solver.cpp:571] Iteration 46220, lr = 0.001 +I0616 10:00:32.001034 9857 solver.cpp:242] Iteration 46240, loss = 0.514587 +I0616 10:00:32.001075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147445 (* 1 = 0.147445 loss) +I0616 10:00:32.001080 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16214 (* 1 = 0.16214 loss) +I0616 10:00:32.001085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.033905 (* 1 = 0.033905 loss) +I0616 10:00:32.001088 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0068221 (* 1 = 0.0068221 loss) +I0616 10:00:32.001092 9857 solver.cpp:571] Iteration 46240, lr = 0.001 +I0616 10:00:43.735164 9857 solver.cpp:242] Iteration 46260, loss = 0.818495 +I0616 10:00:43.735206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250061 (* 1 = 0.250061 loss) +I0616 10:00:43.735211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242019 (* 1 = 0.242019 loss) +I0616 10:00:43.735215 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0511007 (* 1 = 0.0511007 loss) +I0616 10:00:43.735219 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0516944 (* 1 = 0.0516944 loss) +I0616 10:00:43.735222 9857 solver.cpp:571] Iteration 46260, lr = 0.001 +I0616 10:00:55.121901 9857 solver.cpp:242] Iteration 46280, loss = 1.32752 +I0616 10:00:55.121942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260321 (* 1 = 0.260321 loss) +I0616 10:00:55.121948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21715 (* 1 = 0.21715 loss) +I0616 10:00:55.121953 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.02334 (* 1 = 0.02334 loss) +I0616 10:00:55.121955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0984284 (* 1 = 0.0984284 loss) +I0616 10:00:55.121959 9857 solver.cpp:571] Iteration 46280, lr = 0.001 +I0616 10:01:06.858013 9857 solver.cpp:242] Iteration 46300, loss = 0.492874 +I0616 10:01:06.858053 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286199 (* 1 = 0.286199 loss) +I0616 10:01:06.858059 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316624 (* 1 = 0.316624 loss) +I0616 10:01:06.858063 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102119 (* 1 = 0.102119 loss) +I0616 10:01:06.858067 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0576897 (* 1 = 0.0576897 loss) +I0616 10:01:06.858070 9857 solver.cpp:571] Iteration 46300, lr = 0.001 +I0616 10:01:18.372239 9857 solver.cpp:242] Iteration 46320, loss = 0.544635 +I0616 10:01:18.372282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0977467 (* 1 = 0.0977467 loss) +I0616 10:01:18.372287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0976402 (* 1 = 0.0976402 loss) +I0616 10:01:18.372290 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0267584 (* 1 = 0.0267584 loss) +I0616 10:01:18.372294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0529044 (* 1 = 0.0529044 loss) +I0616 10:01:18.372298 9857 solver.cpp:571] Iteration 46320, lr = 0.001 +I0616 10:01:29.742594 9857 solver.cpp:242] Iteration 46340, loss = 0.787378 +I0616 10:01:29.742635 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309545 (* 1 = 0.309545 loss) +I0616 10:01:29.742640 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281171 (* 1 = 0.281171 loss) +I0616 10:01:29.742645 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0462738 (* 1 = 0.0462738 loss) +I0616 10:01:29.742648 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020038 (* 1 = 0.020038 loss) +I0616 10:01:29.742652 9857 solver.cpp:571] Iteration 46340, lr = 0.001 +I0616 10:01:41.175654 9857 solver.cpp:242] Iteration 46360, loss = 0.677361 +I0616 10:01:41.175695 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.555718 (* 1 = 0.555718 loss) +I0616 10:01:41.175701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.406492 (* 1 = 0.406492 loss) +I0616 10:01:41.175705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129102 (* 1 = 0.129102 loss) +I0616 10:01:41.175709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0523493 (* 1 = 0.0523493 loss) +I0616 10:01:41.175712 9857 solver.cpp:571] Iteration 46360, lr = 0.001 +I0616 10:01:52.879259 9857 solver.cpp:242] Iteration 46380, loss = 0.97461 +I0616 10:01:52.879302 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236971 (* 1 = 0.236971 loss) +I0616 10:01:52.879307 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354014 (* 1 = 0.354014 loss) +I0616 10:01:52.879312 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0808538 (* 1 = 0.0808538 loss) +I0616 10:01:52.879314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00943204 (* 1 = 0.00943204 loss) +I0616 10:01:52.879318 9857 solver.cpp:571] Iteration 46380, lr = 0.001 +speed: 0.616s / iter +I0616 10:02:04.662788 9857 solver.cpp:242] Iteration 46400, loss = 0.846217 +I0616 10:02:04.662818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.31371 (* 1 = 0.31371 loss) +I0616 10:02:04.662823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49838 (* 1 = 0.49838 loss) +I0616 10:02:04.662827 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.373172 (* 1 = 0.373172 loss) +I0616 10:02:04.662832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0971714 (* 1 = 0.0971714 loss) +I0616 10:02:04.662835 9857 solver.cpp:571] Iteration 46400, lr = 0.001 +I0616 10:02:16.163393 9857 solver.cpp:242] Iteration 46420, loss = 0.588509 +I0616 10:02:16.163435 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0930355 (* 1 = 0.0930355 loss) +I0616 10:02:16.163440 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274864 (* 1 = 0.274864 loss) +I0616 10:02:16.163444 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0175549 (* 1 = 0.0175549 loss) +I0616 10:02:16.163447 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00882062 (* 1 = 0.00882062 loss) +I0616 10:02:16.163451 9857 solver.cpp:571] Iteration 46420, lr = 0.001 +I0616 10:02:27.348126 9857 solver.cpp:242] Iteration 46440, loss = 0.699706 +I0616 10:02:27.348168 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22518 (* 1 = 0.22518 loss) +I0616 10:02:27.348175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119884 (* 1 = 0.119884 loss) +I0616 10:02:27.348178 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0136792 (* 1 = 0.0136792 loss) +I0616 10:02:27.348182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130438 (* 1 = 0.0130438 loss) +I0616 10:02:27.348186 9857 solver.cpp:571] Iteration 46440, lr = 0.001 +I0616 10:02:38.875553 9857 solver.cpp:242] Iteration 46460, loss = 0.761582 +I0616 10:02:38.875594 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351983 (* 1 = 0.351983 loss) +I0616 10:02:38.875600 9857 solver.cpp:258] Train net output #1: loss_cls = 0.468678 (* 1 = 0.468678 loss) +I0616 10:02:38.875604 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.284977 (* 1 = 0.284977 loss) +I0616 10:02:38.875608 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0305531 (* 1 = 0.0305531 loss) +I0616 10:02:38.875612 9857 solver.cpp:571] Iteration 46460, lr = 0.001 +I0616 10:02:50.402509 9857 solver.cpp:242] Iteration 46480, loss = 0.552675 +I0616 10:02:50.402549 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13705 (* 1 = 0.13705 loss) +I0616 10:02:50.402554 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131729 (* 1 = 0.131729 loss) +I0616 10:02:50.402559 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287765 (* 1 = 0.0287765 loss) +I0616 10:02:50.402562 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00256309 (* 1 = 0.00256309 loss) +I0616 10:02:50.402565 9857 solver.cpp:571] Iteration 46480, lr = 0.001 +I0616 10:03:01.830124 9857 solver.cpp:242] Iteration 46500, loss = 0.963723 +I0616 10:03:01.830165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224313 (* 1 = 0.224313 loss) +I0616 10:03:01.830171 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401675 (* 1 = 0.401675 loss) +I0616 10:03:01.830175 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137552 (* 1 = 0.137552 loss) +I0616 10:03:01.830179 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147891 (* 1 = 0.0147891 loss) +I0616 10:03:01.830183 9857 solver.cpp:571] Iteration 46500, lr = 0.001 +I0616 10:03:13.212693 9857 solver.cpp:242] Iteration 46520, loss = 0.976759 +I0616 10:03:13.212738 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.387586 (* 1 = 0.387586 loss) +I0616 10:03:13.212743 9857 solver.cpp:258] Train net output #1: loss_cls = 0.481053 (* 1 = 0.481053 loss) +I0616 10:03:13.212746 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.342335 (* 1 = 0.342335 loss) +I0616 10:03:13.212750 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.127743 (* 1 = 0.127743 loss) +I0616 10:03:13.212754 9857 solver.cpp:571] Iteration 46520, lr = 0.001 +I0616 10:03:24.868949 9857 solver.cpp:242] Iteration 46540, loss = 0.724737 +I0616 10:03:24.868993 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183775 (* 1 = 0.183775 loss) +I0616 10:03:24.868999 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26448 (* 1 = 0.26448 loss) +I0616 10:03:24.869002 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0380717 (* 1 = 0.0380717 loss) +I0616 10:03:24.869006 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286001 (* 1 = 0.0286001 loss) +I0616 10:03:24.869009 9857 solver.cpp:571] Iteration 46540, lr = 0.001 +I0616 10:03:36.284900 9857 solver.cpp:242] Iteration 46560, loss = 0.878395 +I0616 10:03:36.284942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149716 (* 1 = 0.149716 loss) +I0616 10:03:36.284950 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217562 (* 1 = 0.217562 loss) +I0616 10:03:36.284953 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178425 (* 1 = 0.178425 loss) +I0616 10:03:36.284957 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0328562 (* 1 = 0.0328562 loss) +I0616 10:03:36.284960 9857 solver.cpp:571] Iteration 46560, lr = 0.001 +I0616 10:03:47.743727 9857 solver.cpp:242] Iteration 46580, loss = 0.773863 +I0616 10:03:47.743770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109016 (* 1 = 0.109016 loss) +I0616 10:03:47.743777 9857 solver.cpp:258] Train net output #1: loss_cls = 0.380678 (* 1 = 0.380678 loss) +I0616 10:03:47.743780 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0940433 (* 1 = 0.0940433 loss) +I0616 10:03:47.743783 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0508964 (* 1 = 0.0508964 loss) +I0616 10:03:47.743788 9857 solver.cpp:571] Iteration 46580, lr = 0.001 +speed: 0.615s / iter +I0616 10:03:59.133733 9857 solver.cpp:242] Iteration 46600, loss = 0.572635 +I0616 10:03:59.133775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210057 (* 1 = 0.210057 loss) +I0616 10:03:59.133780 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194078 (* 1 = 0.194078 loss) +I0616 10:03:59.133785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0305615 (* 1 = 0.0305615 loss) +I0616 10:03:59.133790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0196784 (* 1 = 0.0196784 loss) +I0616 10:03:59.133793 9857 solver.cpp:571] Iteration 46600, lr = 0.001 +I0616 10:04:10.891507 9857 solver.cpp:242] Iteration 46620, loss = 0.734108 +I0616 10:04:10.891551 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10977 (* 1 = 0.10977 loss) +I0616 10:04:10.891556 9857 solver.cpp:258] Train net output #1: loss_cls = 0.73891 (* 1 = 0.73891 loss) +I0616 10:04:10.891559 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.196803 (* 1 = 0.196803 loss) +I0616 10:04:10.891563 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122708 (* 1 = 0.0122708 loss) +I0616 10:04:10.891567 9857 solver.cpp:571] Iteration 46620, lr = 0.001 +I0616 10:04:22.634090 9857 solver.cpp:242] Iteration 46640, loss = 1.13472 +I0616 10:04:22.634132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379013 (* 1 = 0.379013 loss) +I0616 10:04:22.634137 9857 solver.cpp:258] Train net output #1: loss_cls = 0.630008 (* 1 = 0.630008 loss) +I0616 10:04:22.634141 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.258636 (* 1 = 0.258636 loss) +I0616 10:04:22.634145 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.171414 (* 1 = 0.171414 loss) +I0616 10:04:22.634148 9857 solver.cpp:571] Iteration 46640, lr = 0.001 +I0616 10:04:34.336648 9857 solver.cpp:242] Iteration 46660, loss = 0.782388 +I0616 10:04:34.336689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360252 (* 1 = 0.360252 loss) +I0616 10:04:34.336695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216446 (* 1 = 0.216446 loss) +I0616 10:04:34.336699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0549608 (* 1 = 0.0549608 loss) +I0616 10:04:34.336704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0923636 (* 1 = 0.0923636 loss) +I0616 10:04:34.336706 9857 solver.cpp:571] Iteration 46660, lr = 0.001 +I0616 10:04:45.892957 9857 solver.cpp:242] Iteration 46680, loss = 0.704198 +I0616 10:04:45.892999 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138769 (* 1 = 0.138769 loss) +I0616 10:04:45.893004 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172532 (* 1 = 0.172532 loss) +I0616 10:04:45.893009 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0464242 (* 1 = 0.0464242 loss) +I0616 10:04:45.893013 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.173813 (* 1 = 0.173813 loss) +I0616 10:04:45.893016 9857 solver.cpp:571] Iteration 46680, lr = 0.001 +I0616 10:04:57.465032 9857 solver.cpp:242] Iteration 46700, loss = 0.44874 +I0616 10:04:57.465075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205074 (* 1 = 0.205074 loss) +I0616 10:04:57.465080 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271404 (* 1 = 0.271404 loss) +I0616 10:04:57.465085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0744121 (* 1 = 0.0744121 loss) +I0616 10:04:57.465087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000722717 (* 1 = 0.000722717 loss) +I0616 10:04:57.465091 9857 solver.cpp:571] Iteration 46700, lr = 0.001 +I0616 10:05:09.094243 9857 solver.cpp:242] Iteration 46720, loss = 1.00457 +I0616 10:05:09.094283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409675 (* 1 = 0.409675 loss) +I0616 10:05:09.094290 9857 solver.cpp:258] Train net output #1: loss_cls = 0.730382 (* 1 = 0.730382 loss) +I0616 10:05:09.094293 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183167 (* 1 = 0.183167 loss) +I0616 10:05:09.094297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0623877 (* 1 = 0.0623877 loss) +I0616 10:05:09.094301 9857 solver.cpp:571] Iteration 46720, lr = 0.001 +I0616 10:05:20.657397 9857 solver.cpp:242] Iteration 46740, loss = 0.50242 +I0616 10:05:20.657439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297708 (* 1 = 0.297708 loss) +I0616 10:05:20.657445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173609 (* 1 = 0.173609 loss) +I0616 10:05:20.657449 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0659821 (* 1 = 0.0659821 loss) +I0616 10:05:20.657454 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00993565 (* 1 = 0.00993565 loss) +I0616 10:05:20.657456 9857 solver.cpp:571] Iteration 46740, lr = 0.001 +I0616 10:05:32.329428 9857 solver.cpp:242] Iteration 46760, loss = 1.27125 +I0616 10:05:32.329470 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272962 (* 1 = 0.272962 loss) +I0616 10:05:32.329475 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00607 (* 1 = 1.00607 loss) +I0616 10:05:32.329479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.361712 (* 1 = 0.361712 loss) +I0616 10:05:32.329483 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.503707 (* 1 = 0.503707 loss) +I0616 10:05:32.329488 9857 solver.cpp:571] Iteration 46760, lr = 0.001 +I0616 10:05:43.856956 9857 solver.cpp:242] Iteration 46780, loss = 0.773125 +I0616 10:05:43.856997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174395 (* 1 = 0.174395 loss) +I0616 10:05:43.857002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24217 (* 1 = 0.24217 loss) +I0616 10:05:43.857007 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0653312 (* 1 = 0.0653312 loss) +I0616 10:05:43.857009 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174598 (* 1 = 0.0174598 loss) +I0616 10:05:43.857013 9857 solver.cpp:571] Iteration 46780, lr = 0.001 +speed: 0.615s / iter +I0616 10:05:55.382936 9857 solver.cpp:242] Iteration 46800, loss = 0.478636 +I0616 10:05:55.382977 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283638 (* 1 = 0.283638 loss) +I0616 10:05:55.382982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244824 (* 1 = 0.244824 loss) +I0616 10:05:55.382987 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0737726 (* 1 = 0.0737726 loss) +I0616 10:05:55.382992 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.120829 (* 1 = 0.120829 loss) +I0616 10:05:55.382997 9857 solver.cpp:571] Iteration 46800, lr = 0.001 +I0616 10:06:06.925385 9857 solver.cpp:242] Iteration 46820, loss = 0.357571 +I0616 10:06:06.925427 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169196 (* 1 = 0.169196 loss) +I0616 10:06:06.925432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18753 (* 1 = 0.18753 loss) +I0616 10:06:06.925437 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0159235 (* 1 = 0.0159235 loss) +I0616 10:06:06.925441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215619 (* 1 = 0.0215619 loss) +I0616 10:06:06.925444 9857 solver.cpp:571] Iteration 46820, lr = 0.001 +I0616 10:06:18.579207 9857 solver.cpp:242] Iteration 46840, loss = 0.753888 +I0616 10:06:18.579251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359702 (* 1 = 0.359702 loss) +I0616 10:06:18.579255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392531 (* 1 = 0.392531 loss) +I0616 10:06:18.579259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.255898 (* 1 = 0.255898 loss) +I0616 10:06:18.579263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0547846 (* 1 = 0.0547846 loss) +I0616 10:06:18.579267 9857 solver.cpp:571] Iteration 46840, lr = 0.001 +I0616 10:06:30.181639 9857 solver.cpp:242] Iteration 46860, loss = 0.42912 +I0616 10:06:30.181682 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0619302 (* 1 = 0.0619302 loss) +I0616 10:06:30.181689 9857 solver.cpp:258] Train net output #1: loss_cls = 0.03887 (* 1 = 0.03887 loss) +I0616 10:06:30.181692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0370704 (* 1 = 0.0370704 loss) +I0616 10:06:30.181696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00271732 (* 1 = 0.00271732 loss) +I0616 10:06:30.181700 9857 solver.cpp:571] Iteration 46860, lr = 0.001 +I0616 10:06:41.923171 9857 solver.cpp:242] Iteration 46880, loss = 0.708493 +I0616 10:06:41.923213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307989 (* 1 = 0.307989 loss) +I0616 10:06:41.923218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.691427 (* 1 = 0.691427 loss) +I0616 10:06:41.923223 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138089 (* 1 = 0.138089 loss) +I0616 10:06:41.923226 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207248 (* 1 = 0.0207248 loss) +I0616 10:06:41.923230 9857 solver.cpp:571] Iteration 46880, lr = 0.001 +I0616 10:06:53.186826 9857 solver.cpp:242] Iteration 46900, loss = 0.844557 +I0616 10:06:53.186868 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10882 (* 1 = 0.10882 loss) +I0616 10:06:53.186874 9857 solver.cpp:258] Train net output #1: loss_cls = 0.141574 (* 1 = 0.141574 loss) +I0616 10:06:53.186878 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0916194 (* 1 = 0.0916194 loss) +I0616 10:06:53.186882 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0237092 (* 1 = 0.0237092 loss) +I0616 10:06:53.186885 9857 solver.cpp:571] Iteration 46900, lr = 0.001 +I0616 10:07:04.715414 9857 solver.cpp:242] Iteration 46920, loss = 0.776843 +I0616 10:07:04.715456 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220185 (* 1 = 0.220185 loss) +I0616 10:07:04.715461 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323648 (* 1 = 0.323648 loss) +I0616 10:07:04.715466 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00227051 (* 1 = 0.00227051 loss) +I0616 10:07:04.715469 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00245788 (* 1 = 0.00245788 loss) +I0616 10:07:04.715473 9857 solver.cpp:571] Iteration 46920, lr = 0.001 +I0616 10:07:16.285662 9857 solver.cpp:242] Iteration 46940, loss = 0.667773 +I0616 10:07:16.285704 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0788659 (* 1 = 0.0788659 loss) +I0616 10:07:16.285711 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235454 (* 1 = 0.235454 loss) +I0616 10:07:16.285714 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0106285 (* 1 = 0.0106285 loss) +I0616 10:07:16.285717 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0071097 (* 1 = 0.0071097 loss) +I0616 10:07:16.285722 9857 solver.cpp:571] Iteration 46940, lr = 0.001 +I0616 10:07:27.671041 9857 solver.cpp:242] Iteration 46960, loss = 0.3151 +I0616 10:07:27.671082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129435 (* 1 = 0.129435 loss) +I0616 10:07:27.671087 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153814 (* 1 = 0.153814 loss) +I0616 10:07:27.671092 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0238286 (* 1 = 0.0238286 loss) +I0616 10:07:27.671094 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150028 (* 1 = 0.0150028 loss) +I0616 10:07:27.671098 9857 solver.cpp:571] Iteration 46960, lr = 0.001 +I0616 10:07:39.186586 9857 solver.cpp:242] Iteration 46980, loss = 0.825868 +I0616 10:07:39.186630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171262 (* 1 = 0.171262 loss) +I0616 10:07:39.186635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110677 (* 1 = 0.110677 loss) +I0616 10:07:39.186640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0231113 (* 1 = 0.0231113 loss) +I0616 10:07:39.186645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0069327 (* 1 = 0.0069327 loss) +I0616 10:07:39.186647 9857 solver.cpp:571] Iteration 46980, lr = 0.001 +speed: 0.615s / iter +I0616 10:07:50.665480 9857 solver.cpp:242] Iteration 47000, loss = 0.52534 +I0616 10:07:50.665520 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125331 (* 1 = 0.125331 loss) +I0616 10:07:50.665526 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242425 (* 1 = 0.242425 loss) +I0616 10:07:50.665530 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0793111 (* 1 = 0.0793111 loss) +I0616 10:07:50.665534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014692 (* 1 = 0.014692 loss) +I0616 10:07:50.665539 9857 solver.cpp:571] Iteration 47000, lr = 0.001 +I0616 10:08:02.416731 9857 solver.cpp:242] Iteration 47020, loss = 1.06998 +I0616 10:08:02.416774 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302131 (* 1 = 0.302131 loss) +I0616 10:08:02.416779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306077 (* 1 = 0.306077 loss) +I0616 10:08:02.416784 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.235725 (* 1 = 0.235725 loss) +I0616 10:08:02.416787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.115415 (* 1 = 0.115415 loss) +I0616 10:08:02.416790 9857 solver.cpp:571] Iteration 47020, lr = 0.001 +I0616 10:08:14.087391 9857 solver.cpp:242] Iteration 47040, loss = 2.04136 +I0616 10:08:14.087430 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337282 (* 1 = 0.337282 loss) +I0616 10:08:14.087436 9857 solver.cpp:258] Train net output #1: loss_cls = 1.83009 (* 1 = 1.83009 loss) +I0616 10:08:14.087440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.216269 (* 1 = 0.216269 loss) +I0616 10:08:14.087445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0256791 (* 1 = 0.0256791 loss) +I0616 10:08:14.087448 9857 solver.cpp:571] Iteration 47040, lr = 0.001 +I0616 10:08:25.751144 9857 solver.cpp:242] Iteration 47060, loss = 0.52921 +I0616 10:08:25.751186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12433 (* 1 = 0.12433 loss) +I0616 10:08:25.751193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171079 (* 1 = 0.171079 loss) +I0616 10:08:25.751196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0494263 (* 1 = 0.0494263 loss) +I0616 10:08:25.751199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215705 (* 1 = 0.0215705 loss) +I0616 10:08:25.751204 9857 solver.cpp:571] Iteration 47060, lr = 0.001 +I0616 10:08:37.278368 9857 solver.cpp:242] Iteration 47080, loss = 0.402845 +I0616 10:08:37.278409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228334 (* 1 = 0.228334 loss) +I0616 10:08:37.278414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152227 (* 1 = 0.152227 loss) +I0616 10:08:37.278419 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0768543 (* 1 = 0.0768543 loss) +I0616 10:08:37.278424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102547 (* 1 = 0.102547 loss) +I0616 10:08:37.278427 9857 solver.cpp:571] Iteration 47080, lr = 0.001 +I0616 10:08:48.822197 9857 solver.cpp:242] Iteration 47100, loss = 0.80484 +I0616 10:08:48.822239 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17309 (* 1 = 0.17309 loss) +I0616 10:08:48.822244 9857 solver.cpp:258] Train net output #1: loss_cls = 0.383328 (* 1 = 0.383328 loss) +I0616 10:08:48.822249 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.405189 (* 1 = 0.405189 loss) +I0616 10:08:48.822252 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.158029 (* 1 = 0.158029 loss) +I0616 10:08:48.822257 9857 solver.cpp:571] Iteration 47100, lr = 0.001 +I0616 10:09:00.214412 9857 solver.cpp:242] Iteration 47120, loss = 0.765792 +I0616 10:09:00.214454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386269 (* 1 = 0.386269 loss) +I0616 10:09:00.214460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.634433 (* 1 = 0.634433 loss) +I0616 10:09:00.214464 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135498 (* 1 = 0.135498 loss) +I0616 10:09:00.214468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.05272 (* 1 = 0.05272 loss) +I0616 10:09:00.214473 9857 solver.cpp:571] Iteration 47120, lr = 0.001 +I0616 10:09:11.757163 9857 solver.cpp:242] Iteration 47140, loss = 0.297198 +I0616 10:09:11.757205 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0362959 (* 1 = 0.0362959 loss) +I0616 10:09:11.757211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0830724 (* 1 = 0.0830724 loss) +I0616 10:09:11.757215 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0312846 (* 1 = 0.0312846 loss) +I0616 10:09:11.757220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0368603 (* 1 = 0.0368603 loss) +I0616 10:09:11.757223 9857 solver.cpp:571] Iteration 47140, lr = 0.001 +I0616 10:09:23.459106 9857 solver.cpp:242] Iteration 47160, loss = 0.416754 +I0616 10:09:23.459147 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187181 (* 1 = 0.187181 loss) +I0616 10:09:23.459153 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217683 (* 1 = 0.217683 loss) +I0616 10:09:23.459157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0478825 (* 1 = 0.0478825 loss) +I0616 10:09:23.459161 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0310261 (* 1 = 0.0310261 loss) +I0616 10:09:23.459164 9857 solver.cpp:571] Iteration 47160, lr = 0.001 +I0616 10:09:34.727715 9857 solver.cpp:242] Iteration 47180, loss = 0.712601 +I0616 10:09:34.727757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158423 (* 1 = 0.158423 loss) +I0616 10:09:34.727763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229553 (* 1 = 0.229553 loss) +I0616 10:09:34.727767 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176734 (* 1 = 0.0176734 loss) +I0616 10:09:34.727771 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226753 (* 1 = 0.0226753 loss) +I0616 10:09:34.727776 9857 solver.cpp:571] Iteration 47180, lr = 0.001 +speed: 0.615s / iter +I0616 10:09:46.113132 9857 solver.cpp:242] Iteration 47200, loss = 0.442064 +I0616 10:09:46.113175 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0909045 (* 1 = 0.0909045 loss) +I0616 10:09:46.113180 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231998 (* 1 = 0.231998 loss) +I0616 10:09:46.113184 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0340067 (* 1 = 0.0340067 loss) +I0616 10:09:46.113188 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00896697 (* 1 = 0.00896697 loss) +I0616 10:09:46.113193 9857 solver.cpp:571] Iteration 47200, lr = 0.001 +I0616 10:09:57.723971 9857 solver.cpp:242] Iteration 47220, loss = 0.863641 +I0616 10:09:57.724011 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338033 (* 1 = 0.338033 loss) +I0616 10:09:57.724017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.462691 (* 1 = 0.462691 loss) +I0616 10:09:57.724021 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129414 (* 1 = 0.129414 loss) +I0616 10:09:57.724025 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0505293 (* 1 = 0.0505293 loss) +I0616 10:09:57.724030 9857 solver.cpp:571] Iteration 47220, lr = 0.001 +I0616 10:10:09.340754 9857 solver.cpp:242] Iteration 47240, loss = 0.586136 +I0616 10:10:09.340798 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166877 (* 1 = 0.166877 loss) +I0616 10:10:09.340804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272125 (* 1 = 0.272125 loss) +I0616 10:10:09.340808 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0454615 (* 1 = 0.0454615 loss) +I0616 10:10:09.340812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00507393 (* 1 = 0.00507393 loss) +I0616 10:10:09.340816 9857 solver.cpp:571] Iteration 47240, lr = 0.001 +I0616 10:10:20.986207 9857 solver.cpp:242] Iteration 47260, loss = 0.807124 +I0616 10:10:20.986249 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0807591 (* 1 = 0.0807591 loss) +I0616 10:10:20.986255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192901 (* 1 = 0.192901 loss) +I0616 10:10:20.986260 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.071908 (* 1 = 0.071908 loss) +I0616 10:10:20.986263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111029 (* 1 = 0.111029 loss) +I0616 10:10:20.986268 9857 solver.cpp:571] Iteration 47260, lr = 0.001 +I0616 10:10:32.668043 9857 solver.cpp:242] Iteration 47280, loss = 0.786133 +I0616 10:10:32.668083 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176312 (* 1 = 0.176312 loss) +I0616 10:10:32.668088 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288997 (* 1 = 0.288997 loss) +I0616 10:10:32.668093 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0701682 (* 1 = 0.0701682 loss) +I0616 10:10:32.668097 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144843 (* 1 = 0.0144843 loss) +I0616 10:10:32.668100 9857 solver.cpp:571] Iteration 47280, lr = 0.001 +I0616 10:10:44.344660 9857 solver.cpp:242] Iteration 47300, loss = 0.611045 +I0616 10:10:44.344702 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111175 (* 1 = 0.111175 loss) +I0616 10:10:44.344707 9857 solver.cpp:258] Train net output #1: loss_cls = 0.106291 (* 1 = 0.106291 loss) +I0616 10:10:44.344712 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00994898 (* 1 = 0.00994898 loss) +I0616 10:10:44.344714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00485557 (* 1 = 0.00485557 loss) +I0616 10:10:44.344718 9857 solver.cpp:571] Iteration 47300, lr = 0.001 +I0616 10:10:55.834837 9857 solver.cpp:242] Iteration 47320, loss = 0.605134 +I0616 10:10:55.834880 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19577 (* 1 = 0.19577 loss) +I0616 10:10:55.834885 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21945 (* 1 = 0.21945 loss) +I0616 10:10:55.834890 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139911 (* 1 = 0.0139911 loss) +I0616 10:10:55.834893 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00646991 (* 1 = 0.00646991 loss) +I0616 10:10:55.834897 9857 solver.cpp:571] Iteration 47320, lr = 0.001 +I0616 10:11:07.439841 9857 solver.cpp:242] Iteration 47340, loss = 0.682127 +I0616 10:11:07.439883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358619 (* 1 = 0.358619 loss) +I0616 10:11:07.439889 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257897 (* 1 = 0.257897 loss) +I0616 10:11:07.439893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0503173 (* 1 = 0.0503173 loss) +I0616 10:11:07.439898 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216673 (* 1 = 0.0216673 loss) +I0616 10:11:07.439900 9857 solver.cpp:571] Iteration 47340, lr = 0.001 +I0616 10:11:18.822223 9857 solver.cpp:242] Iteration 47360, loss = 0.46607 +I0616 10:11:18.822264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233733 (* 1 = 0.233733 loss) +I0616 10:11:18.822270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257539 (* 1 = 0.257539 loss) +I0616 10:11:18.822274 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122338 (* 1 = 0.122338 loss) +I0616 10:11:18.822278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199748 (* 1 = 0.0199748 loss) +I0616 10:11:18.822281 9857 solver.cpp:571] Iteration 47360, lr = 0.001 +I0616 10:11:30.120199 9857 solver.cpp:242] Iteration 47380, loss = 0.926854 +I0616 10:11:30.120241 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290786 (* 1 = 0.290786 loss) +I0616 10:11:30.120246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.455879 (* 1 = 0.455879 loss) +I0616 10:11:30.120250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.186192 (* 1 = 0.186192 loss) +I0616 10:11:30.120254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159756 (* 1 = 0.159756 loss) +I0616 10:11:30.120257 9857 solver.cpp:571] Iteration 47380, lr = 0.001 +speed: 0.615s / iter +I0616 10:11:41.543812 9857 solver.cpp:242] Iteration 47400, loss = 0.346153 +I0616 10:11:41.543853 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168851 (* 1 = 0.168851 loss) +I0616 10:11:41.543859 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189042 (* 1 = 0.189042 loss) +I0616 10:11:41.543864 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0315748 (* 1 = 0.0315748 loss) +I0616 10:11:41.543867 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0336091 (* 1 = 0.0336091 loss) +I0616 10:11:41.543871 9857 solver.cpp:571] Iteration 47400, lr = 0.001 +I0616 10:11:53.147845 9857 solver.cpp:242] Iteration 47420, loss = 0.596538 +I0616 10:11:53.147886 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202021 (* 1 = 0.202021 loss) +I0616 10:11:53.147891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359128 (* 1 = 0.359128 loss) +I0616 10:11:53.147896 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153934 (* 1 = 0.153934 loss) +I0616 10:11:53.147899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104874 (* 1 = 0.104874 loss) +I0616 10:11:53.147903 9857 solver.cpp:571] Iteration 47420, lr = 0.001 +I0616 10:12:04.558548 9857 solver.cpp:242] Iteration 47440, loss = 1.09972 +I0616 10:12:04.558590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.36222 (* 1 = 0.36222 loss) +I0616 10:12:04.558596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.522961 (* 1 = 0.522961 loss) +I0616 10:12:04.558600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246501 (* 1 = 0.246501 loss) +I0616 10:12:04.558604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0645333 (* 1 = 0.0645333 loss) +I0616 10:12:04.558607 9857 solver.cpp:571] Iteration 47440, lr = 0.001 +I0616 10:12:16.285802 9857 solver.cpp:242] Iteration 47460, loss = 0.918085 +I0616 10:12:16.285845 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16072 (* 1 = 0.16072 loss) +I0616 10:12:16.285851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225532 (* 1 = 0.225532 loss) +I0616 10:12:16.285854 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0884644 (* 1 = 0.0884644 loss) +I0616 10:12:16.285858 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.204193 (* 1 = 0.204193 loss) +I0616 10:12:16.285862 9857 solver.cpp:571] Iteration 47460, lr = 0.001 +I0616 10:12:27.964483 9857 solver.cpp:242] Iteration 47480, loss = 1.06206 +I0616 10:12:27.964524 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299839 (* 1 = 0.299839 loss) +I0616 10:12:27.964529 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375071 (* 1 = 0.375071 loss) +I0616 10:12:27.964534 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144821 (* 1 = 0.144821 loss) +I0616 10:12:27.964537 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.02112 (* 1 = 0.02112 loss) +I0616 10:12:27.964540 9857 solver.cpp:571] Iteration 47480, lr = 0.001 +I0616 10:12:39.573309 9857 solver.cpp:242] Iteration 47500, loss = 0.592091 +I0616 10:12:39.573350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394389 (* 1 = 0.394389 loss) +I0616 10:12:39.573355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211983 (* 1 = 0.211983 loss) +I0616 10:12:39.573359 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0522294 (* 1 = 0.0522294 loss) +I0616 10:12:39.573364 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0548064 (* 1 = 0.0548064 loss) +I0616 10:12:39.573367 9857 solver.cpp:571] Iteration 47500, lr = 0.001 +I0616 10:12:51.207197 9857 solver.cpp:242] Iteration 47520, loss = 0.785137 +I0616 10:12:51.207238 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.395412 (* 1 = 0.395412 loss) +I0616 10:12:51.207245 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251481 (* 1 = 0.251481 loss) +I0616 10:12:51.207249 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0960423 (* 1 = 0.0960423 loss) +I0616 10:12:51.207253 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306822 (* 1 = 0.0306822 loss) +I0616 10:12:51.207257 9857 solver.cpp:571] Iteration 47520, lr = 0.001 +I0616 10:13:02.529278 9857 solver.cpp:242] Iteration 47540, loss = 0.98778 +I0616 10:13:02.529319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308624 (* 1 = 0.308624 loss) +I0616 10:13:02.529325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.355793 (* 1 = 0.355793 loss) +I0616 10:13:02.529328 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.42489 (* 1 = 0.42489 loss) +I0616 10:13:02.529332 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0542255 (* 1 = 0.0542255 loss) +I0616 10:13:02.529336 9857 solver.cpp:571] Iteration 47540, lr = 0.001 +I0616 10:13:13.996155 9857 solver.cpp:242] Iteration 47560, loss = 0.390523 +I0616 10:13:13.996196 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157414 (* 1 = 0.157414 loss) +I0616 10:13:13.996202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206933 (* 1 = 0.206933 loss) +I0616 10:13:13.996206 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0869859 (* 1 = 0.0869859 loss) +I0616 10:13:13.996211 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111154 (* 1 = 0.0111154 loss) +I0616 10:13:13.996214 9857 solver.cpp:571] Iteration 47560, lr = 0.001 +I0616 10:13:25.805642 9857 solver.cpp:242] Iteration 47580, loss = 0.764074 +I0616 10:13:25.805683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285196 (* 1 = 0.285196 loss) +I0616 10:13:25.805690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306136 (* 1 = 0.306136 loss) +I0616 10:13:25.805693 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.040766 (* 1 = 0.040766 loss) +I0616 10:13:25.805696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011888 (* 1 = 0.011888 loss) +I0616 10:13:25.805701 9857 solver.cpp:571] Iteration 47580, lr = 0.001 +speed: 0.615s / iter +I0616 10:13:37.285171 9857 solver.cpp:242] Iteration 47600, loss = 0.78607 +I0616 10:13:37.285212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307562 (* 1 = 0.307562 loss) +I0616 10:13:37.285218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.361234 (* 1 = 0.361234 loss) +I0616 10:13:37.285221 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0672974 (* 1 = 0.0672974 loss) +I0616 10:13:37.285226 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0582106 (* 1 = 0.0582106 loss) +I0616 10:13:37.285233 9857 solver.cpp:571] Iteration 47600, lr = 0.001 +I0616 10:13:48.897966 9857 solver.cpp:242] Iteration 47620, loss = 1.05364 +I0616 10:13:48.898010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396626 (* 1 = 0.396626 loss) +I0616 10:13:48.898015 9857 solver.cpp:258] Train net output #1: loss_cls = 1.00877 (* 1 = 1.00877 loss) +I0616 10:13:48.898020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188105 (* 1 = 0.188105 loss) +I0616 10:13:48.898023 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136029 (* 1 = 0.136029 loss) +I0616 10:13:48.898027 9857 solver.cpp:571] Iteration 47620, lr = 0.001 +I0616 10:14:00.477193 9857 solver.cpp:242] Iteration 47640, loss = 0.320845 +I0616 10:14:00.477234 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0739564 (* 1 = 0.0739564 loss) +I0616 10:14:00.477241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120948 (* 1 = 0.120948 loss) +I0616 10:14:00.477244 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00797779 (* 1 = 0.00797779 loss) +I0616 10:14:00.477248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00817106 (* 1 = 0.00817106 loss) +I0616 10:14:00.477252 9857 solver.cpp:571] Iteration 47640, lr = 0.001 +I0616 10:14:11.917742 9857 solver.cpp:242] Iteration 47660, loss = 0.629276 +I0616 10:14:11.917783 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283475 (* 1 = 0.283475 loss) +I0616 10:14:11.917789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.657688 (* 1 = 0.657688 loss) +I0616 10:14:11.917793 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0379565 (* 1 = 0.0379565 loss) +I0616 10:14:11.917798 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0590384 (* 1 = 0.0590384 loss) +I0616 10:14:11.917801 9857 solver.cpp:571] Iteration 47660, lr = 0.001 +I0616 10:14:23.381314 9857 solver.cpp:242] Iteration 47680, loss = 0.823446 +I0616 10:14:23.381356 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182213 (* 1 = 0.182213 loss) +I0616 10:14:23.381361 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305542 (* 1 = 0.305542 loss) +I0616 10:14:23.381364 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125846 (* 1 = 0.125846 loss) +I0616 10:14:23.381368 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0447427 (* 1 = 0.0447427 loss) +I0616 10:14:23.381372 9857 solver.cpp:571] Iteration 47680, lr = 0.001 +I0616 10:14:34.942275 9857 solver.cpp:242] Iteration 47700, loss = 0.966412 +I0616 10:14:34.942317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.368611 (* 1 = 0.368611 loss) +I0616 10:14:34.942322 9857 solver.cpp:258] Train net output #1: loss_cls = 0.411233 (* 1 = 0.411233 loss) +I0616 10:14:34.942325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0229134 (* 1 = 0.0229134 loss) +I0616 10:14:34.942329 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0583843 (* 1 = 0.0583843 loss) +I0616 10:14:34.942333 9857 solver.cpp:571] Iteration 47700, lr = 0.001 +I0616 10:14:46.605134 9857 solver.cpp:242] Iteration 47720, loss = 0.533922 +I0616 10:14:46.605177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149408 (* 1 = 0.149408 loss) +I0616 10:14:46.605182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188125 (* 1 = 0.188125 loss) +I0616 10:14:46.605187 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0983496 (* 1 = 0.0983496 loss) +I0616 10:14:46.605190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0178957 (* 1 = 0.0178957 loss) +I0616 10:14:46.605195 9857 solver.cpp:571] Iteration 47720, lr = 0.001 +I0616 10:14:58.236361 9857 solver.cpp:242] Iteration 47740, loss = 0.583454 +I0616 10:14:58.236402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0702426 (* 1 = 0.0702426 loss) +I0616 10:14:58.236408 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148235 (* 1 = 0.148235 loss) +I0616 10:14:58.236413 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0178896 (* 1 = 0.0178896 loss) +I0616 10:14:58.236416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000913033 (* 1 = 0.000913033 loss) +I0616 10:14:58.236420 9857 solver.cpp:571] Iteration 47740, lr = 0.001 +I0616 10:15:09.824585 9857 solver.cpp:242] Iteration 47760, loss = 1.16576 +I0616 10:15:09.824626 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.447369 (* 1 = 0.447369 loss) +I0616 10:15:09.824632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.582796 (* 1 = 0.582796 loss) +I0616 10:15:09.824636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0857273 (* 1 = 0.0857273 loss) +I0616 10:15:09.824640 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01785 (* 1 = 0.01785 loss) +I0616 10:15:09.824645 9857 solver.cpp:571] Iteration 47760, lr = 0.001 +I0616 10:15:21.036602 9857 solver.cpp:242] Iteration 47780, loss = 1.27019 +I0616 10:15:21.036643 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0988357 (* 1 = 0.0988357 loss) +I0616 10:15:21.036649 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102225 (* 1 = 0.102225 loss) +I0616 10:15:21.036654 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0160947 (* 1 = 0.0160947 loss) +I0616 10:15:21.036659 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00619274 (* 1 = 0.00619274 loss) +I0616 10:15:21.036661 9857 solver.cpp:571] Iteration 47780, lr = 0.001 +speed: 0.615s / iter +I0616 10:15:32.790740 9857 solver.cpp:242] Iteration 47800, loss = 0.62618 +I0616 10:15:32.790782 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304658 (* 1 = 0.304658 loss) +I0616 10:15:32.790788 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438894 (* 1 = 0.438894 loss) +I0616 10:15:32.790792 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.191268 (* 1 = 0.191268 loss) +I0616 10:15:32.790796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0749974 (* 1 = 0.0749974 loss) +I0616 10:15:32.790801 9857 solver.cpp:571] Iteration 47800, lr = 0.001 +I0616 10:15:44.538784 9857 solver.cpp:242] Iteration 47820, loss = 0.893826 +I0616 10:15:44.538825 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206321 (* 1 = 0.206321 loss) +I0616 10:15:44.538831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.549799 (* 1 = 0.549799 loss) +I0616 10:15:44.538836 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.2414 (* 1 = 0.2414 loss) +I0616 10:15:44.538841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0472311 (* 1 = 0.0472311 loss) +I0616 10:15:44.538843 9857 solver.cpp:571] Iteration 47820, lr = 0.001 +I0616 10:15:56.045330 9857 solver.cpp:242] Iteration 47840, loss = 0.44395 +I0616 10:15:56.045372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129724 (* 1 = 0.129724 loss) +I0616 10:15:56.045377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158815 (* 1 = 0.158815 loss) +I0616 10:15:56.045380 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112571 (* 1 = 0.0112571 loss) +I0616 10:15:56.045384 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00239608 (* 1 = 0.00239608 loss) +I0616 10:15:56.045388 9857 solver.cpp:571] Iteration 47840, lr = 0.001 +I0616 10:16:07.708381 9857 solver.cpp:242] Iteration 47860, loss = 0.709731 +I0616 10:16:07.708425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171235 (* 1 = 0.171235 loss) +I0616 10:16:07.708431 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183632 (* 1 = 0.183632 loss) +I0616 10:16:07.708434 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0893192 (* 1 = 0.0893192 loss) +I0616 10:16:07.708438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0506107 (* 1 = 0.0506107 loss) +I0616 10:16:07.708442 9857 solver.cpp:571] Iteration 47860, lr = 0.001 +I0616 10:16:19.236714 9857 solver.cpp:242] Iteration 47880, loss = 0.92792 +I0616 10:16:19.236757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311648 (* 1 = 0.311648 loss) +I0616 10:16:19.236763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269251 (* 1 = 0.269251 loss) +I0616 10:16:19.236768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201853 (* 1 = 0.201853 loss) +I0616 10:16:19.236771 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0312121 (* 1 = 0.0312121 loss) +I0616 10:16:19.236775 9857 solver.cpp:571] Iteration 47880, lr = 0.001 +I0616 10:16:30.956307 9857 solver.cpp:242] Iteration 47900, loss = 0.798541 +I0616 10:16:30.956349 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.314784 (* 1 = 0.314784 loss) +I0616 10:16:30.956354 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372404 (* 1 = 0.372404 loss) +I0616 10:16:30.956358 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149997 (* 1 = 0.149997 loss) +I0616 10:16:30.956362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.066683 (* 1 = 0.066683 loss) +I0616 10:16:30.956367 9857 solver.cpp:571] Iteration 47900, lr = 0.001 +I0616 10:16:42.382139 9857 solver.cpp:242] Iteration 47920, loss = 1.09448 +I0616 10:16:42.382169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.361951 (* 1 = 0.361951 loss) +I0616 10:16:42.382174 9857 solver.cpp:258] Train net output #1: loss_cls = 0.484579 (* 1 = 0.484579 loss) +I0616 10:16:42.382179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.516309 (* 1 = 0.516309 loss) +I0616 10:16:42.382182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.505931 (* 1 = 0.505931 loss) +I0616 10:16:42.382186 9857 solver.cpp:571] Iteration 47920, lr = 0.001 +I0616 10:16:53.987000 9857 solver.cpp:242] Iteration 47940, loss = 0.85305 +I0616 10:16:53.987042 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358335 (* 1 = 0.358335 loss) +I0616 10:16:53.987047 9857 solver.cpp:258] Train net output #1: loss_cls = 0.334878 (* 1 = 0.334878 loss) +I0616 10:16:53.987051 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246127 (* 1 = 0.246127 loss) +I0616 10:16:53.987056 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130997 (* 1 = 0.0130997 loss) +I0616 10:16:53.987058 9857 solver.cpp:571] Iteration 47940, lr = 0.001 +I0616 10:17:05.472935 9857 solver.cpp:242] Iteration 47960, loss = 0.87739 +I0616 10:17:05.472975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143805 (* 1 = 0.143805 loss) +I0616 10:17:05.472980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201261 (* 1 = 0.201261 loss) +I0616 10:17:05.472985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139245 (* 1 = 0.139245 loss) +I0616 10:17:05.472987 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.234729 (* 1 = 0.234729 loss) +I0616 10:17:05.472991 9857 solver.cpp:571] Iteration 47960, lr = 0.001 +I0616 10:17:17.010324 9857 solver.cpp:242] Iteration 47980, loss = 0.532471 +I0616 10:17:17.010365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100811 (* 1 = 0.100811 loss) +I0616 10:17:17.010370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175787 (* 1 = 0.175787 loss) +I0616 10:17:17.010375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0344385 (* 1 = 0.0344385 loss) +I0616 10:17:17.010378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390367 (* 1 = 0.0390367 loss) +I0616 10:17:17.010381 9857 solver.cpp:571] Iteration 47980, lr = 0.001 +speed: 0.614s / iter +I0616 10:17:28.528636 9857 solver.cpp:242] Iteration 48000, loss = 0.913305 +I0616 10:17:28.528679 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220287 (* 1 = 0.220287 loss) +I0616 10:17:28.528684 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253358 (* 1 = 0.253358 loss) +I0616 10:17:28.528688 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.132875 (* 1 = 0.132875 loss) +I0616 10:17:28.528692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120509 (* 1 = 0.0120509 loss) +I0616 10:17:28.528697 9857 solver.cpp:571] Iteration 48000, lr = 0.001 +I0616 10:17:39.784438 9857 solver.cpp:242] Iteration 48020, loss = 0.958822 +I0616 10:17:39.784481 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122757 (* 1 = 0.122757 loss) +I0616 10:17:39.784485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242418 (* 1 = 0.242418 loss) +I0616 10:17:39.784489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.214209 (* 1 = 0.214209 loss) +I0616 10:17:39.784493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.25069 (* 1 = 0.25069 loss) +I0616 10:17:39.784497 9857 solver.cpp:571] Iteration 48020, lr = 0.001 +I0616 10:17:51.454365 9857 solver.cpp:242] Iteration 48040, loss = 0.490783 +I0616 10:17:51.454408 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138499 (* 1 = 0.138499 loss) +I0616 10:17:51.454414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250316 (* 1 = 0.250316 loss) +I0616 10:17:51.454418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0915879 (* 1 = 0.0915879 loss) +I0616 10:17:51.454421 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334199 (* 1 = 0.0334199 loss) +I0616 10:17:51.454426 9857 solver.cpp:571] Iteration 48040, lr = 0.001 +I0616 10:18:02.900259 9857 solver.cpp:242] Iteration 48060, loss = 0.856998 +I0616 10:18:02.900300 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299697 (* 1 = 0.299697 loss) +I0616 10:18:02.900305 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244736 (* 1 = 0.244736 loss) +I0616 10:18:02.900310 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165038 (* 1 = 0.165038 loss) +I0616 10:18:02.900315 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183197 (* 1 = 0.0183197 loss) +I0616 10:18:02.900317 9857 solver.cpp:571] Iteration 48060, lr = 0.001 +I0616 10:18:14.425349 9857 solver.cpp:242] Iteration 48080, loss = 1.06558 +I0616 10:18:14.425390 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234145 (* 1 = 0.234145 loss) +I0616 10:18:14.425396 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316548 (* 1 = 0.316548 loss) +I0616 10:18:14.425400 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0475746 (* 1 = 0.0475746 loss) +I0616 10:18:14.425405 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0086444 (* 1 = 0.0086444 loss) +I0616 10:18:14.425408 9857 solver.cpp:571] Iteration 48080, lr = 0.001 +I0616 10:18:26.219038 9857 solver.cpp:242] Iteration 48100, loss = 0.820549 +I0616 10:18:26.219077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197314 (* 1 = 0.197314 loss) +I0616 10:18:26.219084 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185043 (* 1 = 0.185043 loss) +I0616 10:18:26.219087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0815949 (* 1 = 0.0815949 loss) +I0616 10:18:26.219091 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127993 (* 1 = 0.0127993 loss) +I0616 10:18:26.219094 9857 solver.cpp:571] Iteration 48100, lr = 0.001 +I0616 10:18:37.985231 9857 solver.cpp:242] Iteration 48120, loss = 0.799216 +I0616 10:18:37.985272 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.415256 (* 1 = 0.415256 loss) +I0616 10:18:37.985278 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332207 (* 1 = 0.332207 loss) +I0616 10:18:37.985282 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112227 (* 1 = 0.112227 loss) +I0616 10:18:37.985286 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319163 (* 1 = 0.0319163 loss) +I0616 10:18:37.985291 9857 solver.cpp:571] Iteration 48120, lr = 0.001 +I0616 10:18:49.574584 9857 solver.cpp:242] Iteration 48140, loss = 0.747976 +I0616 10:18:49.574626 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271978 (* 1 = 0.271978 loss) +I0616 10:18:49.574631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.385729 (* 1 = 0.385729 loss) +I0616 10:18:49.574636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0311987 (* 1 = 0.0311987 loss) +I0616 10:18:49.574640 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0682497 (* 1 = 0.0682497 loss) +I0616 10:18:49.574643 9857 solver.cpp:571] Iteration 48140, lr = 0.001 +I0616 10:19:01.091784 9857 solver.cpp:242] Iteration 48160, loss = 0.812651 +I0616 10:19:01.091826 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16807 (* 1 = 0.16807 loss) +I0616 10:19:01.091832 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237968 (* 1 = 0.237968 loss) +I0616 10:19:01.091836 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0427548 (* 1 = 0.0427548 loss) +I0616 10:19:01.091840 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0581134 (* 1 = 0.0581134 loss) +I0616 10:19:01.091845 9857 solver.cpp:571] Iteration 48160, lr = 0.001 +I0616 10:19:12.874006 9857 solver.cpp:242] Iteration 48180, loss = 0.433959 +I0616 10:19:12.874047 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0724229 (* 1 = 0.0724229 loss) +I0616 10:19:12.874053 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131063 (* 1 = 0.131063 loss) +I0616 10:19:12.874058 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0311229 (* 1 = 0.0311229 loss) +I0616 10:19:12.874061 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189207 (* 1 = 0.0189207 loss) +I0616 10:19:12.874064 9857 solver.cpp:571] Iteration 48180, lr = 0.001 +speed: 0.614s / iter +I0616 10:19:24.274498 9857 solver.cpp:242] Iteration 48200, loss = 1.25778 +I0616 10:19:24.274541 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244631 (* 1 = 0.244631 loss) +I0616 10:19:24.274546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.454965 (* 1 = 0.454965 loss) +I0616 10:19:24.274550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201576 (* 1 = 0.201576 loss) +I0616 10:19:24.274554 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0703593 (* 1 = 0.0703593 loss) +I0616 10:19:24.274559 9857 solver.cpp:571] Iteration 48200, lr = 0.001 +I0616 10:19:35.980610 9857 solver.cpp:242] Iteration 48220, loss = 0.28742 +I0616 10:19:35.980654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0794785 (* 1 = 0.0794785 loss) +I0616 10:19:35.980659 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0728634 (* 1 = 0.0728634 loss) +I0616 10:19:35.980664 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120124 (* 1 = 0.120124 loss) +I0616 10:19:35.980667 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160846 (* 1 = 0.0160846 loss) +I0616 10:19:35.980671 9857 solver.cpp:571] Iteration 48220, lr = 0.001 +I0616 10:19:47.627607 9857 solver.cpp:242] Iteration 48240, loss = 0.533866 +I0616 10:19:47.627650 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190888 (* 1 = 0.190888 loss) +I0616 10:19:47.627655 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178043 (* 1 = 0.178043 loss) +I0616 10:19:47.627660 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389296 (* 1 = 0.0389296 loss) +I0616 10:19:47.627663 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0631277 (* 1 = 0.0631277 loss) +I0616 10:19:47.627667 9857 solver.cpp:571] Iteration 48240, lr = 0.001 +I0616 10:19:59.070945 9857 solver.cpp:242] Iteration 48260, loss = 0.316649 +I0616 10:19:59.070987 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0924933 (* 1 = 0.0924933 loss) +I0616 10:19:59.070993 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234261 (* 1 = 0.234261 loss) +I0616 10:19:59.070997 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537647 (* 1 = 0.0537647 loss) +I0616 10:19:59.071002 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00806447 (* 1 = 0.00806447 loss) +I0616 10:19:59.071004 9857 solver.cpp:571] Iteration 48260, lr = 0.001 +I0616 10:20:10.599196 9857 solver.cpp:242] Iteration 48280, loss = 0.453154 +I0616 10:20:10.599239 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177285 (* 1 = 0.177285 loss) +I0616 10:20:10.599246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177678 (* 1 = 0.177678 loss) +I0616 10:20:10.599249 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0872819 (* 1 = 0.0872819 loss) +I0616 10:20:10.599253 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013181 (* 1 = 0.013181 loss) +I0616 10:20:10.599257 9857 solver.cpp:571] Iteration 48280, lr = 0.001 +I0616 10:20:22.183995 9857 solver.cpp:242] Iteration 48300, loss = 0.577046 +I0616 10:20:22.184036 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169293 (* 1 = 0.169293 loss) +I0616 10:20:22.184042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.327971 (* 1 = 0.327971 loss) +I0616 10:20:22.184046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101466 (* 1 = 0.101466 loss) +I0616 10:20:22.184051 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00713908 (* 1 = 0.00713908 loss) +I0616 10:20:22.184054 9857 solver.cpp:571] Iteration 48300, lr = 0.001 +I0616 10:20:34.023876 9857 solver.cpp:242] Iteration 48320, loss = 0.641158 +I0616 10:20:34.023917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190223 (* 1 = 0.190223 loss) +I0616 10:20:34.023923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188984 (* 1 = 0.188984 loss) +I0616 10:20:34.023927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0231798 (* 1 = 0.0231798 loss) +I0616 10:20:34.023931 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029458 (* 1 = 0.029458 loss) +I0616 10:20:34.023936 9857 solver.cpp:571] Iteration 48320, lr = 0.001 +I0616 10:20:45.586961 9857 solver.cpp:242] Iteration 48340, loss = 0.668671 +I0616 10:20:45.587002 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111686 (* 1 = 0.111686 loss) +I0616 10:20:45.587008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16231 (* 1 = 0.16231 loss) +I0616 10:20:45.587013 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0518686 (* 1 = 0.0518686 loss) +I0616 10:20:45.587016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235643 (* 1 = 0.0235643 loss) +I0616 10:20:45.587020 9857 solver.cpp:571] Iteration 48340, lr = 0.001 +I0616 10:20:57.061509 9857 solver.cpp:242] Iteration 48360, loss = 1.21502 +I0616 10:20:57.061550 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346296 (* 1 = 0.346296 loss) +I0616 10:20:57.061555 9857 solver.cpp:258] Train net output #1: loss_cls = 0.522166 (* 1 = 0.522166 loss) +I0616 10:20:57.061560 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178981 (* 1 = 0.178981 loss) +I0616 10:20:57.061563 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.563895 (* 1 = 0.563895 loss) +I0616 10:20:57.061568 9857 solver.cpp:571] Iteration 48360, lr = 0.001 +I0616 10:21:08.505091 9857 solver.cpp:242] Iteration 48380, loss = 0.792789 +I0616 10:21:08.505134 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.381999 (* 1 = 0.381999 loss) +I0616 10:21:08.505139 9857 solver.cpp:258] Train net output #1: loss_cls = 0.484868 (* 1 = 0.484868 loss) +I0616 10:21:08.505143 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163778 (* 1 = 0.163778 loss) +I0616 10:21:08.505147 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.151214 (* 1 = 0.151214 loss) +I0616 10:21:08.505151 9857 solver.cpp:571] Iteration 48380, lr = 0.001 +speed: 0.614s / iter +I0616 10:21:20.218787 9857 solver.cpp:242] Iteration 48400, loss = 0.967677 +I0616 10:21:20.218828 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262285 (* 1 = 0.262285 loss) +I0616 10:21:20.218834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182809 (* 1 = 0.182809 loss) +I0616 10:21:20.218838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152347 (* 1 = 0.152347 loss) +I0616 10:21:20.218842 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225443 (* 1 = 0.0225443 loss) +I0616 10:21:20.218845 9857 solver.cpp:571] Iteration 48400, lr = 0.001 +I0616 10:21:31.866192 9857 solver.cpp:242] Iteration 48420, loss = 1.21698 +I0616 10:21:31.866235 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.385772 (* 1 = 0.385772 loss) +I0616 10:21:31.866240 9857 solver.cpp:258] Train net output #1: loss_cls = 0.617627 (* 1 = 0.617627 loss) +I0616 10:21:31.866245 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178406 (* 1 = 0.178406 loss) +I0616 10:21:31.866248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.114733 (* 1 = 0.114733 loss) +I0616 10:21:31.866252 9857 solver.cpp:571] Iteration 48420, lr = 0.001 +I0616 10:21:43.376269 9857 solver.cpp:242] Iteration 48440, loss = 0.741401 +I0616 10:21:43.376312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183566 (* 1 = 0.183566 loss) +I0616 10:21:43.376317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.362337 (* 1 = 0.362337 loss) +I0616 10:21:43.376322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0240911 (* 1 = 0.0240911 loss) +I0616 10:21:43.376327 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00267892 (* 1 = 0.00267892 loss) +I0616 10:21:43.376330 9857 solver.cpp:571] Iteration 48440, lr = 0.001 +I0616 10:21:55.067196 9857 solver.cpp:242] Iteration 48460, loss = 0.861606 +I0616 10:21:55.067236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306974 (* 1 = 0.306974 loss) +I0616 10:21:55.067241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278765 (* 1 = 0.278765 loss) +I0616 10:21:55.067246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0921436 (* 1 = 0.0921436 loss) +I0616 10:21:55.067250 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0929365 (* 1 = 0.0929365 loss) +I0616 10:21:55.067253 9857 solver.cpp:571] Iteration 48460, lr = 0.001 +I0616 10:22:06.668867 9857 solver.cpp:242] Iteration 48480, loss = 0.491655 +I0616 10:22:06.668910 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0845842 (* 1 = 0.0845842 loss) +I0616 10:22:06.668916 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194459 (* 1 = 0.194459 loss) +I0616 10:22:06.668920 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0415436 (* 1 = 0.0415436 loss) +I0616 10:22:06.668925 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0300982 (* 1 = 0.0300982 loss) +I0616 10:22:06.668928 9857 solver.cpp:571] Iteration 48480, lr = 0.001 +I0616 10:22:18.191910 9857 solver.cpp:242] Iteration 48500, loss = 1.05802 +I0616 10:22:18.191949 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260541 (* 1 = 0.260541 loss) +I0616 10:22:18.191956 9857 solver.cpp:258] Train net output #1: loss_cls = 0.497028 (* 1 = 0.497028 loss) +I0616 10:22:18.191959 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123093 (* 1 = 0.123093 loss) +I0616 10:22:18.191963 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162489 (* 1 = 0.0162489 loss) +I0616 10:22:18.191967 9857 solver.cpp:571] Iteration 48500, lr = 0.001 +I0616 10:22:29.969210 9857 solver.cpp:242] Iteration 48520, loss = 0.841512 +I0616 10:22:29.969254 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322271 (* 1 = 0.322271 loss) +I0616 10:22:29.969259 9857 solver.cpp:258] Train net output #1: loss_cls = 0.580005 (* 1 = 0.580005 loss) +I0616 10:22:29.969264 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00372856 (* 1 = 0.00372856 loss) +I0616 10:22:29.969267 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00746821 (* 1 = 0.00746821 loss) +I0616 10:22:29.969271 9857 solver.cpp:571] Iteration 48520, lr = 0.001 +I0616 10:22:41.263964 9857 solver.cpp:242] Iteration 48540, loss = 0.442449 +I0616 10:22:41.264006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0958508 (* 1 = 0.0958508 loss) +I0616 10:22:41.264013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230857 (* 1 = 0.230857 loss) +I0616 10:22:41.264016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0430229 (* 1 = 0.0430229 loss) +I0616 10:22:41.264020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00435917 (* 1 = 0.00435917 loss) +I0616 10:22:41.264024 9857 solver.cpp:571] Iteration 48540, lr = 0.001 +I0616 10:22:52.767463 9857 solver.cpp:242] Iteration 48560, loss = 0.318754 +I0616 10:22:52.767504 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10004 (* 1 = 0.10004 loss) +I0616 10:22:52.767510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134499 (* 1 = 0.134499 loss) +I0616 10:22:52.767514 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0100598 (* 1 = 0.0100598 loss) +I0616 10:22:52.767518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153016 (* 1 = 0.0153016 loss) +I0616 10:22:52.767523 9857 solver.cpp:571] Iteration 48560, lr = 0.001 +I0616 10:23:04.377915 9857 solver.cpp:242] Iteration 48580, loss = 1.05139 +I0616 10:23:04.377957 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173649 (* 1 = 0.173649 loss) +I0616 10:23:04.377964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233052 (* 1 = 0.233052 loss) +I0616 10:23:04.377967 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0346382 (* 1 = 0.0346382 loss) +I0616 10:23:04.377970 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112614 (* 1 = 0.0112614 loss) +I0616 10:23:04.377974 9857 solver.cpp:571] Iteration 48580, lr = 0.001 +speed: 0.614s / iter +I0616 10:23:16.036069 9857 solver.cpp:242] Iteration 48600, loss = 0.646058 +I0616 10:23:16.036111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29073 (* 1 = 0.29073 loss) +I0616 10:23:16.036118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328594 (* 1 = 0.328594 loss) +I0616 10:23:16.036121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.195895 (* 1 = 0.195895 loss) +I0616 10:23:16.036125 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0436418 (* 1 = 0.0436418 loss) +I0616 10:23:16.036128 9857 solver.cpp:571] Iteration 48600, lr = 0.001 +I0616 10:23:27.727109 9857 solver.cpp:242] Iteration 48620, loss = 0.408754 +I0616 10:23:27.727154 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102925 (* 1 = 0.102925 loss) +I0616 10:23:27.727159 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183691 (* 1 = 0.183691 loss) +I0616 10:23:27.727162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00427236 (* 1 = 0.00427236 loss) +I0616 10:23:27.727166 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138431 (* 1 = 0.0138431 loss) +I0616 10:23:27.727170 9857 solver.cpp:571] Iteration 48620, lr = 0.001 +I0616 10:23:39.504865 9857 solver.cpp:242] Iteration 48640, loss = 0.412276 +I0616 10:23:39.504909 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136445 (* 1 = 0.136445 loss) +I0616 10:23:39.504914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225961 (* 1 = 0.225961 loss) +I0616 10:23:39.504919 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0136341 (* 1 = 0.0136341 loss) +I0616 10:23:39.504922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00669156 (* 1 = 0.00669156 loss) +I0616 10:23:39.504925 9857 solver.cpp:571] Iteration 48640, lr = 0.001 +I0616 10:23:50.771152 9857 solver.cpp:242] Iteration 48660, loss = 0.330616 +I0616 10:23:50.771193 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0661962 (* 1 = 0.0661962 loss) +I0616 10:23:50.771198 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180731 (* 1 = 0.180731 loss) +I0616 10:23:50.771201 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0680449 (* 1 = 0.0680449 loss) +I0616 10:23:50.771205 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00384676 (* 1 = 0.00384676 loss) +I0616 10:23:50.771209 9857 solver.cpp:571] Iteration 48660, lr = 0.001 +I0616 10:24:02.405792 9857 solver.cpp:242] Iteration 48680, loss = 0.852681 +I0616 10:24:02.405833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101073 (* 1 = 0.101073 loss) +I0616 10:24:02.405839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225355 (* 1 = 0.225355 loss) +I0616 10:24:02.405843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.270704 (* 1 = 0.270704 loss) +I0616 10:24:02.405848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00659068 (* 1 = 0.00659068 loss) +I0616 10:24:02.405851 9857 solver.cpp:571] Iteration 48680, lr = 0.001 +I0616 10:24:13.889631 9857 solver.cpp:242] Iteration 48700, loss = 0.769504 +I0616 10:24:13.889672 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259565 (* 1 = 0.259565 loss) +I0616 10:24:13.889678 9857 solver.cpp:258] Train net output #1: loss_cls = 0.452814 (* 1 = 0.452814 loss) +I0616 10:24:13.889683 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124914 (* 1 = 0.124914 loss) +I0616 10:24:13.889686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.178965 (* 1 = 0.178965 loss) +I0616 10:24:13.889690 9857 solver.cpp:571] Iteration 48700, lr = 0.001 +I0616 10:24:25.502797 9857 solver.cpp:242] Iteration 48720, loss = 0.428404 +I0616 10:24:25.502840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127929 (* 1 = 0.127929 loss) +I0616 10:24:25.502846 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0939452 (* 1 = 0.0939452 loss) +I0616 10:24:25.502849 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0410702 (* 1 = 0.0410702 loss) +I0616 10:24:25.502853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00937195 (* 1 = 0.00937195 loss) +I0616 10:24:25.502856 9857 solver.cpp:571] Iteration 48720, lr = 0.001 +I0616 10:24:36.975956 9857 solver.cpp:242] Iteration 48740, loss = 0.73733 +I0616 10:24:36.975997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121369 (* 1 = 0.121369 loss) +I0616 10:24:36.976003 9857 solver.cpp:258] Train net output #1: loss_cls = 0.365207 (* 1 = 0.365207 loss) +I0616 10:24:36.976007 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00860697 (* 1 = 0.00860697 loss) +I0616 10:24:36.976011 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00838496 (* 1 = 0.00838496 loss) +I0616 10:24:36.976016 9857 solver.cpp:571] Iteration 48740, lr = 0.001 +I0616 10:24:48.668570 9857 solver.cpp:242] Iteration 48760, loss = 1.44827 +I0616 10:24:48.668612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192395 (* 1 = 0.192395 loss) +I0616 10:24:48.668617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315904 (* 1 = 0.315904 loss) +I0616 10:24:48.668622 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389575 (* 1 = 0.0389575 loss) +I0616 10:24:48.668625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163876 (* 1 = 0.0163876 loss) +I0616 10:24:48.668629 9857 solver.cpp:571] Iteration 48760, lr = 0.001 +I0616 10:25:00.119755 9857 solver.cpp:242] Iteration 48780, loss = 0.53777 +I0616 10:25:00.119781 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237866 (* 1 = 0.237866 loss) +I0616 10:25:00.119801 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278893 (* 1 = 0.278893 loss) +I0616 10:25:00.119804 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.182372 (* 1 = 0.182372 loss) +I0616 10:25:00.119808 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0361642 (* 1 = 0.0361642 loss) +I0616 10:25:00.119812 9857 solver.cpp:571] Iteration 48780, lr = 0.001 +speed: 0.614s / iter +I0616 10:25:11.862982 9857 solver.cpp:242] Iteration 48800, loss = 0.539746 +I0616 10:25:11.863024 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185107 (* 1 = 0.185107 loss) +I0616 10:25:11.863030 9857 solver.cpp:258] Train net output #1: loss_cls = 0.382734 (* 1 = 0.382734 loss) +I0616 10:25:11.863034 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120354 (* 1 = 0.120354 loss) +I0616 10:25:11.863039 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00432849 (* 1 = 0.00432849 loss) +I0616 10:25:11.863042 9857 solver.cpp:571] Iteration 48800, lr = 0.001 +I0616 10:25:23.219411 9857 solver.cpp:242] Iteration 48820, loss = 0.462737 +I0616 10:25:23.219454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14798 (* 1 = 0.14798 loss) +I0616 10:25:23.219460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146643 (* 1 = 0.146643 loss) +I0616 10:25:23.219463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0616867 (* 1 = 0.0616867 loss) +I0616 10:25:23.219467 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015011 (* 1 = 0.015011 loss) +I0616 10:25:23.219471 9857 solver.cpp:571] Iteration 48820, lr = 0.001 +I0616 10:25:34.877720 9857 solver.cpp:242] Iteration 48840, loss = 0.513287 +I0616 10:25:34.877763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168805 (* 1 = 0.168805 loss) +I0616 10:25:34.877768 9857 solver.cpp:258] Train net output #1: loss_cls = 0.327233 (* 1 = 0.327233 loss) +I0616 10:25:34.877773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0919212 (* 1 = 0.0919212 loss) +I0616 10:25:34.877775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159327 (* 1 = 0.159327 loss) +I0616 10:25:34.877779 9857 solver.cpp:571] Iteration 48840, lr = 0.001 +I0616 10:25:46.500643 9857 solver.cpp:242] Iteration 48860, loss = 0.815781 +I0616 10:25:46.500684 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332129 (* 1 = 0.332129 loss) +I0616 10:25:46.500690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204773 (* 1 = 0.204773 loss) +I0616 10:25:46.500694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.028092 (* 1 = 0.028092 loss) +I0616 10:25:46.500699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0551902 (* 1 = 0.0551902 loss) +I0616 10:25:46.500702 9857 solver.cpp:571] Iteration 48860, lr = 0.001 +I0616 10:25:58.022817 9857 solver.cpp:242] Iteration 48880, loss = 0.844361 +I0616 10:25:58.022860 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.289132 (* 1 = 0.289132 loss) +I0616 10:25:58.022866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49124 (* 1 = 0.49124 loss) +I0616 10:25:58.022869 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220027 (* 1 = 0.220027 loss) +I0616 10:25:58.022873 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404231 (* 1 = 0.0404231 loss) +I0616 10:25:58.022877 9857 solver.cpp:571] Iteration 48880, lr = 0.001 +I0616 10:26:09.650667 9857 solver.cpp:242] Iteration 48900, loss = 0.333778 +I0616 10:26:09.650709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0867312 (* 1 = 0.0867312 loss) +I0616 10:26:09.650715 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0851202 (* 1 = 0.0851202 loss) +I0616 10:26:09.650719 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100788 (* 1 = 0.100788 loss) +I0616 10:26:09.650722 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0304718 (* 1 = 0.0304718 loss) +I0616 10:26:09.650727 9857 solver.cpp:571] Iteration 48900, lr = 0.001 +I0616 10:26:21.025288 9857 solver.cpp:242] Iteration 48920, loss = 0.473588 +I0616 10:26:21.025331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.096546 (* 1 = 0.096546 loss) +I0616 10:26:21.025336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136546 (* 1 = 0.136546 loss) +I0616 10:26:21.025341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0430811 (* 1 = 0.0430811 loss) +I0616 10:26:21.025344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00458771 (* 1 = 0.00458771 loss) +I0616 10:26:21.025347 9857 solver.cpp:571] Iteration 48920, lr = 0.001 +I0616 10:26:32.597364 9857 solver.cpp:242] Iteration 48940, loss = 0.575927 +I0616 10:26:32.597404 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16769 (* 1 = 0.16769 loss) +I0616 10:26:32.597410 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19232 (* 1 = 0.19232 loss) +I0616 10:26:32.597414 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.08969 (* 1 = 0.08969 loss) +I0616 10:26:32.597419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0736348 (* 1 = 0.0736348 loss) +I0616 10:26:32.597422 9857 solver.cpp:571] Iteration 48940, lr = 0.001 +I0616 10:26:43.849722 9857 solver.cpp:242] Iteration 48960, loss = 0.424377 +I0616 10:26:43.849764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174917 (* 1 = 0.174917 loss) +I0616 10:26:43.849771 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270398 (* 1 = 0.270398 loss) +I0616 10:26:43.849774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0670547 (* 1 = 0.0670547 loss) +I0616 10:26:43.849778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00699995 (* 1 = 0.00699995 loss) +I0616 10:26:43.849782 9857 solver.cpp:571] Iteration 48960, lr = 0.001 +I0616 10:26:55.389148 9857 solver.cpp:242] Iteration 48980, loss = 0.40979 +I0616 10:26:55.389190 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17924 (* 1 = 0.17924 loss) +I0616 10:26:55.389196 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12249 (* 1 = 0.12249 loss) +I0616 10:26:55.389200 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0333501 (* 1 = 0.0333501 loss) +I0616 10:26:55.389204 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0237657 (* 1 = 0.0237657 loss) +I0616 10:26:55.389207 9857 solver.cpp:571] Iteration 48980, lr = 0.001 +speed: 0.614s / iter +I0616 10:27:06.816503 9857 solver.cpp:242] Iteration 49000, loss = 0.931533 +I0616 10:27:06.816545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259494 (* 1 = 0.259494 loss) +I0616 10:27:06.816550 9857 solver.cpp:258] Train net output #1: loss_cls = 0.501387 (* 1 = 0.501387 loss) +I0616 10:27:06.816553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.399772 (* 1 = 0.399772 loss) +I0616 10:27:06.816557 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.121229 (* 1 = 0.121229 loss) +I0616 10:27:06.816561 9857 solver.cpp:571] Iteration 49000, lr = 0.001 +I0616 10:27:18.143082 9857 solver.cpp:242] Iteration 49020, loss = 0.467242 +I0616 10:27:18.143126 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.085566 (* 1 = 0.085566 loss) +I0616 10:27:18.143131 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10172 (* 1 = 0.10172 loss) +I0616 10:27:18.143134 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117464 (* 1 = 0.117464 loss) +I0616 10:27:18.143138 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012463 (* 1 = 0.012463 loss) +I0616 10:27:18.143142 9857 solver.cpp:571] Iteration 49020, lr = 0.001 +I0616 10:27:29.652122 9857 solver.cpp:242] Iteration 49040, loss = 1.05817 +I0616 10:27:29.652164 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315525 (* 1 = 0.315525 loss) +I0616 10:27:29.652170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.587968 (* 1 = 0.587968 loss) +I0616 10:27:29.652174 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210632 (* 1 = 0.210632 loss) +I0616 10:27:29.652179 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0684771 (* 1 = 0.0684771 loss) +I0616 10:27:29.652181 9857 solver.cpp:571] Iteration 49040, lr = 0.001 +I0616 10:27:41.372903 9857 solver.cpp:242] Iteration 49060, loss = 0.657151 +I0616 10:27:41.372946 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108776 (* 1 = 0.108776 loss) +I0616 10:27:41.372951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0765592 (* 1 = 0.0765592 loss) +I0616 10:27:41.372956 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00696331 (* 1 = 0.00696331 loss) +I0616 10:27:41.372959 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00382711 (* 1 = 0.00382711 loss) +I0616 10:27:41.372963 9857 solver.cpp:571] Iteration 49060, lr = 0.001 +I0616 10:27:52.798287 9857 solver.cpp:242] Iteration 49080, loss = 0.320927 +I0616 10:27:52.798327 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101715 (* 1 = 0.101715 loss) +I0616 10:27:52.798333 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128204 (* 1 = 0.128204 loss) +I0616 10:27:52.798337 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228704 (* 1 = 0.0228704 loss) +I0616 10:27:52.798341 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145946 (* 1 = 0.0145946 loss) +I0616 10:27:52.798346 9857 solver.cpp:571] Iteration 49080, lr = 0.001 +I0616 10:28:04.575774 9857 solver.cpp:242] Iteration 49100, loss = 0.754142 +I0616 10:28:04.575817 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212767 (* 1 = 0.212767 loss) +I0616 10:28:04.575824 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23333 (* 1 = 0.23333 loss) +I0616 10:28:04.575827 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.216427 (* 1 = 0.216427 loss) +I0616 10:28:04.575831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255946 (* 1 = 0.0255946 loss) +I0616 10:28:04.575835 9857 solver.cpp:571] Iteration 49100, lr = 0.001 +I0616 10:28:16.076861 9857 solver.cpp:242] Iteration 49120, loss = 0.942889 +I0616 10:28:16.076903 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130805 (* 1 = 0.130805 loss) +I0616 10:28:16.076908 9857 solver.cpp:258] Train net output #1: loss_cls = 0.451592 (* 1 = 0.451592 loss) +I0616 10:28:16.076912 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.2175 (* 1 = 0.2175 loss) +I0616 10:28:16.076916 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0148284 (* 1 = 0.0148284 loss) +I0616 10:28:16.076920 9857 solver.cpp:571] Iteration 49120, lr = 0.001 +I0616 10:28:27.858145 9857 solver.cpp:242] Iteration 49140, loss = 0.561937 +I0616 10:28:27.858188 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0506386 (* 1 = 0.0506386 loss) +I0616 10:28:27.858194 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0628491 (* 1 = 0.0628491 loss) +I0616 10:28:27.858198 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00976514 (* 1 = 0.00976514 loss) +I0616 10:28:27.858201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00772162 (* 1 = 0.00772162 loss) +I0616 10:28:27.858206 9857 solver.cpp:571] Iteration 49140, lr = 0.001 +I0616 10:28:39.484341 9857 solver.cpp:242] Iteration 49160, loss = 0.694656 +I0616 10:28:39.484385 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215975 (* 1 = 0.215975 loss) +I0616 10:28:39.484390 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225503 (* 1 = 0.225503 loss) +I0616 10:28:39.484395 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.183242 (* 1 = 0.183242 loss) +I0616 10:28:39.484398 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0526116 (* 1 = 0.0526116 loss) +I0616 10:28:39.484405 9857 solver.cpp:571] Iteration 49160, lr = 0.001 +I0616 10:28:50.770566 9857 solver.cpp:242] Iteration 49180, loss = 0.798641 +I0616 10:28:50.770603 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154124 (* 1 = 0.154124 loss) +I0616 10:28:50.770609 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215348 (* 1 = 0.215348 loss) +I0616 10:28:50.770614 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0430245 (* 1 = 0.0430245 loss) +I0616 10:28:50.770617 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0380193 (* 1 = 0.0380193 loss) +I0616 10:28:50.770622 9857 solver.cpp:571] Iteration 49180, lr = 0.001 +speed: 0.613s / iter +I0616 10:29:02.457898 9857 solver.cpp:242] Iteration 49200, loss = 0.419189 +I0616 10:29:02.457942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0554066 (* 1 = 0.0554066 loss) +I0616 10:29:02.457947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176065 (* 1 = 0.176065 loss) +I0616 10:29:02.457950 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0842198 (* 1 = 0.0842198 loss) +I0616 10:29:02.457954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142235 (* 1 = 0.0142235 loss) +I0616 10:29:02.457958 9857 solver.cpp:571] Iteration 49200, lr = 0.001 +I0616 10:29:13.857177 9857 solver.cpp:242] Iteration 49220, loss = 0.379101 +I0616 10:29:13.857220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140182 (* 1 = 0.140182 loss) +I0616 10:29:13.857226 9857 solver.cpp:258] Train net output #1: loss_cls = 0.198485 (* 1 = 0.198485 loss) +I0616 10:29:13.857230 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0243233 (* 1 = 0.0243233 loss) +I0616 10:29:13.857234 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0258462 (* 1 = 0.0258462 loss) +I0616 10:29:13.857239 9857 solver.cpp:571] Iteration 49220, lr = 0.001 +I0616 10:29:25.418973 9857 solver.cpp:242] Iteration 49240, loss = 0.851544 +I0616 10:29:25.419015 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24925 (* 1 = 0.24925 loss) +I0616 10:29:25.419020 9857 solver.cpp:258] Train net output #1: loss_cls = 0.34495 (* 1 = 0.34495 loss) +I0616 10:29:25.419024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159753 (* 1 = 0.159753 loss) +I0616 10:29:25.419028 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397941 (* 1 = 0.0397941 loss) +I0616 10:29:25.419033 9857 solver.cpp:571] Iteration 49240, lr = 0.001 +I0616 10:29:36.809711 9857 solver.cpp:242] Iteration 49260, loss = 1.22853 +I0616 10:29:36.809753 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411794 (* 1 = 0.411794 loss) +I0616 10:29:36.809758 9857 solver.cpp:258] Train net output #1: loss_cls = 0.520355 (* 1 = 0.520355 loss) +I0616 10:29:36.809762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0206286 (* 1 = 0.0206286 loss) +I0616 10:29:36.809767 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133091 (* 1 = 0.0133091 loss) +I0616 10:29:36.809770 9857 solver.cpp:571] Iteration 49260, lr = 0.001 +I0616 10:29:48.088740 9857 solver.cpp:242] Iteration 49280, loss = 0.376506 +I0616 10:29:48.088780 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173563 (* 1 = 0.173563 loss) +I0616 10:29:48.088786 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253087 (* 1 = 0.253087 loss) +I0616 10:29:48.088790 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0223022 (* 1 = 0.0223022 loss) +I0616 10:29:48.088794 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00343637 (* 1 = 0.00343637 loss) +I0616 10:29:48.088798 9857 solver.cpp:571] Iteration 49280, lr = 0.001 +I0616 10:29:59.404235 9857 solver.cpp:242] Iteration 49300, loss = 1.05854 +I0616 10:29:59.404278 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.562489 (* 1 = 0.562489 loss) +I0616 10:29:59.404284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.539326 (* 1 = 0.539326 loss) +I0616 10:29:59.404287 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.274458 (* 1 = 0.274458 loss) +I0616 10:29:59.404290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323218 (* 1 = 0.0323218 loss) +I0616 10:29:59.404294 9857 solver.cpp:571] Iteration 49300, lr = 0.001 +I0616 10:30:10.837258 9857 solver.cpp:242] Iteration 49320, loss = 0.456383 +I0616 10:30:10.837299 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164161 (* 1 = 0.164161 loss) +I0616 10:30:10.837304 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32631 (* 1 = 0.32631 loss) +I0616 10:30:10.837308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0341394 (* 1 = 0.0341394 loss) +I0616 10:30:10.837312 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0213205 (* 1 = 0.0213205 loss) +I0616 10:30:10.837316 9857 solver.cpp:571] Iteration 49320, lr = 0.001 +I0616 10:30:22.317340 9857 solver.cpp:242] Iteration 49340, loss = 0.565301 +I0616 10:30:22.317383 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141903 (* 1 = 0.141903 loss) +I0616 10:30:22.317389 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322956 (* 1 = 0.322956 loss) +I0616 10:30:22.317394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0522358 (* 1 = 0.0522358 loss) +I0616 10:30:22.317396 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125432 (* 1 = 0.0125432 loss) +I0616 10:30:22.317400 9857 solver.cpp:571] Iteration 49340, lr = 0.001 +I0616 10:30:33.831774 9857 solver.cpp:242] Iteration 49360, loss = 0.848135 +I0616 10:30:33.831815 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413207 (* 1 = 0.413207 loss) +I0616 10:30:33.831821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343676 (* 1 = 0.343676 loss) +I0616 10:30:33.831825 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.066281 (* 1 = 0.066281 loss) +I0616 10:30:33.831830 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109799 (* 1 = 0.0109799 loss) +I0616 10:30:33.831835 9857 solver.cpp:571] Iteration 49360, lr = 0.001 +I0616 10:30:45.341387 9857 solver.cpp:242] Iteration 49380, loss = 0.403098 +I0616 10:30:45.341429 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725143 (* 1 = 0.0725143 loss) +I0616 10:30:45.341435 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168359 (* 1 = 0.168359 loss) +I0616 10:30:45.341439 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0327997 (* 1 = 0.0327997 loss) +I0616 10:30:45.341444 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0280437 (* 1 = 0.0280437 loss) +I0616 10:30:45.341446 9857 solver.cpp:571] Iteration 49380, lr = 0.001 +speed: 0.613s / iter +I0616 10:30:57.058404 9857 solver.cpp:242] Iteration 49400, loss = 1.06035 +I0616 10:30:57.058445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.423499 (* 1 = 0.423499 loss) +I0616 10:30:57.058451 9857 solver.cpp:258] Train net output #1: loss_cls = 0.858563 (* 1 = 0.858563 loss) +I0616 10:30:57.058455 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.287271 (* 1 = 0.287271 loss) +I0616 10:30:57.058459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136356 (* 1 = 0.136356 loss) +I0616 10:30:57.058462 9857 solver.cpp:571] Iteration 49400, lr = 0.001 +I0616 10:31:08.851464 9857 solver.cpp:242] Iteration 49420, loss = 0.478297 +I0616 10:31:08.851507 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139922 (* 1 = 0.139922 loss) +I0616 10:31:08.851513 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271403 (* 1 = 0.271403 loss) +I0616 10:31:08.851517 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138735 (* 1 = 0.138735 loss) +I0616 10:31:08.851521 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0185085 (* 1 = 0.0185085 loss) +I0616 10:31:08.851526 9857 solver.cpp:571] Iteration 49420, lr = 0.001 +I0616 10:31:20.413398 9857 solver.cpp:242] Iteration 49440, loss = 1.16874 +I0616 10:31:20.413436 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.4895 (* 1 = 0.4895 loss) +I0616 10:31:20.413442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.450255 (* 1 = 0.450255 loss) +I0616 10:31:20.413446 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.196127 (* 1 = 0.196127 loss) +I0616 10:31:20.413450 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0596668 (* 1 = 0.0596668 loss) +I0616 10:31:20.413453 9857 solver.cpp:571] Iteration 49440, lr = 0.001 +I0616 10:31:31.912418 9857 solver.cpp:242] Iteration 49460, loss = 0.918721 +I0616 10:31:31.912461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181272 (* 1 = 0.181272 loss) +I0616 10:31:31.912467 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22413 (* 1 = 0.22413 loss) +I0616 10:31:31.912470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0186498 (* 1 = 0.0186498 loss) +I0616 10:31:31.912473 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0233684 (* 1 = 0.0233684 loss) +I0616 10:31:31.912477 9857 solver.cpp:571] Iteration 49460, lr = 0.001 +I0616 10:31:43.650514 9857 solver.cpp:242] Iteration 49480, loss = 0.816401 +I0616 10:31:43.650557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173091 (* 1 = 0.173091 loss) +I0616 10:31:43.650563 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253358 (* 1 = 0.253358 loss) +I0616 10:31:43.650568 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0543451 (* 1 = 0.0543451 loss) +I0616 10:31:43.650571 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.068969 (* 1 = 0.068969 loss) +I0616 10:31:43.650575 9857 solver.cpp:571] Iteration 49480, lr = 0.001 +I0616 10:31:55.301288 9857 solver.cpp:242] Iteration 49500, loss = 0.821089 +I0616 10:31:55.301331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.454584 (* 1 = 0.454584 loss) +I0616 10:31:55.301337 9857 solver.cpp:258] Train net output #1: loss_cls = 0.409962 (* 1 = 0.409962 loss) +I0616 10:31:55.301340 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193642 (* 1 = 0.193642 loss) +I0616 10:31:55.301344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.12238 (* 1 = 0.12238 loss) +I0616 10:31:55.301348 9857 solver.cpp:571] Iteration 49500, lr = 0.001 +I0616 10:32:06.598886 9857 solver.cpp:242] Iteration 49520, loss = 0.254239 +I0616 10:32:06.598927 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0654173 (* 1 = 0.0654173 loss) +I0616 10:32:06.598933 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214428 (* 1 = 0.214428 loss) +I0616 10:32:06.598937 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0125746 (* 1 = 0.0125746 loss) +I0616 10:32:06.598940 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108904 (* 1 = 0.0108904 loss) +I0616 10:32:06.598944 9857 solver.cpp:571] Iteration 49520, lr = 0.001 +I0616 10:32:18.273560 9857 solver.cpp:242] Iteration 49540, loss = 0.924484 +I0616 10:32:18.273602 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270954 (* 1 = 0.270954 loss) +I0616 10:32:18.273607 9857 solver.cpp:258] Train net output #1: loss_cls = 0.690819 (* 1 = 0.690819 loss) +I0616 10:32:18.273612 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109856 (* 1 = 0.109856 loss) +I0616 10:32:18.273617 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0600185 (* 1 = 0.0600185 loss) +I0616 10:32:18.273619 9857 solver.cpp:571] Iteration 49540, lr = 0.001 +I0616 10:32:29.968770 9857 solver.cpp:242] Iteration 49560, loss = 0.781101 +I0616 10:32:29.968811 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351459 (* 1 = 0.351459 loss) +I0616 10:32:29.968817 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375774 (* 1 = 0.375774 loss) +I0616 10:32:29.968822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0799169 (* 1 = 0.0799169 loss) +I0616 10:32:29.968825 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235591 (* 1 = 0.0235591 loss) +I0616 10:32:29.968829 9857 solver.cpp:571] Iteration 49560, lr = 0.001 +I0616 10:32:41.516822 9857 solver.cpp:242] Iteration 49580, loss = 1.15113 +I0616 10:32:41.516865 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205463 (* 1 = 0.205463 loss) +I0616 10:32:41.516870 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402583 (* 1 = 0.402583 loss) +I0616 10:32:41.516875 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190705 (* 1 = 0.190705 loss) +I0616 10:32:41.516878 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0363263 (* 1 = 0.0363263 loss) +I0616 10:32:41.516882 9857 solver.cpp:571] Iteration 49580, lr = 0.001 +speed: 0.613s / iter +I0616 10:32:53.153432 9857 solver.cpp:242] Iteration 49600, loss = 1.02895 +I0616 10:32:53.153475 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163668 (* 1 = 0.163668 loss) +I0616 10:32:53.153481 9857 solver.cpp:258] Train net output #1: loss_cls = 0.346726 (* 1 = 0.346726 loss) +I0616 10:32:53.153484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12211 (* 1 = 0.12211 loss) +I0616 10:32:53.153488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0218449 (* 1 = 0.0218449 loss) +I0616 10:32:53.153491 9857 solver.cpp:571] Iteration 49600, lr = 0.001 +I0616 10:33:04.427608 9857 solver.cpp:242] Iteration 49620, loss = 0.365824 +I0616 10:33:04.427649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109001 (* 1 = 0.109001 loss) +I0616 10:33:04.427655 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0925146 (* 1 = 0.0925146 loss) +I0616 10:33:04.427659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0250431 (* 1 = 0.0250431 loss) +I0616 10:33:04.427664 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255993 (* 1 = 0.0255993 loss) +I0616 10:33:04.427666 9857 solver.cpp:571] Iteration 49620, lr = 0.001 +I0616 10:33:15.965555 9857 solver.cpp:242] Iteration 49640, loss = 1.35237 +I0616 10:33:15.965598 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.481317 (* 1 = 0.481317 loss) +I0616 10:33:15.965603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.762861 (* 1 = 0.762861 loss) +I0616 10:33:15.965607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.131762 (* 1 = 0.131762 loss) +I0616 10:33:15.965611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.541117 (* 1 = 0.541117 loss) +I0616 10:33:15.965615 9857 solver.cpp:571] Iteration 49640, lr = 0.001 +I0616 10:33:27.508852 9857 solver.cpp:242] Iteration 49660, loss = 1.18373 +I0616 10:33:27.508908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315546 (* 1 = 0.315546 loss) +I0616 10:33:27.508913 9857 solver.cpp:258] Train net output #1: loss_cls = 0.809924 (* 1 = 0.809924 loss) +I0616 10:33:27.508918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.308718 (* 1 = 0.308718 loss) +I0616 10:33:27.508921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.066415 (* 1 = 0.066415 loss) +I0616 10:33:27.508925 9857 solver.cpp:571] Iteration 49660, lr = 0.001 +I0616 10:33:39.206771 9857 solver.cpp:242] Iteration 49680, loss = 0.716749 +I0616 10:33:39.206812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.36521 (* 1 = 0.36521 loss) +I0616 10:33:39.206818 9857 solver.cpp:258] Train net output #1: loss_cls = 0.352135 (* 1 = 0.352135 loss) +I0616 10:33:39.206822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158592 (* 1 = 0.158592 loss) +I0616 10:33:39.206825 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.026445 (* 1 = 0.026445 loss) +I0616 10:33:39.206830 9857 solver.cpp:571] Iteration 49680, lr = 0.001 +I0616 10:33:50.791188 9857 solver.cpp:242] Iteration 49700, loss = 0.322827 +I0616 10:33:50.791229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128975 (* 1 = 0.128975 loss) +I0616 10:33:50.791234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167783 (* 1 = 0.167783 loss) +I0616 10:33:50.791239 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0685913 (* 1 = 0.0685913 loss) +I0616 10:33:50.791242 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00995334 (* 1 = 0.00995334 loss) +I0616 10:33:50.791246 9857 solver.cpp:571] Iteration 49700, lr = 0.001 +I0616 10:34:02.572899 9857 solver.cpp:242] Iteration 49720, loss = 0.279609 +I0616 10:34:02.572942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0712659 (* 1 = 0.0712659 loss) +I0616 10:34:02.572947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0898391 (* 1 = 0.0898391 loss) +I0616 10:34:02.572952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0101538 (* 1 = 0.0101538 loss) +I0616 10:34:02.572954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127467 (* 1 = 0.0127467 loss) +I0616 10:34:02.572958 9857 solver.cpp:571] Iteration 49720, lr = 0.001 +I0616 10:34:14.020547 9857 solver.cpp:242] Iteration 49740, loss = 0.247792 +I0616 10:34:14.020589 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0630624 (* 1 = 0.0630624 loss) +I0616 10:34:14.020596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0994419 (* 1 = 0.0994419 loss) +I0616 10:34:14.020599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0684674 (* 1 = 0.0684674 loss) +I0616 10:34:14.020602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337198 (* 1 = 0.0337198 loss) +I0616 10:34:14.020606 9857 solver.cpp:571] Iteration 49740, lr = 0.001 +I0616 10:34:25.534898 9857 solver.cpp:242] Iteration 49760, loss = 0.519226 +I0616 10:34:25.534940 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150301 (* 1 = 0.150301 loss) +I0616 10:34:25.534945 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344023 (* 1 = 0.344023 loss) +I0616 10:34:25.534950 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190191 (* 1 = 0.190191 loss) +I0616 10:34:25.534953 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.024324 (* 1 = 0.024324 loss) +I0616 10:34:25.534957 9857 solver.cpp:571] Iteration 49760, lr = 0.001 +I0616 10:34:37.279325 9857 solver.cpp:242] Iteration 49780, loss = 1.97241 +I0616 10:34:37.279367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261804 (* 1 = 0.261804 loss) +I0616 10:34:37.279373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.238296 (* 1 = 0.238296 loss) +I0616 10:34:37.279377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.508109 (* 1 = 0.508109 loss) +I0616 10:34:37.279381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0510522 (* 1 = 0.0510522 loss) +I0616 10:34:37.279386 9857 solver.cpp:571] Iteration 49780, lr = 0.001 +speed: 0.613s / iter +I0616 10:34:48.819458 9857 solver.cpp:242] Iteration 49800, loss = 0.998674 +I0616 10:34:48.819499 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302355 (* 1 = 0.302355 loss) +I0616 10:34:48.819504 9857 solver.cpp:258] Train net output #1: loss_cls = 0.417631 (* 1 = 0.417631 loss) +I0616 10:34:48.819509 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149411 (* 1 = 0.149411 loss) +I0616 10:34:48.819512 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0545232 (* 1 = 0.0545232 loss) +I0616 10:34:48.819516 9857 solver.cpp:571] Iteration 49800, lr = 0.001 +I0616 10:35:00.457412 9857 solver.cpp:242] Iteration 49820, loss = 0.554328 +I0616 10:35:00.457453 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256577 (* 1 = 0.256577 loss) +I0616 10:35:00.457458 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317404 (* 1 = 0.317404 loss) +I0616 10:35:00.457463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102088 (* 1 = 0.102088 loss) +I0616 10:35:00.457468 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0383504 (* 1 = 0.0383504 loss) +I0616 10:35:00.457471 9857 solver.cpp:571] Iteration 49820, lr = 0.001 +I0616 10:35:12.133996 9857 solver.cpp:242] Iteration 49840, loss = 0.786263 +I0616 10:35:12.134039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104254 (* 1 = 0.104254 loss) +I0616 10:35:12.134044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250481 (* 1 = 0.250481 loss) +I0616 10:35:12.134049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0474234 (* 1 = 0.0474234 loss) +I0616 10:35:12.134052 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0239409 (* 1 = 0.0239409 loss) +I0616 10:35:12.134057 9857 solver.cpp:571] Iteration 49840, lr = 0.001 +I0616 10:35:23.852252 9857 solver.cpp:242] Iteration 49860, loss = 0.719786 +I0616 10:35:23.852294 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299331 (* 1 = 0.299331 loss) +I0616 10:35:23.852300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.474838 (* 1 = 0.474838 loss) +I0616 10:35:23.852304 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.177774 (* 1 = 0.177774 loss) +I0616 10:35:23.852308 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.025209 (* 1 = 0.025209 loss) +I0616 10:35:23.852311 9857 solver.cpp:571] Iteration 49860, lr = 0.001 +I0616 10:35:35.545979 9857 solver.cpp:242] Iteration 49880, loss = 0.295512 +I0616 10:35:35.546021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0646787 (* 1 = 0.0646787 loss) +I0616 10:35:35.546026 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186384 (* 1 = 0.186384 loss) +I0616 10:35:35.546030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0351618 (* 1 = 0.0351618 loss) +I0616 10:35:35.546033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179348 (* 1 = 0.0179348 loss) +I0616 10:35:35.546037 9857 solver.cpp:571] Iteration 49880, lr = 0.001 +I0616 10:35:47.213721 9857 solver.cpp:242] Iteration 49900, loss = 1.07459 +I0616 10:35:47.213762 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257217 (* 1 = 0.257217 loss) +I0616 10:35:47.213767 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321565 (* 1 = 0.321565 loss) +I0616 10:35:47.213770 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154173 (* 1 = 0.154173 loss) +I0616 10:35:47.213774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0584924 (* 1 = 0.0584924 loss) +I0616 10:35:47.213778 9857 solver.cpp:571] Iteration 49900, lr = 0.001 +I0616 10:35:58.781735 9857 solver.cpp:242] Iteration 49920, loss = 0.78125 +I0616 10:35:58.781779 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.243932 (* 1 = 0.243932 loss) +I0616 10:35:58.781785 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375214 (* 1 = 0.375214 loss) +I0616 10:35:58.781788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108208 (* 1 = 0.108208 loss) +I0616 10:35:58.781792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0241017 (* 1 = 0.0241017 loss) +I0616 10:35:58.781796 9857 solver.cpp:571] Iteration 49920, lr = 0.001 +I0616 10:36:10.331224 9857 solver.cpp:242] Iteration 49940, loss = 0.42309 +I0616 10:36:10.331265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197023 (* 1 = 0.197023 loss) +I0616 10:36:10.331271 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171581 (* 1 = 0.171581 loss) +I0616 10:36:10.331275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0509544 (* 1 = 0.0509544 loss) +I0616 10:36:10.331279 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0633571 (* 1 = 0.0633571 loss) +I0616 10:36:10.331282 9857 solver.cpp:571] Iteration 49940, lr = 0.001 +I0616 10:36:21.660677 9857 solver.cpp:242] Iteration 49960, loss = 0.813317 +I0616 10:36:21.660719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120914 (* 1 = 0.120914 loss) +I0616 10:36:21.660724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222108 (* 1 = 0.222108 loss) +I0616 10:36:21.660729 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0138606 (* 1 = 0.0138606 loss) +I0616 10:36:21.660732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205163 (* 1 = 0.0205163 loss) +I0616 10:36:21.660737 9857 solver.cpp:571] Iteration 49960, lr = 0.001 +I0616 10:36:33.465029 9857 solver.cpp:242] Iteration 49980, loss = 0.752613 +I0616 10:36:33.465056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351995 (* 1 = 0.351995 loss) +I0616 10:36:33.465062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.541706 (* 1 = 0.541706 loss) +I0616 10:36:33.465067 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0515165 (* 1 = 0.0515165 loss) +I0616 10:36:33.465071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165596 (* 1 = 0.0165596 loss) +I0616 10:36:33.465075 9857 solver.cpp:571] Iteration 49980, lr = 0.001 +speed: 0.613s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_50000.caffemodel +I0616 10:36:47.132661 9857 solver.cpp:242] Iteration 50000, loss = 0.809124 +I0616 10:36:47.132709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30969 (* 1 = 0.30969 loss) +I0616 10:36:47.132717 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303346 (* 1 = 0.303346 loss) +I0616 10:36:47.132738 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115861 (* 1 = 0.115861 loss) +I0616 10:36:47.132745 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714875 (* 1 = 0.0714875 loss) +I0616 10:36:47.132751 9857 solver.cpp:571] Iteration 50000, lr = 0.0001 +I0616 10:36:58.400987 9857 solver.cpp:242] Iteration 50020, loss = 1.72578 +I0616 10:36:58.401029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267173 (* 1 = 0.267173 loss) +I0616 10:36:58.401036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280706 (* 1 = 0.280706 loss) +I0616 10:36:58.401039 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.182667 (* 1 = 0.182667 loss) +I0616 10:36:58.401042 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0713534 (* 1 = 0.0713534 loss) +I0616 10:36:58.401046 9857 solver.cpp:571] Iteration 50020, lr = 0.0001 +I0616 10:37:09.830502 9857 solver.cpp:242] Iteration 50040, loss = 1.26796 +I0616 10:37:09.830543 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287877 (* 1 = 0.287877 loss) +I0616 10:37:09.830548 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559332 (* 1 = 0.559332 loss) +I0616 10:37:09.830551 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.244756 (* 1 = 0.244756 loss) +I0616 10:37:09.830555 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.170593 (* 1 = 0.170593 loss) +I0616 10:37:09.830559 9857 solver.cpp:571] Iteration 50040, lr = 0.0001 +I0616 10:37:21.486098 9857 solver.cpp:242] Iteration 50060, loss = 0.681614 +I0616 10:37:21.486140 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366736 (* 1 = 0.366736 loss) +I0616 10:37:21.486145 9857 solver.cpp:258] Train net output #1: loss_cls = 0.604396 (* 1 = 0.604396 loss) +I0616 10:37:21.486150 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136084 (* 1 = 0.136084 loss) +I0616 10:37:21.486153 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.059979 (* 1 = 0.059979 loss) +I0616 10:37:21.486156 9857 solver.cpp:571] Iteration 50060, lr = 0.0001 +I0616 10:37:32.991972 9857 solver.cpp:242] Iteration 50080, loss = 0.612986 +I0616 10:37:32.992013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306872 (* 1 = 0.306872 loss) +I0616 10:37:32.992018 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24254 (* 1 = 0.24254 loss) +I0616 10:37:32.992022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0631478 (* 1 = 0.0631478 loss) +I0616 10:37:32.992027 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030032 (* 1 = 0.030032 loss) +I0616 10:37:32.992030 9857 solver.cpp:571] Iteration 50080, lr = 0.0001 +I0616 10:37:44.278830 9857 solver.cpp:242] Iteration 50100, loss = 0.719141 +I0616 10:37:44.278870 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159997 (* 1 = 0.159997 loss) +I0616 10:37:44.278875 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218132 (* 1 = 0.218132 loss) +I0616 10:37:44.278880 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0475563 (* 1 = 0.0475563 loss) +I0616 10:37:44.278883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010505 (* 1 = 0.010505 loss) +I0616 10:37:44.278887 9857 solver.cpp:571] Iteration 50100, lr = 0.0001 +I0616 10:37:55.745004 9857 solver.cpp:242] Iteration 50120, loss = 0.500033 +I0616 10:37:55.745046 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111518 (* 1 = 0.111518 loss) +I0616 10:37:55.745052 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203329 (* 1 = 0.203329 loss) +I0616 10:37:55.745056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0594999 (* 1 = 0.0594999 loss) +I0616 10:37:55.745060 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253223 (* 1 = 0.0253223 loss) +I0616 10:37:55.745066 9857 solver.cpp:571] Iteration 50120, lr = 0.0001 +I0616 10:38:07.143718 9857 solver.cpp:242] Iteration 50140, loss = 1.2501 +I0616 10:38:07.143759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305245 (* 1 = 0.305245 loss) +I0616 10:38:07.143764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.773533 (* 1 = 0.773533 loss) +I0616 10:38:07.143769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.344096 (* 1 = 0.344096 loss) +I0616 10:38:07.143772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.628325 (* 1 = 0.628325 loss) +I0616 10:38:07.143776 9857 solver.cpp:571] Iteration 50140, lr = 0.0001 +I0616 10:38:18.671015 9857 solver.cpp:242] Iteration 50160, loss = 0.447738 +I0616 10:38:18.671056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167479 (* 1 = 0.167479 loss) +I0616 10:38:18.671061 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0933944 (* 1 = 0.0933944 loss) +I0616 10:38:18.671066 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249522 (* 1 = 0.249522 loss) +I0616 10:38:18.671068 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235178 (* 1 = 0.0235178 loss) +I0616 10:38:18.671072 9857 solver.cpp:571] Iteration 50160, lr = 0.0001 +I0616 10:38:30.309870 9857 solver.cpp:242] Iteration 50180, loss = 0.649514 +I0616 10:38:30.309912 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.06381 (* 1 = 0.06381 loss) +I0616 10:38:30.309917 9857 solver.cpp:258] Train net output #1: loss_cls = 0.091105 (* 1 = 0.091105 loss) +I0616 10:38:30.309921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0486028 (* 1 = 0.0486028 loss) +I0616 10:38:30.309926 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149133 (* 1 = 0.0149133 loss) +I0616 10:38:30.309929 9857 solver.cpp:571] Iteration 50180, lr = 0.0001 +speed: 0.613s / iter +I0616 10:38:41.637765 9857 solver.cpp:242] Iteration 50200, loss = 0.697306 +I0616 10:38:41.637806 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10948 (* 1 = 0.10948 loss) +I0616 10:38:41.637812 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253015 (* 1 = 0.253015 loss) +I0616 10:38:41.637816 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0876799 (* 1 = 0.0876799 loss) +I0616 10:38:41.637820 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139783 (* 1 = 0.0139783 loss) +I0616 10:38:41.637823 9857 solver.cpp:571] Iteration 50200, lr = 0.0001 +I0616 10:38:53.014158 9857 solver.cpp:242] Iteration 50220, loss = 0.358451 +I0616 10:38:53.014199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0736215 (* 1 = 0.0736215 loss) +I0616 10:38:53.014204 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160216 (* 1 = 0.160216 loss) +I0616 10:38:53.014209 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.061732 (* 1 = 0.061732 loss) +I0616 10:38:53.014212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0403586 (* 1 = 0.0403586 loss) +I0616 10:38:53.014216 9857 solver.cpp:571] Iteration 50220, lr = 0.0001 +I0616 10:39:04.721089 9857 solver.cpp:242] Iteration 50240, loss = 0.786951 +I0616 10:39:04.721129 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341931 (* 1 = 0.341931 loss) +I0616 10:39:04.721134 9857 solver.cpp:258] Train net output #1: loss_cls = 0.30062 (* 1 = 0.30062 loss) +I0616 10:39:04.721138 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155259 (* 1 = 0.155259 loss) +I0616 10:39:04.721143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175674 (* 1 = 0.0175674 loss) +I0616 10:39:04.721146 9857 solver.cpp:571] Iteration 50240, lr = 0.0001 +I0616 10:39:16.357909 9857 solver.cpp:242] Iteration 50260, loss = 0.344387 +I0616 10:39:16.357951 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103065 (* 1 = 0.103065 loss) +I0616 10:39:16.357957 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164916 (* 1 = 0.164916 loss) +I0616 10:39:16.357961 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537787 (* 1 = 0.0537787 loss) +I0616 10:39:16.357964 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00491519 (* 1 = 0.00491519 loss) +I0616 10:39:16.357969 9857 solver.cpp:571] Iteration 50260, lr = 0.0001 +I0616 10:39:27.697968 9857 solver.cpp:242] Iteration 50280, loss = 0.667345 +I0616 10:39:27.698009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18067 (* 1 = 0.18067 loss) +I0616 10:39:27.698014 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178994 (* 1 = 0.178994 loss) +I0616 10:39:27.698019 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0260071 (* 1 = 0.0260071 loss) +I0616 10:39:27.698022 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028969 (* 1 = 0.028969 loss) +I0616 10:39:27.698026 9857 solver.cpp:571] Iteration 50280, lr = 0.0001 +I0616 10:39:39.235697 9857 solver.cpp:242] Iteration 50300, loss = 0.32767 +I0616 10:39:39.235738 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0829184 (* 1 = 0.0829184 loss) +I0616 10:39:39.235743 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149657 (* 1 = 0.149657 loss) +I0616 10:39:39.235748 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.048939 (* 1 = 0.048939 loss) +I0616 10:39:39.235751 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00965374 (* 1 = 0.00965374 loss) +I0616 10:39:39.235755 9857 solver.cpp:571] Iteration 50300, lr = 0.0001 +I0616 10:39:50.749523 9857 solver.cpp:242] Iteration 50320, loss = 0.347973 +I0616 10:39:50.749563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154337 (* 1 = 0.154337 loss) +I0616 10:39:50.749569 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0987684 (* 1 = 0.0987684 loss) +I0616 10:39:50.749573 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0376023 (* 1 = 0.0376023 loss) +I0616 10:39:50.749577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167714 (* 1 = 0.0167714 loss) +I0616 10:39:50.749580 9857 solver.cpp:571] Iteration 50320, lr = 0.0001 +I0616 10:40:02.486114 9857 solver.cpp:242] Iteration 50340, loss = 0.594528 +I0616 10:40:02.486155 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.325582 (* 1 = 0.325582 loss) +I0616 10:40:02.486160 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498127 (* 1 = 0.498127 loss) +I0616 10:40:02.486165 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0435902 (* 1 = 0.0435902 loss) +I0616 10:40:02.486168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00846489 (* 1 = 0.00846489 loss) +I0616 10:40:02.486171 9857 solver.cpp:571] Iteration 50340, lr = 0.0001 +I0616 10:40:14.001611 9857 solver.cpp:242] Iteration 50360, loss = 0.506711 +I0616 10:40:14.001654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312877 (* 1 = 0.312877 loss) +I0616 10:40:14.001659 9857 solver.cpp:258] Train net output #1: loss_cls = 0.425576 (* 1 = 0.425576 loss) +I0616 10:40:14.001663 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0701835 (* 1 = 0.0701835 loss) +I0616 10:40:14.001667 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235685 (* 1 = 0.0235685 loss) +I0616 10:40:14.001670 9857 solver.cpp:571] Iteration 50360, lr = 0.0001 +I0616 10:40:25.647188 9857 solver.cpp:242] Iteration 50380, loss = 0.40874 +I0616 10:40:25.647230 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175601 (* 1 = 0.175601 loss) +I0616 10:40:25.647236 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302841 (* 1 = 0.302841 loss) +I0616 10:40:25.647240 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0231084 (* 1 = 0.0231084 loss) +I0616 10:40:25.647244 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0354753 (* 1 = 0.0354753 loss) +I0616 10:40:25.647248 9857 solver.cpp:571] Iteration 50380, lr = 0.0001 +speed: 0.613s / iter +I0616 10:40:37.317765 9857 solver.cpp:242] Iteration 50400, loss = 0.717376 +I0616 10:40:37.317808 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0776383 (* 1 = 0.0776383 loss) +I0616 10:40:37.317814 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265853 (* 1 = 0.265853 loss) +I0616 10:40:37.317818 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.198985 (* 1 = 0.198985 loss) +I0616 10:40:37.317822 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00457146 (* 1 = 0.00457146 loss) +I0616 10:40:37.317826 9857 solver.cpp:571] Iteration 50400, lr = 0.0001 +I0616 10:40:48.903760 9857 solver.cpp:242] Iteration 50420, loss = 0.377036 +I0616 10:40:48.903802 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258888 (* 1 = 0.258888 loss) +I0616 10:40:48.903807 9857 solver.cpp:258] Train net output #1: loss_cls = 0.193772 (* 1 = 0.193772 loss) +I0616 10:40:48.903811 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0241295 (* 1 = 0.0241295 loss) +I0616 10:40:48.903815 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00357048 (* 1 = 0.00357048 loss) +I0616 10:40:48.903820 9857 solver.cpp:571] Iteration 50420, lr = 0.0001 +I0616 10:41:00.583129 9857 solver.cpp:242] Iteration 50440, loss = 0.354899 +I0616 10:41:00.583171 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0736953 (* 1 = 0.0736953 loss) +I0616 10:41:00.583178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145763 (* 1 = 0.145763 loss) +I0616 10:41:00.583181 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0314129 (* 1 = 0.0314129 loss) +I0616 10:41:00.583184 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112059 (* 1 = 0.0112059 loss) +I0616 10:41:00.583189 9857 solver.cpp:571] Iteration 50440, lr = 0.0001 +I0616 10:41:12.176394 9857 solver.cpp:242] Iteration 50460, loss = 0.475697 +I0616 10:41:12.176439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.08281 (* 1 = 0.08281 loss) +I0616 10:41:12.176445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20311 (* 1 = 0.20311 loss) +I0616 10:41:12.176448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0751643 (* 1 = 0.0751643 loss) +I0616 10:41:12.176452 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122949 (* 1 = 0.0122949 loss) +I0616 10:41:12.176455 9857 solver.cpp:571] Iteration 50460, lr = 0.0001 +I0616 10:41:23.867660 9857 solver.cpp:242] Iteration 50480, loss = 0.245241 +I0616 10:41:23.867702 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0819114 (* 1 = 0.0819114 loss) +I0616 10:41:23.867707 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118267 (* 1 = 0.118267 loss) +I0616 10:41:23.867710 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0398339 (* 1 = 0.0398339 loss) +I0616 10:41:23.867714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00664654 (* 1 = 0.00664654 loss) +I0616 10:41:23.867717 9857 solver.cpp:571] Iteration 50480, lr = 0.0001 +I0616 10:41:35.422025 9857 solver.cpp:242] Iteration 50500, loss = 1.01139 +I0616 10:41:35.422067 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.412321 (* 1 = 0.412321 loss) +I0616 10:41:35.422073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.633275 (* 1 = 0.633275 loss) +I0616 10:41:35.422077 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.341354 (* 1 = 0.341354 loss) +I0616 10:41:35.422080 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0799918 (* 1 = 0.0799918 loss) +I0616 10:41:35.422085 9857 solver.cpp:571] Iteration 50500, lr = 0.0001 +I0616 10:41:47.058415 9857 solver.cpp:242] Iteration 50520, loss = 0.711164 +I0616 10:41:47.058457 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170665 (* 1 = 0.170665 loss) +I0616 10:41:47.058462 9857 solver.cpp:258] Train net output #1: loss_cls = 0.529709 (* 1 = 0.529709 loss) +I0616 10:41:47.058466 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.273999 (* 1 = 0.273999 loss) +I0616 10:41:47.058470 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.06031 (* 1 = 0.06031 loss) +I0616 10:41:47.058475 9857 solver.cpp:571] Iteration 50520, lr = 0.0001 +I0616 10:41:58.512825 9857 solver.cpp:242] Iteration 50540, loss = 0.944972 +I0616 10:41:58.512867 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.403158 (* 1 = 0.403158 loss) +I0616 10:41:58.512872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.424733 (* 1 = 0.424733 loss) +I0616 10:41:58.512876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.329009 (* 1 = 0.329009 loss) +I0616 10:41:58.512881 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0415227 (* 1 = 0.0415227 loss) +I0616 10:41:58.512884 9857 solver.cpp:571] Iteration 50540, lr = 0.0001 +I0616 10:42:10.045125 9857 solver.cpp:242] Iteration 50560, loss = 0.362061 +I0616 10:42:10.045167 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127574 (* 1 = 0.127574 loss) +I0616 10:42:10.045172 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164121 (* 1 = 0.164121 loss) +I0616 10:42:10.045176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0153908 (* 1 = 0.0153908 loss) +I0616 10:42:10.045181 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0061781 (* 1 = 0.0061781 loss) +I0616 10:42:10.045184 9857 solver.cpp:571] Iteration 50560, lr = 0.0001 +I0616 10:42:21.585731 9857 solver.cpp:242] Iteration 50580, loss = 0.76556 +I0616 10:42:21.585772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0988403 (* 1 = 0.0988403 loss) +I0616 10:42:21.585778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165321 (* 1 = 0.165321 loss) +I0616 10:42:21.585783 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104308 (* 1 = 0.104308 loss) +I0616 10:42:21.585786 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198843 (* 1 = 0.0198843 loss) +I0616 10:42:21.585789 9857 solver.cpp:571] Iteration 50580, lr = 0.0001 +speed: 0.612s / iter +I0616 10:42:32.977506 9857 solver.cpp:242] Iteration 50600, loss = 0.601477 +I0616 10:42:32.977548 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.404166 (* 1 = 0.404166 loss) +I0616 10:42:32.977553 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309705 (* 1 = 0.309705 loss) +I0616 10:42:32.977557 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0586471 (* 1 = 0.0586471 loss) +I0616 10:42:32.977561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0329379 (* 1 = 0.0329379 loss) +I0616 10:42:32.977566 9857 solver.cpp:571] Iteration 50600, lr = 0.0001 +I0616 10:42:44.551704 9857 solver.cpp:242] Iteration 50620, loss = 0.36653 +I0616 10:42:44.551745 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118045 (* 1 = 0.118045 loss) +I0616 10:42:44.551751 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127963 (* 1 = 0.127963 loss) +I0616 10:42:44.551755 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0995928 (* 1 = 0.0995928 loss) +I0616 10:42:44.551759 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.041351 (* 1 = 0.041351 loss) +I0616 10:42:44.551765 9857 solver.cpp:571] Iteration 50620, lr = 0.0001 +I0616 10:42:56.212792 9857 solver.cpp:242] Iteration 50640, loss = 0.46598 +I0616 10:42:56.212834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121095 (* 1 = 0.121095 loss) +I0616 10:42:56.212839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264917 (* 1 = 0.264917 loss) +I0616 10:42:56.212843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0480112 (* 1 = 0.0480112 loss) +I0616 10:42:56.212847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0451735 (* 1 = 0.0451735 loss) +I0616 10:42:56.212852 9857 solver.cpp:571] Iteration 50640, lr = 0.0001 +I0616 10:43:07.700810 9857 solver.cpp:242] Iteration 50660, loss = 0.531607 +I0616 10:43:07.700851 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0714554 (* 1 = 0.0714554 loss) +I0616 10:43:07.700857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145186 (* 1 = 0.145186 loss) +I0616 10:43:07.700861 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0167538 (* 1 = 0.0167538 loss) +I0616 10:43:07.700865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0211612 (* 1 = 0.0211612 loss) +I0616 10:43:07.700868 9857 solver.cpp:571] Iteration 50660, lr = 0.0001 +I0616 10:43:19.241269 9857 solver.cpp:242] Iteration 50680, loss = 0.541409 +I0616 10:43:19.241312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14638 (* 1 = 0.14638 loss) +I0616 10:43:19.241317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209882 (* 1 = 0.209882 loss) +I0616 10:43:19.241320 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0194615 (* 1 = 0.0194615 loss) +I0616 10:43:19.241324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00402733 (* 1 = 0.00402733 loss) +I0616 10:43:19.241328 9857 solver.cpp:571] Iteration 50680, lr = 0.0001 +I0616 10:43:30.788628 9857 solver.cpp:242] Iteration 50700, loss = 0.49047 +I0616 10:43:30.788671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166569 (* 1 = 0.166569 loss) +I0616 10:43:30.788677 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185053 (* 1 = 0.185053 loss) +I0616 10:43:30.788681 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155082 (* 1 = 0.0155082 loss) +I0616 10:43:30.788686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00805846 (* 1 = 0.00805846 loss) +I0616 10:43:30.788689 9857 solver.cpp:571] Iteration 50700, lr = 0.0001 +I0616 10:43:42.355666 9857 solver.cpp:242] Iteration 50720, loss = 0.344908 +I0616 10:43:42.355707 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.069543 (* 1 = 0.069543 loss) +I0616 10:43:42.355712 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0950766 (* 1 = 0.0950766 loss) +I0616 10:43:42.355716 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0225932 (* 1 = 0.0225932 loss) +I0616 10:43:42.355720 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00619411 (* 1 = 0.00619411 loss) +I0616 10:43:42.355725 9857 solver.cpp:571] Iteration 50720, lr = 0.0001 +I0616 10:43:54.027006 9857 solver.cpp:242] Iteration 50740, loss = 0.513272 +I0616 10:43:54.027050 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19347 (* 1 = 0.19347 loss) +I0616 10:43:54.027055 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125765 (* 1 = 0.125765 loss) +I0616 10:43:54.027060 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0365271 (* 1 = 0.0365271 loss) +I0616 10:43:54.027063 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00375706 (* 1 = 0.00375706 loss) +I0616 10:43:54.027066 9857 solver.cpp:571] Iteration 50740, lr = 0.0001 +I0616 10:44:05.735118 9857 solver.cpp:242] Iteration 50760, loss = 0.538197 +I0616 10:44:05.735162 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153922 (* 1 = 0.153922 loss) +I0616 10:44:05.735167 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24694 (* 1 = 0.24694 loss) +I0616 10:44:05.735172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0444259 (* 1 = 0.0444259 loss) +I0616 10:44:05.735175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106651 (* 1 = 0.0106651 loss) +I0616 10:44:05.735179 9857 solver.cpp:571] Iteration 50760, lr = 0.0001 +I0616 10:44:17.360718 9857 solver.cpp:242] Iteration 50780, loss = 0.872875 +I0616 10:44:17.360759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357086 (* 1 = 0.357086 loss) +I0616 10:44:17.360765 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260596 (* 1 = 0.260596 loss) +I0616 10:44:17.360769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102131 (* 1 = 0.102131 loss) +I0616 10:44:17.360774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108169 (* 1 = 0.0108169 loss) +I0616 10:44:17.360780 9857 solver.cpp:571] Iteration 50780, lr = 0.0001 +speed: 0.612s / iter +I0616 10:44:28.804034 9857 solver.cpp:242] Iteration 50800, loss = 0.566414 +I0616 10:44:28.804077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288056 (* 1 = 0.288056 loss) +I0616 10:44:28.804083 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418948 (* 1 = 0.418948 loss) +I0616 10:44:28.804087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0233739 (* 1 = 0.0233739 loss) +I0616 10:44:28.804091 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0297496 (* 1 = 0.0297496 loss) +I0616 10:44:28.804095 9857 solver.cpp:571] Iteration 50800, lr = 0.0001 +I0616 10:44:40.608419 9857 solver.cpp:242] Iteration 50820, loss = 1.15898 +I0616 10:44:40.608461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272952 (* 1 = 0.272952 loss) +I0616 10:44:40.608467 9857 solver.cpp:258] Train net output #1: loss_cls = 0.47599 (* 1 = 0.47599 loss) +I0616 10:44:40.608471 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.564164 (* 1 = 0.564164 loss) +I0616 10:44:40.608474 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0965747 (* 1 = 0.0965747 loss) +I0616 10:44:40.608479 9857 solver.cpp:571] Iteration 50820, lr = 0.0001 +I0616 10:44:52.395527 9857 solver.cpp:242] Iteration 50840, loss = 0.709184 +I0616 10:44:52.395570 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138265 (* 1 = 0.138265 loss) +I0616 10:44:52.395576 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126924 (* 1 = 0.126924 loss) +I0616 10:44:52.395579 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0626808 (* 1 = 0.0626808 loss) +I0616 10:44:52.395582 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0545307 (* 1 = 0.0545307 loss) +I0616 10:44:52.395586 9857 solver.cpp:571] Iteration 50840, lr = 0.0001 +I0616 10:45:03.869011 9857 solver.cpp:242] Iteration 50860, loss = 0.354032 +I0616 10:45:03.869052 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177156 (* 1 = 0.177156 loss) +I0616 10:45:03.869058 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176774 (* 1 = 0.176774 loss) +I0616 10:45:03.869063 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0384943 (* 1 = 0.0384943 loss) +I0616 10:45:03.869066 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00309853 (* 1 = 0.00309853 loss) +I0616 10:45:03.869071 9857 solver.cpp:571] Iteration 50860, lr = 0.0001 +I0616 10:45:15.354975 9857 solver.cpp:242] Iteration 50880, loss = 0.451797 +I0616 10:45:15.355016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32092 (* 1 = 0.32092 loss) +I0616 10:45:15.355021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252879 (* 1 = 0.252879 loss) +I0616 10:45:15.355026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0483774 (* 1 = 0.0483774 loss) +I0616 10:45:15.355031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0560801 (* 1 = 0.0560801 loss) +I0616 10:45:15.355033 9857 solver.cpp:571] Iteration 50880, lr = 0.0001 +I0616 10:45:26.856870 9857 solver.cpp:242] Iteration 50900, loss = 0.317373 +I0616 10:45:26.856914 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0768322 (* 1 = 0.0768322 loss) +I0616 10:45:26.856919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11325 (* 1 = 0.11325 loss) +I0616 10:45:26.856923 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00902391 (* 1 = 0.00902391 loss) +I0616 10:45:26.856926 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00679117 (* 1 = 0.00679117 loss) +I0616 10:45:26.856930 9857 solver.cpp:571] Iteration 50900, lr = 0.0001 +I0616 10:45:38.305038 9857 solver.cpp:242] Iteration 50920, loss = 0.433375 +I0616 10:45:38.305081 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334377 (* 1 = 0.334377 loss) +I0616 10:45:38.305088 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196498 (* 1 = 0.196498 loss) +I0616 10:45:38.305091 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0919673 (* 1 = 0.0919673 loss) +I0616 10:45:38.305095 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130403 (* 1 = 0.0130403 loss) +I0616 10:45:38.305099 9857 solver.cpp:571] Iteration 50920, lr = 0.0001 +I0616 10:45:49.793167 9857 solver.cpp:242] Iteration 50940, loss = 0.769498 +I0616 10:45:49.793210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137928 (* 1 = 0.137928 loss) +I0616 10:45:49.793215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257478 (* 1 = 0.257478 loss) +I0616 10:45:49.793218 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0433503 (* 1 = 0.0433503 loss) +I0616 10:45:49.793222 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306292 (* 1 = 0.0306292 loss) +I0616 10:45:49.793226 9857 solver.cpp:571] Iteration 50940, lr = 0.0001 +I0616 10:46:01.327554 9857 solver.cpp:242] Iteration 50960, loss = 1.01455 +I0616 10:46:01.327595 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350625 (* 1 = 0.350625 loss) +I0616 10:46:01.327600 9857 solver.cpp:258] Train net output #1: loss_cls = 0.519521 (* 1 = 0.519521 loss) +I0616 10:46:01.327605 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.315135 (* 1 = 0.315135 loss) +I0616 10:46:01.327610 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117793 (* 1 = 0.117793 loss) +I0616 10:46:01.327612 9857 solver.cpp:571] Iteration 50960, lr = 0.0001 +I0616 10:46:12.966954 9857 solver.cpp:242] Iteration 50980, loss = 1.00673 +I0616 10:46:12.966997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.462699 (* 1 = 0.462699 loss) +I0616 10:46:12.967002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.530461 (* 1 = 0.530461 loss) +I0616 10:46:12.967006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0959491 (* 1 = 0.0959491 loss) +I0616 10:46:12.967010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111195 (* 1 = 0.111195 loss) +I0616 10:46:12.967015 9857 solver.cpp:571] Iteration 50980, lr = 0.0001 +speed: 0.612s / iter +I0616 10:46:24.550233 9857 solver.cpp:242] Iteration 51000, loss = 0.619693 +I0616 10:46:24.550276 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168672 (* 1 = 0.168672 loss) +I0616 10:46:24.550281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175194 (* 1 = 0.175194 loss) +I0616 10:46:24.550284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0108161 (* 1 = 0.0108161 loss) +I0616 10:46:24.550288 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00680759 (* 1 = 0.00680759 loss) +I0616 10:46:24.550292 9857 solver.cpp:571] Iteration 51000, lr = 0.0001 +I0616 10:46:35.980988 9857 solver.cpp:242] Iteration 51020, loss = 0.754448 +I0616 10:46:35.981029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247429 (* 1 = 0.247429 loss) +I0616 10:46:35.981034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314675 (* 1 = 0.314675 loss) +I0616 10:46:35.981040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0587692 (* 1 = 0.0587692 loss) +I0616 10:46:35.981043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00913535 (* 1 = 0.00913535 loss) +I0616 10:46:35.981046 9857 solver.cpp:571] Iteration 51020, lr = 0.0001 +I0616 10:46:47.706863 9857 solver.cpp:242] Iteration 51040, loss = 0.560477 +I0616 10:46:47.706902 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265294 (* 1 = 0.265294 loss) +I0616 10:46:47.706907 9857 solver.cpp:258] Train net output #1: loss_cls = 0.367618 (* 1 = 0.367618 loss) +I0616 10:46:47.706912 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13236 (* 1 = 0.13236 loss) +I0616 10:46:47.706915 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.039346 (* 1 = 0.039346 loss) +I0616 10:46:47.706918 9857 solver.cpp:571] Iteration 51040, lr = 0.0001 +I0616 10:46:59.160456 9857 solver.cpp:242] Iteration 51060, loss = 0.276995 +I0616 10:46:59.160496 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0867446 (* 1 = 0.0867446 loss) +I0616 10:46:59.160502 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109661 (* 1 = 0.109661 loss) +I0616 10:46:59.160506 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0447715 (* 1 = 0.0447715 loss) +I0616 10:46:59.160511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215754 (* 1 = 0.0215754 loss) +I0616 10:46:59.160513 9857 solver.cpp:571] Iteration 51060, lr = 0.0001 +I0616 10:47:10.578145 9857 solver.cpp:242] Iteration 51080, loss = 0.476505 +I0616 10:47:10.578187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258945 (* 1 = 0.258945 loss) +I0616 10:47:10.578193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153284 (* 1 = 0.153284 loss) +I0616 10:47:10.578197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0458372 (* 1 = 0.0458372 loss) +I0616 10:47:10.578202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00924473 (* 1 = 0.00924473 loss) +I0616 10:47:10.578204 9857 solver.cpp:571] Iteration 51080, lr = 0.0001 +I0616 10:47:21.901115 9857 solver.cpp:242] Iteration 51100, loss = 0.852156 +I0616 10:47:21.901157 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150932 (* 1 = 0.150932 loss) +I0616 10:47:21.901162 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153438 (* 1 = 0.153438 loss) +I0616 10:47:21.901167 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.013378 (* 1 = 0.013378 loss) +I0616 10:47:21.901170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126774 (* 1 = 0.0126774 loss) +I0616 10:47:21.901175 9857 solver.cpp:571] Iteration 51100, lr = 0.0001 +I0616 10:47:33.410872 9857 solver.cpp:242] Iteration 51120, loss = 0.212199 +I0616 10:47:33.410912 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0993464 (* 1 = 0.0993464 loss) +I0616 10:47:33.410917 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111919 (* 1 = 0.111919 loss) +I0616 10:47:33.410922 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00830229 (* 1 = 0.00830229 loss) +I0616 10:47:33.410924 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0025982 (* 1 = 0.0025982 loss) +I0616 10:47:33.410928 9857 solver.cpp:571] Iteration 51120, lr = 0.0001 +I0616 10:47:44.852211 9857 solver.cpp:242] Iteration 51140, loss = 0.895447 +I0616 10:47:44.852254 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324636 (* 1 = 0.324636 loss) +I0616 10:47:44.852260 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514772 (* 1 = 0.514772 loss) +I0616 10:47:44.852264 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121459 (* 1 = 0.121459 loss) +I0616 10:47:44.852268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.043653 (* 1 = 0.043653 loss) +I0616 10:47:44.852273 9857 solver.cpp:571] Iteration 51140, lr = 0.0001 +I0616 10:47:56.519960 9857 solver.cpp:242] Iteration 51160, loss = 0.468875 +I0616 10:47:56.520004 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235847 (* 1 = 0.235847 loss) +I0616 10:47:56.520009 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214936 (* 1 = 0.214936 loss) +I0616 10:47:56.520012 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.074661 (* 1 = 0.074661 loss) +I0616 10:47:56.520016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0309863 (* 1 = 0.0309863 loss) +I0616 10:47:56.520022 9857 solver.cpp:571] Iteration 51160, lr = 0.0001 +I0616 10:48:08.226874 9857 solver.cpp:242] Iteration 51180, loss = 0.750443 +I0616 10:48:08.226917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347479 (* 1 = 0.347479 loss) +I0616 10:48:08.226922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.54378 (* 1 = 0.54378 loss) +I0616 10:48:08.226927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.280644 (* 1 = 0.280644 loss) +I0616 10:48:08.226930 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0956728 (* 1 = 0.0956728 loss) +I0616 10:48:08.226934 9857 solver.cpp:571] Iteration 51180, lr = 0.0001 +speed: 0.612s / iter +I0616 10:48:19.689445 9857 solver.cpp:242] Iteration 51200, loss = 0.390238 +I0616 10:48:19.689488 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0972828 (* 1 = 0.0972828 loss) +I0616 10:48:19.689493 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154063 (* 1 = 0.154063 loss) +I0616 10:48:19.689497 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0510436 (* 1 = 0.0510436 loss) +I0616 10:48:19.689502 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0502216 (* 1 = 0.0502216 loss) +I0616 10:48:19.689504 9857 solver.cpp:571] Iteration 51200, lr = 0.0001 +I0616 10:48:31.190546 9857 solver.cpp:242] Iteration 51220, loss = 1.18263 +I0616 10:48:31.190587 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.456493 (* 1 = 0.456493 loss) +I0616 10:48:31.190593 9857 solver.cpp:258] Train net output #1: loss_cls = 0.758279 (* 1 = 0.758279 loss) +I0616 10:48:31.190598 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158602 (* 1 = 0.158602 loss) +I0616 10:48:31.190600 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107661 (* 1 = 0.107661 loss) +I0616 10:48:31.190604 9857 solver.cpp:571] Iteration 51220, lr = 0.0001 +I0616 10:48:42.704447 9857 solver.cpp:242] Iteration 51240, loss = 0.717229 +I0616 10:48:42.704488 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275527 (* 1 = 0.275527 loss) +I0616 10:48:42.704494 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26512 (* 1 = 0.26512 loss) +I0616 10:48:42.704499 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114709 (* 1 = 0.114709 loss) +I0616 10:48:42.704501 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0823276 (* 1 = 0.0823276 loss) +I0616 10:48:42.704505 9857 solver.cpp:571] Iteration 51240, lr = 0.0001 +I0616 10:48:54.204362 9857 solver.cpp:242] Iteration 51260, loss = 0.379325 +I0616 10:48:54.204404 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0527324 (* 1 = 0.0527324 loss) +I0616 10:48:54.204411 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131744 (* 1 = 0.131744 loss) +I0616 10:48:54.204414 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00372715 (* 1 = 0.00372715 loss) +I0616 10:48:54.204418 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0312492 (* 1 = 0.0312492 loss) +I0616 10:48:54.204421 9857 solver.cpp:571] Iteration 51260, lr = 0.0001 +I0616 10:49:05.798291 9857 solver.cpp:242] Iteration 51280, loss = 0.502921 +I0616 10:49:05.798333 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286713 (* 1 = 0.286713 loss) +I0616 10:49:05.798339 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265294 (* 1 = 0.265294 loss) +I0616 10:49:05.798343 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0560518 (* 1 = 0.0560518 loss) +I0616 10:49:05.798347 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0375696 (* 1 = 0.0375696 loss) +I0616 10:49:05.798352 9857 solver.cpp:571] Iteration 51280, lr = 0.0001 +I0616 10:49:17.413692 9857 solver.cpp:242] Iteration 51300, loss = 0.534965 +I0616 10:49:17.413735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123117 (* 1 = 0.123117 loss) +I0616 10:49:17.413740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156253 (* 1 = 0.156253 loss) +I0616 10:49:17.413744 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163328 (* 1 = 0.0163328 loss) +I0616 10:49:17.413748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00785884 (* 1 = 0.00785884 loss) +I0616 10:49:17.413751 9857 solver.cpp:571] Iteration 51300, lr = 0.0001 +I0616 10:49:29.023181 9857 solver.cpp:242] Iteration 51320, loss = 0.559808 +I0616 10:49:29.023223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116595 (* 1 = 0.116595 loss) +I0616 10:49:29.023228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155988 (* 1 = 0.155988 loss) +I0616 10:49:29.023233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0655562 (* 1 = 0.0655562 loss) +I0616 10:49:29.023236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00788141 (* 1 = 0.00788141 loss) +I0616 10:49:29.023241 9857 solver.cpp:571] Iteration 51320, lr = 0.0001 +I0616 10:49:40.630853 9857 solver.cpp:242] Iteration 51340, loss = 0.771028 +I0616 10:49:40.630897 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222967 (* 1 = 0.222967 loss) +I0616 10:49:40.630902 9857 solver.cpp:258] Train net output #1: loss_cls = 0.454653 (* 1 = 0.454653 loss) +I0616 10:49:40.630905 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0776318 (* 1 = 0.0776318 loss) +I0616 10:49:40.630909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.057604 (* 1 = 0.057604 loss) +I0616 10:49:40.630915 9857 solver.cpp:571] Iteration 51340, lr = 0.0001 +I0616 10:49:52.311918 9857 solver.cpp:242] Iteration 51360, loss = 0.422152 +I0616 10:49:52.311959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0738379 (* 1 = 0.0738379 loss) +I0616 10:49:52.311964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13621 (* 1 = 0.13621 loss) +I0616 10:49:52.311969 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0539134 (* 1 = 0.0539134 loss) +I0616 10:49:52.311972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00509469 (* 1 = 0.00509469 loss) +I0616 10:49:52.311975 9857 solver.cpp:571] Iteration 51360, lr = 0.0001 +I0616 10:50:03.667074 9857 solver.cpp:242] Iteration 51380, loss = 1.1963 +I0616 10:50:03.667115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342108 (* 1 = 0.342108 loss) +I0616 10:50:03.667121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.853072 (* 1 = 0.853072 loss) +I0616 10:50:03.667125 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.238289 (* 1 = 0.238289 loss) +I0616 10:50:03.667129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0937435 (* 1 = 0.0937435 loss) +I0616 10:50:03.667132 9857 solver.cpp:571] Iteration 51380, lr = 0.0001 +speed: 0.612s / iter +I0616 10:50:15.213773 9857 solver.cpp:242] Iteration 51400, loss = 0.44656 +I0616 10:50:15.213814 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106482 (* 1 = 0.106482 loss) +I0616 10:50:15.213819 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214727 (* 1 = 0.214727 loss) +I0616 10:50:15.213824 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14769 (* 1 = 0.14769 loss) +I0616 10:50:15.213826 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0989638 (* 1 = 0.0989638 loss) +I0616 10:50:15.213830 9857 solver.cpp:571] Iteration 51400, lr = 0.0001 +I0616 10:50:26.731276 9857 solver.cpp:242] Iteration 51420, loss = 0.259354 +I0616 10:50:26.731318 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0821695 (* 1 = 0.0821695 loss) +I0616 10:50:26.731323 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104686 (* 1 = 0.104686 loss) +I0616 10:50:26.731328 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0953088 (* 1 = 0.0953088 loss) +I0616 10:50:26.731331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216816 (* 1 = 0.0216816 loss) +I0616 10:50:26.731335 9857 solver.cpp:571] Iteration 51420, lr = 0.0001 +I0616 10:50:38.346186 9857 solver.cpp:242] Iteration 51440, loss = 0.579058 +I0616 10:50:38.346227 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244108 (* 1 = 0.244108 loss) +I0616 10:50:38.346233 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162245 (* 1 = 0.162245 loss) +I0616 10:50:38.346237 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0429538 (* 1 = 0.0429538 loss) +I0616 10:50:38.346241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149765 (* 1 = 0.0149765 loss) +I0616 10:50:38.346245 9857 solver.cpp:571] Iteration 51440, lr = 0.0001 +I0616 10:50:49.735525 9857 solver.cpp:242] Iteration 51460, loss = 0.699045 +I0616 10:50:49.735568 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.441111 (* 1 = 0.441111 loss) +I0616 10:50:49.735572 9857 solver.cpp:258] Train net output #1: loss_cls = 0.648692 (* 1 = 0.648692 loss) +I0616 10:50:49.735577 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0855918 (* 1 = 0.0855918 loss) +I0616 10:50:49.735580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0200268 (* 1 = 0.0200268 loss) +I0616 10:50:49.735584 9857 solver.cpp:571] Iteration 51460, lr = 0.0001 +I0616 10:51:01.145512 9857 solver.cpp:242] Iteration 51480, loss = 0.303159 +I0616 10:51:01.145553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129104 (* 1 = 0.129104 loss) +I0616 10:51:01.145560 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123883 (* 1 = 0.123883 loss) +I0616 10:51:01.145563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0503977 (* 1 = 0.0503977 loss) +I0616 10:51:01.145566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174888 (* 1 = 0.0174888 loss) +I0616 10:51:01.145570 9857 solver.cpp:571] Iteration 51480, lr = 0.0001 +I0616 10:51:12.451788 9857 solver.cpp:242] Iteration 51500, loss = 0.858535 +I0616 10:51:12.451830 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324221 (* 1 = 0.324221 loss) +I0616 10:51:12.451835 9857 solver.cpp:258] Train net output #1: loss_cls = 0.407211 (* 1 = 0.407211 loss) +I0616 10:51:12.451839 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.300248 (* 1 = 0.300248 loss) +I0616 10:51:12.451843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.11605 (* 1 = 0.11605 loss) +I0616 10:51:12.451846 9857 solver.cpp:571] Iteration 51500, lr = 0.0001 +I0616 10:51:24.079572 9857 solver.cpp:242] Iteration 51520, loss = 0.961074 +I0616 10:51:24.079612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326736 (* 1 = 0.326736 loss) +I0616 10:51:24.079617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.575736 (* 1 = 0.575736 loss) +I0616 10:51:24.079622 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157741 (* 1 = 0.157741 loss) +I0616 10:51:24.079625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0171931 (* 1 = 0.0171931 loss) +I0616 10:51:24.079630 9857 solver.cpp:571] Iteration 51520, lr = 0.0001 +I0616 10:51:35.535091 9857 solver.cpp:242] Iteration 51540, loss = 0.909376 +I0616 10:51:35.535133 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22876 (* 1 = 0.22876 loss) +I0616 10:51:35.535140 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328366 (* 1 = 0.328366 loss) +I0616 10:51:35.535143 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224207 (* 1 = 0.224207 loss) +I0616 10:51:35.535146 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117554 (* 1 = 0.117554 loss) +I0616 10:51:35.535151 9857 solver.cpp:571] Iteration 51540, lr = 0.0001 +I0616 10:51:47.322492 9857 solver.cpp:242] Iteration 51560, loss = 0.669997 +I0616 10:51:47.322533 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161299 (* 1 = 0.161299 loss) +I0616 10:51:47.322538 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270108 (* 1 = 0.270108 loss) +I0616 10:51:47.322543 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0853995 (* 1 = 0.0853995 loss) +I0616 10:51:47.322546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0317377 (* 1 = 0.0317377 loss) +I0616 10:51:47.322551 9857 solver.cpp:571] Iteration 51560, lr = 0.0001 +I0616 10:51:59.007791 9857 solver.cpp:242] Iteration 51580, loss = 0.746636 +I0616 10:51:59.007833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210748 (* 1 = 0.210748 loss) +I0616 10:51:59.007838 9857 solver.cpp:258] Train net output #1: loss_cls = 0.695008 (* 1 = 0.695008 loss) +I0616 10:51:59.007843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110422 (* 1 = 0.110422 loss) +I0616 10:51:59.007846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0538685 (* 1 = 0.0538685 loss) +I0616 10:51:59.007850 9857 solver.cpp:571] Iteration 51580, lr = 0.0001 +speed: 0.612s / iter +I0616 10:52:10.887902 9857 solver.cpp:242] Iteration 51600, loss = 0.919418 +I0616 10:52:10.887943 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247753 (* 1 = 0.247753 loss) +I0616 10:52:10.887948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237251 (* 1 = 0.237251 loss) +I0616 10:52:10.887953 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0688744 (* 1 = 0.0688744 loss) +I0616 10:52:10.887956 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0750469 (* 1 = 0.0750469 loss) +I0616 10:52:10.887959 9857 solver.cpp:571] Iteration 51600, lr = 0.0001 +I0616 10:52:22.785380 9857 solver.cpp:242] Iteration 51620, loss = 0.645178 +I0616 10:52:22.785421 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346408 (* 1 = 0.346408 loss) +I0616 10:52:22.785426 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252428 (* 1 = 0.252428 loss) +I0616 10:52:22.785430 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0260091 (* 1 = 0.0260091 loss) +I0616 10:52:22.785434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00848752 (* 1 = 0.00848752 loss) +I0616 10:52:22.785439 9857 solver.cpp:571] Iteration 51620, lr = 0.0001 +I0616 10:52:34.244077 9857 solver.cpp:242] Iteration 51640, loss = 0.288158 +I0616 10:52:34.244119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0679362 (* 1 = 0.0679362 loss) +I0616 10:52:34.244125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150972 (* 1 = 0.150972 loss) +I0616 10:52:34.244129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0641626 (* 1 = 0.0641626 loss) +I0616 10:52:34.244132 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00224922 (* 1 = 0.00224922 loss) +I0616 10:52:34.244137 9857 solver.cpp:571] Iteration 51640, lr = 0.0001 +I0616 10:52:45.753334 9857 solver.cpp:242] Iteration 51660, loss = 0.316261 +I0616 10:52:45.753374 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0578624 (* 1 = 0.0578624 loss) +I0616 10:52:45.753381 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114417 (* 1 = 0.114417 loss) +I0616 10:52:45.753384 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0177193 (* 1 = 0.0177193 loss) +I0616 10:52:45.753387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00227741 (* 1 = 0.00227741 loss) +I0616 10:52:45.753391 9857 solver.cpp:571] Iteration 51660, lr = 0.0001 +I0616 10:52:57.111279 9857 solver.cpp:242] Iteration 51680, loss = 0.945357 +I0616 10:52:57.111320 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251899 (* 1 = 0.251899 loss) +I0616 10:52:57.111326 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384016 (* 1 = 0.384016 loss) +I0616 10:52:57.111330 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119771 (* 1 = 0.119771 loss) +I0616 10:52:57.111335 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156243 (* 1 = 0.156243 loss) +I0616 10:52:57.111338 9857 solver.cpp:571] Iteration 51680, lr = 0.0001 +I0616 10:53:08.940109 9857 solver.cpp:242] Iteration 51700, loss = 0.358188 +I0616 10:53:08.940150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159783 (* 1 = 0.159783 loss) +I0616 10:53:08.940155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174467 (* 1 = 0.174467 loss) +I0616 10:53:08.940160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0025082 (* 1 = 0.0025082 loss) +I0616 10:53:08.940163 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00291306 (* 1 = 0.00291306 loss) +I0616 10:53:08.940167 9857 solver.cpp:571] Iteration 51700, lr = 0.0001 +I0616 10:53:20.469348 9857 solver.cpp:242] Iteration 51720, loss = 0.404091 +I0616 10:53:20.469393 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0620171 (* 1 = 0.0620171 loss) +I0616 10:53:20.469398 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189997 (* 1 = 0.189997 loss) +I0616 10:53:20.469401 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228443 (* 1 = 0.0228443 loss) +I0616 10:53:20.469405 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113081 (* 1 = 0.0113081 loss) +I0616 10:53:20.469408 9857 solver.cpp:571] Iteration 51720, lr = 0.0001 +I0616 10:53:32.055577 9857 solver.cpp:242] Iteration 51740, loss = 0.631216 +I0616 10:53:32.055619 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305237 (* 1 = 0.305237 loss) +I0616 10:53:32.055624 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244862 (* 1 = 0.244862 loss) +I0616 10:53:32.055629 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0392091 (* 1 = 0.0392091 loss) +I0616 10:53:32.055632 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00933924 (* 1 = 0.00933924 loss) +I0616 10:53:32.055635 9857 solver.cpp:571] Iteration 51740, lr = 0.0001 +I0616 10:53:43.868697 9857 solver.cpp:242] Iteration 51760, loss = 0.386147 +I0616 10:53:43.868741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0972831 (* 1 = 0.0972831 loss) +I0616 10:53:43.868746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206244 (* 1 = 0.206244 loss) +I0616 10:53:43.868749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136944 (* 1 = 0.136944 loss) +I0616 10:53:43.868753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140436 (* 1 = 0.0140436 loss) +I0616 10:53:43.868757 9857 solver.cpp:571] Iteration 51760, lr = 0.0001 +I0616 10:53:55.388223 9857 solver.cpp:242] Iteration 51780, loss = 0.419277 +I0616 10:53:55.388265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0977325 (* 1 = 0.0977325 loss) +I0616 10:53:55.388272 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0847905 (* 1 = 0.0847905 loss) +I0616 10:53:55.388275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00605057 (* 1 = 0.00605057 loss) +I0616 10:53:55.388279 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00311087 (* 1 = 0.00311087 loss) +I0616 10:53:55.388283 9857 solver.cpp:571] Iteration 51780, lr = 0.0001 +speed: 0.612s / iter +I0616 10:54:06.787598 9857 solver.cpp:242] Iteration 51800, loss = 0.526359 +I0616 10:54:06.787641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218649 (* 1 = 0.218649 loss) +I0616 10:54:06.787645 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189263 (* 1 = 0.189263 loss) +I0616 10:54:06.787649 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0279803 (* 1 = 0.0279803 loss) +I0616 10:54:06.787653 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0057969 (* 1 = 0.0057969 loss) +I0616 10:54:06.787657 9857 solver.cpp:571] Iteration 51800, lr = 0.0001 +I0616 10:54:18.210880 9857 solver.cpp:242] Iteration 51820, loss = 0.307641 +I0616 10:54:18.210919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107896 (* 1 = 0.107896 loss) +I0616 10:54:18.210924 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180182 (* 1 = 0.180182 loss) +I0616 10:54:18.210929 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0813182 (* 1 = 0.0813182 loss) +I0616 10:54:18.210932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225235 (* 1 = 0.0225235 loss) +I0616 10:54:18.210937 9857 solver.cpp:571] Iteration 51820, lr = 0.0001 +I0616 10:54:29.756934 9857 solver.cpp:242] Iteration 51840, loss = 0.334661 +I0616 10:54:29.756976 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218242 (* 1 = 0.218242 loss) +I0616 10:54:29.756981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197396 (* 1 = 0.197396 loss) +I0616 10:54:29.756985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0210262 (* 1 = 0.0210262 loss) +I0616 10:54:29.756989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011058 (* 1 = 0.011058 loss) +I0616 10:54:29.756992 9857 solver.cpp:571] Iteration 51840, lr = 0.0001 +I0616 10:54:41.294308 9857 solver.cpp:242] Iteration 51860, loss = 0.615008 +I0616 10:54:41.294350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0637619 (* 1 = 0.0637619 loss) +I0616 10:54:41.294356 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108096 (* 1 = 0.108096 loss) +I0616 10:54:41.294360 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0315715 (* 1 = 0.0315715 loss) +I0616 10:54:41.294364 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00338232 (* 1 = 0.00338232 loss) +I0616 10:54:41.294368 9857 solver.cpp:571] Iteration 51860, lr = 0.0001 +I0616 10:54:53.121155 9857 solver.cpp:242] Iteration 51880, loss = 0.888667 +I0616 10:54:53.121196 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0983068 (* 1 = 0.0983068 loss) +I0616 10:54:53.121201 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138845 (* 1 = 0.138845 loss) +I0616 10:54:53.121206 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0161361 (* 1 = 0.0161361 loss) +I0616 10:54:53.121208 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00265098 (* 1 = 0.00265098 loss) +I0616 10:54:53.121212 9857 solver.cpp:571] Iteration 51880, lr = 0.0001 +I0616 10:55:04.683698 9857 solver.cpp:242] Iteration 51900, loss = 0.303662 +I0616 10:55:04.683739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121211 (* 1 = 0.121211 loss) +I0616 10:55:04.683745 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153637 (* 1 = 0.153637 loss) +I0616 10:55:04.683749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00704591 (* 1 = 0.00704591 loss) +I0616 10:55:04.683753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132895 (* 1 = 0.0132895 loss) +I0616 10:55:04.683756 9857 solver.cpp:571] Iteration 51900, lr = 0.0001 +I0616 10:55:16.018925 9857 solver.cpp:242] Iteration 51920, loss = 0.498004 +I0616 10:55:16.018968 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125879 (* 1 = 0.125879 loss) +I0616 10:55:16.018973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280078 (* 1 = 0.280078 loss) +I0616 10:55:16.018977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0918346 (* 1 = 0.0918346 loss) +I0616 10:55:16.018981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125792 (* 1 = 0.0125792 loss) +I0616 10:55:16.018985 9857 solver.cpp:571] Iteration 51920, lr = 0.0001 +I0616 10:55:27.599371 9857 solver.cpp:242] Iteration 51940, loss = 0.3629 +I0616 10:55:27.599411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106631 (* 1 = 0.106631 loss) +I0616 10:55:27.599416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131202 (* 1 = 0.131202 loss) +I0616 10:55:27.599421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0260646 (* 1 = 0.0260646 loss) +I0616 10:55:27.599424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00715631 (* 1 = 0.00715631 loss) +I0616 10:55:27.599428 9857 solver.cpp:571] Iteration 51940, lr = 0.0001 +I0616 10:55:39.132491 9857 solver.cpp:242] Iteration 51960, loss = 0.294824 +I0616 10:55:39.132534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126516 (* 1 = 0.126516 loss) +I0616 10:55:39.132540 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169334 (* 1 = 0.169334 loss) +I0616 10:55:39.132544 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0279804 (* 1 = 0.0279804 loss) +I0616 10:55:39.132549 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00640988 (* 1 = 0.00640988 loss) +I0616 10:55:39.132552 9857 solver.cpp:571] Iteration 51960, lr = 0.0001 +I0616 10:55:50.591430 9857 solver.cpp:242] Iteration 51980, loss = 0.577919 +I0616 10:55:50.591472 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3694 (* 1 = 0.3694 loss) +I0616 10:55:50.591478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172346 (* 1 = 0.172346 loss) +I0616 10:55:50.591482 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0267863 (* 1 = 0.0267863 loss) +I0616 10:55:50.591485 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149379 (* 1 = 0.0149379 loss) +I0616 10:55:50.591490 9857 solver.cpp:571] Iteration 51980, lr = 0.0001 +speed: 0.612s / iter +I0616 10:56:02.166827 9857 solver.cpp:242] Iteration 52000, loss = 0.841287 +I0616 10:56:02.166867 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359464 (* 1 = 0.359464 loss) +I0616 10:56:02.166872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.502866 (* 1 = 0.502866 loss) +I0616 10:56:02.166877 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0714791 (* 1 = 0.0714791 loss) +I0616 10:56:02.166880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0464762 (* 1 = 0.0464762 loss) +I0616 10:56:02.166883 9857 solver.cpp:571] Iteration 52000, lr = 0.0001 +I0616 10:56:13.540091 9857 solver.cpp:242] Iteration 52020, loss = 0.56293 +I0616 10:56:13.540132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127343 (* 1 = 0.127343 loss) +I0616 10:56:13.540137 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150478 (* 1 = 0.150478 loss) +I0616 10:56:13.540141 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0755798 (* 1 = 0.0755798 loss) +I0616 10:56:13.540144 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0504807 (* 1 = 0.0504807 loss) +I0616 10:56:13.540148 9857 solver.cpp:571] Iteration 52020, lr = 0.0001 +I0616 10:56:25.108599 9857 solver.cpp:242] Iteration 52040, loss = 0.793302 +I0616 10:56:25.108640 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3111 (* 1 = 0.3111 loss) +I0616 10:56:25.108646 9857 solver.cpp:258] Train net output #1: loss_cls = 0.429261 (* 1 = 0.429261 loss) +I0616 10:56:25.108650 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0672178 (* 1 = 0.0672178 loss) +I0616 10:56:25.108654 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313622 (* 1 = 0.0313622 loss) +I0616 10:56:25.108659 9857 solver.cpp:571] Iteration 52040, lr = 0.0001 +I0616 10:56:36.814417 9857 solver.cpp:242] Iteration 52060, loss = 0.296989 +I0616 10:56:36.814460 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174477 (* 1 = 0.174477 loss) +I0616 10:56:36.814465 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109541 (* 1 = 0.109541 loss) +I0616 10:56:36.814470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108401 (* 1 = 0.108401 loss) +I0616 10:56:36.814472 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0441434 (* 1 = 0.0441434 loss) +I0616 10:56:36.814476 9857 solver.cpp:571] Iteration 52060, lr = 0.0001 +I0616 10:56:48.295449 9857 solver.cpp:242] Iteration 52080, loss = 0.465082 +I0616 10:56:48.295490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148136 (* 1 = 0.148136 loss) +I0616 10:56:48.295495 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310488 (* 1 = 0.310488 loss) +I0616 10:56:48.295500 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0560588 (* 1 = 0.0560588 loss) +I0616 10:56:48.295505 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334004 (* 1 = 0.0334004 loss) +I0616 10:56:48.295507 9857 solver.cpp:571] Iteration 52080, lr = 0.0001 +I0616 10:56:59.907996 9857 solver.cpp:242] Iteration 52100, loss = 0.506904 +I0616 10:56:59.908037 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120421 (* 1 = 0.120421 loss) +I0616 10:56:59.908043 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170709 (* 1 = 0.170709 loss) +I0616 10:56:59.908047 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0382448 (* 1 = 0.0382448 loss) +I0616 10:56:59.908051 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.002352 (* 1 = 0.002352 loss) +I0616 10:56:59.908054 9857 solver.cpp:571] Iteration 52100, lr = 0.0001 +I0616 10:57:11.527866 9857 solver.cpp:242] Iteration 52120, loss = 0.217781 +I0616 10:57:11.527909 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0798226 (* 1 = 0.0798226 loss) +I0616 10:57:11.527915 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128633 (* 1 = 0.128633 loss) +I0616 10:57:11.527920 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0278398 (* 1 = 0.0278398 loss) +I0616 10:57:11.527923 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00738152 (* 1 = 0.00738152 loss) +I0616 10:57:11.527926 9857 solver.cpp:571] Iteration 52120, lr = 0.0001 +I0616 10:57:22.995663 9857 solver.cpp:242] Iteration 52140, loss = 0.620905 +I0616 10:57:22.995703 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.369801 (* 1 = 0.369801 loss) +I0616 10:57:22.995709 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38914 (* 1 = 0.38914 loss) +I0616 10:57:22.995713 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130676 (* 1 = 0.130676 loss) +I0616 10:57:22.995717 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234353 (* 1 = 0.0234353 loss) +I0616 10:57:22.995720 9857 solver.cpp:571] Iteration 52140, lr = 0.0001 +I0616 10:57:34.499826 9857 solver.cpp:242] Iteration 52160, loss = 0.542209 +I0616 10:57:34.499867 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166631 (* 1 = 0.166631 loss) +I0616 10:57:34.499872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190761 (* 1 = 0.190761 loss) +I0616 10:57:34.499876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0973356 (* 1 = 0.0973356 loss) +I0616 10:57:34.499881 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226575 (* 1 = 0.0226575 loss) +I0616 10:57:34.499884 9857 solver.cpp:571] Iteration 52160, lr = 0.0001 +I0616 10:57:45.815166 9857 solver.cpp:242] Iteration 52180, loss = 0.420964 +I0616 10:57:45.815209 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115197 (* 1 = 0.115197 loss) +I0616 10:57:45.815214 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109922 (* 1 = 0.109922 loss) +I0616 10:57:45.815219 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0544942 (* 1 = 0.0544942 loss) +I0616 10:57:45.815222 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0398706 (* 1 = 0.0398706 loss) +I0616 10:57:45.815227 9857 solver.cpp:571] Iteration 52180, lr = 0.0001 +speed: 0.611s / iter +I0616 10:57:57.335536 9857 solver.cpp:242] Iteration 52200, loss = 0.486805 +I0616 10:57:57.335579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0758889 (* 1 = 0.0758889 loss) +I0616 10:57:57.335585 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18313 (* 1 = 0.18313 loss) +I0616 10:57:57.335589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0629551 (* 1 = 0.0629551 loss) +I0616 10:57:57.335593 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00363824 (* 1 = 0.00363824 loss) +I0616 10:57:57.335597 9857 solver.cpp:571] Iteration 52200, lr = 0.0001 +I0616 10:58:08.850121 9857 solver.cpp:242] Iteration 52220, loss = 1.05985 +I0616 10:58:08.850162 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164707 (* 1 = 0.164707 loss) +I0616 10:58:08.850168 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208944 (* 1 = 0.208944 loss) +I0616 10:58:08.850172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0143055 (* 1 = 0.0143055 loss) +I0616 10:58:08.850175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00208805 (* 1 = 0.00208805 loss) +I0616 10:58:08.850179 9857 solver.cpp:571] Iteration 52220, lr = 0.0001 +I0616 10:58:20.449697 9857 solver.cpp:242] Iteration 52240, loss = 0.386315 +I0616 10:58:20.449739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199641 (* 1 = 0.199641 loss) +I0616 10:58:20.449745 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171931 (* 1 = 0.171931 loss) +I0616 10:58:20.449749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0825688 (* 1 = 0.0825688 loss) +I0616 10:58:20.449753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00652872 (* 1 = 0.00652872 loss) +I0616 10:58:20.449756 9857 solver.cpp:571] Iteration 52240, lr = 0.0001 +I0616 10:58:31.760145 9857 solver.cpp:242] Iteration 52260, loss = 0.363274 +I0616 10:58:31.760188 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135335 (* 1 = 0.135335 loss) +I0616 10:58:31.760195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163836 (* 1 = 0.163836 loss) +I0616 10:58:31.760198 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0677575 (* 1 = 0.0677575 loss) +I0616 10:58:31.760202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012388 (* 1 = 0.012388 loss) +I0616 10:58:31.760206 9857 solver.cpp:571] Iteration 52260, lr = 0.0001 +I0616 10:58:43.522164 9857 solver.cpp:242] Iteration 52280, loss = 0.352952 +I0616 10:58:43.522208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0726214 (* 1 = 0.0726214 loss) +I0616 10:58:43.522213 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166375 (* 1 = 0.166375 loss) +I0616 10:58:43.522217 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00672664 (* 1 = 0.00672664 loss) +I0616 10:58:43.522222 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179603 (* 1 = 0.0179603 loss) +I0616 10:58:43.522224 9857 solver.cpp:571] Iteration 52280, lr = 0.0001 +I0616 10:58:54.878685 9857 solver.cpp:242] Iteration 52300, loss = 0.521119 +I0616 10:58:54.878727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214981 (* 1 = 0.214981 loss) +I0616 10:58:54.878732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252712 (* 1 = 0.252712 loss) +I0616 10:58:54.878737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0970258 (* 1 = 0.0970258 loss) +I0616 10:58:54.878741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015833 (* 1 = 0.015833 loss) +I0616 10:58:54.878744 9857 solver.cpp:571] Iteration 52300, lr = 0.0001 +I0616 10:59:06.571486 9857 solver.cpp:242] Iteration 52320, loss = 0.54097 +I0616 10:59:06.571529 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113283 (* 1 = 0.113283 loss) +I0616 10:59:06.571534 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149853 (* 1 = 0.149853 loss) +I0616 10:59:06.571539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374213 (* 1 = 0.0374213 loss) +I0616 10:59:06.571542 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109959 (* 1 = 0.0109959 loss) +I0616 10:59:06.571547 9857 solver.cpp:571] Iteration 52320, lr = 0.0001 +I0616 10:59:18.204704 9857 solver.cpp:242] Iteration 52340, loss = 0.499924 +I0616 10:59:18.204744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0939083 (* 1 = 0.0939083 loss) +I0616 10:59:18.204751 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121042 (* 1 = 0.121042 loss) +I0616 10:59:18.204756 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0373297 (* 1 = 0.0373297 loss) +I0616 10:59:18.204758 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.155575 (* 1 = 0.155575 loss) +I0616 10:59:18.204762 9857 solver.cpp:571] Iteration 52340, lr = 0.0001 +I0616 10:59:30.015478 9857 solver.cpp:242] Iteration 52360, loss = 0.682177 +I0616 10:59:30.015522 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251052 (* 1 = 0.251052 loss) +I0616 10:59:30.015527 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315625 (* 1 = 0.315625 loss) +I0616 10:59:30.015532 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.177561 (* 1 = 0.177561 loss) +I0616 10:59:30.015534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224616 (* 1 = 0.0224616 loss) +I0616 10:59:30.015540 9857 solver.cpp:571] Iteration 52360, lr = 0.0001 +I0616 10:59:41.564335 9857 solver.cpp:242] Iteration 52380, loss = 0.433066 +I0616 10:59:41.564378 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154324 (* 1 = 0.154324 loss) +I0616 10:59:41.564383 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122998 (* 1 = 0.122998 loss) +I0616 10:59:41.564386 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.095723 (* 1 = 0.095723 loss) +I0616 10:59:41.564390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00276268 (* 1 = 0.00276268 loss) +I0616 10:59:41.564393 9857 solver.cpp:571] Iteration 52380, lr = 0.0001 +speed: 0.611s / iter +I0616 10:59:52.957965 9857 solver.cpp:242] Iteration 52400, loss = 0.613105 +I0616 10:59:52.958009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193093 (* 1 = 0.193093 loss) +I0616 10:59:52.958014 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180194 (* 1 = 0.180194 loss) +I0616 10:59:52.958019 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0667366 (* 1 = 0.0667366 loss) +I0616 10:59:52.958022 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00150222 (* 1 = 0.00150222 loss) +I0616 10:59:52.958026 9857 solver.cpp:571] Iteration 52400, lr = 0.0001 +I0616 11:00:04.447996 9857 solver.cpp:242] Iteration 52420, loss = 0.687268 +I0616 11:00:04.448038 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282015 (* 1 = 0.282015 loss) +I0616 11:00:04.448043 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368171 (* 1 = 0.368171 loss) +I0616 11:00:04.448047 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163386 (* 1 = 0.163386 loss) +I0616 11:00:04.448051 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0151057 (* 1 = 0.0151057 loss) +I0616 11:00:04.448055 9857 solver.cpp:571] Iteration 52420, lr = 0.0001 +I0616 11:00:15.959266 9857 solver.cpp:242] Iteration 52440, loss = 0.489538 +I0616 11:00:15.959308 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100713 (* 1 = 0.100713 loss) +I0616 11:00:15.959314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.290246 (* 1 = 0.290246 loss) +I0616 11:00:15.959318 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0778832 (* 1 = 0.0778832 loss) +I0616 11:00:15.959322 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.094271 (* 1 = 0.094271 loss) +I0616 11:00:15.959326 9857 solver.cpp:571] Iteration 52440, lr = 0.0001 +I0616 11:00:27.537572 9857 solver.cpp:242] Iteration 52460, loss = 0.91541 +I0616 11:00:27.537614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224548 (* 1 = 0.224548 loss) +I0616 11:00:27.537619 9857 solver.cpp:258] Train net output #1: loss_cls = 0.334958 (* 1 = 0.334958 loss) +I0616 11:00:27.537623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116314 (* 1 = 0.116314 loss) +I0616 11:00:27.537627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.215794 (* 1 = 0.215794 loss) +I0616 11:00:27.537631 9857 solver.cpp:571] Iteration 52460, lr = 0.0001 +I0616 11:00:39.123067 9857 solver.cpp:242] Iteration 52480, loss = 0.517346 +I0616 11:00:39.123108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0575889 (* 1 = 0.0575889 loss) +I0616 11:00:39.123114 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121038 (* 1 = 0.121038 loss) +I0616 11:00:39.123118 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00441147 (* 1 = 0.00441147 loss) +I0616 11:00:39.123122 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.036673 (* 1 = 0.036673 loss) +I0616 11:00:39.123126 9857 solver.cpp:571] Iteration 52480, lr = 0.0001 +I0616 11:00:51.002531 9857 solver.cpp:242] Iteration 52500, loss = 0.456511 +I0616 11:00:51.002569 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.08561 (* 1 = 0.08561 loss) +I0616 11:00:51.002575 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16835 (* 1 = 0.16835 loss) +I0616 11:00:51.002579 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.01102 (* 1 = 0.01102 loss) +I0616 11:00:51.002583 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011479 (* 1 = 0.011479 loss) +I0616 11:00:51.002586 9857 solver.cpp:571] Iteration 52500, lr = 0.0001 +I0616 11:01:02.354794 9857 solver.cpp:242] Iteration 52520, loss = 0.475282 +I0616 11:01:02.354835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0858265 (* 1 = 0.0858265 loss) +I0616 11:01:02.354840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185253 (* 1 = 0.185253 loss) +I0616 11:01:02.354845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0120179 (* 1 = 0.0120179 loss) +I0616 11:01:02.354848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224561 (* 1 = 0.0224561 loss) +I0616 11:01:02.354852 9857 solver.cpp:571] Iteration 52520, lr = 0.0001 +I0616 11:01:14.000536 9857 solver.cpp:242] Iteration 52540, loss = 0.432349 +I0616 11:01:14.000577 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0139867 (* 1 = 0.0139867 loss) +I0616 11:01:14.000583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123282 (* 1 = 0.123282 loss) +I0616 11:01:14.000587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0499201 (* 1 = 0.0499201 loss) +I0616 11:01:14.000591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0713408 (* 1 = 0.0713408 loss) +I0616 11:01:14.000594 9857 solver.cpp:571] Iteration 52540, lr = 0.0001 +I0616 11:01:25.388273 9857 solver.cpp:242] Iteration 52560, loss = 0.329722 +I0616 11:01:25.388316 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101463 (* 1 = 0.101463 loss) +I0616 11:01:25.388322 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112696 (* 1 = 0.112696 loss) +I0616 11:01:25.388326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0316682 (* 1 = 0.0316682 loss) +I0616 11:01:25.388329 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00668319 (* 1 = 0.00668319 loss) +I0616 11:01:25.388334 9857 solver.cpp:571] Iteration 52560, lr = 0.0001 +I0616 11:01:37.088408 9857 solver.cpp:242] Iteration 52580, loss = 0.901246 +I0616 11:01:37.088450 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386572 (* 1 = 0.386572 loss) +I0616 11:01:37.088456 9857 solver.cpp:258] Train net output #1: loss_cls = 0.43379 (* 1 = 0.43379 loss) +I0616 11:01:37.088460 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.259869 (* 1 = 0.259869 loss) +I0616 11:01:37.088464 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.373355 (* 1 = 0.373355 loss) +I0616 11:01:37.088467 9857 solver.cpp:571] Iteration 52580, lr = 0.0001 +speed: 0.611s / iter +I0616 11:01:48.686882 9857 solver.cpp:242] Iteration 52600, loss = 0.415229 +I0616 11:01:48.686926 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.079184 (* 1 = 0.079184 loss) +I0616 11:01:48.686946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139335 (* 1 = 0.139335 loss) +I0616 11:01:48.686951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00121015 (* 1 = 0.00121015 loss) +I0616 11:01:48.686955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00334025 (* 1 = 0.00334025 loss) +I0616 11:01:48.686959 9857 solver.cpp:571] Iteration 52600, lr = 0.0001 +I0616 11:02:00.399937 9857 solver.cpp:242] Iteration 52620, loss = 0.592563 +I0616 11:02:00.399981 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392623 (* 1 = 0.392623 loss) +I0616 11:02:00.399986 9857 solver.cpp:258] Train net output #1: loss_cls = 0.553236 (* 1 = 0.553236 loss) +I0616 11:02:00.399991 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106098 (* 1 = 0.106098 loss) +I0616 11:02:00.399994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128149 (* 1 = 0.0128149 loss) +I0616 11:02:00.399997 9857 solver.cpp:571] Iteration 52620, lr = 0.0001 +I0616 11:02:12.124939 9857 solver.cpp:242] Iteration 52640, loss = 0.546117 +I0616 11:02:12.124980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251824 (* 1 = 0.251824 loss) +I0616 11:02:12.124985 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344904 (* 1 = 0.344904 loss) +I0616 11:02:12.124989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119258 (* 1 = 0.119258 loss) +I0616 11:02:12.124994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020782 (* 1 = 0.020782 loss) +I0616 11:02:12.124997 9857 solver.cpp:571] Iteration 52640, lr = 0.0001 +I0616 11:02:23.776566 9857 solver.cpp:242] Iteration 52660, loss = 0.418851 +I0616 11:02:23.776608 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0748972 (* 1 = 0.0748972 loss) +I0616 11:02:23.776614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119413 (* 1 = 0.119413 loss) +I0616 11:02:23.776618 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0619917 (* 1 = 0.0619917 loss) +I0616 11:02:23.776623 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00236216 (* 1 = 0.00236216 loss) +I0616 11:02:23.776626 9857 solver.cpp:571] Iteration 52660, lr = 0.0001 +I0616 11:02:35.351513 9857 solver.cpp:242] Iteration 52680, loss = 0.697289 +I0616 11:02:35.351557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0774061 (* 1 = 0.0774061 loss) +I0616 11:02:35.351562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143656 (* 1 = 0.143656 loss) +I0616 11:02:35.351567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0327289 (* 1 = 0.0327289 loss) +I0616 11:02:35.351570 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175735 (* 1 = 0.0175735 loss) +I0616 11:02:35.351577 9857 solver.cpp:571] Iteration 52680, lr = 0.0001 +I0616 11:02:46.869706 9857 solver.cpp:242] Iteration 52700, loss = 0.421325 +I0616 11:02:46.869748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0752284 (* 1 = 0.0752284 loss) +I0616 11:02:46.869753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139551 (* 1 = 0.139551 loss) +I0616 11:02:46.869757 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172748 (* 1 = 0.172748 loss) +I0616 11:02:46.869761 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0379033 (* 1 = 0.0379033 loss) +I0616 11:02:46.869765 9857 solver.cpp:571] Iteration 52700, lr = 0.0001 +I0616 11:02:58.262944 9857 solver.cpp:242] Iteration 52720, loss = 0.698236 +I0616 11:02:58.262984 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22322 (* 1 = 0.22322 loss) +I0616 11:02:58.262989 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239357 (* 1 = 0.239357 loss) +I0616 11:02:58.262994 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141466 (* 1 = 0.141466 loss) +I0616 11:02:58.262997 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0614987 (* 1 = 0.0614987 loss) +I0616 11:02:58.263000 9857 solver.cpp:571] Iteration 52720, lr = 0.0001 +I0616 11:03:09.898231 9857 solver.cpp:242] Iteration 52740, loss = 0.401406 +I0616 11:03:09.898274 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0656902 (* 1 = 0.0656902 loss) +I0616 11:03:09.898279 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0597763 (* 1 = 0.0597763 loss) +I0616 11:03:09.898284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0166025 (* 1 = 0.0166025 loss) +I0616 11:03:09.898288 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.163601 (* 1 = 0.163601 loss) +I0616 11:03:09.898291 9857 solver.cpp:571] Iteration 52740, lr = 0.0001 +I0616 11:03:21.414779 9857 solver.cpp:242] Iteration 52760, loss = 0.593633 +I0616 11:03:21.414822 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241172 (* 1 = 0.241172 loss) +I0616 11:03:21.414829 9857 solver.cpp:258] Train net output #1: loss_cls = 0.346648 (* 1 = 0.346648 loss) +I0616 11:03:21.414832 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.148616 (* 1 = 0.148616 loss) +I0616 11:03:21.414837 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319105 (* 1 = 0.0319105 loss) +I0616 11:03:21.414842 9857 solver.cpp:571] Iteration 52760, lr = 0.0001 +I0616 11:03:32.863131 9857 solver.cpp:242] Iteration 52780, loss = 1.16343 +I0616 11:03:32.863174 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264545 (* 1 = 0.264545 loss) +I0616 11:03:32.863179 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301954 (* 1 = 0.301954 loss) +I0616 11:03:32.863184 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.099007 (* 1 = 0.099007 loss) +I0616 11:03:32.863188 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0208549 (* 1 = 0.0208549 loss) +I0616 11:03:32.863191 9857 solver.cpp:571] Iteration 52780, lr = 0.0001 +speed: 0.611s / iter +I0616 11:03:44.239941 9857 solver.cpp:242] Iteration 52800, loss = 0.913552 +I0616 11:03:44.239984 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282936 (* 1 = 0.282936 loss) +I0616 11:03:44.239989 9857 solver.cpp:258] Train net output #1: loss_cls = 0.533254 (* 1 = 0.533254 loss) +I0616 11:03:44.239994 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.212289 (* 1 = 0.212289 loss) +I0616 11:03:44.239996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137587 (* 1 = 0.137587 loss) +I0616 11:03:44.240000 9857 solver.cpp:571] Iteration 52800, lr = 0.0001 +I0616 11:03:55.796151 9857 solver.cpp:242] Iteration 52820, loss = 0.259025 +I0616 11:03:55.796193 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.090378 (* 1 = 0.090378 loss) +I0616 11:03:55.796200 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122499 (* 1 = 0.122499 loss) +I0616 11:03:55.796203 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00225622 (* 1 = 0.00225622 loss) +I0616 11:03:55.796207 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00699593 (* 1 = 0.00699593 loss) +I0616 11:03:55.796211 9857 solver.cpp:571] Iteration 52820, lr = 0.0001 +I0616 11:04:07.027170 9857 solver.cpp:242] Iteration 52840, loss = 0.897593 +I0616 11:04:07.027212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192926 (* 1 = 0.192926 loss) +I0616 11:04:07.027218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267353 (* 1 = 0.267353 loss) +I0616 11:04:07.027222 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0606801 (* 1 = 0.0606801 loss) +I0616 11:04:07.027226 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193473 (* 1 = 0.0193473 loss) +I0616 11:04:07.027230 9857 solver.cpp:571] Iteration 52840, lr = 0.0001 +I0616 11:04:18.746345 9857 solver.cpp:242] Iteration 52860, loss = 0.652886 +I0616 11:04:18.746386 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.453695 (* 1 = 0.453695 loss) +I0616 11:04:18.746392 9857 solver.cpp:258] Train net output #1: loss_cls = 0.508545 (* 1 = 0.508545 loss) +I0616 11:04:18.746395 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0729727 (* 1 = 0.0729727 loss) +I0616 11:04:18.746399 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0511206 (* 1 = 0.0511206 loss) +I0616 11:04:18.746402 9857 solver.cpp:571] Iteration 52860, lr = 0.0001 +I0616 11:04:30.347682 9857 solver.cpp:242] Iteration 52880, loss = 0.89404 +I0616 11:04:30.347724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309057 (* 1 = 0.309057 loss) +I0616 11:04:30.347729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.756006 (* 1 = 0.756006 loss) +I0616 11:04:30.347734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0567301 (* 1 = 0.0567301 loss) +I0616 11:04:30.347738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10088 (* 1 = 0.10088 loss) +I0616 11:04:30.347741 9857 solver.cpp:571] Iteration 52880, lr = 0.0001 +I0616 11:04:41.809567 9857 solver.cpp:242] Iteration 52900, loss = 0.337727 +I0616 11:04:41.809610 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112306 (* 1 = 0.112306 loss) +I0616 11:04:41.809617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268013 (* 1 = 0.268013 loss) +I0616 11:04:41.809620 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0362838 (* 1 = 0.0362838 loss) +I0616 11:04:41.809624 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207414 (* 1 = 0.0207414 loss) +I0616 11:04:41.809628 9857 solver.cpp:571] Iteration 52900, lr = 0.0001 +I0616 11:04:53.591176 9857 solver.cpp:242] Iteration 52920, loss = 0.611196 +I0616 11:04:53.591217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211837 (* 1 = 0.211837 loss) +I0616 11:04:53.591223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213881 (* 1 = 0.213881 loss) +I0616 11:04:53.591228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0103434 (* 1 = 0.0103434 loss) +I0616 11:04:53.591231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00521063 (* 1 = 0.00521063 loss) +I0616 11:04:53.591235 9857 solver.cpp:571] Iteration 52920, lr = 0.0001 +I0616 11:05:05.143484 9857 solver.cpp:242] Iteration 52940, loss = 1.19248 +I0616 11:05:05.143524 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228351 (* 1 = 0.228351 loss) +I0616 11:05:05.143530 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375414 (* 1 = 0.375414 loss) +I0616 11:05:05.143534 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.270286 (* 1 = 0.270286 loss) +I0616 11:05:05.143537 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.352666 (* 1 = 0.352666 loss) +I0616 11:05:05.143542 9857 solver.cpp:571] Iteration 52940, lr = 0.0001 +I0616 11:05:16.595888 9857 solver.cpp:242] Iteration 52960, loss = 0.888893 +I0616 11:05:16.595930 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140019 (* 1 = 0.140019 loss) +I0616 11:05:16.595937 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317502 (* 1 = 0.317502 loss) +I0616 11:05:16.595940 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.215699 (* 1 = 0.215699 loss) +I0616 11:05:16.595943 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00766698 (* 1 = 0.00766698 loss) +I0616 11:05:16.595947 9857 solver.cpp:571] Iteration 52960, lr = 0.0001 +I0616 11:05:28.205219 9857 solver.cpp:242] Iteration 52980, loss = 0.662848 +I0616 11:05:28.205260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203739 (* 1 = 0.203739 loss) +I0616 11:05:28.205266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213141 (* 1 = 0.213141 loss) +I0616 11:05:28.205271 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0617104 (* 1 = 0.0617104 loss) +I0616 11:05:28.205274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0516998 (* 1 = 0.0516998 loss) +I0616 11:05:28.205278 9857 solver.cpp:571] Iteration 52980, lr = 0.0001 +speed: 0.611s / iter +I0616 11:05:39.734859 9857 solver.cpp:242] Iteration 53000, loss = 0.385394 +I0616 11:05:39.734901 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0547799 (* 1 = 0.0547799 loss) +I0616 11:05:39.734906 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102866 (* 1 = 0.102866 loss) +I0616 11:05:39.734910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0121325 (* 1 = 0.0121325 loss) +I0616 11:05:39.734915 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00568438 (* 1 = 0.00568438 loss) +I0616 11:05:39.734918 9857 solver.cpp:571] Iteration 53000, lr = 0.0001 +I0616 11:05:51.366690 9857 solver.cpp:242] Iteration 53020, loss = 0.557878 +I0616 11:05:51.366730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.414554 (* 1 = 0.414554 loss) +I0616 11:05:51.366735 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215113 (* 1 = 0.215113 loss) +I0616 11:05:51.366739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.181178 (* 1 = 0.181178 loss) +I0616 11:05:51.366744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245106 (* 1 = 0.0245106 loss) +I0616 11:05:51.366746 9857 solver.cpp:571] Iteration 53020, lr = 0.0001 +I0616 11:06:03.100854 9857 solver.cpp:242] Iteration 53040, loss = 0.374261 +I0616 11:06:03.100896 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185555 (* 1 = 0.185555 loss) +I0616 11:06:03.100903 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225694 (* 1 = 0.225694 loss) +I0616 11:06:03.100906 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0319895 (* 1 = 0.0319895 loss) +I0616 11:06:03.100910 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157033 (* 1 = 0.0157033 loss) +I0616 11:06:03.100914 9857 solver.cpp:571] Iteration 53040, lr = 0.0001 +I0616 11:06:14.544351 9857 solver.cpp:242] Iteration 53060, loss = 0.775332 +I0616 11:06:14.544394 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0650581 (* 1 = 0.0650581 loss) +I0616 11:06:14.544399 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103869 (* 1 = 0.103869 loss) +I0616 11:06:14.544404 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0251496 (* 1 = 0.0251496 loss) +I0616 11:06:14.544407 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00808984 (* 1 = 0.00808984 loss) +I0616 11:06:14.544411 9857 solver.cpp:571] Iteration 53060, lr = 0.0001 +I0616 11:06:26.180582 9857 solver.cpp:242] Iteration 53080, loss = 0.293457 +I0616 11:06:26.180624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10584 (* 1 = 0.10584 loss) +I0616 11:06:26.180630 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14801 (* 1 = 0.14801 loss) +I0616 11:06:26.180634 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.064157 (* 1 = 0.064157 loss) +I0616 11:06:26.180639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0469461 (* 1 = 0.0469461 loss) +I0616 11:06:26.180641 9857 solver.cpp:571] Iteration 53080, lr = 0.0001 +I0616 11:06:37.576256 9857 solver.cpp:242] Iteration 53100, loss = 0.338928 +I0616 11:06:37.576297 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0594052 (* 1 = 0.0594052 loss) +I0616 11:06:37.576303 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0835813 (* 1 = 0.0835813 loss) +I0616 11:06:37.576306 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0492041 (* 1 = 0.0492041 loss) +I0616 11:06:37.576310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00953168 (* 1 = 0.00953168 loss) +I0616 11:06:37.576314 9857 solver.cpp:571] Iteration 53100, lr = 0.0001 +I0616 11:06:49.304033 9857 solver.cpp:242] Iteration 53120, loss = 0.988495 +I0616 11:06:49.304075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0731497 (* 1 = 0.0731497 loss) +I0616 11:06:49.304081 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152935 (* 1 = 0.152935 loss) +I0616 11:06:49.304085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0597694 (* 1 = 0.0597694 loss) +I0616 11:06:49.304090 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0124593 (* 1 = 0.0124593 loss) +I0616 11:06:49.304092 9857 solver.cpp:571] Iteration 53120, lr = 0.0001 +I0616 11:07:00.642910 9857 solver.cpp:242] Iteration 53140, loss = 0.603038 +I0616 11:07:00.642951 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138524 (* 1 = 0.138524 loss) +I0616 11:07:00.642957 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175401 (* 1 = 0.175401 loss) +I0616 11:07:00.642961 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0131856 (* 1 = 0.0131856 loss) +I0616 11:07:00.642966 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0031437 (* 1 = 0.0031437 loss) +I0616 11:07:00.642969 9857 solver.cpp:571] Iteration 53140, lr = 0.0001 +I0616 11:07:11.854317 9857 solver.cpp:242] Iteration 53160, loss = 0.375878 +I0616 11:07:11.854360 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0817449 (* 1 = 0.0817449 loss) +I0616 11:07:11.854367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0793801 (* 1 = 0.0793801 loss) +I0616 11:07:11.854372 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100776 (* 1 = 0.100776 loss) +I0616 11:07:11.854375 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00556659 (* 1 = 0.00556659 loss) +I0616 11:07:11.854379 9857 solver.cpp:571] Iteration 53160, lr = 0.0001 +I0616 11:07:23.405804 9857 solver.cpp:242] Iteration 53180, loss = 0.648326 +I0616 11:07:23.405846 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12091 (* 1 = 0.12091 loss) +I0616 11:07:23.405853 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20072 (* 1 = 0.20072 loss) +I0616 11:07:23.405856 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0326939 (* 1 = 0.0326939 loss) +I0616 11:07:23.405860 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334405 (* 1 = 0.0334405 loss) +I0616 11:07:23.405864 9857 solver.cpp:571] Iteration 53180, lr = 0.0001 +speed: 0.611s / iter +I0616 11:07:34.972033 9857 solver.cpp:242] Iteration 53200, loss = 1.03077 +I0616 11:07:34.972074 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165156 (* 1 = 0.165156 loss) +I0616 11:07:34.972079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235559 (* 1 = 0.235559 loss) +I0616 11:07:34.972084 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0831609 (* 1 = 0.0831609 loss) +I0616 11:07:34.972087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245589 (* 1 = 0.0245589 loss) +I0616 11:07:34.972091 9857 solver.cpp:571] Iteration 53200, lr = 0.0001 +I0616 11:07:46.674311 9857 solver.cpp:242] Iteration 53220, loss = 0.61149 +I0616 11:07:46.674353 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305633 (* 1 = 0.305633 loss) +I0616 11:07:46.674358 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392577 (* 1 = 0.392577 loss) +I0616 11:07:46.674362 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.230517 (* 1 = 0.230517 loss) +I0616 11:07:46.674366 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0574656 (* 1 = 0.0574656 loss) +I0616 11:07:46.674371 9857 solver.cpp:571] Iteration 53220, lr = 0.0001 +I0616 11:07:58.421599 9857 solver.cpp:242] Iteration 53240, loss = 0.701503 +I0616 11:07:58.421640 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192363 (* 1 = 0.192363 loss) +I0616 11:07:58.421645 9857 solver.cpp:258] Train net output #1: loss_cls = 0.353947 (* 1 = 0.353947 loss) +I0616 11:07:58.421649 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.203172 (* 1 = 0.203172 loss) +I0616 11:07:58.421653 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.12332 (* 1 = 0.12332 loss) +I0616 11:07:58.421658 9857 solver.cpp:571] Iteration 53240, lr = 0.0001 +I0616 11:08:09.870748 9857 solver.cpp:242] Iteration 53260, loss = 0.446143 +I0616 11:08:09.870793 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133045 (* 1 = 0.133045 loss) +I0616 11:08:09.870798 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13318 (* 1 = 0.13318 loss) +I0616 11:08:09.870802 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230468 (* 1 = 0.0230468 loss) +I0616 11:08:09.870806 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0331712 (* 1 = 0.0331712 loss) +I0616 11:08:09.870810 9857 solver.cpp:571] Iteration 53260, lr = 0.0001 +I0616 11:08:21.514288 9857 solver.cpp:242] Iteration 53280, loss = 0.544319 +I0616 11:08:21.514331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149554 (* 1 = 0.149554 loss) +I0616 11:08:21.514336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188868 (* 1 = 0.188868 loss) +I0616 11:08:21.514341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0887669 (* 1 = 0.0887669 loss) +I0616 11:08:21.514345 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285042 (* 1 = 0.0285042 loss) +I0616 11:08:21.514348 9857 solver.cpp:571] Iteration 53280, lr = 0.0001 +I0616 11:08:33.208997 9857 solver.cpp:242] Iteration 53300, loss = 1.11488 +I0616 11:08:33.209038 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376301 (* 1 = 0.376301 loss) +I0616 11:08:33.209043 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351773 (* 1 = 0.351773 loss) +I0616 11:08:33.209048 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.380909 (* 1 = 0.380909 loss) +I0616 11:08:33.209051 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0641313 (* 1 = 0.0641313 loss) +I0616 11:08:33.209054 9857 solver.cpp:571] Iteration 53300, lr = 0.0001 +I0616 11:08:44.783923 9857 solver.cpp:242] Iteration 53320, loss = 0.576716 +I0616 11:08:44.783964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206861 (* 1 = 0.206861 loss) +I0616 11:08:44.783970 9857 solver.cpp:258] Train net output #1: loss_cls = 0.261193 (* 1 = 0.261193 loss) +I0616 11:08:44.783974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0269071 (* 1 = 0.0269071 loss) +I0616 11:08:44.783977 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269749 (* 1 = 0.0269749 loss) +I0616 11:08:44.783982 9857 solver.cpp:571] Iteration 53320, lr = 0.0001 +I0616 11:08:56.355080 9857 solver.cpp:242] Iteration 53340, loss = 1.01775 +I0616 11:08:56.355123 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257864 (* 1 = 0.257864 loss) +I0616 11:08:56.355128 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404848 (* 1 = 0.404848 loss) +I0616 11:08:56.355131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.066531 (* 1 = 0.066531 loss) +I0616 11:08:56.355135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126705 (* 1 = 0.126705 loss) +I0616 11:08:56.355139 9857 solver.cpp:571] Iteration 53340, lr = 0.0001 +I0616 11:09:07.973094 9857 solver.cpp:242] Iteration 53360, loss = 0.861043 +I0616 11:09:07.973136 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376609 (* 1 = 0.376609 loss) +I0616 11:09:07.973142 9857 solver.cpp:258] Train net output #1: loss_cls = 0.513471 (* 1 = 0.513471 loss) +I0616 11:09:07.973146 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.402901 (* 1 = 0.402901 loss) +I0616 11:09:07.973150 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144255 (* 1 = 0.144255 loss) +I0616 11:09:07.973153 9857 solver.cpp:571] Iteration 53360, lr = 0.0001 +I0616 11:09:19.472251 9857 solver.cpp:242] Iteration 53380, loss = 0.703742 +I0616 11:09:19.472295 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359829 (* 1 = 0.359829 loss) +I0616 11:09:19.472300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.227154 (* 1 = 0.227154 loss) +I0616 11:09:19.472304 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555996 (* 1 = 0.0555996 loss) +I0616 11:09:19.472307 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00542475 (* 1 = 0.00542475 loss) +I0616 11:09:19.472311 9857 solver.cpp:571] Iteration 53380, lr = 0.0001 +speed: 0.611s / iter +I0616 11:09:30.731905 9857 solver.cpp:242] Iteration 53400, loss = 0.473349 +I0616 11:09:30.731948 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0718611 (* 1 = 0.0718611 loss) +I0616 11:09:30.731955 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136848 (* 1 = 0.136848 loss) +I0616 11:09:30.731958 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228074 (* 1 = 0.0228074 loss) +I0616 11:09:30.731961 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00944634 (* 1 = 0.00944634 loss) +I0616 11:09:30.731966 9857 solver.cpp:571] Iteration 53400, lr = 0.0001 +I0616 11:09:41.972285 9857 solver.cpp:242] Iteration 53420, loss = 0.238849 +I0616 11:09:41.972328 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101515 (* 1 = 0.101515 loss) +I0616 11:09:41.972333 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0911664 (* 1 = 0.0911664 loss) +I0616 11:09:41.972337 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0284637 (* 1 = 0.0284637 loss) +I0616 11:09:41.972342 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179696 (* 1 = 0.0179696 loss) +I0616 11:09:41.972345 9857 solver.cpp:571] Iteration 53420, lr = 0.0001 +I0616 11:09:53.235888 9857 solver.cpp:242] Iteration 53440, loss = 0.782595 +I0616 11:09:53.235927 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0967946 (* 1 = 0.0967946 loss) +I0616 11:09:53.235934 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136978 (* 1 = 0.136978 loss) +I0616 11:09:53.235937 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0268588 (* 1 = 0.0268588 loss) +I0616 11:09:53.235941 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00604182 (* 1 = 0.00604182 loss) +I0616 11:09:53.235944 9857 solver.cpp:571] Iteration 53440, lr = 0.0001 +I0616 11:10:04.979831 9857 solver.cpp:242] Iteration 53460, loss = 0.75918 +I0616 11:10:04.979873 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0888669 (* 1 = 0.0888669 loss) +I0616 11:10:04.979879 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18446 (* 1 = 0.18446 loss) +I0616 11:10:04.979883 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.053517 (* 1 = 0.053517 loss) +I0616 11:10:04.979887 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10799 (* 1 = 0.10799 loss) +I0616 11:10:04.979890 9857 solver.cpp:571] Iteration 53460, lr = 0.0001 +I0616 11:10:16.687326 9857 solver.cpp:242] Iteration 53480, loss = 0.689404 +I0616 11:10:16.687369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220931 (* 1 = 0.220931 loss) +I0616 11:10:16.687374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207055 (* 1 = 0.207055 loss) +I0616 11:10:16.687379 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.044443 (* 1 = 0.044443 loss) +I0616 11:10:16.687382 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117749 (* 1 = 0.117749 loss) +I0616 11:10:16.687386 9857 solver.cpp:571] Iteration 53480, lr = 0.0001 +I0616 11:10:27.929105 9857 solver.cpp:242] Iteration 53500, loss = 1.51006 +I0616 11:10:27.929147 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353646 (* 1 = 0.353646 loss) +I0616 11:10:27.929153 9857 solver.cpp:258] Train net output #1: loss_cls = 0.48882 (* 1 = 0.48882 loss) +I0616 11:10:27.929157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.171688 (* 1 = 0.171688 loss) +I0616 11:10:27.929162 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0773539 (* 1 = 0.0773539 loss) +I0616 11:10:27.929164 9857 solver.cpp:571] Iteration 53500, lr = 0.0001 +I0616 11:10:39.474337 9857 solver.cpp:242] Iteration 53520, loss = 1.48101 +I0616 11:10:39.474380 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155725 (* 1 = 0.155725 loss) +I0616 11:10:39.474385 9857 solver.cpp:258] Train net output #1: loss_cls = 0.39311 (* 1 = 0.39311 loss) +I0616 11:10:39.474388 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.426637 (* 1 = 0.426637 loss) +I0616 11:10:39.474391 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.675829 (* 1 = 0.675829 loss) +I0616 11:10:39.474395 9857 solver.cpp:571] Iteration 53520, lr = 0.0001 +I0616 11:10:51.183240 9857 solver.cpp:242] Iteration 53540, loss = 0.821637 +I0616 11:10:51.183279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.40074 (* 1 = 0.40074 loss) +I0616 11:10:51.183284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298651 (* 1 = 0.298651 loss) +I0616 11:10:51.183289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135147 (* 1 = 0.135147 loss) +I0616 11:10:51.183292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.140332 (* 1 = 0.140332 loss) +I0616 11:10:51.183296 9857 solver.cpp:571] Iteration 53540, lr = 0.0001 +I0616 11:11:02.794544 9857 solver.cpp:242] Iteration 53560, loss = 0.524167 +I0616 11:11:02.794585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228623 (* 1 = 0.228623 loss) +I0616 11:11:02.794591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301019 (* 1 = 0.301019 loss) +I0616 11:11:02.794595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159365 (* 1 = 0.159365 loss) +I0616 11:11:02.794600 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01701 (* 1 = 0.01701 loss) +I0616 11:11:02.794603 9857 solver.cpp:571] Iteration 53560, lr = 0.0001 +I0616 11:11:14.430860 9857 solver.cpp:242] Iteration 53580, loss = 0.427719 +I0616 11:11:14.430902 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18757 (* 1 = 0.18757 loss) +I0616 11:11:14.430907 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124081 (* 1 = 0.124081 loss) +I0616 11:11:14.430912 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00955302 (* 1 = 0.00955302 loss) +I0616 11:11:14.430917 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145524 (* 1 = 0.0145524 loss) +I0616 11:11:14.430919 9857 solver.cpp:571] Iteration 53580, lr = 0.0001 +speed: 0.611s / iter +I0616 11:11:25.820051 9857 solver.cpp:242] Iteration 53600, loss = 0.671704 +I0616 11:11:25.820094 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334862 (* 1 = 0.334862 loss) +I0616 11:11:25.820099 9857 solver.cpp:258] Train net output #1: loss_cls = 0.348919 (* 1 = 0.348919 loss) +I0616 11:11:25.820103 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0821151 (* 1 = 0.0821151 loss) +I0616 11:11:25.820106 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.154039 (* 1 = 0.154039 loss) +I0616 11:11:25.820111 9857 solver.cpp:571] Iteration 53600, lr = 0.0001 +I0616 11:11:37.335805 9857 solver.cpp:242] Iteration 53620, loss = 0.535842 +I0616 11:11:37.335846 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0959173 (* 1 = 0.0959173 loss) +I0616 11:11:37.335851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144494 (* 1 = 0.144494 loss) +I0616 11:11:37.335855 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0463674 (* 1 = 0.0463674 loss) +I0616 11:11:37.335860 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00588576 (* 1 = 0.00588576 loss) +I0616 11:11:37.335863 9857 solver.cpp:571] Iteration 53620, lr = 0.0001 +I0616 11:11:49.123334 9857 solver.cpp:242] Iteration 53640, loss = 0.790725 +I0616 11:11:49.123378 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241655 (* 1 = 0.241655 loss) +I0616 11:11:49.123383 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306988 (* 1 = 0.306988 loss) +I0616 11:11:49.123386 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0300505 (* 1 = 0.0300505 loss) +I0616 11:11:49.123390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0567807 (* 1 = 0.0567807 loss) +I0616 11:11:49.123394 9857 solver.cpp:571] Iteration 53640, lr = 0.0001 +I0616 11:12:00.788763 9857 solver.cpp:242] Iteration 53660, loss = 0.311907 +I0616 11:12:00.788806 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1191 (* 1 = 0.1191 loss) +I0616 11:12:00.788811 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200375 (* 1 = 0.200375 loss) +I0616 11:12:00.788815 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0254354 (* 1 = 0.0254354 loss) +I0616 11:12:00.788820 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00689728 (* 1 = 0.00689728 loss) +I0616 11:12:00.788822 9857 solver.cpp:571] Iteration 53660, lr = 0.0001 +I0616 11:12:12.207322 9857 solver.cpp:242] Iteration 53680, loss = 1.13985 +I0616 11:12:12.207365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.400307 (* 1 = 0.400307 loss) +I0616 11:12:12.207370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.774164 (* 1 = 0.774164 loss) +I0616 11:12:12.207373 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.393907 (* 1 = 0.393907 loss) +I0616 11:12:12.207378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0830627 (* 1 = 0.0830627 loss) +I0616 11:12:12.207381 9857 solver.cpp:571] Iteration 53680, lr = 0.0001 +I0616 11:12:23.910517 9857 solver.cpp:242] Iteration 53700, loss = 0.460113 +I0616 11:12:23.910560 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114396 (* 1 = 0.114396 loss) +I0616 11:12:23.910567 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206015 (* 1 = 0.206015 loss) +I0616 11:12:23.910570 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0515191 (* 1 = 0.0515191 loss) +I0616 11:12:23.910574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00685678 (* 1 = 0.00685678 loss) +I0616 11:12:23.910578 9857 solver.cpp:571] Iteration 53700, lr = 0.0001 +I0616 11:12:35.408977 9857 solver.cpp:242] Iteration 53720, loss = 0.431998 +I0616 11:12:35.409018 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.076821 (* 1 = 0.076821 loss) +I0616 11:12:35.409024 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111414 (* 1 = 0.111414 loss) +I0616 11:12:35.409029 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0377606 (* 1 = 0.0377606 loss) +I0616 11:12:35.409031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0028372 (* 1 = 0.0028372 loss) +I0616 11:12:35.409035 9857 solver.cpp:571] Iteration 53720, lr = 0.0001 +I0616 11:12:47.179886 9857 solver.cpp:242] Iteration 53740, loss = 0.375854 +I0616 11:12:47.179929 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155726 (* 1 = 0.155726 loss) +I0616 11:12:47.179934 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113207 (* 1 = 0.113207 loss) +I0616 11:12:47.179939 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0142621 (* 1 = 0.0142621 loss) +I0616 11:12:47.179942 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00290005 (* 1 = 0.00290005 loss) +I0616 11:12:47.179946 9857 solver.cpp:571] Iteration 53740, lr = 0.0001 +I0616 11:12:58.912595 9857 solver.cpp:242] Iteration 53760, loss = 0.567046 +I0616 11:12:58.912638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163644 (* 1 = 0.163644 loss) +I0616 11:12:58.912643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257758 (* 1 = 0.257758 loss) +I0616 11:12:58.912647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0040487 (* 1 = 0.0040487 loss) +I0616 11:12:58.912652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00302948 (* 1 = 0.00302948 loss) +I0616 11:12:58.912655 9857 solver.cpp:571] Iteration 53760, lr = 0.0001 +I0616 11:13:10.465854 9857 solver.cpp:242] Iteration 53780, loss = 0.266678 +I0616 11:13:10.465896 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0987276 (* 1 = 0.0987276 loss) +I0616 11:13:10.465901 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0962307 (* 1 = 0.0962307 loss) +I0616 11:13:10.465905 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0173237 (* 1 = 0.0173237 loss) +I0616 11:13:10.465909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00638544 (* 1 = 0.00638544 loss) +I0616 11:13:10.465914 9857 solver.cpp:571] Iteration 53780, lr = 0.0001 +speed: 0.610s / iter +I0616 11:13:22.036432 9857 solver.cpp:242] Iteration 53800, loss = 0.986127 +I0616 11:13:22.036473 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382517 (* 1 = 0.382517 loss) +I0616 11:13:22.036478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.472378 (* 1 = 0.472378 loss) +I0616 11:13:22.036483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200136 (* 1 = 0.200136 loss) +I0616 11:13:22.036486 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10193 (* 1 = 0.10193 loss) +I0616 11:13:22.036489 9857 solver.cpp:571] Iteration 53800, lr = 0.0001 +I0616 11:13:33.600002 9857 solver.cpp:242] Iteration 53820, loss = 0.357807 +I0616 11:13:33.600042 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132773 (* 1 = 0.132773 loss) +I0616 11:13:33.600049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194885 (* 1 = 0.194885 loss) +I0616 11:13:33.600052 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.060443 (* 1 = 0.060443 loss) +I0616 11:13:33.600055 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144813 (* 1 = 0.0144813 loss) +I0616 11:13:33.600059 9857 solver.cpp:571] Iteration 53820, lr = 0.0001 +I0616 11:13:45.198979 9857 solver.cpp:242] Iteration 53840, loss = 0.467106 +I0616 11:13:45.199019 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124799 (* 1 = 0.124799 loss) +I0616 11:13:45.199024 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178178 (* 1 = 0.178178 loss) +I0616 11:13:45.199028 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0568346 (* 1 = 0.0568346 loss) +I0616 11:13:45.199033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0118049 (* 1 = 0.0118049 loss) +I0616 11:13:45.199035 9857 solver.cpp:571] Iteration 53840, lr = 0.0001 +I0616 11:13:56.712339 9857 solver.cpp:242] Iteration 53860, loss = 0.432492 +I0616 11:13:56.712383 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120647 (* 1 = 0.120647 loss) +I0616 11:13:56.712388 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134971 (* 1 = 0.134971 loss) +I0616 11:13:56.712393 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287025 (* 1 = 0.0287025 loss) +I0616 11:13:56.712396 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00821393 (* 1 = 0.00821393 loss) +I0616 11:13:56.712400 9857 solver.cpp:571] Iteration 53860, lr = 0.0001 +I0616 11:14:08.287564 9857 solver.cpp:242] Iteration 53880, loss = 1.09371 +I0616 11:14:08.287606 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.349711 (* 1 = 0.349711 loss) +I0616 11:14:08.287611 9857 solver.cpp:258] Train net output #1: loss_cls = 0.483714 (* 1 = 0.483714 loss) +I0616 11:14:08.287614 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15922 (* 1 = 0.15922 loss) +I0616 11:14:08.287618 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.166371 (* 1 = 0.166371 loss) +I0616 11:14:08.287622 9857 solver.cpp:571] Iteration 53880, lr = 0.0001 +I0616 11:14:19.990991 9857 solver.cpp:242] Iteration 53900, loss = 0.412989 +I0616 11:14:19.991034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.077118 (* 1 = 0.077118 loss) +I0616 11:14:19.991040 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182009 (* 1 = 0.182009 loss) +I0616 11:14:19.991044 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0117575 (* 1 = 0.0117575 loss) +I0616 11:14:19.991047 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183706 (* 1 = 0.0183706 loss) +I0616 11:14:19.991051 9857 solver.cpp:571] Iteration 53900, lr = 0.0001 +I0616 11:14:31.234272 9857 solver.cpp:242] Iteration 53920, loss = 1.33903 +I0616 11:14:31.234315 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32947 (* 1 = 0.32947 loss) +I0616 11:14:31.234320 9857 solver.cpp:258] Train net output #1: loss_cls = 0.46661 (* 1 = 0.46661 loss) +I0616 11:14:31.234324 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0622195 (* 1 = 0.0622195 loss) +I0616 11:14:31.234328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111312 (* 1 = 0.111312 loss) +I0616 11:14:31.234333 9857 solver.cpp:571] Iteration 53920, lr = 0.0001 +I0616 11:14:42.718233 9857 solver.cpp:242] Iteration 53940, loss = 0.397753 +I0616 11:14:42.718276 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178969 (* 1 = 0.178969 loss) +I0616 11:14:42.718281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244197 (* 1 = 0.244197 loss) +I0616 11:14:42.718286 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0755091 (* 1 = 0.0755091 loss) +I0616 11:14:42.718289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0623848 (* 1 = 0.0623848 loss) +I0616 11:14:42.718293 9857 solver.cpp:571] Iteration 53940, lr = 0.0001 +I0616 11:14:54.229313 9857 solver.cpp:242] Iteration 53960, loss = 0.926574 +I0616 11:14:54.229357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.355192 (* 1 = 0.355192 loss) +I0616 11:14:54.229362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370419 (* 1 = 0.370419 loss) +I0616 11:14:54.229365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160616 (* 1 = 0.160616 loss) +I0616 11:14:54.229369 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110989 (* 1 = 0.110989 loss) +I0616 11:14:54.229373 9857 solver.cpp:571] Iteration 53960, lr = 0.0001 +I0616 11:15:05.572672 9857 solver.cpp:242] Iteration 53980, loss = 0.61754 +I0616 11:15:05.572713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16282 (* 1 = 0.16282 loss) +I0616 11:15:05.572718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17311 (* 1 = 0.17311 loss) +I0616 11:15:05.572722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0553264 (* 1 = 0.0553264 loss) +I0616 11:15:05.572726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00860225 (* 1 = 0.00860225 loss) +I0616 11:15:05.572731 9857 solver.cpp:571] Iteration 53980, lr = 0.0001 +speed: 0.610s / iter +I0616 11:15:17.165652 9857 solver.cpp:242] Iteration 54000, loss = 0.368647 +I0616 11:15:17.165694 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0868903 (* 1 = 0.0868903 loss) +I0616 11:15:17.165699 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101545 (* 1 = 0.101545 loss) +I0616 11:15:17.165704 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287284 (* 1 = 0.0287284 loss) +I0616 11:15:17.165707 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01059 (* 1 = 0.01059 loss) +I0616 11:15:17.165710 9857 solver.cpp:571] Iteration 54000, lr = 0.0001 +I0616 11:15:28.522347 9857 solver.cpp:242] Iteration 54020, loss = 0.905481 +I0616 11:15:28.522389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.410174 (* 1 = 0.410174 loss) +I0616 11:15:28.522394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328871 (* 1 = 0.328871 loss) +I0616 11:15:28.522398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.236466 (* 1 = 0.236466 loss) +I0616 11:15:28.522403 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0734005 (* 1 = 0.0734005 loss) +I0616 11:15:28.522406 9857 solver.cpp:571] Iteration 54020, lr = 0.0001 +I0616 11:15:40.148309 9857 solver.cpp:242] Iteration 54040, loss = 0.566981 +I0616 11:15:40.148350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188012 (* 1 = 0.188012 loss) +I0616 11:15:40.148356 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249468 (* 1 = 0.249468 loss) +I0616 11:15:40.148360 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136568 (* 1 = 0.136568 loss) +I0616 11:15:40.148365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0677176 (* 1 = 0.0677176 loss) +I0616 11:15:40.148368 9857 solver.cpp:571] Iteration 54040, lr = 0.0001 +I0616 11:15:51.707301 9857 solver.cpp:242] Iteration 54060, loss = 0.319067 +I0616 11:15:51.707342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152191 (* 1 = 0.152191 loss) +I0616 11:15:51.707347 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15579 (* 1 = 0.15579 loss) +I0616 11:15:51.707351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0399003 (* 1 = 0.0399003 loss) +I0616 11:15:51.707355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00765653 (* 1 = 0.00765653 loss) +I0616 11:15:51.707358 9857 solver.cpp:571] Iteration 54060, lr = 0.0001 +I0616 11:16:03.283221 9857 solver.cpp:242] Iteration 54080, loss = 0.361729 +I0616 11:16:03.283265 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127712 (* 1 = 0.127712 loss) +I0616 11:16:03.283270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162211 (* 1 = 0.162211 loss) +I0616 11:16:03.283274 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212321 (* 1 = 0.0212321 loss) +I0616 11:16:03.283278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0488058 (* 1 = 0.0488058 loss) +I0616 11:16:03.283282 9857 solver.cpp:571] Iteration 54080, lr = 0.0001 +I0616 11:16:14.842164 9857 solver.cpp:242] Iteration 54100, loss = 0.928402 +I0616 11:16:14.842207 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394622 (* 1 = 0.394622 loss) +I0616 11:16:14.842226 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418302 (* 1 = 0.418302 loss) +I0616 11:16:14.842231 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0746797 (* 1 = 0.0746797 loss) +I0616 11:16:14.842233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0315813 (* 1 = 0.0315813 loss) +I0616 11:16:14.842237 9857 solver.cpp:571] Iteration 54100, lr = 0.0001 +I0616 11:16:26.687351 9857 solver.cpp:242] Iteration 54120, loss = 0.823699 +I0616 11:16:26.687394 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253209 (* 1 = 0.253209 loss) +I0616 11:16:26.687399 9857 solver.cpp:258] Train net output #1: loss_cls = 0.331035 (* 1 = 0.331035 loss) +I0616 11:16:26.687404 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0369814 (* 1 = 0.0369814 loss) +I0616 11:16:26.687407 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0219735 (* 1 = 0.0219735 loss) +I0616 11:16:26.687412 9857 solver.cpp:571] Iteration 54120, lr = 0.0001 +I0616 11:16:38.099939 9857 solver.cpp:242] Iteration 54140, loss = 1.00129 +I0616 11:16:38.099982 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0834621 (* 1 = 0.0834621 loss) +I0616 11:16:38.099988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209834 (* 1 = 0.209834 loss) +I0616 11:16:38.099992 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123118 (* 1 = 0.123118 loss) +I0616 11:16:38.099995 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.335196 (* 1 = 0.335196 loss) +I0616 11:16:38.099999 9857 solver.cpp:571] Iteration 54140, lr = 0.0001 +I0616 11:16:49.625811 9857 solver.cpp:242] Iteration 54160, loss = 0.340871 +I0616 11:16:49.625851 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161669 (* 1 = 0.161669 loss) +I0616 11:16:49.625857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222691 (* 1 = 0.222691 loss) +I0616 11:16:49.625861 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0373295 (* 1 = 0.0373295 loss) +I0616 11:16:49.625865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254356 (* 1 = 0.0254356 loss) +I0616 11:16:49.625869 9857 solver.cpp:571] Iteration 54160, lr = 0.0001 +I0616 11:17:01.277348 9857 solver.cpp:242] Iteration 54180, loss = 0.84163 +I0616 11:17:01.277391 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.496464 (* 1 = 0.496464 loss) +I0616 11:17:01.277396 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276063 (* 1 = 0.276063 loss) +I0616 11:17:01.277401 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0936595 (* 1 = 0.0936595 loss) +I0616 11:17:01.277405 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.063043 (* 1 = 0.063043 loss) +I0616 11:17:01.277408 9857 solver.cpp:571] Iteration 54180, lr = 0.0001 +speed: 0.610s / iter +I0616 11:17:12.915077 9857 solver.cpp:242] Iteration 54200, loss = 0.763324 +I0616 11:17:12.915118 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.463586 (* 1 = 0.463586 loss) +I0616 11:17:12.915124 9857 solver.cpp:258] Train net output #1: loss_cls = 0.425912 (* 1 = 0.425912 loss) +I0616 11:17:12.915128 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0805079 (* 1 = 0.0805079 loss) +I0616 11:17:12.915132 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.127727 (* 1 = 0.127727 loss) +I0616 11:17:12.915135 9857 solver.cpp:571] Iteration 54200, lr = 0.0001 +I0616 11:17:24.609884 9857 solver.cpp:242] Iteration 54220, loss = 0.444315 +I0616 11:17:24.609926 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106646 (* 1 = 0.106646 loss) +I0616 11:17:24.609932 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116391 (* 1 = 0.116391 loss) +I0616 11:17:24.609936 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0239367 (* 1 = 0.0239367 loss) +I0616 11:17:24.609940 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0266251 (* 1 = 0.0266251 loss) +I0616 11:17:24.609943 9857 solver.cpp:571] Iteration 54220, lr = 0.0001 +I0616 11:17:36.065520 9857 solver.cpp:242] Iteration 54240, loss = 0.227326 +I0616 11:17:36.065560 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0461399 (* 1 = 0.0461399 loss) +I0616 11:17:36.065567 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0741594 (* 1 = 0.0741594 loss) +I0616 11:17:36.065570 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0347197 (* 1 = 0.0347197 loss) +I0616 11:17:36.065574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00768 (* 1 = 0.00768 loss) +I0616 11:17:36.065577 9857 solver.cpp:571] Iteration 54240, lr = 0.0001 +I0616 11:17:47.676586 9857 solver.cpp:242] Iteration 54260, loss = 0.569518 +I0616 11:17:47.676630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0734728 (* 1 = 0.0734728 loss) +I0616 11:17:47.676635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0844547 (* 1 = 0.0844547 loss) +I0616 11:17:47.676640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155262 (* 1 = 0.0155262 loss) +I0616 11:17:47.676642 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00696142 (* 1 = 0.00696142 loss) +I0616 11:17:47.676646 9857 solver.cpp:571] Iteration 54260, lr = 0.0001 +I0616 11:17:59.221185 9857 solver.cpp:242] Iteration 54280, loss = 0.494281 +I0616 11:17:59.221227 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114484 (* 1 = 0.114484 loss) +I0616 11:17:59.221233 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232491 (* 1 = 0.232491 loss) +I0616 11:17:59.221237 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0113096 (* 1 = 0.0113096 loss) +I0616 11:17:59.221241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00120874 (* 1 = 0.00120874 loss) +I0616 11:17:59.221246 9857 solver.cpp:571] Iteration 54280, lr = 0.0001 +I0616 11:18:10.535719 9857 solver.cpp:242] Iteration 54300, loss = 1.26118 +I0616 11:18:10.535763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360935 (* 1 = 0.360935 loss) +I0616 11:18:10.535768 9857 solver.cpp:258] Train net output #1: loss_cls = 0.489303 (* 1 = 0.489303 loss) +I0616 11:18:10.535771 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0961934 (* 1 = 0.0961934 loss) +I0616 11:18:10.535775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0740816 (* 1 = 0.0740816 loss) +I0616 11:18:10.535778 9857 solver.cpp:571] Iteration 54300, lr = 0.0001 +I0616 11:18:22.145759 9857 solver.cpp:242] Iteration 54320, loss = 1.0335 +I0616 11:18:22.145802 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303499 (* 1 = 0.303499 loss) +I0616 11:18:22.145808 9857 solver.cpp:258] Train net output #1: loss_cls = 0.674361 (* 1 = 0.674361 loss) +I0616 11:18:22.145812 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166237 (* 1 = 0.166237 loss) +I0616 11:18:22.145817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0336161 (* 1 = 0.0336161 loss) +I0616 11:18:22.145822 9857 solver.cpp:571] Iteration 54320, lr = 0.0001 +I0616 11:18:33.681674 9857 solver.cpp:242] Iteration 54340, loss = 1.0743 +I0616 11:18:33.681715 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352053 (* 1 = 0.352053 loss) +I0616 11:18:33.681721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.427697 (* 1 = 0.427697 loss) +I0616 11:18:33.681725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158603 (* 1 = 0.158603 loss) +I0616 11:18:33.681728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.499361 (* 1 = 0.499361 loss) +I0616 11:18:33.681732 9857 solver.cpp:571] Iteration 54340, lr = 0.0001 +I0616 11:18:45.269182 9857 solver.cpp:242] Iteration 54360, loss = 0.495739 +I0616 11:18:45.269223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239379 (* 1 = 0.239379 loss) +I0616 11:18:45.269229 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170868 (* 1 = 0.170868 loss) +I0616 11:18:45.269233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0792722 (* 1 = 0.0792722 loss) +I0616 11:18:45.269237 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0790013 (* 1 = 0.0790013 loss) +I0616 11:18:45.269242 9857 solver.cpp:571] Iteration 54360, lr = 0.0001 +I0616 11:18:56.895367 9857 solver.cpp:242] Iteration 54380, loss = 0.243656 +I0616 11:18:56.895409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0516176 (* 1 = 0.0516176 loss) +I0616 11:18:56.895414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0802833 (* 1 = 0.0802833 loss) +I0616 11:18:56.895418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112147 (* 1 = 0.0112147 loss) +I0616 11:18:56.895422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179443 (* 1 = 0.0179443 loss) +I0616 11:18:56.895426 9857 solver.cpp:571] Iteration 54380, lr = 0.0001 +speed: 0.610s / iter +I0616 11:19:08.241436 9857 solver.cpp:242] Iteration 54400, loss = 0.570004 +I0616 11:19:08.241474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135564 (* 1 = 0.135564 loss) +I0616 11:19:08.241480 9857 solver.cpp:258] Train net output #1: loss_cls = 0.362463 (* 1 = 0.362463 loss) +I0616 11:19:08.241484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0199076 (* 1 = 0.0199076 loss) +I0616 11:19:08.241488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.212852 (* 1 = 0.212852 loss) +I0616 11:19:08.241492 9857 solver.cpp:571] Iteration 54400, lr = 0.0001 +I0616 11:19:19.747239 9857 solver.cpp:242] Iteration 54420, loss = 0.485882 +I0616 11:19:19.747282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271682 (* 1 = 0.271682 loss) +I0616 11:19:19.747287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.383831 (* 1 = 0.383831 loss) +I0616 11:19:19.747292 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0569877 (* 1 = 0.0569877 loss) +I0616 11:19:19.747295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337272 (* 1 = 0.0337272 loss) +I0616 11:19:19.747298 9857 solver.cpp:571] Iteration 54420, lr = 0.0001 +I0616 11:19:31.131342 9857 solver.cpp:242] Iteration 54440, loss = 1.1787 +I0616 11:19:31.131386 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221183 (* 1 = 0.221183 loss) +I0616 11:19:31.131391 9857 solver.cpp:258] Train net output #1: loss_cls = 0.356908 (* 1 = 0.356908 loss) +I0616 11:19:31.131395 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.220173 (* 1 = 0.220173 loss) +I0616 11:19:31.131398 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.162933 (* 1 = 0.162933 loss) +I0616 11:19:31.131402 9857 solver.cpp:571] Iteration 54440, lr = 0.0001 +I0616 11:19:42.744014 9857 solver.cpp:242] Iteration 54460, loss = 0.369337 +I0616 11:19:42.744056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152053 (* 1 = 0.152053 loss) +I0616 11:19:42.744062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155653 (* 1 = 0.155653 loss) +I0616 11:19:42.744066 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0708033 (* 1 = 0.0708033 loss) +I0616 11:19:42.744071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00679085 (* 1 = 0.00679085 loss) +I0616 11:19:42.744073 9857 solver.cpp:571] Iteration 54460, lr = 0.0001 +I0616 11:19:54.584265 9857 solver.cpp:242] Iteration 54480, loss = 0.359664 +I0616 11:19:54.584303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0661326 (* 1 = 0.0661326 loss) +I0616 11:19:54.584308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0764379 (* 1 = 0.0764379 loss) +I0616 11:19:54.584313 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.004377 (* 1 = 0.004377 loss) +I0616 11:19:54.584316 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00370972 (* 1 = 0.00370972 loss) +I0616 11:19:54.584321 9857 solver.cpp:571] Iteration 54480, lr = 0.0001 +I0616 11:20:05.971321 9857 solver.cpp:242] Iteration 54500, loss = 0.371882 +I0616 11:20:05.971364 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151441 (* 1 = 0.151441 loss) +I0616 11:20:05.971382 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176144 (* 1 = 0.176144 loss) +I0616 11:20:05.971386 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105556 (* 1 = 0.105556 loss) +I0616 11:20:05.971390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102514 (* 1 = 0.0102514 loss) +I0616 11:20:05.971395 9857 solver.cpp:571] Iteration 54500, lr = 0.0001 +I0616 11:20:17.280673 9857 solver.cpp:242] Iteration 54520, loss = 0.371894 +I0616 11:20:17.280710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0844081 (* 1 = 0.0844081 loss) +I0616 11:20:17.280716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163052 (* 1 = 0.163052 loss) +I0616 11:20:17.280720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00457119 (* 1 = 0.00457119 loss) +I0616 11:20:17.280725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0809813 (* 1 = 0.0809813 loss) +I0616 11:20:17.280727 9857 solver.cpp:571] Iteration 54520, lr = 0.0001 +I0616 11:20:28.795840 9857 solver.cpp:242] Iteration 54540, loss = 0.523147 +I0616 11:20:28.795883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260437 (* 1 = 0.260437 loss) +I0616 11:20:28.795888 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310931 (* 1 = 0.310931 loss) +I0616 11:20:28.795892 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138961 (* 1 = 0.138961 loss) +I0616 11:20:28.795897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0263843 (* 1 = 0.0263843 loss) +I0616 11:20:28.795900 9857 solver.cpp:571] Iteration 54540, lr = 0.0001 +I0616 11:20:40.226743 9857 solver.cpp:242] Iteration 54560, loss = 0.739228 +I0616 11:20:40.226785 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115908 (* 1 = 0.115908 loss) +I0616 11:20:40.226793 9857 solver.cpp:258] Train net output #1: loss_cls = 0.30038 (* 1 = 0.30038 loss) +I0616 11:20:40.226796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0308833 (* 1 = 0.0308833 loss) +I0616 11:20:40.226800 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00903757 (* 1 = 0.00903757 loss) +I0616 11:20:40.226804 9857 solver.cpp:571] Iteration 54560, lr = 0.0001 +I0616 11:20:52.085194 9857 solver.cpp:242] Iteration 54580, loss = 0.34843 +I0616 11:20:52.085235 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172582 (* 1 = 0.172582 loss) +I0616 11:20:52.085242 9857 solver.cpp:258] Train net output #1: loss_cls = 0.198367 (* 1 = 0.198367 loss) +I0616 11:20:52.085245 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101032 (* 1 = 0.101032 loss) +I0616 11:20:52.085249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0417105 (* 1 = 0.0417105 loss) +I0616 11:20:52.085255 9857 solver.cpp:571] Iteration 54580, lr = 0.0001 +speed: 0.610s / iter +I0616 11:21:03.571560 9857 solver.cpp:242] Iteration 54600, loss = 0.657109 +I0616 11:21:03.571604 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252315 (* 1 = 0.252315 loss) +I0616 11:21:03.571609 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378347 (* 1 = 0.378347 loss) +I0616 11:21:03.571612 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.302401 (* 1 = 0.302401 loss) +I0616 11:21:03.571616 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.139226 (* 1 = 0.139226 loss) +I0616 11:21:03.571619 9857 solver.cpp:571] Iteration 54600, lr = 0.0001 +I0616 11:21:14.977004 9857 solver.cpp:242] Iteration 54620, loss = 0.646806 +I0616 11:21:14.977046 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329139 (* 1 = 0.329139 loss) +I0616 11:21:14.977051 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249511 (* 1 = 0.249511 loss) +I0616 11:21:14.977056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0765086 (* 1 = 0.0765086 loss) +I0616 11:21:14.977059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0674039 (* 1 = 0.0674039 loss) +I0616 11:21:14.977063 9857 solver.cpp:571] Iteration 54620, lr = 0.0001 +I0616 11:21:26.417122 9857 solver.cpp:242] Iteration 54640, loss = 0.613772 +I0616 11:21:26.417165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329052 (* 1 = 0.329052 loss) +I0616 11:21:26.417171 9857 solver.cpp:258] Train net output #1: loss_cls = 0.652077 (* 1 = 0.652077 loss) +I0616 11:21:26.417174 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0437846 (* 1 = 0.0437846 loss) +I0616 11:21:26.417177 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0704004 (* 1 = 0.0704004 loss) +I0616 11:21:26.417182 9857 solver.cpp:571] Iteration 54640, lr = 0.0001 +I0616 11:21:37.950628 9857 solver.cpp:242] Iteration 54660, loss = 0.774433 +I0616 11:21:37.950669 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302092 (* 1 = 0.302092 loss) +I0616 11:21:37.950675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.539335 (* 1 = 0.539335 loss) +I0616 11:21:37.950678 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294609 (* 1 = 0.294609 loss) +I0616 11:21:37.950682 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.210348 (* 1 = 0.210348 loss) +I0616 11:21:37.950685 9857 solver.cpp:571] Iteration 54660, lr = 0.0001 +I0616 11:21:49.644588 9857 solver.cpp:242] Iteration 54680, loss = 0.202235 +I0616 11:21:49.644631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.044105 (* 1 = 0.044105 loss) +I0616 11:21:49.644637 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102782 (* 1 = 0.102782 loss) +I0616 11:21:49.644641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0204471 (* 1 = 0.0204471 loss) +I0616 11:21:49.644645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0361519 (* 1 = 0.0361519 loss) +I0616 11:21:49.644649 9857 solver.cpp:571] Iteration 54680, lr = 0.0001 +I0616 11:22:00.965667 9857 solver.cpp:242] Iteration 54700, loss = 0.640469 +I0616 11:22:00.965708 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.07984 (* 1 = 0.07984 loss) +I0616 11:22:00.965713 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159017 (* 1 = 0.159017 loss) +I0616 11:22:00.965718 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0201625 (* 1 = 0.0201625 loss) +I0616 11:22:00.965721 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164129 (* 1 = 0.0164129 loss) +I0616 11:22:00.965725 9857 solver.cpp:571] Iteration 54700, lr = 0.0001 +I0616 11:22:12.579280 9857 solver.cpp:242] Iteration 54720, loss = 0.398465 +I0616 11:22:12.579337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210212 (* 1 = 0.210212 loss) +I0616 11:22:12.579355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251886 (* 1 = 0.251886 loss) +I0616 11:22:12.579360 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0855572 (* 1 = 0.0855572 loss) +I0616 11:22:12.579363 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.02006 (* 1 = 0.02006 loss) +I0616 11:22:12.579367 9857 solver.cpp:571] Iteration 54720, lr = 0.0001 +I0616 11:22:24.256641 9857 solver.cpp:242] Iteration 54740, loss = 0.622967 +I0616 11:22:24.256683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186074 (* 1 = 0.186074 loss) +I0616 11:22:24.256688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270476 (* 1 = 0.270476 loss) +I0616 11:22:24.256692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112674 (* 1 = 0.112674 loss) +I0616 11:22:24.256696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0717217 (* 1 = 0.0717217 loss) +I0616 11:22:24.256700 9857 solver.cpp:571] Iteration 54740, lr = 0.0001 +I0616 11:22:35.902143 9857 solver.cpp:242] Iteration 54760, loss = 0.813659 +I0616 11:22:35.902185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371133 (* 1 = 0.371133 loss) +I0616 11:22:35.902190 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323902 (* 1 = 0.323902 loss) +I0616 11:22:35.902195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0733804 (* 1 = 0.0733804 loss) +I0616 11:22:35.902199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0618395 (* 1 = 0.0618395 loss) +I0616 11:22:35.902202 9857 solver.cpp:571] Iteration 54760, lr = 0.0001 +I0616 11:22:47.576792 9857 solver.cpp:242] Iteration 54780, loss = 0.505592 +I0616 11:22:47.576834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0803162 (* 1 = 0.0803162 loss) +I0616 11:22:47.576840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207334 (* 1 = 0.207334 loss) +I0616 11:22:47.576844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143838 (* 1 = 0.143838 loss) +I0616 11:22:47.576848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.335073 (* 1 = 0.335073 loss) +I0616 11:22:47.576854 9857 solver.cpp:571] Iteration 54780, lr = 0.0001 +speed: 0.610s / iter +I0616 11:22:59.094220 9857 solver.cpp:242] Iteration 54800, loss = 0.250829 +I0616 11:22:59.094261 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0930761 (* 1 = 0.0930761 loss) +I0616 11:22:59.094269 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142283 (* 1 = 0.142283 loss) +I0616 11:22:59.094272 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0103821 (* 1 = 0.0103821 loss) +I0616 11:22:59.094276 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00525743 (* 1 = 0.00525743 loss) +I0616 11:22:59.094280 9857 solver.cpp:571] Iteration 54800, lr = 0.0001 +I0616 11:23:10.425279 9857 solver.cpp:242] Iteration 54820, loss = 0.774934 +I0616 11:23:10.425321 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0678074 (* 1 = 0.0678074 loss) +I0616 11:23:10.425328 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0670681 (* 1 = 0.0670681 loss) +I0616 11:23:10.425331 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00640765 (* 1 = 0.00640765 loss) +I0616 11:23:10.425335 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00132758 (* 1 = 0.00132758 loss) +I0616 11:23:10.425339 9857 solver.cpp:571] Iteration 54820, lr = 0.0001 +I0616 11:23:22.130306 9857 solver.cpp:242] Iteration 54840, loss = 0.63226 +I0616 11:23:22.130347 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118963 (* 1 = 0.118963 loss) +I0616 11:23:22.130352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0958721 (* 1 = 0.0958721 loss) +I0616 11:23:22.130357 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105797 (* 1 = 0.105797 loss) +I0616 11:23:22.130360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197683 (* 1 = 0.0197683 loss) +I0616 11:23:22.130363 9857 solver.cpp:571] Iteration 54840, lr = 0.0001 +I0616 11:23:34.071308 9857 solver.cpp:242] Iteration 54860, loss = 0.266926 +I0616 11:23:34.071348 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123451 (* 1 = 0.123451 loss) +I0616 11:23:34.071354 9857 solver.cpp:258] Train net output #1: loss_cls = 0.133515 (* 1 = 0.133515 loss) +I0616 11:23:34.071358 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00214817 (* 1 = 0.00214817 loss) +I0616 11:23:34.071362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014987 (* 1 = 0.014987 loss) +I0616 11:23:34.071365 9857 solver.cpp:571] Iteration 54860, lr = 0.0001 +I0616 11:23:45.517400 9857 solver.cpp:242] Iteration 54880, loss = 1.30685 +I0616 11:23:45.517442 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315358 (* 1 = 0.315358 loss) +I0616 11:23:45.517447 9857 solver.cpp:258] Train net output #1: loss_cls = 0.562153 (* 1 = 0.562153 loss) +I0616 11:23:45.517452 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0547618 (* 1 = 0.0547618 loss) +I0616 11:23:45.517455 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269239 (* 1 = 0.0269239 loss) +I0616 11:23:45.517459 9857 solver.cpp:571] Iteration 54880, lr = 0.0001 +I0616 11:23:57.240506 9857 solver.cpp:242] Iteration 54900, loss = 1.3388 +I0616 11:23:57.240550 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.420347 (* 1 = 0.420347 loss) +I0616 11:23:57.240556 9857 solver.cpp:258] Train net output #1: loss_cls = 0.428113 (* 1 = 0.428113 loss) +I0616 11:23:57.240559 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.262729 (* 1 = 0.262729 loss) +I0616 11:23:57.240563 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0909979 (* 1 = 0.0909979 loss) +I0616 11:23:57.240566 9857 solver.cpp:571] Iteration 54900, lr = 0.0001 +I0616 11:24:08.722628 9857 solver.cpp:242] Iteration 54920, loss = 1.28281 +I0616 11:24:08.722671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.402026 (* 1 = 0.402026 loss) +I0616 11:24:08.722676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.494735 (* 1 = 0.494735 loss) +I0616 11:24:08.722681 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.617066 (* 1 = 0.617066 loss) +I0616 11:24:08.722683 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.164599 (* 1 = 0.164599 loss) +I0616 11:24:08.722687 9857 solver.cpp:571] Iteration 54920, lr = 0.0001 +I0616 11:24:20.256309 9857 solver.cpp:242] Iteration 54940, loss = 0.670437 +I0616 11:24:20.256350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0699046 (* 1 = 0.0699046 loss) +I0616 11:24:20.256356 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167885 (* 1 = 0.167885 loss) +I0616 11:24:20.256361 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212584 (* 1 = 0.0212584 loss) +I0616 11:24:20.256363 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00646074 (* 1 = 0.00646074 loss) +I0616 11:24:20.256367 9857 solver.cpp:571] Iteration 54940, lr = 0.0001 +I0616 11:24:31.921720 9857 solver.cpp:242] Iteration 54960, loss = 0.831685 +I0616 11:24:31.921761 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285197 (* 1 = 0.285197 loss) +I0616 11:24:31.921767 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270479 (* 1 = 0.270479 loss) +I0616 11:24:31.921772 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0602397 (* 1 = 0.0602397 loss) +I0616 11:24:31.921775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114284 (* 1 = 0.0114284 loss) +I0616 11:24:31.921779 9857 solver.cpp:571] Iteration 54960, lr = 0.0001 +I0616 11:24:43.456423 9857 solver.cpp:242] Iteration 54980, loss = 1.16436 +I0616 11:24:43.456465 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337268 (* 1 = 0.337268 loss) +I0616 11:24:43.456471 9857 solver.cpp:258] Train net output #1: loss_cls = 0.688504 (* 1 = 0.688504 loss) +I0616 11:24:43.456475 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.454149 (* 1 = 0.454149 loss) +I0616 11:24:43.456480 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0508934 (* 1 = 0.0508934 loss) +I0616 11:24:43.456483 9857 solver.cpp:571] Iteration 54980, lr = 0.0001 +speed: 0.610s / iter +I0616 11:24:54.990443 9857 solver.cpp:242] Iteration 55000, loss = 0.240219 +I0616 11:24:54.990484 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0744676 (* 1 = 0.0744676 loss) +I0616 11:24:54.990489 9857 solver.cpp:258] Train net output #1: loss_cls = 0.090693 (* 1 = 0.090693 loss) +I0616 11:24:54.990494 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00787997 (* 1 = 0.00787997 loss) +I0616 11:24:54.990497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00811501 (* 1 = 0.00811501 loss) +I0616 11:24:54.990501 9857 solver.cpp:571] Iteration 55000, lr = 0.0001 +I0616 11:25:06.401340 9857 solver.cpp:242] Iteration 55020, loss = 0.224791 +I0616 11:25:06.401382 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0374744 (* 1 = 0.0374744 loss) +I0616 11:25:06.401388 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114406 (* 1 = 0.114406 loss) +I0616 11:25:06.401392 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0321304 (* 1 = 0.0321304 loss) +I0616 11:25:06.401396 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109871 (* 1 = 0.0109871 loss) +I0616 11:25:06.401401 9857 solver.cpp:571] Iteration 55020, lr = 0.0001 +I0616 11:25:18.216558 9857 solver.cpp:242] Iteration 55040, loss = 0.446506 +I0616 11:25:18.216599 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143869 (* 1 = 0.143869 loss) +I0616 11:25:18.216604 9857 solver.cpp:258] Train net output #1: loss_cls = 0.469101 (* 1 = 0.469101 loss) +I0616 11:25:18.216609 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0335227 (* 1 = 0.0335227 loss) +I0616 11:25:18.216612 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255961 (* 1 = 0.0255961 loss) +I0616 11:25:18.216616 9857 solver.cpp:571] Iteration 55040, lr = 0.0001 +I0616 11:25:29.820518 9857 solver.cpp:242] Iteration 55060, loss = 1.0761 +I0616 11:25:29.820559 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338432 (* 1 = 0.338432 loss) +I0616 11:25:29.820564 9857 solver.cpp:258] Train net output #1: loss_cls = 0.445941 (* 1 = 0.445941 loss) +I0616 11:25:29.820569 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.244994 (* 1 = 0.244994 loss) +I0616 11:25:29.820572 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.403517 (* 1 = 0.403517 loss) +I0616 11:25:29.820576 9857 solver.cpp:571] Iteration 55060, lr = 0.0001 +I0616 11:25:41.380317 9857 solver.cpp:242] Iteration 55080, loss = 0.434427 +I0616 11:25:41.380360 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0829017 (* 1 = 0.0829017 loss) +I0616 11:25:41.380367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124359 (* 1 = 0.124359 loss) +I0616 11:25:41.380370 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112325 (* 1 = 0.0112325 loss) +I0616 11:25:41.380374 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0185089 (* 1 = 0.0185089 loss) +I0616 11:25:41.380378 9857 solver.cpp:571] Iteration 55080, lr = 0.0001 +I0616 11:25:52.816450 9857 solver.cpp:242] Iteration 55100, loss = 0.345155 +I0616 11:25:52.816491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157269 (* 1 = 0.157269 loss) +I0616 11:25:52.816498 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162846 (* 1 = 0.162846 loss) +I0616 11:25:52.816501 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0260844 (* 1 = 0.0260844 loss) +I0616 11:25:52.816505 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00979772 (* 1 = 0.00979772 loss) +I0616 11:25:52.816509 9857 solver.cpp:571] Iteration 55100, lr = 0.0001 +I0616 11:26:04.089491 9857 solver.cpp:242] Iteration 55120, loss = 0.480954 +I0616 11:26:04.089534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284021 (* 1 = 0.284021 loss) +I0616 11:26:04.089539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219527 (* 1 = 0.219527 loss) +I0616 11:26:04.089542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15897 (* 1 = 0.15897 loss) +I0616 11:26:04.089546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108163 (* 1 = 0.108163 loss) +I0616 11:26:04.089550 9857 solver.cpp:571] Iteration 55120, lr = 0.0001 +I0616 11:26:15.466728 9857 solver.cpp:242] Iteration 55140, loss = 0.68317 +I0616 11:26:15.466775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.405538 (* 1 = 0.405538 loss) +I0616 11:26:15.466783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.53801 (* 1 = 0.53801 loss) +I0616 11:26:15.466786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.229782 (* 1 = 0.229782 loss) +I0616 11:26:15.466790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179879 (* 1 = 0.0179879 loss) +I0616 11:26:15.466794 9857 solver.cpp:571] Iteration 55140, lr = 0.0001 +I0616 11:26:27.118798 9857 solver.cpp:242] Iteration 55160, loss = 0.818888 +I0616 11:26:27.118840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.420893 (* 1 = 0.420893 loss) +I0616 11:26:27.118845 9857 solver.cpp:258] Train net output #1: loss_cls = 0.547313 (* 1 = 0.547313 loss) +I0616 11:26:27.118850 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.24114 (* 1 = 0.24114 loss) +I0616 11:26:27.118854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.202777 (* 1 = 0.202777 loss) +I0616 11:26:27.118857 9857 solver.cpp:571] Iteration 55160, lr = 0.0001 +I0616 11:26:38.668067 9857 solver.cpp:242] Iteration 55180, loss = 0.521589 +I0616 11:26:38.668109 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254465 (* 1 = 0.254465 loss) +I0616 11:26:38.668115 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221439 (* 1 = 0.221439 loss) +I0616 11:26:38.668119 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0716552 (* 1 = 0.0716552 loss) +I0616 11:26:38.668123 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.118959 (* 1 = 0.118959 loss) +I0616 11:26:38.668128 9857 solver.cpp:571] Iteration 55180, lr = 0.0001 +speed: 0.610s / iter +I0616 11:26:50.324352 9857 solver.cpp:242] Iteration 55200, loss = 0.215607 +I0616 11:26:50.324393 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112044 (* 1 = 0.112044 loss) +I0616 11:26:50.324398 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0919366 (* 1 = 0.0919366 loss) +I0616 11:26:50.324403 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0379919 (* 1 = 0.0379919 loss) +I0616 11:26:50.324406 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00410191 (* 1 = 0.00410191 loss) +I0616 11:26:50.324410 9857 solver.cpp:571] Iteration 55200, lr = 0.0001 +I0616 11:27:01.929818 9857 solver.cpp:242] Iteration 55220, loss = 0.929151 +I0616 11:27:01.929862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251799 (* 1 = 0.251799 loss) +I0616 11:27:01.929867 9857 solver.cpp:258] Train net output #1: loss_cls = 0.389215 (* 1 = 0.389215 loss) +I0616 11:27:01.929872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172286 (* 1 = 0.172286 loss) +I0616 11:27:01.929875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170728 (* 1 = 0.0170728 loss) +I0616 11:27:01.929879 9857 solver.cpp:571] Iteration 55220, lr = 0.0001 +I0616 11:27:13.595402 9857 solver.cpp:242] Iteration 55240, loss = 0.718993 +I0616 11:27:13.595445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331275 (* 1 = 0.331275 loss) +I0616 11:27:13.595450 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426929 (* 1 = 0.426929 loss) +I0616 11:27:13.595454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134893 (* 1 = 0.134893 loss) +I0616 11:27:13.595458 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.141593 (* 1 = 0.141593 loss) +I0616 11:27:13.595463 9857 solver.cpp:571] Iteration 55240, lr = 0.0001 +I0616 11:27:25.333732 9857 solver.cpp:242] Iteration 55260, loss = 0.934322 +I0616 11:27:25.333775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27046 (* 1 = 0.27046 loss) +I0616 11:27:25.333781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289354 (* 1 = 0.289354 loss) +I0616 11:27:25.333784 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.257257 (* 1 = 0.257257 loss) +I0616 11:27:25.333788 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0886963 (* 1 = 0.0886963 loss) +I0616 11:27:25.333792 9857 solver.cpp:571] Iteration 55260, lr = 0.0001 +I0616 11:27:36.869676 9857 solver.cpp:242] Iteration 55280, loss = 0.938853 +I0616 11:27:36.869717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202391 (* 1 = 0.202391 loss) +I0616 11:27:36.869722 9857 solver.cpp:258] Train net output #1: loss_cls = 0.325802 (* 1 = 0.325802 loss) +I0616 11:27:36.869727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0597758 (* 1 = 0.0597758 loss) +I0616 11:27:36.869730 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00612391 (* 1 = 0.00612391 loss) +I0616 11:27:36.869735 9857 solver.cpp:571] Iteration 55280, lr = 0.0001 +I0616 11:27:48.380462 9857 solver.cpp:242] Iteration 55300, loss = 0.581293 +I0616 11:27:48.380504 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272045 (* 1 = 0.272045 loss) +I0616 11:27:48.380509 9857 solver.cpp:258] Train net output #1: loss_cls = 0.450701 (* 1 = 0.450701 loss) +I0616 11:27:48.380513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0654155 (* 1 = 0.0654155 loss) +I0616 11:27:48.380517 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325698 (* 1 = 0.0325698 loss) +I0616 11:27:48.380520 9857 solver.cpp:571] Iteration 55300, lr = 0.0001 +I0616 11:27:59.861753 9857 solver.cpp:242] Iteration 55320, loss = 0.696771 +I0616 11:27:59.861796 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0960198 (* 1 = 0.0960198 loss) +I0616 11:27:59.861801 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128566 (* 1 = 0.128566 loss) +I0616 11:27:59.861805 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0583466 (* 1 = 0.0583466 loss) +I0616 11:27:59.861809 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0268993 (* 1 = 0.0268993 loss) +I0616 11:27:59.861814 9857 solver.cpp:571] Iteration 55320, lr = 0.0001 +I0616 11:28:11.462780 9857 solver.cpp:242] Iteration 55340, loss = 0.731448 +I0616 11:28:11.462819 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316892 (* 1 = 0.316892 loss) +I0616 11:28:11.462826 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287381 (* 1 = 0.287381 loss) +I0616 11:28:11.462829 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0527512 (* 1 = 0.0527512 loss) +I0616 11:28:11.462833 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0858823 (* 1 = 0.0858823 loss) +I0616 11:28:11.462836 9857 solver.cpp:571] Iteration 55340, lr = 0.0001 +I0616 11:28:22.946863 9857 solver.cpp:242] Iteration 55360, loss = 0.243149 +I0616 11:28:22.946907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0730745 (* 1 = 0.0730745 loss) +I0616 11:28:22.946912 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0864427 (* 1 = 0.0864427 loss) +I0616 11:28:22.946915 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0106549 (* 1 = 0.0106549 loss) +I0616 11:28:22.946919 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00429718 (* 1 = 0.00429718 loss) +I0616 11:28:22.946923 9857 solver.cpp:571] Iteration 55360, lr = 0.0001 +I0616 11:28:34.144644 9857 solver.cpp:242] Iteration 55380, loss = 0.731577 +I0616 11:28:34.144687 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296689 (* 1 = 0.296689 loss) +I0616 11:28:34.144692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.427583 (* 1 = 0.427583 loss) +I0616 11:28:34.144697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.221158 (* 1 = 0.221158 loss) +I0616 11:28:34.144701 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.182604 (* 1 = 0.182604 loss) +I0616 11:28:34.144704 9857 solver.cpp:571] Iteration 55380, lr = 0.0001 +speed: 0.609s / iter +I0616 11:28:45.744853 9857 solver.cpp:242] Iteration 55400, loss = 0.618318 +I0616 11:28:45.744890 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157658 (* 1 = 0.157658 loss) +I0616 11:28:45.744896 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4655 (* 1 = 0.4655 loss) +I0616 11:28:45.744900 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112713 (* 1 = 0.112713 loss) +I0616 11:28:45.744904 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119496 (* 1 = 0.0119496 loss) +I0616 11:28:45.744907 9857 solver.cpp:571] Iteration 55400, lr = 0.0001 +I0616 11:28:57.195586 9857 solver.cpp:242] Iteration 55420, loss = 0.926491 +I0616 11:28:57.195628 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367767 (* 1 = 0.367767 loss) +I0616 11:28:57.195633 9857 solver.cpp:258] Train net output #1: loss_cls = 0.7845 (* 1 = 0.7845 loss) +I0616 11:28:57.195637 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.302596 (* 1 = 0.302596 loss) +I0616 11:28:57.195641 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.106498 (* 1 = 0.106498 loss) +I0616 11:28:57.195646 9857 solver.cpp:571] Iteration 55420, lr = 0.0001 +I0616 11:29:08.773713 9857 solver.cpp:242] Iteration 55440, loss = 0.441019 +I0616 11:29:08.773756 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224252 (* 1 = 0.224252 loss) +I0616 11:29:08.773761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252029 (* 1 = 0.252029 loss) +I0616 11:29:08.773766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0553926 (* 1 = 0.0553926 loss) +I0616 11:29:08.773769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.044694 (* 1 = 0.044694 loss) +I0616 11:29:08.773773 9857 solver.cpp:571] Iteration 55440, lr = 0.0001 +I0616 11:29:20.088651 9857 solver.cpp:242] Iteration 55460, loss = 0.517567 +I0616 11:29:20.088695 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0518362 (* 1 = 0.0518362 loss) +I0616 11:29:20.088701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0753019 (* 1 = 0.0753019 loss) +I0616 11:29:20.088704 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00972559 (* 1 = 0.00972559 loss) +I0616 11:29:20.088708 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138689 (* 1 = 0.0138689 loss) +I0616 11:29:20.088712 9857 solver.cpp:571] Iteration 55460, lr = 0.0001 +I0616 11:29:31.746793 9857 solver.cpp:242] Iteration 55480, loss = 0.489533 +I0616 11:29:31.746834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324728 (* 1 = 0.324728 loss) +I0616 11:29:31.746840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235961 (* 1 = 0.235961 loss) +I0616 11:29:31.746843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0847603 (* 1 = 0.0847603 loss) +I0616 11:29:31.746847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165631 (* 1 = 0.0165631 loss) +I0616 11:29:31.746850 9857 solver.cpp:571] Iteration 55480, lr = 0.0001 +I0616 11:29:43.228492 9857 solver.cpp:242] Iteration 55500, loss = 0.388195 +I0616 11:29:43.228534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163028 (* 1 = 0.163028 loss) +I0616 11:29:43.228539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20622 (* 1 = 0.20622 loss) +I0616 11:29:43.228544 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0764711 (* 1 = 0.0764711 loss) +I0616 11:29:43.228549 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286657 (* 1 = 0.0286657 loss) +I0616 11:29:43.228551 9857 solver.cpp:571] Iteration 55500, lr = 0.0001 +I0616 11:29:54.648283 9857 solver.cpp:242] Iteration 55520, loss = 0.850121 +I0616 11:29:54.648325 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158292 (* 1 = 0.158292 loss) +I0616 11:29:54.648331 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123901 (* 1 = 0.123901 loss) +I0616 11:29:54.648335 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0379674 (* 1 = 0.0379674 loss) +I0616 11:29:54.648339 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0243785 (* 1 = 0.0243785 loss) +I0616 11:29:54.648344 9857 solver.cpp:571] Iteration 55520, lr = 0.0001 +I0616 11:30:06.218240 9857 solver.cpp:242] Iteration 55540, loss = 0.674829 +I0616 11:30:06.218283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0751069 (* 1 = 0.0751069 loss) +I0616 11:30:06.218288 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108461 (* 1 = 0.108461 loss) +I0616 11:30:06.218292 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0485695 (* 1 = 0.0485695 loss) +I0616 11:30:06.218297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0789823 (* 1 = 0.0789823 loss) +I0616 11:30:06.218299 9857 solver.cpp:571] Iteration 55540, lr = 0.0001 +I0616 11:30:17.972056 9857 solver.cpp:242] Iteration 55560, loss = 0.448052 +I0616 11:30:17.972100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133027 (* 1 = 0.133027 loss) +I0616 11:30:17.972105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164907 (* 1 = 0.164907 loss) +I0616 11:30:17.972110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0417506 (* 1 = 0.0417506 loss) +I0616 11:30:17.972113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00687636 (* 1 = 0.00687636 loss) +I0616 11:30:17.972117 9857 solver.cpp:571] Iteration 55560, lr = 0.0001 +I0616 11:30:29.437716 9857 solver.cpp:242] Iteration 55580, loss = 0.943818 +I0616 11:30:29.437758 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337352 (* 1 = 0.337352 loss) +I0616 11:30:29.437764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.661371 (* 1 = 0.661371 loss) +I0616 11:30:29.437768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.288857 (* 1 = 0.288857 loss) +I0616 11:30:29.437772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.260402 (* 1 = 0.260402 loss) +I0616 11:30:29.437777 9857 solver.cpp:571] Iteration 55580, lr = 0.0001 +speed: 0.609s / iter +I0616 11:30:40.929045 9857 solver.cpp:242] Iteration 55600, loss = 0.527798 +I0616 11:30:40.929087 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0496198 (* 1 = 0.0496198 loss) +I0616 11:30:40.929093 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108307 (* 1 = 0.108307 loss) +I0616 11:30:40.929097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102539 (* 1 = 0.102539 loss) +I0616 11:30:40.929100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111083 (* 1 = 0.111083 loss) +I0616 11:30:40.929105 9857 solver.cpp:571] Iteration 55600, lr = 0.0001 +I0616 11:30:52.334257 9857 solver.cpp:242] Iteration 55620, loss = 0.836419 +I0616 11:30:52.334300 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.348756 (* 1 = 0.348756 loss) +I0616 11:30:52.334305 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249421 (* 1 = 0.249421 loss) +I0616 11:30:52.334309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102783 (* 1 = 0.102783 loss) +I0616 11:30:52.334312 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.046784 (* 1 = 0.046784 loss) +I0616 11:30:52.334316 9857 solver.cpp:571] Iteration 55620, lr = 0.0001 +I0616 11:31:03.861714 9857 solver.cpp:242] Iteration 55640, loss = 0.7367 +I0616 11:31:03.861757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286901 (* 1 = 0.286901 loss) +I0616 11:31:03.861763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.457211 (* 1 = 0.457211 loss) +I0616 11:31:03.861766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114787 (* 1 = 0.114787 loss) +I0616 11:31:03.861769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0155827 (* 1 = 0.0155827 loss) +I0616 11:31:03.861773 9857 solver.cpp:571] Iteration 55640, lr = 0.0001 +I0616 11:31:15.442454 9857 solver.cpp:242] Iteration 55660, loss = 0.476752 +I0616 11:31:15.442498 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102404 (* 1 = 0.102404 loss) +I0616 11:31:15.442503 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202372 (* 1 = 0.202372 loss) +I0616 11:31:15.442507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0756078 (* 1 = 0.0756078 loss) +I0616 11:31:15.442512 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0340572 (* 1 = 0.0340572 loss) +I0616 11:31:15.442514 9857 solver.cpp:571] Iteration 55660, lr = 0.0001 +I0616 11:31:27.193861 9857 solver.cpp:242] Iteration 55680, loss = 0.29626 +I0616 11:31:27.193904 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131148 (* 1 = 0.131148 loss) +I0616 11:31:27.193910 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111955 (* 1 = 0.111955 loss) +I0616 11:31:27.193914 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0581113 (* 1 = 0.0581113 loss) +I0616 11:31:27.193917 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0317891 (* 1 = 0.0317891 loss) +I0616 11:31:27.193922 9857 solver.cpp:571] Iteration 55680, lr = 0.0001 +I0616 11:31:38.763108 9857 solver.cpp:242] Iteration 55700, loss = 0.54507 +I0616 11:31:38.763150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.062635 (* 1 = 0.062635 loss) +I0616 11:31:38.763155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107709 (* 1 = 0.107709 loss) +I0616 11:31:38.763160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115737 (* 1 = 0.115737 loss) +I0616 11:31:38.763164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00878694 (* 1 = 0.00878694 loss) +I0616 11:31:38.763167 9857 solver.cpp:571] Iteration 55700, lr = 0.0001 +I0616 11:31:50.486716 9857 solver.cpp:242] Iteration 55720, loss = 0.384834 +I0616 11:31:50.486760 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114671 (* 1 = 0.114671 loss) +I0616 11:31:50.486765 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142538 (* 1 = 0.142538 loss) +I0616 11:31:50.486770 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0197255 (* 1 = 0.0197255 loss) +I0616 11:31:50.486774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00344169 (* 1 = 0.00344169 loss) +I0616 11:31:50.486778 9857 solver.cpp:571] Iteration 55720, lr = 0.0001 +I0616 11:32:01.798549 9857 solver.cpp:242] Iteration 55740, loss = 0.958745 +I0616 11:32:01.798589 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.532109 (* 1 = 0.532109 loss) +I0616 11:32:01.798594 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338344 (* 1 = 0.338344 loss) +I0616 11:32:01.798599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0508891 (* 1 = 0.0508891 loss) +I0616 11:32:01.798602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0386998 (* 1 = 0.0386998 loss) +I0616 11:32:01.798606 9857 solver.cpp:571] Iteration 55740, lr = 0.0001 +I0616 11:32:13.037494 9857 solver.cpp:242] Iteration 55760, loss = 0.441906 +I0616 11:32:13.037536 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102578 (* 1 = 0.102578 loss) +I0616 11:32:13.037542 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175026 (* 1 = 0.175026 loss) +I0616 11:32:13.037546 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0254473 (* 1 = 0.0254473 loss) +I0616 11:32:13.037549 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00426703 (* 1 = 0.00426703 loss) +I0616 11:32:13.037554 9857 solver.cpp:571] Iteration 55760, lr = 0.0001 +I0616 11:32:24.648092 9857 solver.cpp:242] Iteration 55780, loss = 0.291864 +I0616 11:32:24.648135 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0608168 (* 1 = 0.0608168 loss) +I0616 11:32:24.648141 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0788484 (* 1 = 0.0788484 loss) +I0616 11:32:24.648145 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.081768 (* 1 = 0.081768 loss) +I0616 11:32:24.648149 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.007116 (* 1 = 0.007116 loss) +I0616 11:32:24.648154 9857 solver.cpp:571] Iteration 55780, lr = 0.0001 +speed: 0.609s / iter +I0616 11:32:36.265615 9857 solver.cpp:242] Iteration 55800, loss = 0.591446 +I0616 11:32:36.265658 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107411 (* 1 = 0.107411 loss) +I0616 11:32:36.265663 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12625 (* 1 = 0.12625 loss) +I0616 11:32:36.265667 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0474851 (* 1 = 0.0474851 loss) +I0616 11:32:36.265671 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0270829 (* 1 = 0.0270829 loss) +I0616 11:32:36.265676 9857 solver.cpp:571] Iteration 55800, lr = 0.0001 +I0616 11:32:47.713404 9857 solver.cpp:242] Iteration 55820, loss = 0.461564 +I0616 11:32:47.713448 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30337 (* 1 = 0.30337 loss) +I0616 11:32:47.713452 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19328 (* 1 = 0.19328 loss) +I0616 11:32:47.713456 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0578698 (* 1 = 0.0578698 loss) +I0616 11:32:47.713460 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.123547 (* 1 = 0.123547 loss) +I0616 11:32:47.713464 9857 solver.cpp:571] Iteration 55820, lr = 0.0001 +I0616 11:32:59.372686 9857 solver.cpp:242] Iteration 55840, loss = 0.674255 +I0616 11:32:59.372723 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376027 (* 1 = 0.376027 loss) +I0616 11:32:59.372730 9857 solver.cpp:258] Train net output #1: loss_cls = 0.397184 (* 1 = 0.397184 loss) +I0616 11:32:59.372733 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0661303 (* 1 = 0.0661303 loss) +I0616 11:32:59.372737 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0517236 (* 1 = 0.0517236 loss) +I0616 11:32:59.372740 9857 solver.cpp:571] Iteration 55840, lr = 0.0001 +I0616 11:33:10.876433 9857 solver.cpp:242] Iteration 55860, loss = 0.559128 +I0616 11:33:10.876477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336675 (* 1 = 0.336675 loss) +I0616 11:33:10.876482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.474203 (* 1 = 0.474203 loss) +I0616 11:33:10.876485 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.052238 (* 1 = 0.052238 loss) +I0616 11:33:10.876489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240314 (* 1 = 0.0240314 loss) +I0616 11:33:10.876493 9857 solver.cpp:571] Iteration 55860, lr = 0.0001 +I0616 11:33:22.437001 9857 solver.cpp:242] Iteration 55880, loss = 1.21782 +I0616 11:33:22.437043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386948 (* 1 = 0.386948 loss) +I0616 11:33:22.437048 9857 solver.cpp:258] Train net output #1: loss_cls = 0.82276 (* 1 = 0.82276 loss) +I0616 11:33:22.437053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.296768 (* 1 = 0.296768 loss) +I0616 11:33:22.437057 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.304591 (* 1 = 0.304591 loss) +I0616 11:33:22.437062 9857 solver.cpp:571] Iteration 55880, lr = 0.0001 +I0616 11:33:33.548933 9857 solver.cpp:242] Iteration 55900, loss = 0.476321 +I0616 11:33:33.548975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107145 (* 1 = 0.107145 loss) +I0616 11:33:33.548980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100055 (* 1 = 0.100055 loss) +I0616 11:33:33.548985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0539018 (* 1 = 0.0539018 loss) +I0616 11:33:33.548990 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015367 (* 1 = 0.015367 loss) +I0616 11:33:33.548992 9857 solver.cpp:571] Iteration 55900, lr = 0.0001 +I0616 11:33:45.150988 9857 solver.cpp:242] Iteration 55920, loss = 0.491464 +I0616 11:33:45.151029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.055178 (* 1 = 0.055178 loss) +I0616 11:33:45.151034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114212 (* 1 = 0.114212 loss) +I0616 11:33:45.151038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0185629 (* 1 = 0.0185629 loss) +I0616 11:33:45.151042 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198689 (* 1 = 0.0198689 loss) +I0616 11:33:45.151046 9857 solver.cpp:571] Iteration 55920, lr = 0.0001 +I0616 11:33:56.450059 9857 solver.cpp:242] Iteration 55940, loss = 0.688133 +I0616 11:33:56.450100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324412 (* 1 = 0.324412 loss) +I0616 11:33:56.450106 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24609 (* 1 = 0.24609 loss) +I0616 11:33:56.450110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0172647 (* 1 = 0.0172647 loss) +I0616 11:33:56.450114 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0340826 (* 1 = 0.0340826 loss) +I0616 11:33:56.450119 9857 solver.cpp:571] Iteration 55940, lr = 0.0001 +I0616 11:34:08.044703 9857 solver.cpp:242] Iteration 55960, loss = 0.404318 +I0616 11:34:08.044730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0567566 (* 1 = 0.0567566 loss) +I0616 11:34:08.044750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0490918 (* 1 = 0.0490918 loss) +I0616 11:34:08.044754 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00208478 (* 1 = 0.00208478 loss) +I0616 11:34:08.044757 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000395248 (* 1 = 0.000395248 loss) +I0616 11:34:08.044761 9857 solver.cpp:571] Iteration 55960, lr = 0.0001 +I0616 11:34:19.685478 9857 solver.cpp:242] Iteration 55980, loss = 0.68354 +I0616 11:34:19.685521 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265223 (* 1 = 0.265223 loss) +I0616 11:34:19.685528 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219832 (* 1 = 0.219832 loss) +I0616 11:34:19.685531 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136139 (* 1 = 0.136139 loss) +I0616 11:34:19.685534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162426 (* 1 = 0.0162426 loss) +I0616 11:34:19.685539 9857 solver.cpp:571] Iteration 55980, lr = 0.0001 +speed: 0.609s / iter +I0616 11:34:31.323596 9857 solver.cpp:242] Iteration 56000, loss = 1.146 +I0616 11:34:31.323638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.573786 (* 1 = 0.573786 loss) +I0616 11:34:31.323643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426115 (* 1 = 0.426115 loss) +I0616 11:34:31.323647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.408262 (* 1 = 0.408262 loss) +I0616 11:34:31.323652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.402155 (* 1 = 0.402155 loss) +I0616 11:34:31.323655 9857 solver.cpp:571] Iteration 56000, lr = 0.0001 +I0616 11:34:42.531929 9857 solver.cpp:242] Iteration 56020, loss = 0.329019 +I0616 11:34:42.531971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0393393 (* 1 = 0.0393393 loss) +I0616 11:34:42.531976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115973 (* 1 = 0.115973 loss) +I0616 11:34:42.531980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0882147 (* 1 = 0.0882147 loss) +I0616 11:34:42.531985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00439911 (* 1 = 0.00439911 loss) +I0616 11:34:42.531988 9857 solver.cpp:571] Iteration 56020, lr = 0.0001 +I0616 11:34:54.252936 9857 solver.cpp:242] Iteration 56040, loss = 0.786081 +I0616 11:34:54.252979 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359567 (* 1 = 0.359567 loss) +I0616 11:34:54.252985 9857 solver.cpp:258] Train net output #1: loss_cls = 0.726672 (* 1 = 0.726672 loss) +I0616 11:34:54.252988 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1078 (* 1 = 0.1078 loss) +I0616 11:34:54.252992 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0629798 (* 1 = 0.0629798 loss) +I0616 11:34:54.252995 9857 solver.cpp:571] Iteration 56040, lr = 0.0001 +I0616 11:35:05.904486 9857 solver.cpp:242] Iteration 56060, loss = 0.539659 +I0616 11:35:05.904528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169032 (* 1 = 0.169032 loss) +I0616 11:35:05.904534 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165511 (* 1 = 0.165511 loss) +I0616 11:35:05.904538 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0515403 (* 1 = 0.0515403 loss) +I0616 11:35:05.904542 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00492835 (* 1 = 0.00492835 loss) +I0616 11:35:05.904546 9857 solver.cpp:571] Iteration 56060, lr = 0.0001 +I0616 11:35:17.293377 9857 solver.cpp:242] Iteration 56080, loss = 0.641594 +I0616 11:35:17.293418 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1462 (* 1 = 0.1462 loss) +I0616 11:35:17.293424 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264023 (* 1 = 0.264023 loss) +I0616 11:35:17.293428 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0265485 (* 1 = 0.0265485 loss) +I0616 11:35:17.293432 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14068 (* 1 = 0.14068 loss) +I0616 11:35:17.293437 9857 solver.cpp:571] Iteration 56080, lr = 0.0001 +I0616 11:35:28.899082 9857 solver.cpp:242] Iteration 56100, loss = 0.329844 +I0616 11:35:28.899123 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247606 (* 1 = 0.247606 loss) +I0616 11:35:28.899129 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16342 (* 1 = 0.16342 loss) +I0616 11:35:28.899133 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0362551 (* 1 = 0.0362551 loss) +I0616 11:35:28.899137 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226939 (* 1 = 0.0226939 loss) +I0616 11:35:28.899140 9857 solver.cpp:571] Iteration 56100, lr = 0.0001 +I0616 11:35:40.409999 9857 solver.cpp:242] Iteration 56120, loss = 0.765266 +I0616 11:35:40.410042 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215442 (* 1 = 0.215442 loss) +I0616 11:35:40.410046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286287 (* 1 = 0.286287 loss) +I0616 11:35:40.410050 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206039 (* 1 = 0.206039 loss) +I0616 11:35:40.410054 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0303668 (* 1 = 0.0303668 loss) +I0616 11:35:40.410058 9857 solver.cpp:571] Iteration 56120, lr = 0.0001 +I0616 11:35:51.927021 9857 solver.cpp:242] Iteration 56140, loss = 0.630309 +I0616 11:35:51.927060 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101746 (* 1 = 0.101746 loss) +I0616 11:35:51.927067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162245 (* 1 = 0.162245 loss) +I0616 11:35:51.927070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0896359 (* 1 = 0.0896359 loss) +I0616 11:35:51.927073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00322819 (* 1 = 0.00322819 loss) +I0616 11:35:51.927078 9857 solver.cpp:571] Iteration 56140, lr = 0.0001 +I0616 11:36:03.402201 9857 solver.cpp:242] Iteration 56160, loss = 0.643192 +I0616 11:36:03.402230 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318419 (* 1 = 0.318419 loss) +I0616 11:36:03.402235 9857 solver.cpp:258] Train net output #1: loss_cls = 0.39691 (* 1 = 0.39691 loss) +I0616 11:36:03.402240 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0963659 (* 1 = 0.0963659 loss) +I0616 11:36:03.402243 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0832432 (* 1 = 0.0832432 loss) +I0616 11:36:03.402246 9857 solver.cpp:571] Iteration 56160, lr = 0.0001 +I0616 11:36:14.869617 9857 solver.cpp:242] Iteration 56180, loss = 0.239562 +I0616 11:36:14.869657 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131652 (* 1 = 0.131652 loss) +I0616 11:36:14.869663 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1598 (* 1 = 0.1598 loss) +I0616 11:36:14.869668 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0127739 (* 1 = 0.0127739 loss) +I0616 11:36:14.869670 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181899 (* 1 = 0.0181899 loss) +I0616 11:36:14.869674 9857 solver.cpp:571] Iteration 56180, lr = 0.0001 +speed: 0.609s / iter +I0616 11:36:26.442760 9857 solver.cpp:242] Iteration 56200, loss = 0.437644 +I0616 11:36:26.442801 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24869 (* 1 = 0.24869 loss) +I0616 11:36:26.442806 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304724 (* 1 = 0.304724 loss) +I0616 11:36:26.442811 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389839 (* 1 = 0.0389839 loss) +I0616 11:36:26.442814 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.054799 (* 1 = 0.054799 loss) +I0616 11:36:26.442818 9857 solver.cpp:571] Iteration 56200, lr = 0.0001 +I0616 11:36:37.953572 9857 solver.cpp:242] Iteration 56220, loss = 0.562418 +I0616 11:36:37.953614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.246716 (* 1 = 0.246716 loss) +I0616 11:36:37.953620 9857 solver.cpp:258] Train net output #1: loss_cls = 0.497348 (* 1 = 0.497348 loss) +I0616 11:36:37.953624 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134404 (* 1 = 0.134404 loss) +I0616 11:36:37.953629 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.050376 (* 1 = 0.050376 loss) +I0616 11:36:37.953632 9857 solver.cpp:571] Iteration 56220, lr = 0.0001 +I0616 11:36:49.528067 9857 solver.cpp:242] Iteration 56240, loss = 0.592052 +I0616 11:36:49.528110 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23964 (* 1 = 0.23964 loss) +I0616 11:36:49.528115 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313041 (* 1 = 0.313041 loss) +I0616 11:36:49.528120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119675 (* 1 = 0.119675 loss) +I0616 11:36:49.528123 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0652524 (* 1 = 0.0652524 loss) +I0616 11:36:49.528127 9857 solver.cpp:571] Iteration 56240, lr = 0.0001 +I0616 11:37:01.332809 9857 solver.cpp:242] Iteration 56260, loss = 0.595609 +I0616 11:37:01.332852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288521 (* 1 = 0.288521 loss) +I0616 11:37:01.332857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236077 (* 1 = 0.236077 loss) +I0616 11:37:01.332861 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0546655 (* 1 = 0.0546655 loss) +I0616 11:37:01.332865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030611 (* 1 = 0.030611 loss) +I0616 11:37:01.332868 9857 solver.cpp:571] Iteration 56260, lr = 0.0001 +I0616 11:37:13.029239 9857 solver.cpp:242] Iteration 56280, loss = 0.615847 +I0616 11:37:13.029280 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233887 (* 1 = 0.233887 loss) +I0616 11:37:13.029286 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210186 (* 1 = 0.210186 loss) +I0616 11:37:13.029290 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0121758 (* 1 = 0.0121758 loss) +I0616 11:37:13.029294 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226412 (* 1 = 0.0226412 loss) +I0616 11:37:13.029297 9857 solver.cpp:571] Iteration 56280, lr = 0.0001 +I0616 11:37:24.659548 9857 solver.cpp:242] Iteration 56300, loss = 0.540735 +I0616 11:37:24.659590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150605 (* 1 = 0.150605 loss) +I0616 11:37:24.659596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268805 (* 1 = 0.268805 loss) +I0616 11:37:24.659600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133806 (* 1 = 0.133806 loss) +I0616 11:37:24.659605 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0418699 (* 1 = 0.0418699 loss) +I0616 11:37:24.659607 9857 solver.cpp:571] Iteration 56300, lr = 0.0001 +I0616 11:37:36.386775 9857 solver.cpp:242] Iteration 56320, loss = 0.489259 +I0616 11:37:36.386816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0577982 (* 1 = 0.0577982 loss) +I0616 11:37:36.386822 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10198 (* 1 = 0.10198 loss) +I0616 11:37:36.386826 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00634138 (* 1 = 0.00634138 loss) +I0616 11:37:36.386831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00693853 (* 1 = 0.00693853 loss) +I0616 11:37:36.386833 9857 solver.cpp:571] Iteration 56320, lr = 0.0001 +I0616 11:37:47.773721 9857 solver.cpp:242] Iteration 56340, loss = 0.409966 +I0616 11:37:47.773763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249337 (* 1 = 0.249337 loss) +I0616 11:37:47.773769 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17862 (* 1 = 0.17862 loss) +I0616 11:37:47.773773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0334869 (* 1 = 0.0334869 loss) +I0616 11:37:47.773777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0217755 (* 1 = 0.0217755 loss) +I0616 11:37:47.773780 9857 solver.cpp:571] Iteration 56340, lr = 0.0001 +I0616 11:37:59.081954 9857 solver.cpp:242] Iteration 56360, loss = 0.613065 +I0616 11:37:59.081995 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237115 (* 1 = 0.237115 loss) +I0616 11:37:59.082001 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278876 (* 1 = 0.278876 loss) +I0616 11:37:59.082005 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0577603 (* 1 = 0.0577603 loss) +I0616 11:37:59.082010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.04463 (* 1 = 0.04463 loss) +I0616 11:37:59.082013 9857 solver.cpp:571] Iteration 56360, lr = 0.0001 +I0616 11:38:10.412178 9857 solver.cpp:242] Iteration 56380, loss = 0.739711 +I0616 11:38:10.412221 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352579 (* 1 = 0.352579 loss) +I0616 11:38:10.412227 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231149 (* 1 = 0.231149 loss) +I0616 11:38:10.412231 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0376445 (* 1 = 0.0376445 loss) +I0616 11:38:10.412235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130039 (* 1 = 0.0130039 loss) +I0616 11:38:10.412238 9857 solver.cpp:571] Iteration 56380, lr = 0.0001 +speed: 0.609s / iter +I0616 11:38:21.992287 9857 solver.cpp:242] Iteration 56400, loss = 0.603961 +I0616 11:38:21.992331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.448777 (* 1 = 0.448777 loss) +I0616 11:38:21.992336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431094 (* 1 = 0.431094 loss) +I0616 11:38:21.992341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0781038 (* 1 = 0.0781038 loss) +I0616 11:38:21.992343 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103925 (* 1 = 0.0103925 loss) +I0616 11:38:21.992347 9857 solver.cpp:571] Iteration 56400, lr = 0.0001 +I0616 11:38:33.422062 9857 solver.cpp:242] Iteration 56420, loss = 0.875404 +I0616 11:38:33.422104 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0522456 (* 1 = 0.0522456 loss) +I0616 11:38:33.422111 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129202 (* 1 = 0.129202 loss) +I0616 11:38:33.422114 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0032098 (* 1 = 0.0032098 loss) +I0616 11:38:33.422118 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00810113 (* 1 = 0.00810113 loss) +I0616 11:38:33.422124 9857 solver.cpp:571] Iteration 56420, lr = 0.0001 +I0616 11:38:45.181501 9857 solver.cpp:242] Iteration 56440, loss = 0.542045 +I0616 11:38:45.181541 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298209 (* 1 = 0.298209 loss) +I0616 11:38:45.181546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32211 (* 1 = 0.32211 loss) +I0616 11:38:45.181550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108312 (* 1 = 0.108312 loss) +I0616 11:38:45.181555 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150172 (* 1 = 0.0150172 loss) +I0616 11:38:45.181558 9857 solver.cpp:571] Iteration 56440, lr = 0.0001 +I0616 11:38:56.841744 9857 solver.cpp:242] Iteration 56460, loss = 0.577971 +I0616 11:38:56.841787 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353994 (* 1 = 0.353994 loss) +I0616 11:38:56.841792 9857 solver.cpp:258] Train net output #1: loss_cls = 0.420171 (* 1 = 0.420171 loss) +I0616 11:38:56.841797 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0270035 (* 1 = 0.0270035 loss) +I0616 11:38:56.841801 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0639058 (* 1 = 0.0639058 loss) +I0616 11:38:56.841804 9857 solver.cpp:571] Iteration 56460, lr = 0.0001 +I0616 11:39:08.415248 9857 solver.cpp:242] Iteration 56480, loss = 0.53349 +I0616 11:39:08.415288 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196158 (* 1 = 0.196158 loss) +I0616 11:39:08.415293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219578 (* 1 = 0.219578 loss) +I0616 11:39:08.415298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.071873 (* 1 = 0.071873 loss) +I0616 11:39:08.415302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128893 (* 1 = 0.128893 loss) +I0616 11:39:08.415305 9857 solver.cpp:571] Iteration 56480, lr = 0.0001 +I0616 11:39:20.019121 9857 solver.cpp:242] Iteration 56500, loss = 0.381202 +I0616 11:39:20.019165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113891 (* 1 = 0.113891 loss) +I0616 11:39:20.019170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103477 (* 1 = 0.103477 loss) +I0616 11:39:20.019175 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0392609 (* 1 = 0.0392609 loss) +I0616 11:39:20.019178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.057901 (* 1 = 0.057901 loss) +I0616 11:39:20.019181 9857 solver.cpp:571] Iteration 56500, lr = 0.0001 +I0616 11:39:31.547086 9857 solver.cpp:242] Iteration 56520, loss = 0.4542 +I0616 11:39:31.547127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123268 (* 1 = 0.123268 loss) +I0616 11:39:31.547132 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120197 (* 1 = 0.120197 loss) +I0616 11:39:31.547137 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00753995 (* 1 = 0.00753995 loss) +I0616 11:39:31.547140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00354125 (* 1 = 0.00354125 loss) +I0616 11:39:31.547143 9857 solver.cpp:571] Iteration 56520, lr = 0.0001 +I0616 11:39:43.239805 9857 solver.cpp:242] Iteration 56540, loss = 0.74572 +I0616 11:39:43.239846 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238326 (* 1 = 0.238326 loss) +I0616 11:39:43.239852 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231081 (* 1 = 0.231081 loss) +I0616 11:39:43.239856 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135439 (* 1 = 0.135439 loss) +I0616 11:39:43.239861 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0452004 (* 1 = 0.0452004 loss) +I0616 11:39:43.239863 9857 solver.cpp:571] Iteration 56540, lr = 0.0001 +I0616 11:39:54.752882 9857 solver.cpp:242] Iteration 56560, loss = 0.588107 +I0616 11:39:54.752926 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184208 (* 1 = 0.184208 loss) +I0616 11:39:54.752931 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136193 (* 1 = 0.136193 loss) +I0616 11:39:54.752935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0589268 (* 1 = 0.0589268 loss) +I0616 11:39:54.752939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113224 (* 1 = 0.0113224 loss) +I0616 11:39:54.752943 9857 solver.cpp:571] Iteration 56560, lr = 0.0001 +I0616 11:40:06.311812 9857 solver.cpp:242] Iteration 56580, loss = 1.00092 +I0616 11:40:06.311856 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304873 (* 1 = 0.304873 loss) +I0616 11:40:06.311861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.818176 (* 1 = 0.818176 loss) +I0616 11:40:06.311864 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.267056 (* 1 = 0.267056 loss) +I0616 11:40:06.311868 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.121658 (* 1 = 0.121658 loss) +I0616 11:40:06.311872 9857 solver.cpp:571] Iteration 56580, lr = 0.0001 +speed: 0.609s / iter +I0616 11:40:17.860724 9857 solver.cpp:242] Iteration 56600, loss = 1.06178 +I0616 11:40:17.860769 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234059 (* 1 = 0.234059 loss) +I0616 11:40:17.860774 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432928 (* 1 = 0.432928 loss) +I0616 11:40:17.860777 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0463835 (* 1 = 0.0463835 loss) +I0616 11:40:17.860781 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0801129 (* 1 = 0.0801129 loss) +I0616 11:40:17.860785 9857 solver.cpp:571] Iteration 56600, lr = 0.0001 +I0616 11:40:29.589578 9857 solver.cpp:242] Iteration 56620, loss = 0.843374 +I0616 11:40:29.589620 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0769917 (* 1 = 0.0769917 loss) +I0616 11:40:29.589625 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343995 (* 1 = 0.343995 loss) +I0616 11:40:29.589629 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0802841 (* 1 = 0.0802841 loss) +I0616 11:40:29.589633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.070423 (* 1 = 0.070423 loss) +I0616 11:40:29.589637 9857 solver.cpp:571] Iteration 56620, lr = 0.0001 +I0616 11:40:41.063017 9857 solver.cpp:242] Iteration 56640, loss = 0.83438 +I0616 11:40:41.063060 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300962 (* 1 = 0.300962 loss) +I0616 11:40:41.063066 9857 solver.cpp:258] Train net output #1: loss_cls = 0.285618 (* 1 = 0.285618 loss) +I0616 11:40:41.063069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145788 (* 1 = 0.145788 loss) +I0616 11:40:41.063072 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.419015 (* 1 = 0.419015 loss) +I0616 11:40:41.063076 9857 solver.cpp:571] Iteration 56640, lr = 0.0001 +I0616 11:40:52.738943 9857 solver.cpp:242] Iteration 56660, loss = 0.380404 +I0616 11:40:52.738986 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213286 (* 1 = 0.213286 loss) +I0616 11:40:52.738991 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139117 (* 1 = 0.139117 loss) +I0616 11:40:52.738994 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0754092 (* 1 = 0.0754092 loss) +I0616 11:40:52.738998 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00252443 (* 1 = 0.00252443 loss) +I0616 11:40:52.739002 9857 solver.cpp:571] Iteration 56660, lr = 0.0001 +I0616 11:41:04.213675 9857 solver.cpp:242] Iteration 56680, loss = 0.601476 +I0616 11:41:04.213716 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303462 (* 1 = 0.303462 loss) +I0616 11:41:04.213721 9857 solver.cpp:258] Train net output #1: loss_cls = 0.498576 (* 1 = 0.498576 loss) +I0616 11:41:04.213726 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107616 (* 1 = 0.107616 loss) +I0616 11:41:04.213729 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197693 (* 1 = 0.0197693 loss) +I0616 11:41:04.213733 9857 solver.cpp:571] Iteration 56680, lr = 0.0001 +I0616 11:41:15.580808 9857 solver.cpp:242] Iteration 56700, loss = 0.491867 +I0616 11:41:15.580852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0528993 (* 1 = 0.0528993 loss) +I0616 11:41:15.580857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0651844 (* 1 = 0.0651844 loss) +I0616 11:41:15.580860 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0100054 (* 1 = 0.0100054 loss) +I0616 11:41:15.580863 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00154859 (* 1 = 0.00154859 loss) +I0616 11:41:15.580868 9857 solver.cpp:571] Iteration 56700, lr = 0.0001 +I0616 11:41:27.272200 9857 solver.cpp:242] Iteration 56720, loss = 0.349657 +I0616 11:41:27.272241 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0866073 (* 1 = 0.0866073 loss) +I0616 11:41:27.272248 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116463 (* 1 = 0.116463 loss) +I0616 11:41:27.272251 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.015742 (* 1 = 0.015742 loss) +I0616 11:41:27.272254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0059027 (* 1 = 0.0059027 loss) +I0616 11:41:27.272258 9857 solver.cpp:571] Iteration 56720, lr = 0.0001 +I0616 11:41:38.790158 9857 solver.cpp:242] Iteration 56740, loss = 0.902883 +I0616 11:41:38.790201 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357453 (* 1 = 0.357453 loss) +I0616 11:41:38.790206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.546374 (* 1 = 0.546374 loss) +I0616 11:41:38.790211 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.308631 (* 1 = 0.308631 loss) +I0616 11:41:38.790215 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14914 (* 1 = 0.14914 loss) +I0616 11:41:38.790218 9857 solver.cpp:571] Iteration 56740, lr = 0.0001 +I0616 11:41:50.595010 9857 solver.cpp:242] Iteration 56760, loss = 1.1795 +I0616 11:41:50.595051 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.273839 (* 1 = 0.273839 loss) +I0616 11:41:50.595057 9857 solver.cpp:258] Train net output #1: loss_cls = 0.452355 (* 1 = 0.452355 loss) +I0616 11:41:50.595060 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.273337 (* 1 = 0.273337 loss) +I0616 11:41:50.595064 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0449936 (* 1 = 0.0449936 loss) +I0616 11:41:50.595069 9857 solver.cpp:571] Iteration 56760, lr = 0.0001 +I0616 11:42:02.173977 9857 solver.cpp:242] Iteration 56780, loss = 0.29059 +I0616 11:42:02.174020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101146 (* 1 = 0.101146 loss) +I0616 11:42:02.174026 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124256 (* 1 = 0.124256 loss) +I0616 11:42:02.174029 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0309118 (* 1 = 0.0309118 loss) +I0616 11:42:02.174033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0532079 (* 1 = 0.0532079 loss) +I0616 11:42:02.174036 9857 solver.cpp:571] Iteration 56780, lr = 0.0001 +speed: 0.609s / iter +I0616 11:42:13.660084 9857 solver.cpp:242] Iteration 56800, loss = 1.13168 +I0616 11:42:13.660125 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310329 (* 1 = 0.310329 loss) +I0616 11:42:13.660130 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150128 (* 1 = 0.150128 loss) +I0616 11:42:13.660135 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0227363 (* 1 = 0.0227363 loss) +I0616 11:42:13.660138 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00534739 (* 1 = 0.00534739 loss) +I0616 11:42:13.660141 9857 solver.cpp:571] Iteration 56800, lr = 0.0001 +I0616 11:42:24.995570 9857 solver.cpp:242] Iteration 56820, loss = 0.738313 +I0616 11:42:24.995611 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0383736 (* 1 = 0.0383736 loss) +I0616 11:42:24.995617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177583 (* 1 = 0.177583 loss) +I0616 11:42:24.995621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108392 (* 1 = 0.108392 loss) +I0616 11:42:24.995625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0515027 (* 1 = 0.0515027 loss) +I0616 11:42:24.995628 9857 solver.cpp:571] Iteration 56820, lr = 0.0001 +I0616 11:42:36.336472 9857 solver.cpp:242] Iteration 56840, loss = 1.16665 +I0616 11:42:36.336513 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.273661 (* 1 = 0.273661 loss) +I0616 11:42:36.336519 9857 solver.cpp:258] Train net output #1: loss_cls = 0.227725 (* 1 = 0.227725 loss) +I0616 11:42:36.336524 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0238507 (* 1 = 0.0238507 loss) +I0616 11:42:36.336527 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104083 (* 1 = 0.104083 loss) +I0616 11:42:36.336530 9857 solver.cpp:571] Iteration 56840, lr = 0.0001 +I0616 11:42:47.917757 9857 solver.cpp:242] Iteration 56860, loss = 0.444778 +I0616 11:42:47.917798 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0914918 (* 1 = 0.0914918 loss) +I0616 11:42:47.917804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351985 (* 1 = 0.351985 loss) +I0616 11:42:47.917807 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0175341 (* 1 = 0.0175341 loss) +I0616 11:42:47.917811 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0485234 (* 1 = 0.0485234 loss) +I0616 11:42:47.917815 9857 solver.cpp:571] Iteration 56860, lr = 0.0001 +I0616 11:42:59.558233 9857 solver.cpp:242] Iteration 56880, loss = 0.314134 +I0616 11:42:59.558275 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0956044 (* 1 = 0.0956044 loss) +I0616 11:42:59.558280 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187665 (* 1 = 0.187665 loss) +I0616 11:42:59.558284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0349176 (* 1 = 0.0349176 loss) +I0616 11:42:59.558289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0479769 (* 1 = 0.0479769 loss) +I0616 11:42:59.558291 9857 solver.cpp:571] Iteration 56880, lr = 0.0001 +I0616 11:43:11.078004 9857 solver.cpp:242] Iteration 56900, loss = 0.65343 +I0616 11:43:11.078047 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164714 (* 1 = 0.164714 loss) +I0616 11:43:11.078052 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303837 (* 1 = 0.303837 loss) +I0616 11:43:11.078057 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126968 (* 1 = 0.126968 loss) +I0616 11:43:11.078059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.113408 (* 1 = 0.113408 loss) +I0616 11:43:11.078063 9857 solver.cpp:571] Iteration 56900, lr = 0.0001 +I0616 11:43:22.465137 9857 solver.cpp:242] Iteration 56920, loss = 0.238205 +I0616 11:43:22.465180 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.097192 (* 1 = 0.097192 loss) +I0616 11:43:22.465186 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0947158 (* 1 = 0.0947158 loss) +I0616 11:43:22.465190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0131081 (* 1 = 0.0131081 loss) +I0616 11:43:22.465193 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00246375 (* 1 = 0.00246375 loss) +I0616 11:43:22.465198 9857 solver.cpp:571] Iteration 56920, lr = 0.0001 +I0616 11:43:34.133391 9857 solver.cpp:242] Iteration 56940, loss = 0.719883 +I0616 11:43:34.133431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.350571 (* 1 = 0.350571 loss) +I0616 11:43:34.133436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388504 (* 1 = 0.388504 loss) +I0616 11:43:34.133440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184187 (* 1 = 0.184187 loss) +I0616 11:43:34.133445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0578671 (* 1 = 0.0578671 loss) +I0616 11:43:34.133448 9857 solver.cpp:571] Iteration 56940, lr = 0.0001 +I0616 11:43:45.786397 9857 solver.cpp:242] Iteration 56960, loss = 0.422715 +I0616 11:43:45.786439 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117196 (* 1 = 0.117196 loss) +I0616 11:43:45.786445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176936 (* 1 = 0.176936 loss) +I0616 11:43:45.786448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.021155 (* 1 = 0.021155 loss) +I0616 11:43:45.786453 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116643 (* 1 = 0.0116643 loss) +I0616 11:43:45.786456 9857 solver.cpp:571] Iteration 56960, lr = 0.0001 +I0616 11:43:57.543151 9857 solver.cpp:242] Iteration 56980, loss = 0.689237 +I0616 11:43:57.543193 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252226 (* 1 = 0.252226 loss) +I0616 11:43:57.543198 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38224 (* 1 = 0.38224 loss) +I0616 11:43:57.543202 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142865 (* 1 = 0.142865 loss) +I0616 11:43:57.543206 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0272958 (* 1 = 0.0272958 loss) +I0616 11:43:57.543210 9857 solver.cpp:571] Iteration 56980, lr = 0.0001 +speed: 0.609s / iter +I0616 11:44:09.166710 9857 solver.cpp:242] Iteration 57000, loss = 0.499827 +I0616 11:44:09.166752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18959 (* 1 = 0.18959 loss) +I0616 11:44:09.166764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231857 (* 1 = 0.231857 loss) +I0616 11:44:09.166769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109579 (* 1 = 0.109579 loss) +I0616 11:44:09.166772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0779691 (* 1 = 0.0779691 loss) +I0616 11:44:09.166776 9857 solver.cpp:571] Iteration 57000, lr = 0.0001 +I0616 11:44:20.735369 9857 solver.cpp:242] Iteration 57020, loss = 0.859758 +I0616 11:44:20.735410 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.512792 (* 1 = 0.512792 loss) +I0616 11:44:20.735415 9857 solver.cpp:258] Train net output #1: loss_cls = 0.84049 (* 1 = 0.84049 loss) +I0616 11:44:20.735420 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105668 (* 1 = 0.105668 loss) +I0616 11:44:20.735424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00942532 (* 1 = 0.00942532 loss) +I0616 11:44:20.735427 9857 solver.cpp:571] Iteration 57020, lr = 0.0001 +I0616 11:44:32.165138 9857 solver.cpp:242] Iteration 57040, loss = 1.10127 +I0616 11:44:32.165180 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.226022 (* 1 = 0.226022 loss) +I0616 11:44:32.165185 9857 solver.cpp:258] Train net output #1: loss_cls = 0.43327 (* 1 = 0.43327 loss) +I0616 11:44:32.165190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.206114 (* 1 = 0.206114 loss) +I0616 11:44:32.165194 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.13545 (* 1 = 0.13545 loss) +I0616 11:44:32.165197 9857 solver.cpp:571] Iteration 57040, lr = 0.0001 +I0616 11:44:43.721400 9857 solver.cpp:242] Iteration 57060, loss = 1.01538 +I0616 11:44:43.721443 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218007 (* 1 = 0.218007 loss) +I0616 11:44:43.721449 9857 solver.cpp:258] Train net output #1: loss_cls = 0.583291 (* 1 = 0.583291 loss) +I0616 11:44:43.721453 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0577542 (* 1 = 0.0577542 loss) +I0616 11:44:43.721457 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0822482 (* 1 = 0.0822482 loss) +I0616 11:44:43.721460 9857 solver.cpp:571] Iteration 57060, lr = 0.0001 +I0616 11:44:55.435209 9857 solver.cpp:242] Iteration 57080, loss = 0.733759 +I0616 11:44:55.435250 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242046 (* 1 = 0.242046 loss) +I0616 11:44:55.435256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.415872 (* 1 = 0.415872 loss) +I0616 11:44:55.435259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197533 (* 1 = 0.197533 loss) +I0616 11:44:55.435263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.272198 (* 1 = 0.272198 loss) +I0616 11:44:55.435269 9857 solver.cpp:571] Iteration 57080, lr = 0.0001 +I0616 11:45:06.834486 9857 solver.cpp:242] Iteration 57100, loss = 0.674489 +I0616 11:45:06.834527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357935 (* 1 = 0.357935 loss) +I0616 11:45:06.834532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.390943 (* 1 = 0.390943 loss) +I0616 11:45:06.834537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0978065 (* 1 = 0.0978065 loss) +I0616 11:45:06.834540 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0597292 (* 1 = 0.0597292 loss) +I0616 11:45:06.834544 9857 solver.cpp:571] Iteration 57100, lr = 0.0001 +I0616 11:45:18.383417 9857 solver.cpp:242] Iteration 57120, loss = 0.762706 +I0616 11:45:18.383460 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101514 (* 1 = 0.101514 loss) +I0616 11:45:18.383466 9857 solver.cpp:258] Train net output #1: loss_cls = 0.25493 (* 1 = 0.25493 loss) +I0616 11:45:18.383469 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0507589 (* 1 = 0.0507589 loss) +I0616 11:45:18.383473 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0281058 (* 1 = 0.0281058 loss) +I0616 11:45:18.383476 9857 solver.cpp:571] Iteration 57120, lr = 0.0001 +I0616 11:45:29.849369 9857 solver.cpp:242] Iteration 57140, loss = 0.455341 +I0616 11:45:29.849411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100415 (* 1 = 0.100415 loss) +I0616 11:45:29.849416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108524 (* 1 = 0.108524 loss) +I0616 11:45:29.849421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0417562 (* 1 = 0.0417562 loss) +I0616 11:45:29.849424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108308 (* 1 = 0.0108308 loss) +I0616 11:45:29.849428 9857 solver.cpp:571] Iteration 57140, lr = 0.0001 +I0616 11:45:41.305117 9857 solver.cpp:242] Iteration 57160, loss = 0.645765 +I0616 11:45:41.305158 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253779 (* 1 = 0.253779 loss) +I0616 11:45:41.305165 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266198 (* 1 = 0.266198 loss) +I0616 11:45:41.305168 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.261103 (* 1 = 0.261103 loss) +I0616 11:45:41.305172 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0442237 (* 1 = 0.0442237 loss) +I0616 11:45:41.305176 9857 solver.cpp:571] Iteration 57160, lr = 0.0001 +I0616 11:45:52.827333 9857 solver.cpp:242] Iteration 57180, loss = 0.571888 +I0616 11:45:52.827375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179148 (* 1 = 0.179148 loss) +I0616 11:45:52.827380 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203838 (* 1 = 0.203838 loss) +I0616 11:45:52.827384 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0172778 (* 1 = 0.0172778 loss) +I0616 11:45:52.827389 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222569 (* 1 = 0.0222569 loss) +I0616 11:45:52.827391 9857 solver.cpp:571] Iteration 57180, lr = 0.0001 +speed: 0.608s / iter +I0616 11:46:04.305219 9857 solver.cpp:242] Iteration 57200, loss = 0.243168 +I0616 11:46:04.305260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0663793 (* 1 = 0.0663793 loss) +I0616 11:46:04.305266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128432 (* 1 = 0.128432 loss) +I0616 11:46:04.305270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.095853 (* 1 = 0.095853 loss) +I0616 11:46:04.305274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010227 (* 1 = 0.010227 loss) +I0616 11:46:04.305277 9857 solver.cpp:571] Iteration 57200, lr = 0.0001 +I0616 11:46:16.042291 9857 solver.cpp:242] Iteration 57220, loss = 0.769481 +I0616 11:46:16.042333 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324016 (* 1 = 0.324016 loss) +I0616 11:46:16.042338 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255874 (* 1 = 0.255874 loss) +I0616 11:46:16.042342 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.196709 (* 1 = 0.196709 loss) +I0616 11:46:16.042346 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.057594 (* 1 = 0.057594 loss) +I0616 11:46:16.042349 9857 solver.cpp:571] Iteration 57220, lr = 0.0001 +I0616 11:46:27.762502 9857 solver.cpp:242] Iteration 57240, loss = 0.777371 +I0616 11:46:27.762543 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250098 (* 1 = 0.250098 loss) +I0616 11:46:27.762549 9857 solver.cpp:258] Train net output #1: loss_cls = 0.44645 (* 1 = 0.44645 loss) +I0616 11:46:27.762553 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117181 (* 1 = 0.117181 loss) +I0616 11:46:27.762557 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.383723 (* 1 = 0.383723 loss) +I0616 11:46:27.762560 9857 solver.cpp:571] Iteration 57240, lr = 0.0001 +I0616 11:46:39.409678 9857 solver.cpp:242] Iteration 57260, loss = 0.306633 +I0616 11:46:39.409719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0684094 (* 1 = 0.0684094 loss) +I0616 11:46:39.409725 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100725 (* 1 = 0.100725 loss) +I0616 11:46:39.409729 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.16687 (* 1 = 0.16687 loss) +I0616 11:46:39.409732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260259 (* 1 = 0.0260259 loss) +I0616 11:46:39.409736 9857 solver.cpp:571] Iteration 57260, lr = 0.0001 +I0616 11:46:50.989295 9857 solver.cpp:242] Iteration 57280, loss = 0.512279 +I0616 11:46:50.989337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166234 (* 1 = 0.166234 loss) +I0616 11:46:50.989342 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247831 (* 1 = 0.247831 loss) +I0616 11:46:50.989346 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0908444 (* 1 = 0.0908444 loss) +I0616 11:46:50.989351 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0403575 (* 1 = 0.0403575 loss) +I0616 11:46:50.989354 9857 solver.cpp:571] Iteration 57280, lr = 0.0001 +I0616 11:47:02.658686 9857 solver.cpp:242] Iteration 57300, loss = 0.443884 +I0616 11:47:02.658727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21744 (* 1 = 0.21744 loss) +I0616 11:47:02.658733 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286236 (* 1 = 0.286236 loss) +I0616 11:47:02.658737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0681361 (* 1 = 0.0681361 loss) +I0616 11:47:02.658741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.04333 (* 1 = 0.04333 loss) +I0616 11:47:02.658745 9857 solver.cpp:571] Iteration 57300, lr = 0.0001 +I0616 11:47:14.319355 9857 solver.cpp:242] Iteration 57320, loss = 0.403907 +I0616 11:47:14.319398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183868 (* 1 = 0.183868 loss) +I0616 11:47:14.319403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204274 (* 1 = 0.204274 loss) +I0616 11:47:14.319407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0648566 (* 1 = 0.0648566 loss) +I0616 11:47:14.319411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0418357 (* 1 = 0.0418357 loss) +I0616 11:47:14.319414 9857 solver.cpp:571] Iteration 57320, lr = 0.0001 +I0616 11:47:25.781540 9857 solver.cpp:242] Iteration 57340, loss = 0.341086 +I0616 11:47:25.781581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0866856 (* 1 = 0.0866856 loss) +I0616 11:47:25.781586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126437 (* 1 = 0.126437 loss) +I0616 11:47:25.781591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0128094 (* 1 = 0.0128094 loss) +I0616 11:47:25.781594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107057 (* 1 = 0.0107057 loss) +I0616 11:47:25.781599 9857 solver.cpp:571] Iteration 57340, lr = 0.0001 +I0616 11:47:37.223846 9857 solver.cpp:242] Iteration 57360, loss = 0.346557 +I0616 11:47:37.223888 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0949603 (* 1 = 0.0949603 loss) +I0616 11:47:37.223893 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174536 (* 1 = 0.174536 loss) +I0616 11:47:37.223897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0859179 (* 1 = 0.0859179 loss) +I0616 11:47:37.223901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181741 (* 1 = 0.0181741 loss) +I0616 11:47:37.223904 9857 solver.cpp:571] Iteration 57360, lr = 0.0001 +I0616 11:47:48.621237 9857 solver.cpp:242] Iteration 57380, loss = 1.04377 +I0616 11:47:48.621276 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304811 (* 1 = 0.304811 loss) +I0616 11:47:48.621282 9857 solver.cpp:258] Train net output #1: loss_cls = 0.393511 (* 1 = 0.393511 loss) +I0616 11:47:48.621286 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103953 (* 1 = 0.103953 loss) +I0616 11:47:48.621290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.415305 (* 1 = 0.415305 loss) +I0616 11:47:48.621294 9857 solver.cpp:571] Iteration 57380, lr = 0.0001 +speed: 0.608s / iter +I0616 11:48:00.240025 9857 solver.cpp:242] Iteration 57400, loss = 0.492748 +I0616 11:48:00.240066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10567 (* 1 = 0.10567 loss) +I0616 11:48:00.240072 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131612 (* 1 = 0.131612 loss) +I0616 11:48:00.240075 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0198509 (* 1 = 0.0198509 loss) +I0616 11:48:00.240079 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0110619 (* 1 = 0.0110619 loss) +I0616 11:48:00.240083 9857 solver.cpp:571] Iteration 57400, lr = 0.0001 +I0616 11:48:11.698556 9857 solver.cpp:242] Iteration 57420, loss = 0.638445 +I0616 11:48:11.698597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.226917 (* 1 = 0.226917 loss) +I0616 11:48:11.698603 9857 solver.cpp:258] Train net output #1: loss_cls = 0.273546 (* 1 = 0.273546 loss) +I0616 11:48:11.698607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.207141 (* 1 = 0.207141 loss) +I0616 11:48:11.698611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.304379 (* 1 = 0.304379 loss) +I0616 11:48:11.698616 9857 solver.cpp:571] Iteration 57420, lr = 0.0001 +I0616 11:48:23.300649 9857 solver.cpp:242] Iteration 57440, loss = 0.979469 +I0616 11:48:23.300689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.366363 (* 1 = 0.366363 loss) +I0616 11:48:23.300695 9857 solver.cpp:258] Train net output #1: loss_cls = 0.816059 (* 1 = 0.816059 loss) +I0616 11:48:23.300699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.320894 (* 1 = 0.320894 loss) +I0616 11:48:23.300704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0747604 (* 1 = 0.0747604 loss) +I0616 11:48:23.300709 9857 solver.cpp:571] Iteration 57440, lr = 0.0001 +I0616 11:48:34.824373 9857 solver.cpp:242] Iteration 57460, loss = 0.787087 +I0616 11:48:34.824414 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21563 (* 1 = 0.21563 loss) +I0616 11:48:34.824419 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185247 (* 1 = 0.185247 loss) +I0616 11:48:34.824424 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0491165 (* 1 = 0.0491165 loss) +I0616 11:48:34.824426 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136128 (* 1 = 0.0136128 loss) +I0616 11:48:34.824430 9857 solver.cpp:571] Iteration 57460, lr = 0.0001 +I0616 11:48:46.239889 9857 solver.cpp:242] Iteration 57480, loss = 0.331402 +I0616 11:48:46.239931 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0648765 (* 1 = 0.0648765 loss) +I0616 11:48:46.239938 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0870299 (* 1 = 0.0870299 loss) +I0616 11:48:46.239941 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0181639 (* 1 = 0.0181639 loss) +I0616 11:48:46.239944 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120716 (* 1 = 0.0120716 loss) +I0616 11:48:46.239948 9857 solver.cpp:571] Iteration 57480, lr = 0.0001 +I0616 11:48:57.760177 9857 solver.cpp:242] Iteration 57500, loss = 0.733004 +I0616 11:48:57.760220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209217 (* 1 = 0.209217 loss) +I0616 11:48:57.760224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296545 (* 1 = 0.296545 loss) +I0616 11:48:57.760228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113476 (* 1 = 0.113476 loss) +I0616 11:48:57.760232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0453175 (* 1 = 0.0453175 loss) +I0616 11:48:57.760236 9857 solver.cpp:571] Iteration 57500, lr = 0.0001 +I0616 11:49:09.425330 9857 solver.cpp:242] Iteration 57520, loss = 0.98644 +I0616 11:49:09.425374 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421784 (* 1 = 0.421784 loss) +I0616 11:49:09.425379 9857 solver.cpp:258] Train net output #1: loss_cls = 0.782895 (* 1 = 0.782895 loss) +I0616 11:49:09.425382 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.422167 (* 1 = 0.422167 loss) +I0616 11:49:09.425386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.160028 (* 1 = 0.160028 loss) +I0616 11:49:09.425390 9857 solver.cpp:571] Iteration 57520, lr = 0.0001 +I0616 11:49:21.063697 9857 solver.cpp:242] Iteration 57540, loss = 0.990327 +I0616 11:49:21.063741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424513 (* 1 = 0.424513 loss) +I0616 11:49:21.063746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.521478 (* 1 = 0.521478 loss) +I0616 11:49:21.063750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.308414 (* 1 = 0.308414 loss) +I0616 11:49:21.063755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.508242 (* 1 = 0.508242 loss) +I0616 11:49:21.063757 9857 solver.cpp:571] Iteration 57540, lr = 0.0001 +I0616 11:49:32.548923 9857 solver.cpp:242] Iteration 57560, loss = 0.311829 +I0616 11:49:32.548966 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.119677 (* 1 = 0.119677 loss) +I0616 11:49:32.548971 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149423 (* 1 = 0.149423 loss) +I0616 11:49:32.548975 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0426171 (* 1 = 0.0426171 loss) +I0616 11:49:32.548979 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0298144 (* 1 = 0.0298144 loss) +I0616 11:49:32.548982 9857 solver.cpp:571] Iteration 57560, lr = 0.0001 +I0616 11:49:44.128829 9857 solver.cpp:242] Iteration 57580, loss = 0.455712 +I0616 11:49:44.128871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128016 (* 1 = 0.128016 loss) +I0616 11:49:44.128877 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0809999 (* 1 = 0.0809999 loss) +I0616 11:49:44.128881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0987962 (* 1 = 0.0987962 loss) +I0616 11:49:44.128885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269003 (* 1 = 0.0269003 loss) +I0616 11:49:44.128888 9857 solver.cpp:571] Iteration 57580, lr = 0.0001 +speed: 0.608s / iter +I0616 11:49:55.816179 9857 solver.cpp:242] Iteration 57600, loss = 0.794669 +I0616 11:49:55.816220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220669 (* 1 = 0.220669 loss) +I0616 11:49:55.816226 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218009 (* 1 = 0.218009 loss) +I0616 11:49:55.816231 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141542 (* 1 = 0.141542 loss) +I0616 11:49:55.816233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0953574 (* 1 = 0.0953574 loss) +I0616 11:49:55.816237 9857 solver.cpp:571] Iteration 57600, lr = 0.0001 +I0616 11:50:07.265787 9857 solver.cpp:242] Iteration 57620, loss = 0.800289 +I0616 11:50:07.265830 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.423348 (* 1 = 0.423348 loss) +I0616 11:50:07.265835 9857 solver.cpp:258] Train net output #1: loss_cls = 0.519082 (* 1 = 0.519082 loss) +I0616 11:50:07.265838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151929 (* 1 = 0.151929 loss) +I0616 11:50:07.265842 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137831 (* 1 = 0.137831 loss) +I0616 11:50:07.265846 9857 solver.cpp:571] Iteration 57620, lr = 0.0001 +I0616 11:50:18.862386 9857 solver.cpp:242] Iteration 57640, loss = 0.539891 +I0616 11:50:18.862431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0960554 (* 1 = 0.0960554 loss) +I0616 11:50:18.862437 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200415 (* 1 = 0.200415 loss) +I0616 11:50:18.862440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0345696 (* 1 = 0.0345696 loss) +I0616 11:50:18.862444 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.001354 (* 1 = 0.001354 loss) +I0616 11:50:18.862447 9857 solver.cpp:571] Iteration 57640, lr = 0.0001 +I0616 11:50:30.463069 9857 solver.cpp:242] Iteration 57660, loss = 0.266008 +I0616 11:50:30.463110 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0876264 (* 1 = 0.0876264 loss) +I0616 11:50:30.463116 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108563 (* 1 = 0.108563 loss) +I0616 11:50:30.463120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00280586 (* 1 = 0.00280586 loss) +I0616 11:50:30.463124 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00224841 (* 1 = 0.00224841 loss) +I0616 11:50:30.463129 9857 solver.cpp:571] Iteration 57660, lr = 0.0001 +I0616 11:50:41.823992 9857 solver.cpp:242] Iteration 57680, loss = 0.668517 +I0616 11:50:41.824021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.400141 (* 1 = 0.400141 loss) +I0616 11:50:41.824028 9857 solver.cpp:258] Train net output #1: loss_cls = 0.412112 (* 1 = 0.412112 loss) +I0616 11:50:41.824031 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141964 (* 1 = 0.141964 loss) +I0616 11:50:41.824035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116909 (* 1 = 0.116909 loss) +I0616 11:50:41.824039 9857 solver.cpp:571] Iteration 57680, lr = 0.0001 +I0616 11:50:53.421869 9857 solver.cpp:242] Iteration 57700, loss = 0.744172 +I0616 11:50:53.421910 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282638 (* 1 = 0.282638 loss) +I0616 11:50:53.421916 9857 solver.cpp:258] Train net output #1: loss_cls = 0.347528 (* 1 = 0.347528 loss) +I0616 11:50:53.421921 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13969 (* 1 = 0.13969 loss) +I0616 11:50:53.421924 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202295 (* 1 = 0.0202295 loss) +I0616 11:50:53.421927 9857 solver.cpp:571] Iteration 57700, lr = 0.0001 +I0616 11:51:05.023612 9857 solver.cpp:242] Iteration 57720, loss = 1.19747 +I0616 11:51:05.023653 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.488874 (* 1 = 0.488874 loss) +I0616 11:51:05.023658 9857 solver.cpp:258] Train net output #1: loss_cls = 0.686912 (* 1 = 0.686912 loss) +I0616 11:51:05.023661 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.367635 (* 1 = 0.367635 loss) +I0616 11:51:05.023665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0785229 (* 1 = 0.0785229 loss) +I0616 11:51:05.023671 9857 solver.cpp:571] Iteration 57720, lr = 0.0001 +I0616 11:51:16.707691 9857 solver.cpp:242] Iteration 57740, loss = 0.325298 +I0616 11:51:16.707731 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227254 (* 1 = 0.227254 loss) +I0616 11:51:16.707736 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194352 (* 1 = 0.194352 loss) +I0616 11:51:16.707739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0180802 (* 1 = 0.0180802 loss) +I0616 11:51:16.707742 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011862 (* 1 = 0.011862 loss) +I0616 11:51:16.707746 9857 solver.cpp:571] Iteration 57740, lr = 0.0001 +I0616 11:51:28.139071 9857 solver.cpp:242] Iteration 57760, loss = 0.359847 +I0616 11:51:28.139113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106145 (* 1 = 0.106145 loss) +I0616 11:51:28.139119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103256 (* 1 = 0.103256 loss) +I0616 11:51:28.139123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0283704 (* 1 = 0.0283704 loss) +I0616 11:51:28.139127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00245026 (* 1 = 0.00245026 loss) +I0616 11:51:28.139132 9857 solver.cpp:571] Iteration 57760, lr = 0.0001 +I0616 11:51:39.815989 9857 solver.cpp:242] Iteration 57780, loss = 0.316683 +I0616 11:51:39.816030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135224 (* 1 = 0.135224 loss) +I0616 11:51:39.816036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.25271 (* 1 = 0.25271 loss) +I0616 11:51:39.816040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230014 (* 1 = 0.0230014 loss) +I0616 11:51:39.816045 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00626588 (* 1 = 0.00626588 loss) +I0616 11:51:39.816047 9857 solver.cpp:571] Iteration 57780, lr = 0.0001 +speed: 0.608s / iter +I0616 11:51:51.384047 9857 solver.cpp:242] Iteration 57800, loss = 0.515868 +I0616 11:51:51.384089 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.065034 (* 1 = 0.065034 loss) +I0616 11:51:51.384095 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160694 (* 1 = 0.160694 loss) +I0616 11:51:51.384099 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0161516 (* 1 = 0.0161516 loss) +I0616 11:51:51.384104 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00846196 (* 1 = 0.00846196 loss) +I0616 11:51:51.384109 9857 solver.cpp:571] Iteration 57800, lr = 0.0001 +I0616 11:52:02.958292 9857 solver.cpp:242] Iteration 57820, loss = 0.982211 +I0616 11:52:02.958333 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256002 (* 1 = 0.256002 loss) +I0616 11:52:02.958339 9857 solver.cpp:258] Train net output #1: loss_cls = 0.367895 (* 1 = 0.367895 loss) +I0616 11:52:02.958343 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0730269 (* 1 = 0.0730269 loss) +I0616 11:52:02.958348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0751138 (* 1 = 0.0751138 loss) +I0616 11:52:02.958350 9857 solver.cpp:571] Iteration 57820, lr = 0.0001 +I0616 11:52:14.658030 9857 solver.cpp:242] Iteration 57840, loss = 0.42208 +I0616 11:52:14.658073 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0878427 (* 1 = 0.0878427 loss) +I0616 11:52:14.658079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147757 (* 1 = 0.147757 loss) +I0616 11:52:14.658083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0226598 (* 1 = 0.0226598 loss) +I0616 11:52:14.658087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174187 (* 1 = 0.0174187 loss) +I0616 11:52:14.658090 9857 solver.cpp:571] Iteration 57840, lr = 0.0001 +I0616 11:52:26.356822 9857 solver.cpp:242] Iteration 57860, loss = 0.413864 +I0616 11:52:26.356863 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14075 (* 1 = 0.14075 loss) +I0616 11:52:26.356868 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196419 (* 1 = 0.196419 loss) +I0616 11:52:26.356871 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0281969 (* 1 = 0.0281969 loss) +I0616 11:52:26.356875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0241554 (* 1 = 0.0241554 loss) +I0616 11:52:26.356879 9857 solver.cpp:571] Iteration 57860, lr = 0.0001 +I0616 11:52:38.094391 9857 solver.cpp:242] Iteration 57880, loss = 0.447631 +I0616 11:52:38.094432 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187298 (* 1 = 0.187298 loss) +I0616 11:52:38.094437 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171634 (* 1 = 0.171634 loss) +I0616 11:52:38.094442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0314761 (* 1 = 0.0314761 loss) +I0616 11:52:38.094445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107109 (* 1 = 0.0107109 loss) +I0616 11:52:38.094449 9857 solver.cpp:571] Iteration 57880, lr = 0.0001 +I0616 11:52:49.766386 9857 solver.cpp:242] Iteration 57900, loss = 0.579623 +I0616 11:52:49.766425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104268 (* 1 = 0.104268 loss) +I0616 11:52:49.766430 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11834 (* 1 = 0.11834 loss) +I0616 11:52:49.766434 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0103136 (* 1 = 0.0103136 loss) +I0616 11:52:49.766438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260618 (* 1 = 0.0260618 loss) +I0616 11:52:49.766443 9857 solver.cpp:571] Iteration 57900, lr = 0.0001 +I0616 11:53:01.338449 9857 solver.cpp:242] Iteration 57920, loss = 0.774189 +I0616 11:53:01.338491 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374986 (* 1 = 0.374986 loss) +I0616 11:53:01.338496 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205158 (* 1 = 0.205158 loss) +I0616 11:53:01.338501 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110384 (* 1 = 0.110384 loss) +I0616 11:53:01.338505 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119689 (* 1 = 0.0119689 loss) +I0616 11:53:01.338508 9857 solver.cpp:571] Iteration 57920, lr = 0.0001 +I0616 11:53:12.766738 9857 solver.cpp:242] Iteration 57940, loss = 0.490975 +I0616 11:53:12.766787 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0700874 (* 1 = 0.0700874 loss) +I0616 11:53:12.766793 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19151 (* 1 = 0.19151 loss) +I0616 11:53:12.766796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0070551 (* 1 = 0.0070551 loss) +I0616 11:53:12.766799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00276972 (* 1 = 0.00276972 loss) +I0616 11:53:12.766803 9857 solver.cpp:571] Iteration 57940, lr = 0.0001 +I0616 11:53:24.678477 9857 solver.cpp:242] Iteration 57960, loss = 1.00882 +I0616 11:53:24.678519 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357173 (* 1 = 0.357173 loss) +I0616 11:53:24.678524 9857 solver.cpp:258] Train net output #1: loss_cls = 0.450139 (* 1 = 0.450139 loss) +I0616 11:53:24.678529 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.241133 (* 1 = 0.241133 loss) +I0616 11:53:24.678531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.412049 (* 1 = 0.412049 loss) +I0616 11:53:24.678535 9857 solver.cpp:571] Iteration 57960, lr = 0.0001 +I0616 11:53:36.085969 9857 solver.cpp:242] Iteration 57980, loss = 0.563196 +I0616 11:53:36.086011 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0527887 (* 1 = 0.0527887 loss) +I0616 11:53:36.086017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0981316 (* 1 = 0.0981316 loss) +I0616 11:53:36.086021 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155281 (* 1 = 0.155281 loss) +I0616 11:53:36.086024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00415981 (* 1 = 0.00415981 loss) +I0616 11:53:36.086029 9857 solver.cpp:571] Iteration 57980, lr = 0.0001 +speed: 0.608s / iter +I0616 11:53:47.924706 9857 solver.cpp:242] Iteration 58000, loss = 0.419733 +I0616 11:53:47.924747 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168703 (* 1 = 0.168703 loss) +I0616 11:53:47.924752 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263424 (* 1 = 0.263424 loss) +I0616 11:53:47.924757 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107218 (* 1 = 0.107218 loss) +I0616 11:53:47.924760 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157561 (* 1 = 0.0157561 loss) +I0616 11:53:47.924764 9857 solver.cpp:571] Iteration 58000, lr = 0.0001 +I0616 11:53:59.378135 9857 solver.cpp:242] Iteration 58020, loss = 0.530325 +I0616 11:53:59.378175 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159868 (* 1 = 0.159868 loss) +I0616 11:53:59.378182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.501307 (* 1 = 0.501307 loss) +I0616 11:53:59.378186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165346 (* 1 = 0.165346 loss) +I0616 11:53:59.378190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225131 (* 1 = 0.0225131 loss) +I0616 11:53:59.378195 9857 solver.cpp:571] Iteration 58020, lr = 0.0001 +I0616 11:54:10.989176 9857 solver.cpp:242] Iteration 58040, loss = 0.559104 +I0616 11:54:10.989217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12599 (* 1 = 0.12599 loss) +I0616 11:54:10.989223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17168 (* 1 = 0.17168 loss) +I0616 11:54:10.989228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0151784 (* 1 = 0.0151784 loss) +I0616 11:54:10.989231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101394 (* 1 = 0.0101394 loss) +I0616 11:54:10.989234 9857 solver.cpp:571] Iteration 58040, lr = 0.0001 +I0616 11:54:22.267844 9857 solver.cpp:242] Iteration 58060, loss = 0.580144 +I0616 11:54:22.267885 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225792 (* 1 = 0.225792 loss) +I0616 11:54:22.267891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314901 (* 1 = 0.314901 loss) +I0616 11:54:22.267895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0917585 (* 1 = 0.0917585 loss) +I0616 11:54:22.267899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0493038 (* 1 = 0.0493038 loss) +I0616 11:54:22.267902 9857 solver.cpp:571] Iteration 58060, lr = 0.0001 +I0616 11:54:33.846529 9857 solver.cpp:242] Iteration 58080, loss = 0.422556 +I0616 11:54:33.846570 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292902 (* 1 = 0.292902 loss) +I0616 11:54:33.846575 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204719 (* 1 = 0.204719 loss) +I0616 11:54:33.846580 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0639323 (* 1 = 0.0639323 loss) +I0616 11:54:33.846583 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0445032 (* 1 = 0.0445032 loss) +I0616 11:54:33.846586 9857 solver.cpp:571] Iteration 58080, lr = 0.0001 +I0616 11:54:45.288525 9857 solver.cpp:242] Iteration 58100, loss = 0.682099 +I0616 11:54:45.288568 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.470085 (* 1 = 0.470085 loss) +I0616 11:54:45.288574 9857 solver.cpp:258] Train net output #1: loss_cls = 0.379986 (* 1 = 0.379986 loss) +I0616 11:54:45.288578 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.164817 (* 1 = 0.164817 loss) +I0616 11:54:45.288581 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0326739 (* 1 = 0.0326739 loss) +I0616 11:54:45.288586 9857 solver.cpp:571] Iteration 58100, lr = 0.0001 +I0616 11:54:56.864872 9857 solver.cpp:242] Iteration 58120, loss = 0.484934 +I0616 11:54:56.864917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160047 (* 1 = 0.160047 loss) +I0616 11:54:56.864922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.327001 (* 1 = 0.327001 loss) +I0616 11:54:56.864925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.217265 (* 1 = 0.217265 loss) +I0616 11:54:56.864929 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00813042 (* 1 = 0.00813042 loss) +I0616 11:54:56.864933 9857 solver.cpp:571] Iteration 58120, lr = 0.0001 +I0616 11:55:08.264219 9857 solver.cpp:242] Iteration 58140, loss = 0.360844 +I0616 11:55:08.264261 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0936278 (* 1 = 0.0936278 loss) +I0616 11:55:08.264266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183483 (* 1 = 0.183483 loss) +I0616 11:55:08.264269 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0246388 (* 1 = 0.0246388 loss) +I0616 11:55:08.264273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227983 (* 1 = 0.0227983 loss) +I0616 11:55:08.264276 9857 solver.cpp:571] Iteration 58140, lr = 0.0001 +I0616 11:55:19.773989 9857 solver.cpp:242] Iteration 58160, loss = 0.888479 +I0616 11:55:19.774031 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.31442 (* 1 = 0.31442 loss) +I0616 11:55:19.774037 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253401 (* 1 = 0.253401 loss) +I0616 11:55:19.774041 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0966431 (* 1 = 0.0966431 loss) +I0616 11:55:19.774045 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0385433 (* 1 = 0.0385433 loss) +I0616 11:55:19.774049 9857 solver.cpp:571] Iteration 58160, lr = 0.0001 +I0616 11:55:31.445696 9857 solver.cpp:242] Iteration 58180, loss = 0.750276 +I0616 11:55:31.445737 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265209 (* 1 = 0.265209 loss) +I0616 11:55:31.445744 9857 solver.cpp:258] Train net output #1: loss_cls = 0.435315 (* 1 = 0.435315 loss) +I0616 11:55:31.445747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.251897 (* 1 = 0.251897 loss) +I0616 11:55:31.445750 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.193327 (* 1 = 0.193327 loss) +I0616 11:55:31.445755 9857 solver.cpp:571] Iteration 58180, lr = 0.0001 +speed: 0.608s / iter +I0616 11:55:43.040624 9857 solver.cpp:242] Iteration 58200, loss = 0.261104 +I0616 11:55:43.040666 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0918631 (* 1 = 0.0918631 loss) +I0616 11:55:43.040671 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142446 (* 1 = 0.142446 loss) +I0616 11:55:43.040675 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0272726 (* 1 = 0.0272726 loss) +I0616 11:55:43.040679 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00805684 (* 1 = 0.00805684 loss) +I0616 11:55:43.040683 9857 solver.cpp:571] Iteration 58200, lr = 0.0001 +I0616 11:55:54.531879 9857 solver.cpp:242] Iteration 58220, loss = 0.588584 +I0616 11:55:54.531922 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231295 (* 1 = 0.231295 loss) +I0616 11:55:54.531929 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170107 (* 1 = 0.170107 loss) +I0616 11:55:54.531932 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.269976 (* 1 = 0.269976 loss) +I0616 11:55:54.531936 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.131446 (* 1 = 0.131446 loss) +I0616 11:55:54.531939 9857 solver.cpp:571] Iteration 58220, lr = 0.0001 +I0616 11:56:06.111397 9857 solver.cpp:242] Iteration 58240, loss = 0.687929 +I0616 11:56:06.111438 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17934 (* 1 = 0.17934 loss) +I0616 11:56:06.111444 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195198 (* 1 = 0.195198 loss) +I0616 11:56:06.111449 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119419 (* 1 = 0.0119419 loss) +I0616 11:56:06.111452 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00758678 (* 1 = 0.00758678 loss) +I0616 11:56:06.111456 9857 solver.cpp:571] Iteration 58240, lr = 0.0001 +I0616 11:56:17.140542 9857 solver.cpp:242] Iteration 58260, loss = 0.24628 +I0616 11:56:17.140583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0364374 (* 1 = 0.0364374 loss) +I0616 11:56:17.140589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146071 (* 1 = 0.146071 loss) +I0616 11:56:17.140594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00776148 (* 1 = 0.00776148 loss) +I0616 11:56:17.140597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00460813 (* 1 = 0.00460813 loss) +I0616 11:56:17.140601 9857 solver.cpp:571] Iteration 58260, lr = 0.0001 +I0616 11:56:28.417845 9857 solver.cpp:242] Iteration 58280, loss = 0.293574 +I0616 11:56:28.417887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105612 (* 1 = 0.105612 loss) +I0616 11:56:28.417893 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195195 (* 1 = 0.195195 loss) +I0616 11:56:28.417897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112576 (* 1 = 0.0112576 loss) +I0616 11:56:28.417901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00114386 (* 1 = 0.00114386 loss) +I0616 11:56:28.417904 9857 solver.cpp:571] Iteration 58280, lr = 0.0001 +I0616 11:56:39.908841 9857 solver.cpp:242] Iteration 58300, loss = 0.314357 +I0616 11:56:39.908884 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0674607 (* 1 = 0.0674607 loss) +I0616 11:56:39.908888 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144247 (* 1 = 0.144247 loss) +I0616 11:56:39.908892 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10751 (* 1 = 0.10751 loss) +I0616 11:56:39.908896 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00218231 (* 1 = 0.00218231 loss) +I0616 11:56:39.908900 9857 solver.cpp:571] Iteration 58300, lr = 0.0001 +I0616 11:56:51.175319 9857 solver.cpp:242] Iteration 58320, loss = 0.562568 +I0616 11:56:51.175362 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0689604 (* 1 = 0.0689604 loss) +I0616 11:56:51.175367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17545 (* 1 = 0.17545 loss) +I0616 11:56:51.175371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0352024 (* 1 = 0.0352024 loss) +I0616 11:56:51.175375 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152712 (* 1 = 0.0152712 loss) +I0616 11:56:51.175379 9857 solver.cpp:571] Iteration 58320, lr = 0.0001 +I0616 11:57:02.548732 9857 solver.cpp:242] Iteration 58340, loss = 0.615976 +I0616 11:57:02.548774 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19998 (* 1 = 0.19998 loss) +I0616 11:57:02.548779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.282592 (* 1 = 0.282592 loss) +I0616 11:57:02.548784 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0814722 (* 1 = 0.0814722 loss) +I0616 11:57:02.548789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0936116 (* 1 = 0.0936116 loss) +I0616 11:57:02.548791 9857 solver.cpp:571] Iteration 58340, lr = 0.0001 +I0616 11:57:14.006075 9857 solver.cpp:242] Iteration 58360, loss = 0.627743 +I0616 11:57:14.006116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17532 (* 1 = 0.17532 loss) +I0616 11:57:14.006122 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245893 (* 1 = 0.245893 loss) +I0616 11:57:14.006126 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1194 (* 1 = 0.1194 loss) +I0616 11:57:14.006130 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143264 (* 1 = 0.0143264 loss) +I0616 11:57:14.006134 9857 solver.cpp:571] Iteration 58360, lr = 0.0001 +I0616 11:57:25.361539 9857 solver.cpp:242] Iteration 58380, loss = 0.539982 +I0616 11:57:25.361582 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.240136 (* 1 = 0.240136 loss) +I0616 11:57:25.361587 9857 solver.cpp:258] Train net output #1: loss_cls = 0.293095 (* 1 = 0.293095 loss) +I0616 11:57:25.361590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150829 (* 1 = 0.150829 loss) +I0616 11:57:25.361594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0277613 (* 1 = 0.0277613 loss) +I0616 11:57:25.361598 9857 solver.cpp:571] Iteration 58380, lr = 0.0001 +speed: 0.608s / iter +I0616 11:57:36.986516 9857 solver.cpp:242] Iteration 58400, loss = 0.5266 +I0616 11:57:36.986557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231797 (* 1 = 0.231797 loss) +I0616 11:57:36.986563 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375757 (* 1 = 0.375757 loss) +I0616 11:57:36.986567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0663636 (* 1 = 0.0663636 loss) +I0616 11:57:36.986572 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0967152 (* 1 = 0.0967152 loss) +I0616 11:57:36.986574 9857 solver.cpp:571] Iteration 58400, lr = 0.0001 +I0616 11:57:48.279103 9857 solver.cpp:242] Iteration 58420, loss = 0.774883 +I0616 11:57:48.279145 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287096 (* 1 = 0.287096 loss) +I0616 11:57:48.279150 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281913 (* 1 = 0.281913 loss) +I0616 11:57:48.279153 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0737258 (* 1 = 0.0737258 loss) +I0616 11:57:48.279157 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.399193 (* 1 = 0.399193 loss) +I0616 11:57:48.279160 9857 solver.cpp:571] Iteration 58420, lr = 0.0001 +I0616 11:57:59.519678 9857 solver.cpp:242] Iteration 58440, loss = 0.487478 +I0616 11:57:59.519721 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112596 (* 1 = 0.112596 loss) +I0616 11:57:59.519726 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0876151 (* 1 = 0.0876151 loss) +I0616 11:57:59.519731 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395191 (* 1 = 0.0395191 loss) +I0616 11:57:59.519733 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00777155 (* 1 = 0.00777155 loss) +I0616 11:57:59.519737 9857 solver.cpp:571] Iteration 58440, lr = 0.0001 +I0616 11:58:11.121506 9857 solver.cpp:242] Iteration 58460, loss = 0.590404 +I0616 11:58:11.121549 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20029 (* 1 = 0.20029 loss) +I0616 11:58:11.121554 9857 solver.cpp:258] Train net output #1: loss_cls = 0.372842 (* 1 = 0.372842 loss) +I0616 11:58:11.121558 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0691124 (* 1 = 0.0691124 loss) +I0616 11:58:11.121562 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162791 (* 1 = 0.0162791 loss) +I0616 11:58:11.121567 9857 solver.cpp:571] Iteration 58460, lr = 0.0001 +I0616 11:58:22.575104 9857 solver.cpp:242] Iteration 58480, loss = 0.439414 +I0616 11:58:22.575145 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13517 (* 1 = 0.13517 loss) +I0616 11:58:22.575150 9857 solver.cpp:258] Train net output #1: loss_cls = 0.256895 (* 1 = 0.256895 loss) +I0616 11:58:22.575155 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0306732 (* 1 = 0.0306732 loss) +I0616 11:58:22.575158 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0911262 (* 1 = 0.0911262 loss) +I0616 11:58:22.575162 9857 solver.cpp:571] Iteration 58480, lr = 0.0001 +I0616 11:58:34.090277 9857 solver.cpp:242] Iteration 58500, loss = 0.806518 +I0616 11:58:34.090319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0640094 (* 1 = 0.0640094 loss) +I0616 11:58:34.090324 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102611 (* 1 = 0.102611 loss) +I0616 11:58:34.090328 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0312516 (* 1 = 0.0312516 loss) +I0616 11:58:34.090332 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00747802 (* 1 = 0.00747802 loss) +I0616 11:58:34.090335 9857 solver.cpp:571] Iteration 58500, lr = 0.0001 +I0616 11:58:45.630108 9857 solver.cpp:242] Iteration 58520, loss = 0.5963 +I0616 11:58:45.630151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.356856 (* 1 = 0.356856 loss) +I0616 11:58:45.630157 9857 solver.cpp:258] Train net output #1: loss_cls = 0.48124 (* 1 = 0.48124 loss) +I0616 11:58:45.630162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0871863 (* 1 = 0.0871863 loss) +I0616 11:58:45.630164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334464 (* 1 = 0.0334464 loss) +I0616 11:58:45.630168 9857 solver.cpp:571] Iteration 58520, lr = 0.0001 +I0616 11:58:57.157722 9857 solver.cpp:242] Iteration 58540, loss = 0.410227 +I0616 11:58:57.157764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231229 (* 1 = 0.231229 loss) +I0616 11:58:57.157770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192377 (* 1 = 0.192377 loss) +I0616 11:58:57.157774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0779349 (* 1 = 0.0779349 loss) +I0616 11:58:57.157778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0228505 (* 1 = 0.0228505 loss) +I0616 11:58:57.157781 9857 solver.cpp:571] Iteration 58540, lr = 0.0001 +I0616 11:59:08.936331 9857 solver.cpp:242] Iteration 58560, loss = 0.506334 +I0616 11:59:08.936372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168721 (* 1 = 0.168721 loss) +I0616 11:59:08.936378 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248135 (* 1 = 0.248135 loss) +I0616 11:59:08.936383 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101448 (* 1 = 0.101448 loss) +I0616 11:59:08.936386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0814664 (* 1 = 0.0814664 loss) +I0616 11:59:08.936390 9857 solver.cpp:571] Iteration 58560, lr = 0.0001 +I0616 11:59:20.498945 9857 solver.cpp:242] Iteration 58580, loss = 0.632342 +I0616 11:59:20.498987 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.090896 (* 1 = 0.090896 loss) +I0616 11:59:20.498993 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218139 (* 1 = 0.218139 loss) +I0616 11:59:20.498997 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106358 (* 1 = 0.106358 loss) +I0616 11:59:20.499001 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.33376 (* 1 = 0.33376 loss) +I0616 11:59:20.499004 9857 solver.cpp:571] Iteration 58580, lr = 0.0001 +speed: 0.608s / iter +I0616 11:59:31.717213 9857 solver.cpp:242] Iteration 58600, loss = 0.209712 +I0616 11:59:31.717255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.065797 (* 1 = 0.065797 loss) +I0616 11:59:31.717275 9857 solver.cpp:258] Train net output #1: loss_cls = 0.095284 (* 1 = 0.095284 loss) +I0616 11:59:31.717279 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0046692 (* 1 = 0.0046692 loss) +I0616 11:59:31.717283 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00679319 (* 1 = 0.00679319 loss) +I0616 11:59:31.717286 9857 solver.cpp:571] Iteration 58600, lr = 0.0001 +I0616 11:59:43.037739 9857 solver.cpp:242] Iteration 58620, loss = 0.931202 +I0616 11:59:43.037781 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265834 (* 1 = 0.265834 loss) +I0616 11:59:43.037786 9857 solver.cpp:258] Train net output #1: loss_cls = 0.439504 (* 1 = 0.439504 loss) +I0616 11:59:43.037791 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0957181 (* 1 = 0.0957181 loss) +I0616 11:59:43.037796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0993922 (* 1 = 0.0993922 loss) +I0616 11:59:43.037799 9857 solver.cpp:571] Iteration 58620, lr = 0.0001 +I0616 11:59:54.450505 9857 solver.cpp:242] Iteration 58640, loss = 0.475909 +I0616 11:59:54.450548 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215156 (* 1 = 0.215156 loss) +I0616 11:59:54.450554 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304693 (* 1 = 0.304693 loss) +I0616 11:59:54.450558 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0396647 (* 1 = 0.0396647 loss) +I0616 11:59:54.450562 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404529 (* 1 = 0.0404529 loss) +I0616 11:59:54.450565 9857 solver.cpp:571] Iteration 58640, lr = 0.0001 +I0616 12:00:05.760712 9857 solver.cpp:242] Iteration 58660, loss = 0.404376 +I0616 12:00:05.760756 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0756733 (* 1 = 0.0756733 loss) +I0616 12:00:05.760773 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121307 (* 1 = 0.121307 loss) +I0616 12:00:05.760777 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0245619 (* 1 = 0.0245619 loss) +I0616 12:00:05.760782 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00699565 (* 1 = 0.00699565 loss) +I0616 12:00:05.760784 9857 solver.cpp:571] Iteration 58660, lr = 0.0001 +I0616 12:00:17.518548 9857 solver.cpp:242] Iteration 58680, loss = 0.409193 +I0616 12:00:17.518591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.051688 (* 1 = 0.051688 loss) +I0616 12:00:17.518596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13678 (* 1 = 0.13678 loss) +I0616 12:00:17.518600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0254843 (* 1 = 0.0254843 loss) +I0616 12:00:17.518604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119673 (* 1 = 0.0119673 loss) +I0616 12:00:17.518607 9857 solver.cpp:571] Iteration 58680, lr = 0.0001 +I0616 12:00:29.072968 9857 solver.cpp:242] Iteration 58700, loss = 0.722279 +I0616 12:00:29.073009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215456 (* 1 = 0.215456 loss) +I0616 12:00:29.073014 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316215 (* 1 = 0.316215 loss) +I0616 12:00:29.073019 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138838 (* 1 = 0.138838 loss) +I0616 12:00:29.073022 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0690737 (* 1 = 0.0690737 loss) +I0616 12:00:29.073026 9857 solver.cpp:571] Iteration 58700, lr = 0.0001 +I0616 12:00:40.695396 9857 solver.cpp:242] Iteration 58720, loss = 0.69383 +I0616 12:00:40.695438 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265754 (* 1 = 0.265754 loss) +I0616 12:00:40.695443 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338618 (* 1 = 0.338618 loss) +I0616 12:00:40.695448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106094 (* 1 = 0.106094 loss) +I0616 12:00:40.695452 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167306 (* 1 = 0.0167306 loss) +I0616 12:00:40.695456 9857 solver.cpp:571] Iteration 58720, lr = 0.0001 +I0616 12:00:52.164010 9857 solver.cpp:242] Iteration 58740, loss = 0.607158 +I0616 12:00:52.164048 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390771 (* 1 = 0.390771 loss) +I0616 12:00:52.164054 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354678 (* 1 = 0.354678 loss) +I0616 12:00:52.164058 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144086 (* 1 = 0.144086 loss) +I0616 12:00:52.164062 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0999032 (* 1 = 0.0999032 loss) +I0616 12:00:52.164065 9857 solver.cpp:571] Iteration 58740, lr = 0.0001 +I0616 12:01:04.054616 9857 solver.cpp:242] Iteration 58760, loss = 0.624007 +I0616 12:01:04.054659 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0988648 (* 1 = 0.0988648 loss) +I0616 12:01:04.054664 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161495 (* 1 = 0.161495 loss) +I0616 12:01:04.054669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0436648 (* 1 = 0.0436648 loss) +I0616 12:01:04.054672 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0148817 (* 1 = 0.0148817 loss) +I0616 12:01:04.054677 9857 solver.cpp:571] Iteration 58760, lr = 0.0001 +I0616 12:01:15.307925 9857 solver.cpp:242] Iteration 58780, loss = 0.538608 +I0616 12:01:15.307967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236319 (* 1 = 0.236319 loss) +I0616 12:01:15.307972 9857 solver.cpp:258] Train net output #1: loss_cls = 0.374596 (* 1 = 0.374596 loss) +I0616 12:01:15.307977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106063 (* 1 = 0.106063 loss) +I0616 12:01:15.307981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0585159 (* 1 = 0.0585159 loss) +I0616 12:01:15.307984 9857 solver.cpp:571] Iteration 58780, lr = 0.0001 +speed: 0.608s / iter +I0616 12:01:26.858081 9857 solver.cpp:242] Iteration 58800, loss = 0.433028 +I0616 12:01:26.858124 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132983 (* 1 = 0.132983 loss) +I0616 12:01:26.858129 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255471 (* 1 = 0.255471 loss) +I0616 12:01:26.858134 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0324123 (* 1 = 0.0324123 loss) +I0616 12:01:26.858137 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00360035 (* 1 = 0.00360035 loss) +I0616 12:01:26.858140 9857 solver.cpp:571] Iteration 58800, lr = 0.0001 +I0616 12:01:38.486232 9857 solver.cpp:242] Iteration 58820, loss = 0.461823 +I0616 12:01:38.486274 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.119284 (* 1 = 0.119284 loss) +I0616 12:01:38.486280 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101915 (* 1 = 0.101915 loss) +I0616 12:01:38.486285 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107655 (* 1 = 0.107655 loss) +I0616 12:01:38.486289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00493509 (* 1 = 0.00493509 loss) +I0616 12:01:38.486292 9857 solver.cpp:571] Iteration 58820, lr = 0.0001 +I0616 12:01:50.305342 9857 solver.cpp:242] Iteration 58840, loss = 0.515154 +I0616 12:01:50.305384 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205722 (* 1 = 0.205722 loss) +I0616 12:01:50.305390 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155743 (* 1 = 0.155743 loss) +I0616 12:01:50.305394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0448817 (* 1 = 0.0448817 loss) +I0616 12:01:50.305398 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.109444 (* 1 = 0.109444 loss) +I0616 12:01:50.305402 9857 solver.cpp:571] Iteration 58840, lr = 0.0001 +I0616 12:02:01.831018 9857 solver.cpp:242] Iteration 58860, loss = 0.618748 +I0616 12:02:01.831059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0990558 (* 1 = 0.0990558 loss) +I0616 12:02:01.831064 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130728 (* 1 = 0.130728 loss) +I0616 12:02:01.831068 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0204946 (* 1 = 0.0204946 loss) +I0616 12:02:01.831073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115843 (* 1 = 0.0115843 loss) +I0616 12:02:01.831075 9857 solver.cpp:571] Iteration 58860, lr = 0.0001 +I0616 12:02:13.498780 9857 solver.cpp:242] Iteration 58880, loss = 0.256582 +I0616 12:02:13.498824 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134029 (* 1 = 0.134029 loss) +I0616 12:02:13.498829 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137059 (* 1 = 0.137059 loss) +I0616 12:02:13.498834 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00242363 (* 1 = 0.00242363 loss) +I0616 12:02:13.498838 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103324 (* 1 = 0.0103324 loss) +I0616 12:02:13.498844 9857 solver.cpp:571] Iteration 58880, lr = 0.0001 +I0616 12:02:24.949295 9857 solver.cpp:242] Iteration 58900, loss = 0.416753 +I0616 12:02:24.949337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261707 (* 1 = 0.261707 loss) +I0616 12:02:24.949343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274083 (* 1 = 0.274083 loss) +I0616 12:02:24.949347 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0259165 (* 1 = 0.0259165 loss) +I0616 12:02:24.949352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0688805 (* 1 = 0.0688805 loss) +I0616 12:02:24.949355 9857 solver.cpp:571] Iteration 58900, lr = 0.0001 +I0616 12:02:36.516347 9857 solver.cpp:242] Iteration 58920, loss = 0.461 +I0616 12:02:36.516388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168163 (* 1 = 0.168163 loss) +I0616 12:02:36.516393 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187932 (* 1 = 0.187932 loss) +I0616 12:02:36.516398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00293074 (* 1 = 0.00293074 loss) +I0616 12:02:36.516402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00761037 (* 1 = 0.00761037 loss) +I0616 12:02:36.516405 9857 solver.cpp:571] Iteration 58920, lr = 0.0001 +I0616 12:02:48.206365 9857 solver.cpp:242] Iteration 58940, loss = 0.572067 +I0616 12:02:48.206406 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.295533 (* 1 = 0.295533 loss) +I0616 12:02:48.206413 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222344 (* 1 = 0.222344 loss) +I0616 12:02:48.206416 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0326606 (* 1 = 0.0326606 loss) +I0616 12:02:48.206420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137145 (* 1 = 0.137145 loss) +I0616 12:02:48.206423 9857 solver.cpp:571] Iteration 58940, lr = 0.0001 +I0616 12:02:59.884237 9857 solver.cpp:242] Iteration 58960, loss = 0.661166 +I0616 12:02:59.884277 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137235 (* 1 = 0.137235 loss) +I0616 12:02:59.884284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301613 (* 1 = 0.301613 loss) +I0616 12:02:59.884287 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.201867 (* 1 = 0.201867 loss) +I0616 12:02:59.884290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00314143 (* 1 = 0.00314143 loss) +I0616 12:02:59.884294 9857 solver.cpp:571] Iteration 58960, lr = 0.0001 +I0616 12:03:11.556577 9857 solver.cpp:242] Iteration 58980, loss = 0.416757 +I0616 12:03:11.556620 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225264 (* 1 = 0.225264 loss) +I0616 12:03:11.556625 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236385 (* 1 = 0.236385 loss) +I0616 12:03:11.556629 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0130944 (* 1 = 0.0130944 loss) +I0616 12:03:11.556633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00935224 (* 1 = 0.00935224 loss) +I0616 12:03:11.556638 9857 solver.cpp:571] Iteration 58980, lr = 0.0001 +speed: 0.607s / iter +I0616 12:03:23.155443 9857 solver.cpp:242] Iteration 59000, loss = 1.06794 +I0616 12:03:23.155485 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32441 (* 1 = 0.32441 loss) +I0616 12:03:23.155491 9857 solver.cpp:258] Train net output #1: loss_cls = 0.736468 (* 1 = 0.736468 loss) +I0616 12:03:23.155495 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.270176 (* 1 = 0.270176 loss) +I0616 12:03:23.155498 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0327511 (* 1 = 0.0327511 loss) +I0616 12:03:23.155503 9857 solver.cpp:571] Iteration 59000, lr = 0.0001 +I0616 12:03:34.761732 9857 solver.cpp:242] Iteration 59020, loss = 0.402132 +I0616 12:03:34.761776 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11793 (* 1 = 0.11793 loss) +I0616 12:03:34.761781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192892 (* 1 = 0.192892 loss) +I0616 12:03:34.761786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0766339 (* 1 = 0.0766339 loss) +I0616 12:03:34.761790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101637 (* 1 = 0.0101637 loss) +I0616 12:03:34.761793 9857 solver.cpp:571] Iteration 59020, lr = 0.0001 +I0616 12:03:46.327698 9857 solver.cpp:242] Iteration 59040, loss = 0.171646 +I0616 12:03:46.327741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.046977 (* 1 = 0.046977 loss) +I0616 12:03:46.327747 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0640286 (* 1 = 0.0640286 loss) +I0616 12:03:46.327751 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.042395 (* 1 = 0.042395 loss) +I0616 12:03:46.327754 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00395912 (* 1 = 0.00395912 loss) +I0616 12:03:46.327759 9857 solver.cpp:571] Iteration 59040, lr = 0.0001 +I0616 12:03:58.035882 9857 solver.cpp:242] Iteration 59060, loss = 0.782814 +I0616 12:03:58.035923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238793 (* 1 = 0.238793 loss) +I0616 12:03:58.035929 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270754 (* 1 = 0.270754 loss) +I0616 12:03:58.035933 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.180923 (* 1 = 0.180923 loss) +I0616 12:03:58.035938 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210603 (* 1 = 0.0210603 loss) +I0616 12:03:58.035941 9857 solver.cpp:571] Iteration 59060, lr = 0.0001 +I0616 12:04:09.611127 9857 solver.cpp:242] Iteration 59080, loss = 0.734523 +I0616 12:04:09.611171 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263774 (* 1 = 0.263774 loss) +I0616 12:04:09.611176 9857 solver.cpp:258] Train net output #1: loss_cls = 0.78424 (* 1 = 0.78424 loss) +I0616 12:04:09.611181 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162459 (* 1 = 0.162459 loss) +I0616 12:04:09.611184 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0521211 (* 1 = 0.0521211 loss) +I0616 12:04:09.611187 9857 solver.cpp:571] Iteration 59080, lr = 0.0001 +I0616 12:04:21.030987 9857 solver.cpp:242] Iteration 59100, loss = 0.360527 +I0616 12:04:21.031029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.098804 (* 1 = 0.098804 loss) +I0616 12:04:21.031034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163959 (* 1 = 0.163959 loss) +I0616 12:04:21.031038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0175846 (* 1 = 0.0175846 loss) +I0616 12:04:21.031043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0747531 (* 1 = 0.0747531 loss) +I0616 12:04:21.031046 9857 solver.cpp:571] Iteration 59100, lr = 0.0001 +I0616 12:04:32.426725 9857 solver.cpp:242] Iteration 59120, loss = 0.511791 +I0616 12:04:32.426770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103922 (* 1 = 0.103922 loss) +I0616 12:04:32.426790 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166115 (* 1 = 0.166115 loss) +I0616 12:04:32.426795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0419149 (* 1 = 0.0419149 loss) +I0616 12:04:32.426798 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120206 (* 1 = 0.0120206 loss) +I0616 12:04:32.426802 9857 solver.cpp:571] Iteration 59120, lr = 0.0001 +I0616 12:04:44.163686 9857 solver.cpp:242] Iteration 59140, loss = 0.568137 +I0616 12:04:44.163728 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254297 (* 1 = 0.254297 loss) +I0616 12:04:44.163733 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274939 (* 1 = 0.274939 loss) +I0616 12:04:44.163738 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160898 (* 1 = 0.160898 loss) +I0616 12:04:44.163740 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129444 (* 1 = 0.129444 loss) +I0616 12:04:44.163744 9857 solver.cpp:571] Iteration 59140, lr = 0.0001 +I0616 12:04:55.689486 9857 solver.cpp:242] Iteration 59160, loss = 0.397264 +I0616 12:04:55.689528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0931162 (* 1 = 0.0931162 loss) +I0616 12:04:55.689534 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209267 (* 1 = 0.209267 loss) +I0616 12:04:55.689538 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00673225 (* 1 = 0.00673225 loss) +I0616 12:04:55.689543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254523 (* 1 = 0.0254523 loss) +I0616 12:04:55.689545 9857 solver.cpp:571] Iteration 59160, lr = 0.0001 +I0616 12:05:07.362435 9857 solver.cpp:242] Iteration 59180, loss = 0.467257 +I0616 12:05:07.362476 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202369 (* 1 = 0.202369 loss) +I0616 12:05:07.362481 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204906 (* 1 = 0.204906 loss) +I0616 12:05:07.362486 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0647988 (* 1 = 0.0647988 loss) +I0616 12:05:07.362489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.270987 (* 1 = 0.270987 loss) +I0616 12:05:07.362493 9857 solver.cpp:571] Iteration 59180, lr = 0.0001 +speed: 0.607s / iter +I0616 12:05:18.826537 9857 solver.cpp:242] Iteration 59200, loss = 1.19256 +I0616 12:05:18.826581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302608 (* 1 = 0.302608 loss) +I0616 12:05:18.826586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.545347 (* 1 = 0.545347 loss) +I0616 12:05:18.826591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.345195 (* 1 = 0.345195 loss) +I0616 12:05:18.826594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.36381 (* 1 = 0.36381 loss) +I0616 12:05:18.826598 9857 solver.cpp:571] Iteration 59200, lr = 0.0001 +I0616 12:05:30.485713 9857 solver.cpp:242] Iteration 59220, loss = 0.465227 +I0616 12:05:30.485755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0857924 (* 1 = 0.0857924 loss) +I0616 12:05:30.485761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118198 (* 1 = 0.118198 loss) +I0616 12:05:30.485765 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0061921 (* 1 = 0.0061921 loss) +I0616 12:05:30.485769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120344 (* 1 = 0.0120344 loss) +I0616 12:05:30.485772 9857 solver.cpp:571] Iteration 59220, lr = 0.0001 +I0616 12:05:41.957794 9857 solver.cpp:242] Iteration 59240, loss = 0.759738 +I0616 12:05:41.957837 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117767 (* 1 = 0.117767 loss) +I0616 12:05:41.957844 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211197 (* 1 = 0.211197 loss) +I0616 12:05:41.957847 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.178743 (* 1 = 0.178743 loss) +I0616 12:05:41.957851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278832 (* 1 = 0.0278832 loss) +I0616 12:05:41.957854 9857 solver.cpp:571] Iteration 59240, lr = 0.0001 +I0616 12:05:53.586279 9857 solver.cpp:242] Iteration 59260, loss = 0.342204 +I0616 12:05:53.586320 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114464 (* 1 = 0.114464 loss) +I0616 12:05:53.586326 9857 solver.cpp:258] Train net output #1: loss_cls = 0.228469 (* 1 = 0.228469 loss) +I0616 12:05:53.586330 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0854289 (* 1 = 0.0854289 loss) +I0616 12:05:53.586334 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00671889 (* 1 = 0.00671889 loss) +I0616 12:05:53.586338 9857 solver.cpp:571] Iteration 59260, lr = 0.0001 +I0616 12:06:04.991717 9857 solver.cpp:242] Iteration 59280, loss = 0.943435 +I0616 12:06:04.991760 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149676 (* 1 = 0.149676 loss) +I0616 12:06:04.991765 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154605 (* 1 = 0.154605 loss) +I0616 12:06:04.991770 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0829922 (* 1 = 0.0829922 loss) +I0616 12:06:04.991773 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0378911 (* 1 = 0.0378911 loss) +I0616 12:06:04.991777 9857 solver.cpp:571] Iteration 59280, lr = 0.0001 +I0616 12:06:16.504058 9857 solver.cpp:242] Iteration 59300, loss = 1.27069 +I0616 12:06:16.504101 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193884 (* 1 = 0.193884 loss) +I0616 12:06:16.504106 9857 solver.cpp:258] Train net output #1: loss_cls = 0.357708 (* 1 = 0.357708 loss) +I0616 12:06:16.504111 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.191436 (* 1 = 0.191436 loss) +I0616 12:06:16.504113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.384845 (* 1 = 0.384845 loss) +I0616 12:06:16.504117 9857 solver.cpp:571] Iteration 59300, lr = 0.0001 +I0616 12:06:27.764533 9857 solver.cpp:242] Iteration 59320, loss = 0.431089 +I0616 12:06:27.764575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0840418 (* 1 = 0.0840418 loss) +I0616 12:06:27.764580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0971663 (* 1 = 0.0971663 loss) +I0616 12:06:27.764585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0158664 (* 1 = 0.0158664 loss) +I0616 12:06:27.764588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104513 (* 1 = 0.104513 loss) +I0616 12:06:27.764592 9857 solver.cpp:571] Iteration 59320, lr = 0.0001 +I0616 12:06:39.054682 9857 solver.cpp:242] Iteration 59340, loss = 0.711495 +I0616 12:06:39.054723 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206296 (* 1 = 0.206296 loss) +I0616 12:06:39.054728 9857 solver.cpp:258] Train net output #1: loss_cls = 0.386326 (* 1 = 0.386326 loss) +I0616 12:06:39.054733 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0853998 (* 1 = 0.0853998 loss) +I0616 12:06:39.054736 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.179681 (* 1 = 0.179681 loss) +I0616 12:06:39.054740 9857 solver.cpp:571] Iteration 59340, lr = 0.0001 +I0616 12:06:50.812695 9857 solver.cpp:242] Iteration 59360, loss = 0.44725 +I0616 12:06:50.812736 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159861 (* 1 = 0.159861 loss) +I0616 12:06:50.812741 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157685 (* 1 = 0.157685 loss) +I0616 12:06:50.812746 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.036644 (* 1 = 0.036644 loss) +I0616 12:06:50.812749 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0266197 (* 1 = 0.0266197 loss) +I0616 12:06:50.812752 9857 solver.cpp:571] Iteration 59360, lr = 0.0001 +I0616 12:07:02.486986 9857 solver.cpp:242] Iteration 59380, loss = 0.717848 +I0616 12:07:02.487028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186394 (* 1 = 0.186394 loss) +I0616 12:07:02.487035 9857 solver.cpp:258] Train net output #1: loss_cls = 0.290745 (* 1 = 0.290745 loss) +I0616 12:07:02.487038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101323 (* 1 = 0.101323 loss) +I0616 12:07:02.487042 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275087 (* 1 = 0.0275087 loss) +I0616 12:07:02.487046 9857 solver.cpp:571] Iteration 59380, lr = 0.0001 +speed: 0.607s / iter +I0616 12:07:14.110492 9857 solver.cpp:242] Iteration 59400, loss = 0.770693 +I0616 12:07:14.110535 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0997874 (* 1 = 0.0997874 loss) +I0616 12:07:14.110540 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22772 (* 1 = 0.22772 loss) +I0616 12:07:14.110544 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14219 (* 1 = 0.14219 loss) +I0616 12:07:14.110548 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00616075 (* 1 = 0.00616075 loss) +I0616 12:07:14.110553 9857 solver.cpp:571] Iteration 59400, lr = 0.0001 +I0616 12:07:25.703690 9857 solver.cpp:242] Iteration 59420, loss = 1.33169 +I0616 12:07:25.703730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305315 (* 1 = 0.305315 loss) +I0616 12:07:25.703737 9857 solver.cpp:258] Train net output #1: loss_cls = 0.741266 (* 1 = 0.741266 loss) +I0616 12:07:25.703740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0604878 (* 1 = 0.0604878 loss) +I0616 12:07:25.703743 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012724 (* 1 = 0.012724 loss) +I0616 12:07:25.703747 9857 solver.cpp:571] Iteration 59420, lr = 0.0001 +I0616 12:07:37.328459 9857 solver.cpp:242] Iteration 59440, loss = 0.334198 +I0616 12:07:37.328501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0983269 (* 1 = 0.0983269 loss) +I0616 12:07:37.328506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169137 (* 1 = 0.169137 loss) +I0616 12:07:37.328511 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0650476 (* 1 = 0.0650476 loss) +I0616 12:07:37.328515 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102836 (* 1 = 0.0102836 loss) +I0616 12:07:37.328521 9857 solver.cpp:571] Iteration 59440, lr = 0.0001 +I0616 12:07:48.841485 9857 solver.cpp:242] Iteration 59460, loss = 0.983475 +I0616 12:07:48.841527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183103 (* 1 = 0.183103 loss) +I0616 12:07:48.841532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288225 (* 1 = 0.288225 loss) +I0616 12:07:48.841536 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160821 (* 1 = 0.160821 loss) +I0616 12:07:48.841541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.127463 (* 1 = 0.127463 loss) +I0616 12:07:48.841543 9857 solver.cpp:571] Iteration 59460, lr = 0.0001 +I0616 12:07:59.935672 9857 solver.cpp:242] Iteration 59480, loss = 0.41697 +I0616 12:07:59.935710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0523642 (* 1 = 0.0523642 loss) +I0616 12:07:59.935716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111122 (* 1 = 0.111122 loss) +I0616 12:07:59.935720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0265163 (* 1 = 0.0265163 loss) +I0616 12:07:59.935724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000950494 (* 1 = 0.000950494 loss) +I0616 12:07:59.935729 9857 solver.cpp:571] Iteration 59480, lr = 0.0001 +I0616 12:08:11.502696 9857 solver.cpp:242] Iteration 59500, loss = 0.63373 +I0616 12:08:11.502739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0688816 (* 1 = 0.0688816 loss) +I0616 12:08:11.502744 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0971356 (* 1 = 0.0971356 loss) +I0616 12:08:11.502748 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0288946 (* 1 = 0.0288946 loss) +I0616 12:08:11.502753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00813485 (* 1 = 0.00813485 loss) +I0616 12:08:11.502758 9857 solver.cpp:571] Iteration 59500, lr = 0.0001 +I0616 12:08:23.054903 9857 solver.cpp:242] Iteration 59520, loss = 0.467442 +I0616 12:08:23.054944 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129125 (* 1 = 0.129125 loss) +I0616 12:08:23.054949 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16227 (* 1 = 0.16227 loss) +I0616 12:08:23.054955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219314 (* 1 = 0.219314 loss) +I0616 12:08:23.054957 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.203261 (* 1 = 0.203261 loss) +I0616 12:08:23.054961 9857 solver.cpp:571] Iteration 59520, lr = 0.0001 +I0616 12:08:34.782598 9857 solver.cpp:242] Iteration 59540, loss = 0.547618 +I0616 12:08:34.782639 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129733 (* 1 = 0.129733 loss) +I0616 12:08:34.782644 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0924719 (* 1 = 0.0924719 loss) +I0616 12:08:34.782647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0542656 (* 1 = 0.0542656 loss) +I0616 12:08:34.782651 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0241315 (* 1 = 0.0241315 loss) +I0616 12:08:34.782655 9857 solver.cpp:571] Iteration 59540, lr = 0.0001 +I0616 12:08:46.123913 9857 solver.cpp:242] Iteration 59560, loss = 0.289767 +I0616 12:08:46.123953 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172042 (* 1 = 0.172042 loss) +I0616 12:08:46.123960 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192465 (* 1 = 0.192465 loss) +I0616 12:08:46.123963 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0294067 (* 1 = 0.0294067 loss) +I0616 12:08:46.123967 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0311264 (* 1 = 0.0311264 loss) +I0616 12:08:46.123970 9857 solver.cpp:571] Iteration 59560, lr = 0.0001 +I0616 12:08:57.825368 9857 solver.cpp:242] Iteration 59580, loss = 0.689077 +I0616 12:08:57.825409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0895425 (* 1 = 0.0895425 loss) +I0616 12:08:57.825415 9857 solver.cpp:258] Train net output #1: loss_cls = 0.097205 (* 1 = 0.097205 loss) +I0616 12:08:57.825419 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00993273 (* 1 = 0.00993273 loss) +I0616 12:08:57.825423 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00160525 (* 1 = 0.00160525 loss) +I0616 12:08:57.825428 9857 solver.cpp:571] Iteration 59580, lr = 0.0001 +speed: 0.607s / iter +I0616 12:09:09.358520 9857 solver.cpp:242] Iteration 59600, loss = 0.466499 +I0616 12:09:09.358559 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.378926 (* 1 = 0.378926 loss) +I0616 12:09:09.358566 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272809 (* 1 = 0.272809 loss) +I0616 12:09:09.358569 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0602539 (* 1 = 0.0602539 loss) +I0616 12:09:09.358573 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00710345 (* 1 = 0.00710345 loss) +I0616 12:09:09.358579 9857 solver.cpp:571] Iteration 59600, lr = 0.0001 +I0616 12:09:21.067662 9857 solver.cpp:242] Iteration 59620, loss = 0.336091 +I0616 12:09:21.067703 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0780332 (* 1 = 0.0780332 loss) +I0616 12:09:21.067708 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143206 (* 1 = 0.143206 loss) +I0616 12:09:21.067713 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0296988 (* 1 = 0.0296988 loss) +I0616 12:09:21.067716 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115136 (* 1 = 0.0115136 loss) +I0616 12:09:21.067720 9857 solver.cpp:571] Iteration 59620, lr = 0.0001 +I0616 12:09:32.826758 9857 solver.cpp:242] Iteration 59640, loss = 0.603119 +I0616 12:09:32.826799 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.369369 (* 1 = 0.369369 loss) +I0616 12:09:32.826804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.442538 (* 1 = 0.442538 loss) +I0616 12:09:32.826809 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119755 (* 1 = 0.119755 loss) +I0616 12:09:32.826812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0691599 (* 1 = 0.0691599 loss) +I0616 12:09:32.826817 9857 solver.cpp:571] Iteration 59640, lr = 0.0001 +I0616 12:09:44.190726 9857 solver.cpp:242] Iteration 59660, loss = 0.827484 +I0616 12:09:44.190769 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.380123 (* 1 = 0.380123 loss) +I0616 12:09:44.190775 9857 solver.cpp:258] Train net output #1: loss_cls = 0.411681 (* 1 = 0.411681 loss) +I0616 12:09:44.190779 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.377738 (* 1 = 0.377738 loss) +I0616 12:09:44.190783 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.086236 (* 1 = 0.086236 loss) +I0616 12:09:44.190786 9857 solver.cpp:571] Iteration 59660, lr = 0.0001 +I0616 12:09:55.698622 9857 solver.cpp:242] Iteration 59680, loss = 0.518357 +I0616 12:09:55.698663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341746 (* 1 = 0.341746 loss) +I0616 12:09:55.698669 9857 solver.cpp:258] Train net output #1: loss_cls = 0.355703 (* 1 = 0.355703 loss) +I0616 12:09:55.698673 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.091389 (* 1 = 0.091389 loss) +I0616 12:09:55.698678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0398895 (* 1 = 0.0398895 loss) +I0616 12:09:55.698681 9857 solver.cpp:571] Iteration 59680, lr = 0.0001 +I0616 12:10:06.941437 9857 solver.cpp:242] Iteration 59700, loss = 0.477035 +I0616 12:10:06.941478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0635746 (* 1 = 0.0635746 loss) +I0616 12:10:06.941483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0606524 (* 1 = 0.0606524 loss) +I0616 12:10:06.941488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0356196 (* 1 = 0.0356196 loss) +I0616 12:10:06.941490 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.182838 (* 1 = 0.182838 loss) +I0616 12:10:06.941494 9857 solver.cpp:571] Iteration 59700, lr = 0.0001 +I0616 12:10:18.664268 9857 solver.cpp:242] Iteration 59720, loss = 0.405885 +I0616 12:10:18.664309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228869 (* 1 = 0.228869 loss) +I0616 12:10:18.664314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249254 (* 1 = 0.249254 loss) +I0616 12:10:18.664319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0811277 (* 1 = 0.0811277 loss) +I0616 12:10:18.664322 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0750925 (* 1 = 0.0750925 loss) +I0616 12:10:18.664326 9857 solver.cpp:571] Iteration 59720, lr = 0.0001 +I0616 12:10:30.339651 9857 solver.cpp:242] Iteration 59740, loss = 0.687126 +I0616 12:10:30.339692 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10518 (* 1 = 0.10518 loss) +I0616 12:10:30.339699 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104932 (* 1 = 0.104932 loss) +I0616 12:10:30.339704 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0298669 (* 1 = 0.0298669 loss) +I0616 12:10:30.339707 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106991 (* 1 = 0.0106991 loss) +I0616 12:10:30.339710 9857 solver.cpp:571] Iteration 59740, lr = 0.0001 +I0616 12:10:42.065374 9857 solver.cpp:242] Iteration 59760, loss = 0.209867 +I0616 12:10:42.065415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0690432 (* 1 = 0.0690432 loss) +I0616 12:10:42.065420 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0676338 (* 1 = 0.0676338 loss) +I0616 12:10:42.065424 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00244774 (* 1 = 0.00244774 loss) +I0616 12:10:42.065428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00799436 (* 1 = 0.00799436 loss) +I0616 12:10:42.065433 9857 solver.cpp:571] Iteration 59760, lr = 0.0001 +I0616 12:10:53.450817 9857 solver.cpp:242] Iteration 59780, loss = 0.839735 +I0616 12:10:53.450857 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.457355 (* 1 = 0.457355 loss) +I0616 12:10:53.450862 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514494 (* 1 = 0.514494 loss) +I0616 12:10:53.450866 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.212967 (* 1 = 0.212967 loss) +I0616 12:10:53.450870 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0589221 (* 1 = 0.0589221 loss) +I0616 12:10:53.450875 9857 solver.cpp:571] Iteration 59780, lr = 0.0001 +speed: 0.607s / iter +I0616 12:11:05.070232 9857 solver.cpp:242] Iteration 59800, loss = 0.794868 +I0616 12:11:05.070272 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284078 (* 1 = 0.284078 loss) +I0616 12:11:05.070278 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176633 (* 1 = 0.176633 loss) +I0616 12:11:05.070282 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0876114 (* 1 = 0.0876114 loss) +I0616 12:11:05.070286 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.300946 (* 1 = 0.300946 loss) +I0616 12:11:05.070291 9857 solver.cpp:571] Iteration 59800, lr = 0.0001 +I0616 12:11:16.624213 9857 solver.cpp:242] Iteration 59820, loss = 0.599665 +I0616 12:11:16.624254 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166897 (* 1 = 0.166897 loss) +I0616 12:11:16.624259 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332144 (* 1 = 0.332144 loss) +I0616 12:11:16.624264 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113604 (* 1 = 0.113604 loss) +I0616 12:11:16.624267 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.253262 (* 1 = 0.253262 loss) +I0616 12:11:16.624271 9857 solver.cpp:571] Iteration 59820, lr = 0.0001 +I0616 12:11:28.211248 9857 solver.cpp:242] Iteration 59840, loss = 0.462494 +I0616 12:11:28.211292 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203363 (* 1 = 0.203363 loss) +I0616 12:11:28.211297 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300218 (* 1 = 0.300218 loss) +I0616 12:11:28.211302 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0632772 (* 1 = 0.0632772 loss) +I0616 12:11:28.211305 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0441146 (* 1 = 0.0441146 loss) +I0616 12:11:28.211308 9857 solver.cpp:571] Iteration 59840, lr = 0.0001 +I0616 12:11:39.637742 9857 solver.cpp:242] Iteration 59860, loss = 0.753891 +I0616 12:11:39.637784 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.426143 (* 1 = 0.426143 loss) +I0616 12:11:39.637790 9857 solver.cpp:258] Train net output #1: loss_cls = 0.52958 (* 1 = 0.52958 loss) +I0616 12:11:39.637794 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167355 (* 1 = 0.167355 loss) +I0616 12:11:39.637797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.192186 (* 1 = 0.192186 loss) +I0616 12:11:39.637801 9857 solver.cpp:571] Iteration 59860, lr = 0.0001 +I0616 12:11:51.217375 9857 solver.cpp:242] Iteration 59880, loss = 0.858273 +I0616 12:11:51.217416 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346869 (* 1 = 0.346869 loss) +I0616 12:11:51.217422 9857 solver.cpp:258] Train net output #1: loss_cls = 0.435758 (* 1 = 0.435758 loss) +I0616 12:11:51.217425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160261 (* 1 = 0.160261 loss) +I0616 12:11:51.217429 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.247438 (* 1 = 0.247438 loss) +I0616 12:11:51.217432 9857 solver.cpp:571] Iteration 59880, lr = 0.0001 +I0616 12:12:02.749510 9857 solver.cpp:242] Iteration 59900, loss = 0.680592 +I0616 12:12:02.749550 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179954 (* 1 = 0.179954 loss) +I0616 12:12:02.749557 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159571 (* 1 = 0.159571 loss) +I0616 12:12:02.749560 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.067859 (* 1 = 0.067859 loss) +I0616 12:12:02.749563 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0451318 (* 1 = 0.0451318 loss) +I0616 12:12:02.749567 9857 solver.cpp:571] Iteration 59900, lr = 0.0001 +I0616 12:12:14.468971 9857 solver.cpp:242] Iteration 59920, loss = 0.711121 +I0616 12:12:14.469013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23131 (* 1 = 0.23131 loss) +I0616 12:12:14.469018 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272793 (* 1 = 0.272793 loss) +I0616 12:12:14.469022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0870376 (* 1 = 0.0870376 loss) +I0616 12:12:14.469027 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0588402 (* 1 = 0.0588402 loss) +I0616 12:12:14.469029 9857 solver.cpp:571] Iteration 59920, lr = 0.0001 +I0616 12:12:26.082109 9857 solver.cpp:242] Iteration 59940, loss = 0.21151 +I0616 12:12:26.082151 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0519607 (* 1 = 0.0519607 loss) +I0616 12:12:26.082157 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0857668 (* 1 = 0.0857668 loss) +I0616 12:12:26.082161 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00795954 (* 1 = 0.00795954 loss) +I0616 12:12:26.082165 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108657 (* 1 = 0.0108657 loss) +I0616 12:12:26.082168 9857 solver.cpp:571] Iteration 59940, lr = 0.0001 +I0616 12:12:37.643882 9857 solver.cpp:242] Iteration 59960, loss = 0.678644 +I0616 12:12:37.643923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308327 (* 1 = 0.308327 loss) +I0616 12:12:37.643929 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438232 (* 1 = 0.438232 loss) +I0616 12:12:37.643934 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20825 (* 1 = 0.20825 loss) +I0616 12:12:37.643939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0798185 (* 1 = 0.0798185 loss) +I0616 12:12:37.643941 9857 solver.cpp:571] Iteration 59960, lr = 0.0001 +I0616 12:12:49.221173 9857 solver.cpp:242] Iteration 59980, loss = 0.358439 +I0616 12:12:49.221213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221494 (* 1 = 0.221494 loss) +I0616 12:12:49.221220 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204562 (* 1 = 0.204562 loss) +I0616 12:12:49.221223 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.051567 (* 1 = 0.051567 loss) +I0616 12:12:49.221227 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033733 (* 1 = 0.033733 loss) +I0616 12:12:49.221231 9857 solver.cpp:571] Iteration 59980, lr = 0.0001 +speed: 0.607s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_60000.caffemodel +I0616 12:13:02.373216 9857 solver.cpp:242] Iteration 60000, loss = 0.6359 +I0616 12:13:02.373257 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307842 (* 1 = 0.307842 loss) +I0616 12:13:02.373263 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243839 (* 1 = 0.243839 loss) +I0616 12:13:02.373267 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14428 (* 1 = 0.14428 loss) +I0616 12:13:02.373271 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0477645 (* 1 = 0.0477645 loss) +I0616 12:13:02.373275 9857 solver.cpp:571] Iteration 60000, lr = 0.0001 +I0616 12:13:14.295716 9857 solver.cpp:242] Iteration 60020, loss = 0.730274 +I0616 12:13:14.295758 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253462 (* 1 = 0.253462 loss) +I0616 12:13:14.295763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.541706 (* 1 = 0.541706 loss) +I0616 12:13:14.295768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13605 (* 1 = 0.13605 loss) +I0616 12:13:14.295771 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119835 (* 1 = 0.119835 loss) +I0616 12:13:14.295775 9857 solver.cpp:571] Iteration 60020, lr = 0.0001 +I0616 12:13:25.913525 9857 solver.cpp:242] Iteration 60040, loss = 0.452491 +I0616 12:13:25.913568 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.362202 (* 1 = 0.362202 loss) +I0616 12:13:25.913573 9857 solver.cpp:258] Train net output #1: loss_cls = 0.198378 (* 1 = 0.198378 loss) +I0616 12:13:25.913578 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0621435 (* 1 = 0.0621435 loss) +I0616 12:13:25.913581 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160271 (* 1 = 0.0160271 loss) +I0616 12:13:25.913585 9857 solver.cpp:571] Iteration 60040, lr = 0.0001 +I0616 12:13:37.535548 9857 solver.cpp:242] Iteration 60060, loss = 0.695085 +I0616 12:13:37.535591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0818723 (* 1 = 0.0818723 loss) +I0616 12:13:37.535598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12779 (* 1 = 0.12779 loss) +I0616 12:13:37.535601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141793 (* 1 = 0.141793 loss) +I0616 12:13:37.535605 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252042 (* 1 = 0.0252042 loss) +I0616 12:13:37.535609 9857 solver.cpp:571] Iteration 60060, lr = 0.0001 +I0616 12:13:49.147228 9857 solver.cpp:242] Iteration 60080, loss = 0.415794 +I0616 12:13:49.147270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281239 (* 1 = 0.281239 loss) +I0616 12:13:49.147275 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182454 (* 1 = 0.182454 loss) +I0616 12:13:49.147279 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0587993 (* 1 = 0.0587993 loss) +I0616 12:13:49.147284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0485945 (* 1 = 0.0485945 loss) +I0616 12:13:49.147286 9857 solver.cpp:571] Iteration 60080, lr = 0.0001 +I0616 12:14:00.775338 9857 solver.cpp:242] Iteration 60100, loss = 0.438664 +I0616 12:14:00.775380 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167541 (* 1 = 0.167541 loss) +I0616 12:14:00.775387 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206002 (* 1 = 0.206002 loss) +I0616 12:14:00.775390 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0700923 (* 1 = 0.0700923 loss) +I0616 12:14:00.775394 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.137215 (* 1 = 0.137215 loss) +I0616 12:14:00.775398 9857 solver.cpp:571] Iteration 60100, lr = 0.0001 +I0616 12:14:12.364296 9857 solver.cpp:242] Iteration 60120, loss = 0.309267 +I0616 12:14:12.364339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213301 (* 1 = 0.213301 loss) +I0616 12:14:12.364344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.212668 (* 1 = 0.212668 loss) +I0616 12:14:12.364349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0210745 (* 1 = 0.0210745 loss) +I0616 12:14:12.364353 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100657 (* 1 = 0.0100657 loss) +I0616 12:14:12.364356 9857 solver.cpp:571] Iteration 60120, lr = 0.0001 +I0616 12:14:24.252022 9857 solver.cpp:242] Iteration 60140, loss = 0.913684 +I0616 12:14:24.252063 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.444545 (* 1 = 0.444545 loss) +I0616 12:14:24.252069 9857 solver.cpp:258] Train net output #1: loss_cls = 0.534395 (* 1 = 0.534395 loss) +I0616 12:14:24.252074 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205898 (* 1 = 0.205898 loss) +I0616 12:14:24.252077 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.172212 (* 1 = 0.172212 loss) +I0616 12:14:24.252080 9857 solver.cpp:571] Iteration 60140, lr = 0.0001 +I0616 12:14:35.819902 9857 solver.cpp:242] Iteration 60160, loss = 0.737365 +I0616 12:14:35.819944 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151948 (* 1 = 0.151948 loss) +I0616 12:14:35.819950 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167553 (* 1 = 0.167553 loss) +I0616 12:14:35.819954 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0831648 (* 1 = 0.0831648 loss) +I0616 12:14:35.819958 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246989 (* 1 = 0.0246989 loss) +I0616 12:14:35.819962 9857 solver.cpp:571] Iteration 60160, lr = 0.0001 +I0616 12:14:47.642194 9857 solver.cpp:242] Iteration 60180, loss = 0.435935 +I0616 12:14:47.642236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182957 (* 1 = 0.182957 loss) +I0616 12:14:47.642242 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178698 (* 1 = 0.178698 loss) +I0616 12:14:47.642246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0458133 (* 1 = 0.0458133 loss) +I0616 12:14:47.642251 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0929831 (* 1 = 0.0929831 loss) +I0616 12:14:47.642254 9857 solver.cpp:571] Iteration 60180, lr = 0.0001 +speed: 0.607s / iter +I0616 12:14:59.054075 9857 solver.cpp:242] Iteration 60200, loss = 0.369448 +I0616 12:14:59.054116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217316 (* 1 = 0.217316 loss) +I0616 12:14:59.054121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104954 (* 1 = 0.104954 loss) +I0616 12:14:59.054126 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0387136 (* 1 = 0.0387136 loss) +I0616 12:14:59.054129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00475475 (* 1 = 0.00475475 loss) +I0616 12:14:59.054133 9857 solver.cpp:571] Iteration 60200, lr = 0.0001 +I0616 12:15:10.171025 9857 solver.cpp:242] Iteration 60220, loss = 0.359495 +I0616 12:15:10.171066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148739 (* 1 = 0.148739 loss) +I0616 12:15:10.171072 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102266 (* 1 = 0.102266 loss) +I0616 12:15:10.171077 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00658269 (* 1 = 0.00658269 loss) +I0616 12:15:10.171080 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0208606 (* 1 = 0.0208606 loss) +I0616 12:15:10.171084 9857 solver.cpp:571] Iteration 60220, lr = 0.0001 +I0616 12:15:21.723371 9857 solver.cpp:242] Iteration 60240, loss = 0.556303 +I0616 12:15:21.723412 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294722 (* 1 = 0.294722 loss) +I0616 12:15:21.723417 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343884 (* 1 = 0.343884 loss) +I0616 12:15:21.723423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.132773 (* 1 = 0.132773 loss) +I0616 12:15:21.723426 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.118293 (* 1 = 0.118293 loss) +I0616 12:15:21.723430 9857 solver.cpp:571] Iteration 60240, lr = 0.0001 +I0616 12:15:33.044984 9857 solver.cpp:242] Iteration 60260, loss = 0.839705 +I0616 12:15:33.045027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0880549 (* 1 = 0.0880549 loss) +I0616 12:15:33.045032 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112922 (* 1 = 0.112922 loss) +I0616 12:15:33.045037 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00985246 (* 1 = 0.00985246 loss) +I0616 12:15:33.045040 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00692988 (* 1 = 0.00692988 loss) +I0616 12:15:33.045045 9857 solver.cpp:571] Iteration 60260, lr = 0.0001 +I0616 12:15:44.350144 9857 solver.cpp:242] Iteration 60280, loss = 0.942646 +I0616 12:15:44.350185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184004 (* 1 = 0.184004 loss) +I0616 12:15:44.350191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.57603 (* 1 = 0.57603 loss) +I0616 12:15:44.350195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140443 (* 1 = 0.140443 loss) +I0616 12:15:44.350199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0460145 (* 1 = 0.0460145 loss) +I0616 12:15:44.350203 9857 solver.cpp:571] Iteration 60280, lr = 0.0001 +I0616 12:15:55.964534 9857 solver.cpp:242] Iteration 60300, loss = 0.640651 +I0616 12:15:55.964576 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311958 (* 1 = 0.311958 loss) +I0616 12:15:55.964581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321243 (* 1 = 0.321243 loss) +I0616 12:15:55.964586 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0688914 (* 1 = 0.0688914 loss) +I0616 12:15:55.964591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00791185 (* 1 = 0.00791185 loss) +I0616 12:15:55.964593 9857 solver.cpp:571] Iteration 60300, lr = 0.0001 +I0616 12:16:07.537160 9857 solver.cpp:242] Iteration 60320, loss = 0.528906 +I0616 12:16:07.537202 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0723974 (* 1 = 0.0723974 loss) +I0616 12:16:07.537207 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126439 (* 1 = 0.126439 loss) +I0616 12:16:07.537211 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0088229 (* 1 = 0.0088229 loss) +I0616 12:16:07.537215 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134316 (* 1 = 0.0134316 loss) +I0616 12:16:07.537220 9857 solver.cpp:571] Iteration 60320, lr = 0.0001 +I0616 12:16:19.568514 9857 solver.cpp:242] Iteration 60340, loss = 0.454826 +I0616 12:16:19.568557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176363 (* 1 = 0.176363 loss) +I0616 12:16:19.568563 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230685 (* 1 = 0.230685 loss) +I0616 12:16:19.568567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118326 (* 1 = 0.118326 loss) +I0616 12:16:19.568572 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0672377 (* 1 = 0.0672377 loss) +I0616 12:16:19.568575 9857 solver.cpp:571] Iteration 60340, lr = 0.0001 +I0616 12:16:31.431638 9857 solver.cpp:242] Iteration 60360, loss = 0.680439 +I0616 12:16:31.431680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2124 (* 1 = 0.2124 loss) +I0616 12:16:31.431685 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204982 (* 1 = 0.204982 loss) +I0616 12:16:31.431690 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0639735 (* 1 = 0.0639735 loss) +I0616 12:16:31.431694 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129223 (* 1 = 0.129223 loss) +I0616 12:16:31.431697 9857 solver.cpp:571] Iteration 60360, lr = 0.0001 +I0616 12:16:42.642127 9857 solver.cpp:242] Iteration 60380, loss = 0.795999 +I0616 12:16:42.642154 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343071 (* 1 = 0.343071 loss) +I0616 12:16:42.642173 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251009 (* 1 = 0.251009 loss) +I0616 12:16:42.642176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0801619 (* 1 = 0.0801619 loss) +I0616 12:16:42.642180 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00949056 (* 1 = 0.00949056 loss) +I0616 12:16:42.642199 9857 solver.cpp:571] Iteration 60380, lr = 0.0001 +speed: 0.607s / iter +I0616 12:16:54.132738 9857 solver.cpp:242] Iteration 60400, loss = 0.357475 +I0616 12:16:54.132778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0788792 (* 1 = 0.0788792 loss) +I0616 12:16:54.132784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138529 (* 1 = 0.138529 loss) +I0616 12:16:54.132788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176297 (* 1 = 0.0176297 loss) +I0616 12:16:54.132792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285234 (* 1 = 0.0285234 loss) +I0616 12:16:54.132797 9857 solver.cpp:571] Iteration 60400, lr = 0.0001 +I0616 12:17:05.686444 9857 solver.cpp:242] Iteration 60420, loss = 0.232901 +I0616 12:17:05.686486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0729533 (* 1 = 0.0729533 loss) +I0616 12:17:05.686491 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112083 (* 1 = 0.112083 loss) +I0616 12:17:05.686496 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0249636 (* 1 = 0.0249636 loss) +I0616 12:17:05.686501 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00966813 (* 1 = 0.00966813 loss) +I0616 12:17:05.686504 9857 solver.cpp:571] Iteration 60420, lr = 0.0001 +I0616 12:17:17.194576 9857 solver.cpp:242] Iteration 60440, loss = 0.687122 +I0616 12:17:17.194617 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331542 (* 1 = 0.331542 loss) +I0616 12:17:17.194623 9857 solver.cpp:258] Train net output #1: loss_cls = 0.448754 (* 1 = 0.448754 loss) +I0616 12:17:17.194628 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0685916 (* 1 = 0.0685916 loss) +I0616 12:17:17.194631 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150207 (* 1 = 0.0150207 loss) +I0616 12:17:17.194635 9857 solver.cpp:571] Iteration 60440, lr = 0.0001 +I0616 12:17:28.899801 9857 solver.cpp:242] Iteration 60460, loss = 0.810705 +I0616 12:17:28.899843 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269714 (* 1 = 0.269714 loss) +I0616 12:17:28.899849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.591406 (* 1 = 0.591406 loss) +I0616 12:17:28.899853 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150796 (* 1 = 0.150796 loss) +I0616 12:17:28.899857 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0413559 (* 1 = 0.0413559 loss) +I0616 12:17:28.899862 9857 solver.cpp:571] Iteration 60460, lr = 0.0001 +I0616 12:17:40.404536 9857 solver.cpp:242] Iteration 60480, loss = 0.500392 +I0616 12:17:40.404578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300138 (* 1 = 0.300138 loss) +I0616 12:17:40.404583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.347671 (* 1 = 0.347671 loss) +I0616 12:17:40.404587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0344498 (* 1 = 0.0344498 loss) +I0616 12:17:40.404592 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0358678 (* 1 = 0.0358678 loss) +I0616 12:17:40.404594 9857 solver.cpp:571] Iteration 60480, lr = 0.0001 +I0616 12:17:51.589042 9857 solver.cpp:242] Iteration 60500, loss = 0.36794 +I0616 12:17:51.589087 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166914 (* 1 = 0.166914 loss) +I0616 12:17:51.589092 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252179 (* 1 = 0.252179 loss) +I0616 12:17:51.589097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.117506 (* 1 = 0.117506 loss) +I0616 12:17:51.589100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102275 (* 1 = 0.102275 loss) +I0616 12:17:51.589104 9857 solver.cpp:571] Iteration 60500, lr = 0.0001 +I0616 12:18:03.227164 9857 solver.cpp:242] Iteration 60520, loss = 0.270791 +I0616 12:18:03.227206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0754781 (* 1 = 0.0754781 loss) +I0616 12:18:03.227211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162233 (* 1 = 0.162233 loss) +I0616 12:18:03.227216 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0278776 (* 1 = 0.0278776 loss) +I0616 12:18:03.227221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161536 (* 1 = 0.0161536 loss) +I0616 12:18:03.227223 9857 solver.cpp:571] Iteration 60520, lr = 0.0001 +I0616 12:18:14.719980 9857 solver.cpp:242] Iteration 60540, loss = 0.368169 +I0616 12:18:14.720022 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127144 (* 1 = 0.127144 loss) +I0616 12:18:14.720028 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142502 (* 1 = 0.142502 loss) +I0616 12:18:14.720032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0497312 (* 1 = 0.0497312 loss) +I0616 12:18:14.720036 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0395569 (* 1 = 0.0395569 loss) +I0616 12:18:14.720039 9857 solver.cpp:571] Iteration 60540, lr = 0.0001 +I0616 12:18:26.315737 9857 solver.cpp:242] Iteration 60560, loss = 0.80381 +I0616 12:18:26.315778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371338 (* 1 = 0.371338 loss) +I0616 12:18:26.315784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271993 (* 1 = 0.271993 loss) +I0616 12:18:26.315788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0595779 (* 1 = 0.0595779 loss) +I0616 12:18:26.315791 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0501982 (* 1 = 0.0501982 loss) +I0616 12:18:26.315795 9857 solver.cpp:571] Iteration 60560, lr = 0.0001 +I0616 12:18:37.954195 9857 solver.cpp:242] Iteration 60580, loss = 0.241322 +I0616 12:18:37.954236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113813 (* 1 = 0.113813 loss) +I0616 12:18:37.954241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110484 (* 1 = 0.110484 loss) +I0616 12:18:37.954246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0109753 (* 1 = 0.0109753 loss) +I0616 12:18:37.954249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252354 (* 1 = 0.0252354 loss) +I0616 12:18:37.954252 9857 solver.cpp:571] Iteration 60580, lr = 0.0001 +speed: 0.607s / iter +I0616 12:18:49.484278 9857 solver.cpp:242] Iteration 60600, loss = 0.442566 +I0616 12:18:49.484319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239634 (* 1 = 0.239634 loss) +I0616 12:18:49.484325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233619 (* 1 = 0.233619 loss) +I0616 12:18:49.484330 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0447208 (* 1 = 0.0447208 loss) +I0616 12:18:49.484333 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0229103 (* 1 = 0.0229103 loss) +I0616 12:18:49.484338 9857 solver.cpp:571] Iteration 60600, lr = 0.0001 +I0616 12:19:01.287595 9857 solver.cpp:242] Iteration 60620, loss = 0.411345 +I0616 12:19:01.287637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180279 (* 1 = 0.180279 loss) +I0616 12:19:01.287642 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326791 (* 1 = 0.326791 loss) +I0616 12:19:01.287647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0732401 (* 1 = 0.0732401 loss) +I0616 12:19:01.287650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165009 (* 1 = 0.0165009 loss) +I0616 12:19:01.287654 9857 solver.cpp:571] Iteration 60620, lr = 0.0001 +I0616 12:19:12.769397 9857 solver.cpp:242] Iteration 60640, loss = 0.306948 +I0616 12:19:12.769438 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185202 (* 1 = 0.185202 loss) +I0616 12:19:12.769444 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178769 (* 1 = 0.178769 loss) +I0616 12:19:12.769448 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00887258 (* 1 = 0.00887258 loss) +I0616 12:19:12.769453 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108137 (* 1 = 0.0108137 loss) +I0616 12:19:12.769456 9857 solver.cpp:571] Iteration 60640, lr = 0.0001 +I0616 12:19:24.166262 9857 solver.cpp:242] Iteration 60660, loss = 0.28875 +I0616 12:19:24.166304 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0568926 (* 1 = 0.0568926 loss) +I0616 12:19:24.166311 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0454341 (* 1 = 0.0454341 loss) +I0616 12:19:24.166314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00206752 (* 1 = 0.00206752 loss) +I0616 12:19:24.166318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0280707 (* 1 = 0.0280707 loss) +I0616 12:19:24.166322 9857 solver.cpp:571] Iteration 60660, lr = 0.0001 +I0616 12:19:35.788424 9857 solver.cpp:242] Iteration 60680, loss = 0.146049 +I0616 12:19:35.788465 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0817757 (* 1 = 0.0817757 loss) +I0616 12:19:35.788471 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101948 (* 1 = 0.101948 loss) +I0616 12:19:35.788476 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00833051 (* 1 = 0.00833051 loss) +I0616 12:19:35.788480 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00266815 (* 1 = 0.00266815 loss) +I0616 12:19:35.788483 9857 solver.cpp:571] Iteration 60680, lr = 0.0001 +I0616 12:19:47.481964 9857 solver.cpp:242] Iteration 60700, loss = 0.311654 +I0616 12:19:47.482007 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0826781 (* 1 = 0.0826781 loss) +I0616 12:19:47.482012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157925 (* 1 = 0.157925 loss) +I0616 12:19:47.482017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0831941 (* 1 = 0.0831941 loss) +I0616 12:19:47.482020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00315318 (* 1 = 0.00315318 loss) +I0616 12:19:47.482024 9857 solver.cpp:571] Iteration 60700, lr = 0.0001 +I0616 12:19:59.168433 9857 solver.cpp:242] Iteration 60720, loss = 0.592264 +I0616 12:19:59.168475 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.317647 (* 1 = 0.317647 loss) +I0616 12:19:59.168480 9857 solver.cpp:258] Train net output #1: loss_cls = 0.389659 (* 1 = 0.389659 loss) +I0616 12:19:59.168484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165427 (* 1 = 0.165427 loss) +I0616 12:19:59.168488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.019932 (* 1 = 0.019932 loss) +I0616 12:19:59.168493 9857 solver.cpp:571] Iteration 60720, lr = 0.0001 +I0616 12:20:10.552551 9857 solver.cpp:242] Iteration 60740, loss = 0.323472 +I0616 12:20:10.552593 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129716 (* 1 = 0.129716 loss) +I0616 12:20:10.552598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171694 (* 1 = 0.171694 loss) +I0616 12:20:10.552603 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00604258 (* 1 = 0.00604258 loss) +I0616 12:20:10.552608 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00466806 (* 1 = 0.00466806 loss) +I0616 12:20:10.552610 9857 solver.cpp:571] Iteration 60740, lr = 0.0001 +I0616 12:20:22.161504 9857 solver.cpp:242] Iteration 60760, loss = 0.386148 +I0616 12:20:22.161545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214233 (* 1 = 0.214233 loss) +I0616 12:20:22.161551 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223125 (* 1 = 0.223125 loss) +I0616 12:20:22.161556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152885 (* 1 = 0.152885 loss) +I0616 12:20:22.161558 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0258693 (* 1 = 0.0258693 loss) +I0616 12:20:22.161562 9857 solver.cpp:571] Iteration 60760, lr = 0.0001 +I0616 12:20:33.772996 9857 solver.cpp:242] Iteration 60780, loss = 0.242603 +I0616 12:20:33.773038 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0714128 (* 1 = 0.0714128 loss) +I0616 12:20:33.773044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0945222 (* 1 = 0.0945222 loss) +I0616 12:20:33.773048 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0124802 (* 1 = 0.0124802 loss) +I0616 12:20:33.773052 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00858129 (* 1 = 0.00858129 loss) +I0616 12:20:33.773056 9857 solver.cpp:571] Iteration 60780, lr = 0.0001 +speed: 0.607s / iter +I0616 12:20:45.254173 9857 solver.cpp:242] Iteration 60800, loss = 0.335698 +I0616 12:20:45.254215 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124971 (* 1 = 0.124971 loss) +I0616 12:20:45.254220 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186376 (* 1 = 0.186376 loss) +I0616 12:20:45.254225 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0142943 (* 1 = 0.0142943 loss) +I0616 12:20:45.254228 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00988643 (* 1 = 0.00988643 loss) +I0616 12:20:45.254232 9857 solver.cpp:571] Iteration 60800, lr = 0.0001 +I0616 12:20:56.570325 9857 solver.cpp:242] Iteration 60820, loss = 0.669693 +I0616 12:20:56.570369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305194 (* 1 = 0.305194 loss) +I0616 12:20:56.570374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.434624 (* 1 = 0.434624 loss) +I0616 12:20:56.570379 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0347829 (* 1 = 0.0347829 loss) +I0616 12:20:56.570382 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00690384 (* 1 = 0.00690384 loss) +I0616 12:20:56.570386 9857 solver.cpp:571] Iteration 60820, lr = 0.0001 +I0616 12:21:08.114332 9857 solver.cpp:242] Iteration 60840, loss = 0.464537 +I0616 12:21:08.114374 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0714476 (* 1 = 0.0714476 loss) +I0616 12:21:08.114379 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107779 (* 1 = 0.107779 loss) +I0616 12:21:08.114383 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0402949 (* 1 = 0.0402949 loss) +I0616 12:21:08.114387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0203147 (* 1 = 0.0203147 loss) +I0616 12:21:08.114392 9857 solver.cpp:571] Iteration 60840, lr = 0.0001 +I0616 12:21:19.571640 9857 solver.cpp:242] Iteration 60860, loss = 0.264935 +I0616 12:21:19.571681 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0847201 (* 1 = 0.0847201 loss) +I0616 12:21:19.571687 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0962137 (* 1 = 0.0962137 loss) +I0616 12:21:19.571691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0344806 (* 1 = 0.0344806 loss) +I0616 12:21:19.571696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00423556 (* 1 = 0.00423556 loss) +I0616 12:21:19.571699 9857 solver.cpp:571] Iteration 60860, lr = 0.0001 +I0616 12:21:31.135392 9857 solver.cpp:242] Iteration 60880, loss = 0.538649 +I0616 12:21:31.135433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181831 (* 1 = 0.181831 loss) +I0616 12:21:31.135439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202976 (* 1 = 0.202976 loss) +I0616 12:21:31.135443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0361226 (* 1 = 0.0361226 loss) +I0616 12:21:31.135447 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.032766 (* 1 = 0.032766 loss) +I0616 12:21:31.135452 9857 solver.cpp:571] Iteration 60880, lr = 0.0001 +I0616 12:21:42.369381 9857 solver.cpp:242] Iteration 60900, loss = 0.821707 +I0616 12:21:42.369423 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120333 (* 1 = 0.120333 loss) +I0616 12:21:42.369429 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108556 (* 1 = 0.108556 loss) +I0616 12:21:42.369433 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0168556 (* 1 = 0.0168556 loss) +I0616 12:21:42.369437 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0034898 (* 1 = 0.0034898 loss) +I0616 12:21:42.369441 9857 solver.cpp:571] Iteration 60900, lr = 0.0001 +I0616 12:21:54.135018 9857 solver.cpp:242] Iteration 60920, loss = 0.521624 +I0616 12:21:54.135061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.226407 (* 1 = 0.226407 loss) +I0616 12:21:54.135067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363884 (* 1 = 0.363884 loss) +I0616 12:21:54.135072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0875767 (* 1 = 0.0875767 loss) +I0616 12:21:54.135076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222509 (* 1 = 0.0222509 loss) +I0616 12:21:54.135079 9857 solver.cpp:571] Iteration 60920, lr = 0.0001 +I0616 12:22:05.786483 9857 solver.cpp:242] Iteration 60940, loss = 0.945145 +I0616 12:22:05.786525 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274307 (* 1 = 0.274307 loss) +I0616 12:22:05.786530 9857 solver.cpp:258] Train net output #1: loss_cls = 0.307871 (* 1 = 0.307871 loss) +I0616 12:22:05.786533 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0608195 (* 1 = 0.0608195 loss) +I0616 12:22:05.786537 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.03332 (* 1 = 0.03332 loss) +I0616 12:22:05.786541 9857 solver.cpp:571] Iteration 60940, lr = 0.0001 +I0616 12:22:17.302239 9857 solver.cpp:242] Iteration 60960, loss = 0.485331 +I0616 12:22:17.302283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0617572 (* 1 = 0.0617572 loss) +I0616 12:22:17.302287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112163 (* 1 = 0.112163 loss) +I0616 12:22:17.302292 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0137479 (* 1 = 0.0137479 loss) +I0616 12:22:17.302295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562367 (* 1 = 0.00562367 loss) +I0616 12:22:17.302299 9857 solver.cpp:571] Iteration 60960, lr = 0.0001 +I0616 12:22:28.707496 9857 solver.cpp:242] Iteration 60980, loss = 0.376071 +I0616 12:22:28.707538 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187077 (* 1 = 0.187077 loss) +I0616 12:22:28.707543 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237875 (* 1 = 0.237875 loss) +I0616 12:22:28.707548 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.132479 (* 1 = 0.132479 loss) +I0616 12:22:28.707552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0458158 (* 1 = 0.0458158 loss) +I0616 12:22:28.707556 9857 solver.cpp:571] Iteration 60980, lr = 0.0001 +speed: 0.606s / iter +I0616 12:22:40.102964 9857 solver.cpp:242] Iteration 61000, loss = 0.583738 +I0616 12:22:40.103006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.289524 (* 1 = 0.289524 loss) +I0616 12:22:40.103013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216502 (* 1 = 0.216502 loss) +I0616 12:22:40.103016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0397827 (* 1 = 0.0397827 loss) +I0616 12:22:40.103020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0423802 (* 1 = 0.0423802 loss) +I0616 12:22:40.103024 9857 solver.cpp:571] Iteration 61000, lr = 0.0001 +I0616 12:22:51.565580 9857 solver.cpp:242] Iteration 61020, loss = 0.839534 +I0616 12:22:51.565623 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162474 (* 1 = 0.162474 loss) +I0616 12:22:51.565629 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0960778 (* 1 = 0.0960778 loss) +I0616 12:22:51.565634 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0225895 (* 1 = 0.0225895 loss) +I0616 12:22:51.565636 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00820116 (* 1 = 0.00820116 loss) +I0616 12:22:51.565640 9857 solver.cpp:571] Iteration 61020, lr = 0.0001 +I0616 12:23:03.064587 9857 solver.cpp:242] Iteration 61040, loss = 0.780428 +I0616 12:23:03.064630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316719 (* 1 = 0.316719 loss) +I0616 12:23:03.064635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.520341 (* 1 = 0.520341 loss) +I0616 12:23:03.064640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120381 (* 1 = 0.120381 loss) +I0616 12:23:03.064643 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0318262 (* 1 = 0.0318262 loss) +I0616 12:23:03.064647 9857 solver.cpp:571] Iteration 61040, lr = 0.0001 +I0616 12:23:14.615414 9857 solver.cpp:242] Iteration 61060, loss = 1.15289 +I0616 12:23:14.615458 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272859 (* 1 = 0.272859 loss) +I0616 12:23:14.615463 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298056 (* 1 = 0.298056 loss) +I0616 12:23:14.615468 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114526 (* 1 = 0.114526 loss) +I0616 12:23:14.615471 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.318554 (* 1 = 0.318554 loss) +I0616 12:23:14.615475 9857 solver.cpp:571] Iteration 61060, lr = 0.0001 +I0616 12:23:26.093525 9857 solver.cpp:242] Iteration 61080, loss = 0.607907 +I0616 12:23:26.093567 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153327 (* 1 = 0.153327 loss) +I0616 12:23:26.093572 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333455 (* 1 = 0.333455 loss) +I0616 12:23:26.093577 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109865 (* 1 = 0.109865 loss) +I0616 12:23:26.093581 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.123866 (* 1 = 0.123866 loss) +I0616 12:23:26.093585 9857 solver.cpp:571] Iteration 61080, lr = 0.0001 +I0616 12:23:37.530135 9857 solver.cpp:242] Iteration 61100, loss = 0.255802 +I0616 12:23:37.530176 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0849756 (* 1 = 0.0849756 loss) +I0616 12:23:37.530182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0941316 (* 1 = 0.0941316 loss) +I0616 12:23:37.530186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0479028 (* 1 = 0.0479028 loss) +I0616 12:23:37.530190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120249 (* 1 = 0.0120249 loss) +I0616 12:23:37.530194 9857 solver.cpp:571] Iteration 61100, lr = 0.0001 +I0616 12:23:49.103924 9857 solver.cpp:242] Iteration 61120, loss = 0.811423 +I0616 12:23:49.103965 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320957 (* 1 = 0.320957 loss) +I0616 12:23:49.103971 9857 solver.cpp:258] Train net output #1: loss_cls = 0.586687 (* 1 = 0.586687 loss) +I0616 12:23:49.103976 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161694 (* 1 = 0.161694 loss) +I0616 12:23:49.103979 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.111991 (* 1 = 0.111991 loss) +I0616 12:23:49.103982 9857 solver.cpp:571] Iteration 61120, lr = 0.0001 +I0616 12:24:00.510867 9857 solver.cpp:242] Iteration 61140, loss = 0.528338 +I0616 12:24:00.510908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287492 (* 1 = 0.287492 loss) +I0616 12:24:00.510915 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148057 (* 1 = 0.148057 loss) +I0616 12:24:00.510932 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0528382 (* 1 = 0.0528382 loss) +I0616 12:24:00.510936 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0263382 (* 1 = 0.0263382 loss) +I0616 12:24:00.510941 9857 solver.cpp:571] Iteration 61140, lr = 0.0001 +I0616 12:24:12.202426 9857 solver.cpp:242] Iteration 61160, loss = 0.277455 +I0616 12:24:12.202469 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115696 (* 1 = 0.115696 loss) +I0616 12:24:12.202476 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209401 (* 1 = 0.209401 loss) +I0616 12:24:12.202479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0107794 (* 1 = 0.0107794 loss) +I0616 12:24:12.202483 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202569 (* 1 = 0.0202569 loss) +I0616 12:24:12.202487 9857 solver.cpp:571] Iteration 61160, lr = 0.0001 +I0616 12:24:23.586762 9857 solver.cpp:242] Iteration 61180, loss = 0.558647 +I0616 12:24:23.586804 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0893754 (* 1 = 0.0893754 loss) +I0616 12:24:23.586809 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107159 (* 1 = 0.107159 loss) +I0616 12:24:23.586814 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.243063 (* 1 = 0.243063 loss) +I0616 12:24:23.586818 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0551706 (* 1 = 0.0551706 loss) +I0616 12:24:23.586824 9857 solver.cpp:571] Iteration 61180, lr = 0.0001 +speed: 0.606s / iter +I0616 12:24:35.300956 9857 solver.cpp:242] Iteration 61200, loss = 0.767353 +I0616 12:24:35.300998 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231365 (* 1 = 0.231365 loss) +I0616 12:24:35.301003 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384349 (* 1 = 0.384349 loss) +I0616 12:24:35.301008 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167912 (* 1 = 0.167912 loss) +I0616 12:24:35.301012 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.305383 (* 1 = 0.305383 loss) +I0616 12:24:35.301015 9857 solver.cpp:571] Iteration 61200, lr = 0.0001 +I0616 12:24:47.035686 9857 solver.cpp:242] Iteration 61220, loss = 0.96501 +I0616 12:24:47.035727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136414 (* 1 = 0.136414 loss) +I0616 12:24:47.035733 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166775 (* 1 = 0.166775 loss) +I0616 12:24:47.035737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0144618 (* 1 = 0.0144618 loss) +I0616 12:24:47.035742 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107987 (* 1 = 0.0107987 loss) +I0616 12:24:47.035745 9857 solver.cpp:571] Iteration 61220, lr = 0.0001 +I0616 12:24:58.368821 9857 solver.cpp:242] Iteration 61240, loss = 0.392023 +I0616 12:24:58.368862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0523141 (* 1 = 0.0523141 loss) +I0616 12:24:58.368867 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113001 (* 1 = 0.113001 loss) +I0616 12:24:58.368871 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00800334 (* 1 = 0.00800334 loss) +I0616 12:24:58.368875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00260874 (* 1 = 0.00260874 loss) +I0616 12:24:58.368880 9857 solver.cpp:571] Iteration 61240, lr = 0.0001 +I0616 12:25:09.985087 9857 solver.cpp:242] Iteration 61260, loss = 0.767863 +I0616 12:25:09.985128 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.438666 (* 1 = 0.438666 loss) +I0616 12:25:09.985134 9857 solver.cpp:258] Train net output #1: loss_cls = 0.411288 (* 1 = 0.411288 loss) +I0616 12:25:09.985138 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219314 (* 1 = 0.219314 loss) +I0616 12:25:09.985142 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.201999 (* 1 = 0.201999 loss) +I0616 12:25:09.985147 9857 solver.cpp:571] Iteration 61260, lr = 0.0001 +I0616 12:25:21.677405 9857 solver.cpp:242] Iteration 61280, loss = 0.540808 +I0616 12:25:21.677446 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114321 (* 1 = 0.114321 loss) +I0616 12:25:21.677453 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143505 (* 1 = 0.143505 loss) +I0616 12:25:21.677456 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0174576 (* 1 = 0.0174576 loss) +I0616 12:25:21.677460 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108267 (* 1 = 0.0108267 loss) +I0616 12:25:21.677464 9857 solver.cpp:571] Iteration 61280, lr = 0.0001 +I0616 12:25:33.296535 9857 solver.cpp:242] Iteration 61300, loss = 0.52793 +I0616 12:25:33.296576 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203647 (* 1 = 0.203647 loss) +I0616 12:25:33.296581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169233 (* 1 = 0.169233 loss) +I0616 12:25:33.296586 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0571279 (* 1 = 0.0571279 loss) +I0616 12:25:33.296589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0463152 (* 1 = 0.0463152 loss) +I0616 12:25:33.296593 9857 solver.cpp:571] Iteration 61300, lr = 0.0001 +I0616 12:25:45.060647 9857 solver.cpp:242] Iteration 61320, loss = 1.08872 +I0616 12:25:45.060686 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149195 (* 1 = 0.149195 loss) +I0616 12:25:45.060693 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316062 (* 1 = 0.316062 loss) +I0616 12:25:45.060698 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0964846 (* 1 = 0.0964846 loss) +I0616 12:25:45.060700 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562336 (* 1 = 0.00562336 loss) +I0616 12:25:45.060705 9857 solver.cpp:571] Iteration 61320, lr = 0.0001 +I0616 12:25:56.749626 9857 solver.cpp:242] Iteration 61340, loss = 0.614735 +I0616 12:25:56.749671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2099 (* 1 = 0.2099 loss) +I0616 12:25:56.749676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371326 (* 1 = 0.371326 loss) +I0616 12:25:56.749680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130015 (* 1 = 0.130015 loss) +I0616 12:25:56.749685 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.049678 (* 1 = 0.049678 loss) +I0616 12:25:56.749688 9857 solver.cpp:571] Iteration 61340, lr = 0.0001 +I0616 12:26:08.475942 9857 solver.cpp:242] Iteration 61360, loss = 0.282068 +I0616 12:26:08.475983 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0485491 (* 1 = 0.0485491 loss) +I0616 12:26:08.475989 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156721 (* 1 = 0.156721 loss) +I0616 12:26:08.475993 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274797 (* 1 = 0.0274797 loss) +I0616 12:26:08.475997 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00726374 (* 1 = 0.00726374 loss) +I0616 12:26:08.476001 9857 solver.cpp:571] Iteration 61360, lr = 0.0001 +I0616 12:26:19.919776 9857 solver.cpp:242] Iteration 61380, loss = 0.426938 +I0616 12:26:19.919817 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0918012 (* 1 = 0.0918012 loss) +I0616 12:26:19.919823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0655736 (* 1 = 0.0655736 loss) +I0616 12:26:19.919828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0855087 (* 1 = 0.0855087 loss) +I0616 12:26:19.919831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0243767 (* 1 = 0.0243767 loss) +I0616 12:26:19.919836 9857 solver.cpp:571] Iteration 61380, lr = 0.0001 +speed: 0.606s / iter +I0616 12:26:31.571662 9857 solver.cpp:242] Iteration 61400, loss = 0.375103 +I0616 12:26:31.571705 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0716881 (* 1 = 0.0716881 loss) +I0616 12:26:31.571712 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0546878 (* 1 = 0.0546878 loss) +I0616 12:26:31.571715 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0201125 (* 1 = 0.0201125 loss) +I0616 12:26:31.571718 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230856 (* 1 = 0.0230856 loss) +I0616 12:26:31.571722 9857 solver.cpp:571] Iteration 61400, lr = 0.0001 +I0616 12:26:43.229264 9857 solver.cpp:242] Iteration 61420, loss = 0.50136 +I0616 12:26:43.229305 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239714 (* 1 = 0.239714 loss) +I0616 12:26:43.229310 9857 solver.cpp:258] Train net output #1: loss_cls = 0.2487 (* 1 = 0.2487 loss) +I0616 12:26:43.229315 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.049766 (* 1 = 0.049766 loss) +I0616 12:26:43.229318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0172606 (* 1 = 0.0172606 loss) +I0616 12:26:43.229322 9857 solver.cpp:571] Iteration 61420, lr = 0.0001 +I0616 12:26:54.835850 9857 solver.cpp:242] Iteration 61440, loss = 0.36435 +I0616 12:26:54.835894 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242585 (* 1 = 0.242585 loss) +I0616 12:26:54.835912 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19969 (* 1 = 0.19969 loss) +I0616 12:26:54.835917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0531384 (* 1 = 0.0531384 loss) +I0616 12:26:54.835922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283135 (* 1 = 0.0283135 loss) +I0616 12:26:54.835924 9857 solver.cpp:571] Iteration 61440, lr = 0.0001 +I0616 12:27:06.645989 9857 solver.cpp:242] Iteration 61460, loss = 0.832861 +I0616 12:27:06.646031 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.301398 (* 1 = 0.301398 loss) +I0616 12:27:06.646036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306708 (* 1 = 0.306708 loss) +I0616 12:27:06.646040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058676 (* 1 = 0.058676 loss) +I0616 12:27:06.646044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179377 (* 1 = 0.0179377 loss) +I0616 12:27:06.646049 9857 solver.cpp:571] Iteration 61460, lr = 0.0001 +I0616 12:27:18.259232 9857 solver.cpp:242] Iteration 61480, loss = 0.501097 +I0616 12:27:18.259274 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293393 (* 1 = 0.293393 loss) +I0616 12:27:18.259279 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350977 (* 1 = 0.350977 loss) +I0616 12:27:18.259284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137043 (* 1 = 0.137043 loss) +I0616 12:27:18.259287 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0277107 (* 1 = 0.0277107 loss) +I0616 12:27:18.259291 9857 solver.cpp:571] Iteration 61480, lr = 0.0001 +I0616 12:27:29.909056 9857 solver.cpp:242] Iteration 61500, loss = 0.30168 +I0616 12:27:29.909098 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112785 (* 1 = 0.112785 loss) +I0616 12:27:29.909104 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260327 (* 1 = 0.260327 loss) +I0616 12:27:29.909109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00730146 (* 1 = 0.00730146 loss) +I0616 12:27:29.909113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194426 (* 1 = 0.0194426 loss) +I0616 12:27:29.909117 9857 solver.cpp:571] Iteration 61500, lr = 0.0001 +I0616 12:27:41.534116 9857 solver.cpp:242] Iteration 61520, loss = 0.567083 +I0616 12:27:41.534169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390588 (* 1 = 0.390588 loss) +I0616 12:27:41.534175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.352756 (* 1 = 0.352756 loss) +I0616 12:27:41.534179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167122 (* 1 = 0.167122 loss) +I0616 12:27:41.534183 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0658247 (* 1 = 0.0658247 loss) +I0616 12:27:41.534188 9857 solver.cpp:571] Iteration 61520, lr = 0.0001 +I0616 12:27:53.188768 9857 solver.cpp:242] Iteration 61540, loss = 0.423146 +I0616 12:27:53.188810 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253187 (* 1 = 0.253187 loss) +I0616 12:27:53.188829 9857 solver.cpp:258] Train net output #1: loss_cls = 0.227478 (* 1 = 0.227478 loss) +I0616 12:27:53.188834 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0478721 (* 1 = 0.0478721 loss) +I0616 12:27:53.188838 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00834565 (* 1 = 0.00834565 loss) +I0616 12:27:53.188841 9857 solver.cpp:571] Iteration 61540, lr = 0.0001 +I0616 12:28:04.641274 9857 solver.cpp:242] Iteration 61560, loss = 0.525936 +I0616 12:28:04.641315 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27088 (* 1 = 0.27088 loss) +I0616 12:28:04.641321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.349207 (* 1 = 0.349207 loss) +I0616 12:28:04.641325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0931225 (* 1 = 0.0931225 loss) +I0616 12:28:04.641330 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0754597 (* 1 = 0.0754597 loss) +I0616 12:28:04.641333 9857 solver.cpp:571] Iteration 61560, lr = 0.0001 +I0616 12:28:15.949311 9857 solver.cpp:242] Iteration 61580, loss = 0.275056 +I0616 12:28:15.949352 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0877552 (* 1 = 0.0877552 loss) +I0616 12:28:15.949357 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191148 (* 1 = 0.191148 loss) +I0616 12:28:15.949362 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00649344 (* 1 = 0.00649344 loss) +I0616 12:28:15.949365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00682554 (* 1 = 0.00682554 loss) +I0616 12:28:15.949369 9857 solver.cpp:571] Iteration 61580, lr = 0.0001 +speed: 0.606s / iter +I0616 12:28:27.382385 9857 solver.cpp:242] Iteration 61600, loss = 0.480158 +I0616 12:28:27.382429 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0834918 (* 1 = 0.0834918 loss) +I0616 12:28:27.382436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.318962 (* 1 = 0.318962 loss) +I0616 12:28:27.382439 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20092 (* 1 = 0.20092 loss) +I0616 12:28:27.382443 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00615113 (* 1 = 0.00615113 loss) +I0616 12:28:27.382447 9857 solver.cpp:571] Iteration 61600, lr = 0.0001 +I0616 12:28:39.105268 9857 solver.cpp:242] Iteration 61620, loss = 0.48942 +I0616 12:28:39.105311 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278323 (* 1 = 0.278323 loss) +I0616 12:28:39.105316 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214788 (* 1 = 0.214788 loss) +I0616 12:28:39.105320 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0901372 (* 1 = 0.0901372 loss) +I0616 12:28:39.105324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0281498 (* 1 = 0.0281498 loss) +I0616 12:28:39.105329 9857 solver.cpp:571] Iteration 61620, lr = 0.0001 +I0616 12:28:50.355641 9857 solver.cpp:242] Iteration 61640, loss = 0.437939 +I0616 12:28:50.355684 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0739418 (* 1 = 0.0739418 loss) +I0616 12:28:50.355690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109612 (* 1 = 0.109612 loss) +I0616 12:28:50.355695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00540835 (* 1 = 0.00540835 loss) +I0616 12:28:50.355697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00446161 (* 1 = 0.00446161 loss) +I0616 12:28:50.355701 9857 solver.cpp:571] Iteration 61640, lr = 0.0001 +I0616 12:29:01.895009 9857 solver.cpp:242] Iteration 61660, loss = 0.962505 +I0616 12:29:01.895050 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.430739 (* 1 = 0.430739 loss) +I0616 12:29:01.895056 9857 solver.cpp:258] Train net output #1: loss_cls = 0.584556 (* 1 = 0.584556 loss) +I0616 12:29:01.895061 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146835 (* 1 = 0.146835 loss) +I0616 12:29:01.895063 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0346719 (* 1 = 0.0346719 loss) +I0616 12:29:01.895067 9857 solver.cpp:571] Iteration 61660, lr = 0.0001 +I0616 12:29:13.427439 9857 solver.cpp:242] Iteration 61680, loss = 1.11776 +I0616 12:29:13.427481 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309251 (* 1 = 0.309251 loss) +I0616 12:29:13.427487 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388914 (* 1 = 0.388914 loss) +I0616 12:29:13.427492 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104263 (* 1 = 0.104263 loss) +I0616 12:29:13.427495 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0463992 (* 1 = 0.0463992 loss) +I0616 12:29:13.427500 9857 solver.cpp:571] Iteration 61680, lr = 0.0001 +I0616 12:29:25.174124 9857 solver.cpp:242] Iteration 61700, loss = 0.445343 +I0616 12:29:25.174165 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238005 (* 1 = 0.238005 loss) +I0616 12:29:25.174170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170722 (* 1 = 0.170722 loss) +I0616 12:29:25.174175 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0595816 (* 1 = 0.0595816 loss) +I0616 12:29:25.174178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0342057 (* 1 = 0.0342057 loss) +I0616 12:29:25.174182 9857 solver.cpp:571] Iteration 61700, lr = 0.0001 +I0616 12:29:36.660567 9857 solver.cpp:242] Iteration 61720, loss = 0.298097 +I0616 12:29:36.660609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148914 (* 1 = 0.148914 loss) +I0616 12:29:36.660614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0748452 (* 1 = 0.0748452 loss) +I0616 12:29:36.660619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00701523 (* 1 = 0.00701523 loss) +I0616 12:29:36.660622 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000613677 (* 1 = 0.000613677 loss) +I0616 12:29:36.660626 9857 solver.cpp:571] Iteration 61720, lr = 0.0001 +I0616 12:29:47.979187 9857 solver.cpp:242] Iteration 61740, loss = 0.488478 +I0616 12:29:47.979229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0691148 (* 1 = 0.0691148 loss) +I0616 12:29:47.979234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223344 (* 1 = 0.223344 loss) +I0616 12:29:47.979238 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0427918 (* 1 = 0.0427918 loss) +I0616 12:29:47.979243 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104094 (* 1 = 0.104094 loss) +I0616 12:29:47.979246 9857 solver.cpp:571] Iteration 61740, lr = 0.0001 +I0616 12:29:59.687471 9857 solver.cpp:242] Iteration 61760, loss = 0.30619 +I0616 12:29:59.687515 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0381401 (* 1 = 0.0381401 loss) +I0616 12:29:59.687521 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0759795 (* 1 = 0.0759795 loss) +I0616 12:29:59.687525 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0054773 (* 1 = 0.0054773 loss) +I0616 12:29:59.687530 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00278233 (* 1 = 0.00278233 loss) +I0616 12:29:59.687532 9857 solver.cpp:571] Iteration 61760, lr = 0.0001 +I0616 12:30:11.029964 9857 solver.cpp:242] Iteration 61780, loss = 0.828673 +I0616 12:30:11.030007 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232302 (* 1 = 0.232302 loss) +I0616 12:30:11.030014 9857 solver.cpp:258] Train net output #1: loss_cls = 0.700203 (* 1 = 0.700203 loss) +I0616 12:30:11.030017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109189 (* 1 = 0.109189 loss) +I0616 12:30:11.030021 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133063 (* 1 = 0.0133063 loss) +I0616 12:30:11.030025 9857 solver.cpp:571] Iteration 61780, lr = 0.0001 +speed: 0.606s / iter +I0616 12:30:22.752755 9857 solver.cpp:242] Iteration 61800, loss = 1.00342 +I0616 12:30:22.752795 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.422375 (* 1 = 0.422375 loss) +I0616 12:30:22.752801 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370602 (* 1 = 0.370602 loss) +I0616 12:30:22.752805 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167003 (* 1 = 0.167003 loss) +I0616 12:30:22.752809 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.229604 (* 1 = 0.229604 loss) +I0616 12:30:22.752812 9857 solver.cpp:571] Iteration 61800, lr = 0.0001 +I0616 12:30:34.409961 9857 solver.cpp:242] Iteration 61820, loss = 0.991931 +I0616 12:30:34.410003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.340206 (* 1 = 0.340206 loss) +I0616 12:30:34.410009 9857 solver.cpp:258] Train net output #1: loss_cls = 0.37182 (* 1 = 0.37182 loss) +I0616 12:30:34.410013 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172699 (* 1 = 0.172699 loss) +I0616 12:30:34.410017 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.198806 (* 1 = 0.198806 loss) +I0616 12:30:34.410020 9857 solver.cpp:571] Iteration 61820, lr = 0.0001 +I0616 12:30:45.928086 9857 solver.cpp:242] Iteration 61840, loss = 0.308215 +I0616 12:30:45.928129 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0925959 (* 1 = 0.0925959 loss) +I0616 12:30:45.928135 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206524 (* 1 = 0.206524 loss) +I0616 12:30:45.928140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0302296 (* 1 = 0.0302296 loss) +I0616 12:30:45.928143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00954245 (* 1 = 0.00954245 loss) +I0616 12:30:45.928148 9857 solver.cpp:571] Iteration 61840, lr = 0.0001 +I0616 12:30:57.503131 9857 solver.cpp:242] Iteration 61860, loss = 0.637644 +I0616 12:30:57.503173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183159 (* 1 = 0.183159 loss) +I0616 12:30:57.503178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242745 (* 1 = 0.242745 loss) +I0616 12:30:57.503183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0300854 (* 1 = 0.0300854 loss) +I0616 12:30:57.503186 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276376 (* 1 = 0.0276376 loss) +I0616 12:30:57.503190 9857 solver.cpp:571] Iteration 61860, lr = 0.0001 +I0616 12:31:08.974658 9857 solver.cpp:242] Iteration 61880, loss = 0.277954 +I0616 12:31:08.974699 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0665984 (* 1 = 0.0665984 loss) +I0616 12:31:08.974705 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0652508 (* 1 = 0.0652508 loss) +I0616 12:31:08.974709 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0458037 (* 1 = 0.0458037 loss) +I0616 12:31:08.974714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00901634 (* 1 = 0.00901634 loss) +I0616 12:31:08.974716 9857 solver.cpp:571] Iteration 61880, lr = 0.0001 +I0616 12:31:20.446125 9857 solver.cpp:242] Iteration 61900, loss = 0.591579 +I0616 12:31:20.446166 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.2079 (* 1 = 0.2079 loss) +I0616 12:31:20.446171 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115967 (* 1 = 0.115967 loss) +I0616 12:31:20.446176 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0347787 (* 1 = 0.0347787 loss) +I0616 12:31:20.446179 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0396701 (* 1 = 0.0396701 loss) +I0616 12:31:20.446183 9857 solver.cpp:571] Iteration 61900, lr = 0.0001 +I0616 12:31:32.043248 9857 solver.cpp:242] Iteration 61920, loss = 0.447141 +I0616 12:31:32.043289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19859 (* 1 = 0.19859 loss) +I0616 12:31:32.043295 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272596 (* 1 = 0.272596 loss) +I0616 12:31:32.043299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0607909 (* 1 = 0.0607909 loss) +I0616 12:31:32.043303 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175671 (* 1 = 0.0175671 loss) +I0616 12:31:32.043308 9857 solver.cpp:571] Iteration 61920, lr = 0.0001 +I0616 12:31:43.543017 9857 solver.cpp:242] Iteration 61940, loss = 0.783668 +I0616 12:31:43.543061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114008 (* 1 = 0.114008 loss) +I0616 12:31:43.543066 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140173 (* 1 = 0.140173 loss) +I0616 12:31:43.543071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.045488 (* 1 = 0.045488 loss) +I0616 12:31:43.543074 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.626291 (* 1 = 0.626291 loss) +I0616 12:31:43.543078 9857 solver.cpp:571] Iteration 61940, lr = 0.0001 +I0616 12:31:55.058487 9857 solver.cpp:242] Iteration 61960, loss = 0.630581 +I0616 12:31:55.058528 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.386825 (* 1 = 0.386825 loss) +I0616 12:31:55.058534 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288818 (* 1 = 0.288818 loss) +I0616 12:31:55.058538 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0913285 (* 1 = 0.0913285 loss) +I0616 12:31:55.058542 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0815261 (* 1 = 0.0815261 loss) +I0616 12:31:55.058547 9857 solver.cpp:571] Iteration 61960, lr = 0.0001 +I0616 12:32:06.740433 9857 solver.cpp:242] Iteration 61980, loss = 0.535566 +I0616 12:32:06.740476 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0970767 (* 1 = 0.0970767 loss) +I0616 12:32:06.740481 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155148 (* 1 = 0.155148 loss) +I0616 12:32:06.740485 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0490376 (* 1 = 0.0490376 loss) +I0616 12:32:06.740489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00726056 (* 1 = 0.00726056 loss) +I0616 12:32:06.740494 9857 solver.cpp:571] Iteration 61980, lr = 0.0001 +speed: 0.606s / iter +I0616 12:32:18.196799 9857 solver.cpp:242] Iteration 62000, loss = 0.561977 +I0616 12:32:18.196841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103389 (* 1 = 0.103389 loss) +I0616 12:32:18.196846 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0625632 (* 1 = 0.0625632 loss) +I0616 12:32:18.196851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0682556 (* 1 = 0.0682556 loss) +I0616 12:32:18.196853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00899941 (* 1 = 0.00899941 loss) +I0616 12:32:18.196857 9857 solver.cpp:571] Iteration 62000, lr = 0.0001 +I0616 12:32:29.870409 9857 solver.cpp:242] Iteration 62020, loss = 0.921099 +I0616 12:32:29.870452 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.304008 (* 1 = 0.304008 loss) +I0616 12:32:29.870458 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388498 (* 1 = 0.388498 loss) +I0616 12:32:29.870462 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150397 (* 1 = 0.150397 loss) +I0616 12:32:29.870466 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.122539 (* 1 = 0.122539 loss) +I0616 12:32:29.870471 9857 solver.cpp:571] Iteration 62020, lr = 0.0001 +I0616 12:32:41.546145 9857 solver.cpp:242] Iteration 62040, loss = 0.910913 +I0616 12:32:41.546188 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0381882 (* 1 = 0.0381882 loss) +I0616 12:32:41.546193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0900981 (* 1 = 0.0900981 loss) +I0616 12:32:41.546198 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0247412 (* 1 = 0.0247412 loss) +I0616 12:32:41.546202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0662897 (* 1 = 0.0662897 loss) +I0616 12:32:41.546205 9857 solver.cpp:571] Iteration 62040, lr = 0.0001 +I0616 12:32:53.066998 9857 solver.cpp:242] Iteration 62060, loss = 0.934808 +I0616 12:32:53.067040 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167886 (* 1 = 0.167886 loss) +I0616 12:32:53.067045 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168059 (* 1 = 0.168059 loss) +I0616 12:32:53.067050 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0597081 (* 1 = 0.0597081 loss) +I0616 12:32:53.067054 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.648369 (* 1 = 0.648369 loss) +I0616 12:32:53.067057 9857 solver.cpp:571] Iteration 62060, lr = 0.0001 +I0616 12:33:04.566308 9857 solver.cpp:242] Iteration 62080, loss = 0.582247 +I0616 12:33:04.566350 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106508 (* 1 = 0.106508 loss) +I0616 12:33:04.566355 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214933 (* 1 = 0.214933 loss) +I0616 12:33:04.566359 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.044654 (* 1 = 0.044654 loss) +I0616 12:33:04.566364 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0351665 (* 1 = 0.0351665 loss) +I0616 12:33:04.566367 9857 solver.cpp:571] Iteration 62080, lr = 0.0001 +I0616 12:33:15.822654 9857 solver.cpp:242] Iteration 62100, loss = 0.819415 +I0616 12:33:15.822695 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354475 (* 1 = 0.354475 loss) +I0616 12:33:15.822700 9857 solver.cpp:258] Train net output #1: loss_cls = 0.419579 (* 1 = 0.419579 loss) +I0616 12:33:15.822705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.403804 (* 1 = 0.403804 loss) +I0616 12:33:15.822708 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0733293 (* 1 = 0.0733293 loss) +I0616 12:33:15.822712 9857 solver.cpp:571] Iteration 62100, lr = 0.0001 +I0616 12:33:27.340373 9857 solver.cpp:242] Iteration 62120, loss = 0.770025 +I0616 12:33:27.340415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170327 (* 1 = 0.170327 loss) +I0616 12:33:27.340421 9857 solver.cpp:258] Train net output #1: loss_cls = 0.613509 (* 1 = 0.613509 loss) +I0616 12:33:27.340425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.30785 (* 1 = 0.30785 loss) +I0616 12:33:27.340430 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.237935 (* 1 = 0.237935 loss) +I0616 12:33:27.340433 9857 solver.cpp:571] Iteration 62120, lr = 0.0001 +I0616 12:33:38.946647 9857 solver.cpp:242] Iteration 62140, loss = 0.59907 +I0616 12:33:38.946689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312727 (* 1 = 0.312727 loss) +I0616 12:33:38.946694 9857 solver.cpp:258] Train net output #1: loss_cls = 0.502492 (* 1 = 0.502492 loss) +I0616 12:33:38.946699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0465872 (* 1 = 0.0465872 loss) +I0616 12:33:38.946703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0645049 (* 1 = 0.0645049 loss) +I0616 12:33:38.946707 9857 solver.cpp:571] Iteration 62140, lr = 0.0001 +I0616 12:33:50.769909 9857 solver.cpp:242] Iteration 62160, loss = 0.274717 +I0616 12:33:50.769951 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176437 (* 1 = 0.176437 loss) +I0616 12:33:50.769958 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177538 (* 1 = 0.177538 loss) +I0616 12:33:50.769961 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0257474 (* 1 = 0.0257474 loss) +I0616 12:33:50.769965 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210842 (* 1 = 0.0210842 loss) +I0616 12:33:50.769969 9857 solver.cpp:571] Iteration 62160, lr = 0.0001 +I0616 12:34:02.180580 9857 solver.cpp:242] Iteration 62180, loss = 0.445506 +I0616 12:34:02.180621 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196213 (* 1 = 0.196213 loss) +I0616 12:34:02.180626 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168242 (* 1 = 0.168242 loss) +I0616 12:34:02.180631 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0611282 (* 1 = 0.0611282 loss) +I0616 12:34:02.180635 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0121806 (* 1 = 0.0121806 loss) +I0616 12:34:02.180639 9857 solver.cpp:571] Iteration 62180, lr = 0.0001 +speed: 0.606s / iter +I0616 12:34:13.897573 9857 solver.cpp:242] Iteration 62200, loss = 0.357877 +I0616 12:34:13.897615 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.087078 (* 1 = 0.087078 loss) +I0616 12:34:13.897621 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107633 (* 1 = 0.107633 loss) +I0616 12:34:13.897625 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0122451 (* 1 = 0.0122451 loss) +I0616 12:34:13.897629 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0051585 (* 1 = 0.0051585 loss) +I0616 12:34:13.897634 9857 solver.cpp:571] Iteration 62200, lr = 0.0001 +I0616 12:34:25.083251 9857 solver.cpp:242] Iteration 62220, loss = 1.19531 +I0616 12:34:25.083289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30639 (* 1 = 0.30639 loss) +I0616 12:34:25.083295 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360505 (* 1 = 0.360505 loss) +I0616 12:34:25.083299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.218436 (* 1 = 0.218436 loss) +I0616 12:34:25.083303 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.426808 (* 1 = 0.426808 loss) +I0616 12:34:25.083307 9857 solver.cpp:571] Iteration 62220, lr = 0.0001 +I0616 12:34:36.738402 9857 solver.cpp:242] Iteration 62240, loss = 1.13202 +I0616 12:34:36.738445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334992 (* 1 = 0.334992 loss) +I0616 12:34:36.738451 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345802 (* 1 = 0.345802 loss) +I0616 12:34:36.738456 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.091414 (* 1 = 0.091414 loss) +I0616 12:34:36.738459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103907 (* 1 = 0.103907 loss) +I0616 12:34:36.738462 9857 solver.cpp:571] Iteration 62240, lr = 0.0001 +I0616 12:34:48.570243 9857 solver.cpp:242] Iteration 62260, loss = 0.495594 +I0616 12:34:48.570286 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218913 (* 1 = 0.218913 loss) +I0616 12:34:48.570291 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243594 (* 1 = 0.243594 loss) +I0616 12:34:48.570296 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0355585 (* 1 = 0.0355585 loss) +I0616 12:34:48.570299 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240346 (* 1 = 0.0240346 loss) +I0616 12:34:48.570303 9857 solver.cpp:571] Iteration 62260, lr = 0.0001 +I0616 12:35:00.148124 9857 solver.cpp:242] Iteration 62280, loss = 0.577284 +I0616 12:35:00.148164 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394847 (* 1 = 0.394847 loss) +I0616 12:35:00.148169 9857 solver.cpp:258] Train net output #1: loss_cls = 0.320351 (* 1 = 0.320351 loss) +I0616 12:35:00.148175 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167168 (* 1 = 0.167168 loss) +I0616 12:35:00.148178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0902085 (* 1 = 0.0902085 loss) +I0616 12:35:00.148182 9857 solver.cpp:571] Iteration 62280, lr = 0.0001 +I0616 12:35:11.563925 9857 solver.cpp:242] Iteration 62300, loss = 0.632185 +I0616 12:35:11.563966 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25188 (* 1 = 0.25188 loss) +I0616 12:35:11.563972 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264876 (* 1 = 0.264876 loss) +I0616 12:35:11.563977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0812641 (* 1 = 0.0812641 loss) +I0616 12:35:11.563980 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.023369 (* 1 = 0.023369 loss) +I0616 12:35:11.563983 9857 solver.cpp:571] Iteration 62300, lr = 0.0001 +I0616 12:35:23.064127 9857 solver.cpp:242] Iteration 62320, loss = 0.225273 +I0616 12:35:23.064170 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0451199 (* 1 = 0.0451199 loss) +I0616 12:35:23.064177 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125431 (* 1 = 0.125431 loss) +I0616 12:35:23.064180 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0295181 (* 1 = 0.0295181 loss) +I0616 12:35:23.064184 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167548 (* 1 = 0.0167548 loss) +I0616 12:35:23.064188 9857 solver.cpp:571] Iteration 62320, lr = 0.0001 +I0616 12:35:34.775290 9857 solver.cpp:242] Iteration 62340, loss = 0.409145 +I0616 12:35:34.775331 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11103 (* 1 = 0.11103 loss) +I0616 12:35:34.775336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163749 (* 1 = 0.163749 loss) +I0616 12:35:34.775341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00999543 (* 1 = 0.00999543 loss) +I0616 12:35:34.775344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00270354 (* 1 = 0.00270354 loss) +I0616 12:35:34.775348 9857 solver.cpp:571] Iteration 62340, lr = 0.0001 +I0616 12:35:46.335733 9857 solver.cpp:242] Iteration 62360, loss = 0.429385 +I0616 12:35:46.335773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0990328 (* 1 = 0.0990328 loss) +I0616 12:35:46.335779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0702934 (* 1 = 0.0702934 loss) +I0616 12:35:46.335783 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00733519 (* 1 = 0.00733519 loss) +I0616 12:35:46.335788 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00380181 (* 1 = 0.00380181 loss) +I0616 12:35:46.335791 9857 solver.cpp:571] Iteration 62360, lr = 0.0001 +I0616 12:35:57.642199 9857 solver.cpp:242] Iteration 62380, loss = 0.635427 +I0616 12:35:57.642241 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303859 (* 1 = 0.303859 loss) +I0616 12:35:57.642246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.285393 (* 1 = 0.285393 loss) +I0616 12:35:57.642251 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104621 (* 1 = 0.104621 loss) +I0616 12:35:57.642254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.276897 (* 1 = 0.276897 loss) +I0616 12:35:57.642258 9857 solver.cpp:571] Iteration 62380, lr = 0.0001 +speed: 0.606s / iter +I0616 12:36:09.428608 9857 solver.cpp:242] Iteration 62400, loss = 0.490959 +I0616 12:36:09.428653 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257183 (* 1 = 0.257183 loss) +I0616 12:36:09.428658 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222189 (* 1 = 0.222189 loss) +I0616 12:36:09.428663 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.044228 (* 1 = 0.044228 loss) +I0616 12:36:09.428665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00739507 (* 1 = 0.00739507 loss) +I0616 12:36:09.428669 9857 solver.cpp:571] Iteration 62400, lr = 0.0001 +I0616 12:36:21.205112 9857 solver.cpp:242] Iteration 62420, loss = 0.507588 +I0616 12:36:21.205153 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242122 (* 1 = 0.242122 loss) +I0616 12:36:21.205158 9857 solver.cpp:258] Train net output #1: loss_cls = 0.353625 (* 1 = 0.353625 loss) +I0616 12:36:21.205163 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1031 (* 1 = 0.1031 loss) +I0616 12:36:21.205168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260017 (* 1 = 0.0260017 loss) +I0616 12:36:21.205170 9857 solver.cpp:571] Iteration 62420, lr = 0.0001 +I0616 12:36:32.832801 9857 solver.cpp:242] Iteration 62440, loss = 0.911492 +I0616 12:36:32.832842 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0702162 (* 1 = 0.0702162 loss) +I0616 12:36:32.832849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0980748 (* 1 = 0.0980748 loss) +I0616 12:36:32.832852 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0158395 (* 1 = 0.0158395 loss) +I0616 12:36:32.832856 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00116785 (* 1 = 0.00116785 loss) +I0616 12:36:32.832860 9857 solver.cpp:571] Iteration 62440, lr = 0.0001 +I0616 12:36:44.535435 9857 solver.cpp:242] Iteration 62460, loss = 0.839844 +I0616 12:36:44.535478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235967 (* 1 = 0.235967 loss) +I0616 12:36:44.535485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252784 (* 1 = 0.252784 loss) +I0616 12:36:44.535488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197224 (* 1 = 0.197224 loss) +I0616 12:36:44.535492 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.130963 (* 1 = 0.130963 loss) +I0616 12:36:44.535495 9857 solver.cpp:571] Iteration 62460, lr = 0.0001 +I0616 12:36:56.060688 9857 solver.cpp:242] Iteration 62480, loss = 0.512736 +I0616 12:36:56.060729 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197731 (* 1 = 0.197731 loss) +I0616 12:36:56.060734 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242387 (* 1 = 0.242387 loss) +I0616 12:36:56.060739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0745958 (* 1 = 0.0745958 loss) +I0616 12:36:56.060742 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.127733 (* 1 = 0.127733 loss) +I0616 12:36:56.060746 9857 solver.cpp:571] Iteration 62480, lr = 0.0001 +I0616 12:37:07.691339 9857 solver.cpp:242] Iteration 62500, loss = 0.572855 +I0616 12:37:07.691381 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.062795 (* 1 = 0.062795 loss) +I0616 12:37:07.691387 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134723 (* 1 = 0.134723 loss) +I0616 12:37:07.691391 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0643403 (* 1 = 0.0643403 loss) +I0616 12:37:07.691395 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483269 (* 1 = 0.0483269 loss) +I0616 12:37:07.691398 9857 solver.cpp:571] Iteration 62500, lr = 0.0001 +I0616 12:37:19.155922 9857 solver.cpp:242] Iteration 62520, loss = 0.566541 +I0616 12:37:19.155961 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13831 (* 1 = 0.13831 loss) +I0616 12:37:19.155966 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134843 (* 1 = 0.134843 loss) +I0616 12:37:19.155971 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0123752 (* 1 = 0.0123752 loss) +I0616 12:37:19.155974 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156671 (* 1 = 0.0156671 loss) +I0616 12:37:19.155978 9857 solver.cpp:571] Iteration 62520, lr = 0.0001 +I0616 12:37:30.527034 9857 solver.cpp:242] Iteration 62540, loss = 0.858804 +I0616 12:37:30.527075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342007 (* 1 = 0.342007 loss) +I0616 12:37:30.527081 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368482 (* 1 = 0.368482 loss) +I0616 12:37:30.527084 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.30087 (* 1 = 0.30087 loss) +I0616 12:37:30.527088 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0671015 (* 1 = 0.0671015 loss) +I0616 12:37:30.527091 9857 solver.cpp:571] Iteration 62540, lr = 0.0001 +I0616 12:37:42.318212 9857 solver.cpp:242] Iteration 62560, loss = 0.268366 +I0616 12:37:42.318255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0919892 (* 1 = 0.0919892 loss) +I0616 12:37:42.318260 9857 solver.cpp:258] Train net output #1: loss_cls = 0.117773 (* 1 = 0.117773 loss) +I0616 12:37:42.318265 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139377 (* 1 = 0.0139377 loss) +I0616 12:37:42.318269 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0412495 (* 1 = 0.0412495 loss) +I0616 12:37:42.318272 9857 solver.cpp:571] Iteration 62560, lr = 0.0001 +I0616 12:37:53.994503 9857 solver.cpp:242] Iteration 62580, loss = 0.710598 +I0616 12:37:53.994545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232556 (* 1 = 0.232556 loss) +I0616 12:37:53.994551 9857 solver.cpp:258] Train net output #1: loss_cls = 0.403574 (* 1 = 0.403574 loss) +I0616 12:37:53.994555 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137249 (* 1 = 0.137249 loss) +I0616 12:37:53.994559 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.216727 (* 1 = 0.216727 loss) +I0616 12:37:53.994562 9857 solver.cpp:571] Iteration 62580, lr = 0.0001 +speed: 0.606s / iter +I0616 12:38:05.535558 9857 solver.cpp:242] Iteration 62600, loss = 1.20694 +I0616 12:38:05.535600 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173688 (* 1 = 0.173688 loss) +I0616 12:38:05.535606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268995 (* 1 = 0.268995 loss) +I0616 12:38:05.535610 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0585915 (* 1 = 0.0585915 loss) +I0616 12:38:05.535614 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00594971 (* 1 = 0.00594971 loss) +I0616 12:38:05.535619 9857 solver.cpp:571] Iteration 62600, lr = 0.0001 +I0616 12:38:17.074918 9857 solver.cpp:242] Iteration 62620, loss = 0.277678 +I0616 12:38:17.074960 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0637868 (* 1 = 0.0637868 loss) +I0616 12:38:17.074966 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0803234 (* 1 = 0.0803234 loss) +I0616 12:38:17.074971 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0146309 (* 1 = 0.0146309 loss) +I0616 12:38:17.074975 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00644857 (* 1 = 0.00644857 loss) +I0616 12:38:17.074978 9857 solver.cpp:571] Iteration 62620, lr = 0.0001 +I0616 12:38:28.631115 9857 solver.cpp:242] Iteration 62640, loss = 0.895645 +I0616 12:38:28.631153 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269465 (* 1 = 0.269465 loss) +I0616 12:38:28.631160 9857 solver.cpp:258] Train net output #1: loss_cls = 0.420216 (* 1 = 0.420216 loss) +I0616 12:38:28.631165 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.399179 (* 1 = 0.399179 loss) +I0616 12:38:28.631170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.326421 (* 1 = 0.326421 loss) +I0616 12:38:28.631176 9857 solver.cpp:571] Iteration 62640, lr = 0.0001 +I0616 12:38:40.069936 9857 solver.cpp:242] Iteration 62660, loss = 0.618641 +I0616 12:38:40.069974 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394254 (* 1 = 0.394254 loss) +I0616 12:38:40.069980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.369816 (* 1 = 0.369816 loss) +I0616 12:38:40.069984 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112304 (* 1 = 0.112304 loss) +I0616 12:38:40.069988 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260803 (* 1 = 0.0260803 loss) +I0616 12:38:40.069991 9857 solver.cpp:571] Iteration 62660, lr = 0.0001 +I0616 12:38:51.719851 9857 solver.cpp:242] Iteration 62680, loss = 0.457516 +I0616 12:38:51.719893 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207579 (* 1 = 0.207579 loss) +I0616 12:38:51.719899 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245359 (* 1 = 0.245359 loss) +I0616 12:38:51.719903 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0631135 (* 1 = 0.0631135 loss) +I0616 12:38:51.719907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0282856 (* 1 = 0.0282856 loss) +I0616 12:38:51.719912 9857 solver.cpp:571] Iteration 62680, lr = 0.0001 +I0616 12:39:03.473258 9857 solver.cpp:242] Iteration 62700, loss = 0.588069 +I0616 12:39:03.473299 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108668 (* 1 = 0.108668 loss) +I0616 12:39:03.473304 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172128 (* 1 = 0.172128 loss) +I0616 12:39:03.473309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0494638 (* 1 = 0.0494638 loss) +I0616 12:39:03.473311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00406943 (* 1 = 0.00406943 loss) +I0616 12:39:03.473315 9857 solver.cpp:571] Iteration 62700, lr = 0.0001 +I0616 12:39:14.968328 9857 solver.cpp:242] Iteration 62720, loss = 0.194084 +I0616 12:39:14.968371 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0687593 (* 1 = 0.0687593 loss) +I0616 12:39:14.968376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118527 (* 1 = 0.118527 loss) +I0616 12:39:14.968381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0253802 (* 1 = 0.0253802 loss) +I0616 12:39:14.968385 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0146903 (* 1 = 0.0146903 loss) +I0616 12:39:14.968389 9857 solver.cpp:571] Iteration 62720, lr = 0.0001 +I0616 12:39:26.639714 9857 solver.cpp:242] Iteration 62740, loss = 1.0739 +I0616 12:39:26.639756 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.515588 (* 1 = 0.515588 loss) +I0616 12:39:26.639761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.324839 (* 1 = 0.324839 loss) +I0616 12:39:26.639766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160486 (* 1 = 0.160486 loss) +I0616 12:39:26.639770 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00547598 (* 1 = 0.00547598 loss) +I0616 12:39:26.639775 9857 solver.cpp:571] Iteration 62740, lr = 0.0001 +I0616 12:39:38.270445 9857 solver.cpp:242] Iteration 62760, loss = 0.537228 +I0616 12:39:38.270486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232065 (* 1 = 0.232065 loss) +I0616 12:39:38.270493 9857 solver.cpp:258] Train net output #1: loss_cls = 0.374036 (* 1 = 0.374036 loss) +I0616 12:39:38.270496 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0310293 (* 1 = 0.0310293 loss) +I0616 12:39:38.270500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00769744 (* 1 = 0.00769744 loss) +I0616 12:39:38.270504 9857 solver.cpp:571] Iteration 62760, lr = 0.0001 +I0616 12:39:50.048781 9857 solver.cpp:242] Iteration 62780, loss = 0.309579 +I0616 12:39:50.048823 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114368 (* 1 = 0.114368 loss) +I0616 12:39:50.048828 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139909 (* 1 = 0.139909 loss) +I0616 12:39:50.048833 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0483162 (* 1 = 0.0483162 loss) +I0616 12:39:50.048836 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131798 (* 1 = 0.0131798 loss) +I0616 12:39:50.048840 9857 solver.cpp:571] Iteration 62780, lr = 0.0001 +speed: 0.606s / iter +I0616 12:40:01.767137 9857 solver.cpp:242] Iteration 62800, loss = 0.479237 +I0616 12:40:01.767176 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192334 (* 1 = 0.192334 loss) +I0616 12:40:01.767181 9857 solver.cpp:258] Train net output #1: loss_cls = 0.291339 (* 1 = 0.291339 loss) +I0616 12:40:01.767186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0897203 (* 1 = 0.0897203 loss) +I0616 12:40:01.767190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0824923 (* 1 = 0.0824923 loss) +I0616 12:40:01.767194 9857 solver.cpp:571] Iteration 62800, lr = 0.0001 +I0616 12:40:13.091233 9857 solver.cpp:242] Iteration 62820, loss = 0.524607 +I0616 12:40:13.091275 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270903 (* 1 = 0.270903 loss) +I0616 12:40:13.091281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363963 (* 1 = 0.363963 loss) +I0616 12:40:13.091285 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0575283 (* 1 = 0.0575283 loss) +I0616 12:40:13.091290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.158067 (* 1 = 0.158067 loss) +I0616 12:40:13.091295 9857 solver.cpp:571] Iteration 62820, lr = 0.0001 +I0616 12:40:24.609061 9857 solver.cpp:242] Iteration 62840, loss = 0.563707 +I0616 12:40:24.609103 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280424 (* 1 = 0.280424 loss) +I0616 12:40:24.609109 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23657 (* 1 = 0.23657 loss) +I0616 12:40:24.609114 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.063023 (* 1 = 0.063023 loss) +I0616 12:40:24.609117 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153237 (* 1 = 0.0153237 loss) +I0616 12:40:24.609122 9857 solver.cpp:571] Iteration 62840, lr = 0.0001 +I0616 12:40:36.127511 9857 solver.cpp:242] Iteration 62860, loss = 1.23582 +I0616 12:40:36.127553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.436587 (* 1 = 0.436587 loss) +I0616 12:40:36.127558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.374054 (* 1 = 0.374054 loss) +I0616 12:40:36.127563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115919 (* 1 = 0.115919 loss) +I0616 12:40:36.127567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.287509 (* 1 = 0.287509 loss) +I0616 12:40:36.127570 9857 solver.cpp:571] Iteration 62860, lr = 0.0001 +I0616 12:40:47.886889 9857 solver.cpp:242] Iteration 62880, loss = 0.540192 +I0616 12:40:47.886919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0729669 (* 1 = 0.0729669 loss) +I0616 12:40:47.886924 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152465 (* 1 = 0.152465 loss) +I0616 12:40:47.886929 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0401696 (* 1 = 0.0401696 loss) +I0616 12:40:47.886932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000376004 (* 1 = 0.000376004 loss) +I0616 12:40:47.886936 9857 solver.cpp:571] Iteration 62880, lr = 0.0001 +I0616 12:40:59.296314 9857 solver.cpp:242] Iteration 62900, loss = 0.307483 +I0616 12:40:59.296358 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0501557 (* 1 = 0.0501557 loss) +I0616 12:40:59.296363 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0902175 (* 1 = 0.0902175 loss) +I0616 12:40:59.296368 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00525252 (* 1 = 0.00525252 loss) +I0616 12:40:59.296372 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00823917 (* 1 = 0.00823917 loss) +I0616 12:40:59.296376 9857 solver.cpp:571] Iteration 62900, lr = 0.0001 +I0616 12:41:10.825803 9857 solver.cpp:242] Iteration 62920, loss = 0.701353 +I0616 12:41:10.825841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316629 (* 1 = 0.316629 loss) +I0616 12:41:10.825847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359531 (* 1 = 0.359531 loss) +I0616 12:41:10.825851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0553349 (* 1 = 0.0553349 loss) +I0616 12:41:10.825855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0380266 (* 1 = 0.0380266 loss) +I0616 12:41:10.825860 9857 solver.cpp:571] Iteration 62920, lr = 0.0001 +I0616 12:41:22.252902 9857 solver.cpp:242] Iteration 62940, loss = 0.278319 +I0616 12:41:22.252943 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0788204 (* 1 = 0.0788204 loss) +I0616 12:41:22.252949 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145481 (* 1 = 0.145481 loss) +I0616 12:41:22.252954 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0737744 (* 1 = 0.0737744 loss) +I0616 12:41:22.252957 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127856 (* 1 = 0.0127856 loss) +I0616 12:41:22.252961 9857 solver.cpp:571] Iteration 62940, lr = 0.0001 +I0616 12:41:33.914284 9857 solver.cpp:242] Iteration 62960, loss = 0.267667 +I0616 12:41:33.914325 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0690599 (* 1 = 0.0690599 loss) +I0616 12:41:33.914330 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155385 (* 1 = 0.155385 loss) +I0616 12:41:33.914335 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119759 (* 1 = 0.0119759 loss) +I0616 12:41:33.914340 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129084 (* 1 = 0.0129084 loss) +I0616 12:41:33.914342 9857 solver.cpp:571] Iteration 62960, lr = 0.0001 +I0616 12:41:45.595404 9857 solver.cpp:242] Iteration 62980, loss = 0.364662 +I0616 12:41:45.595446 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105143 (* 1 = 0.105143 loss) +I0616 12:41:45.595451 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0803807 (* 1 = 0.0803807 loss) +I0616 12:41:45.595456 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222813 (* 1 = 0.0222813 loss) +I0616 12:41:45.595459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103737 (* 1 = 0.0103737 loss) +I0616 12:41:45.595463 9857 solver.cpp:571] Iteration 62980, lr = 0.0001 +speed: 0.606s / iter +I0616 12:41:57.237457 9857 solver.cpp:242] Iteration 63000, loss = 0.699927 +I0616 12:41:57.237500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190959 (* 1 = 0.190959 loss) +I0616 12:41:57.237509 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257332 (* 1 = 0.257332 loss) +I0616 12:41:57.237514 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112377 (* 1 = 0.0112377 loss) +I0616 12:41:57.237520 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130742 (* 1 = 0.0130742 loss) +I0616 12:41:57.237526 9857 solver.cpp:571] Iteration 63000, lr = 0.0001 +I0616 12:42:08.663163 9857 solver.cpp:242] Iteration 63020, loss = 0.746908 +I0616 12:42:08.663205 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.367289 (* 1 = 0.367289 loss) +I0616 12:42:08.663211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.419551 (* 1 = 0.419551 loss) +I0616 12:42:08.663215 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149794 (* 1 = 0.149794 loss) +I0616 12:42:08.663218 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.140744 (* 1 = 0.140744 loss) +I0616 12:42:08.663223 9857 solver.cpp:571] Iteration 63020, lr = 0.0001 +I0616 12:42:20.357030 9857 solver.cpp:242] Iteration 63040, loss = 1.00999 +I0616 12:42:20.357074 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170587 (* 1 = 0.170587 loss) +I0616 12:42:20.357079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17178 (* 1 = 0.17178 loss) +I0616 12:42:20.357084 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0422078 (* 1 = 0.0422078 loss) +I0616 12:42:20.357087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0401444 (* 1 = 0.0401444 loss) +I0616 12:42:20.357090 9857 solver.cpp:571] Iteration 63040, lr = 0.0001 +I0616 12:42:31.946254 9857 solver.cpp:242] Iteration 63060, loss = 0.706308 +I0616 12:42:31.946295 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118759 (* 1 = 0.118759 loss) +I0616 12:42:31.946300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199624 (* 1 = 0.199624 loss) +I0616 12:42:31.946305 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0558239 (* 1 = 0.0558239 loss) +I0616 12:42:31.946310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00118215 (* 1 = 0.00118215 loss) +I0616 12:42:31.946313 9857 solver.cpp:571] Iteration 63060, lr = 0.0001 +I0616 12:42:43.531965 9857 solver.cpp:242] Iteration 63080, loss = 0.872391 +I0616 12:42:43.532007 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300392 (* 1 = 0.300392 loss) +I0616 12:42:43.532013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.749297 (* 1 = 0.749297 loss) +I0616 12:42:43.532017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190588 (* 1 = 0.190588 loss) +I0616 12:42:43.532021 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0801574 (* 1 = 0.0801574 loss) +I0616 12:42:43.532024 9857 solver.cpp:571] Iteration 63080, lr = 0.0001 +I0616 12:42:55.060144 9857 solver.cpp:242] Iteration 63100, loss = 0.54642 +I0616 12:42:55.060185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129826 (* 1 = 0.129826 loss) +I0616 12:42:55.060191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172424 (* 1 = 0.172424 loss) +I0616 12:42:55.060195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0936958 (* 1 = 0.0936958 loss) +I0616 12:42:55.060199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00337667 (* 1 = 0.00337667 loss) +I0616 12:42:55.060204 9857 solver.cpp:571] Iteration 63100, lr = 0.0001 +I0616 12:43:06.495355 9857 solver.cpp:242] Iteration 63120, loss = 0.415772 +I0616 12:43:06.495398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23843 (* 1 = 0.23843 loss) +I0616 12:43:06.495404 9857 solver.cpp:258] Train net output #1: loss_cls = 0.416248 (* 1 = 0.416248 loss) +I0616 12:43:06.495409 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.031061 (* 1 = 0.031061 loss) +I0616 12:43:06.495412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0108327 (* 1 = 0.0108327 loss) +I0616 12:43:06.495415 9857 solver.cpp:571] Iteration 63120, lr = 0.0001 +I0616 12:43:17.914361 9857 solver.cpp:242] Iteration 63140, loss = 1.13338 +I0616 12:43:17.914402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389605 (* 1 = 0.389605 loss) +I0616 12:43:17.914407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.599513 (* 1 = 0.599513 loss) +I0616 12:43:17.914412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.294829 (* 1 = 0.294829 loss) +I0616 12:43:17.914415 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0996193 (* 1 = 0.0996193 loss) +I0616 12:43:17.914419 9857 solver.cpp:571] Iteration 63140, lr = 0.0001 +I0616 12:43:29.731603 9857 solver.cpp:242] Iteration 63160, loss = 0.929515 +I0616 12:43:29.731645 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286571 (* 1 = 0.286571 loss) +I0616 12:43:29.731650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.460075 (* 1 = 0.460075 loss) +I0616 12:43:29.731655 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246932 (* 1 = 0.246932 loss) +I0616 12:43:29.731658 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126818 (* 1 = 0.126818 loss) +I0616 12:43:29.731662 9857 solver.cpp:571] Iteration 63160, lr = 0.0001 +I0616 12:43:41.200423 9857 solver.cpp:242] Iteration 63180, loss = 0.314124 +I0616 12:43:41.200465 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0468271 (* 1 = 0.0468271 loss) +I0616 12:43:41.200470 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0957688 (* 1 = 0.0957688 loss) +I0616 12:43:41.200474 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00413663 (* 1 = 0.00413663 loss) +I0616 12:43:41.200479 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00839118 (* 1 = 0.00839118 loss) +I0616 12:43:41.200482 9857 solver.cpp:571] Iteration 63180, lr = 0.0001 +speed: 0.606s / iter +I0616 12:43:53.032167 9857 solver.cpp:242] Iteration 63200, loss = 0.530468 +I0616 12:43:53.032208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309466 (* 1 = 0.309466 loss) +I0616 12:43:53.032214 9857 solver.cpp:258] Train net output #1: loss_cls = 0.421624 (* 1 = 0.421624 loss) +I0616 12:43:53.032218 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110301 (* 1 = 0.110301 loss) +I0616 12:43:53.032222 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251568 (* 1 = 0.0251568 loss) +I0616 12:43:53.032227 9857 solver.cpp:571] Iteration 63200, lr = 0.0001 +I0616 12:44:04.549552 9857 solver.cpp:242] Iteration 63220, loss = 0.452356 +I0616 12:44:04.549595 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204497 (* 1 = 0.204497 loss) +I0616 12:44:04.549600 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223418 (* 1 = 0.223418 loss) +I0616 12:44:04.549604 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0170049 (* 1 = 0.0170049 loss) +I0616 12:44:04.549608 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0110305 (* 1 = 0.0110305 loss) +I0616 12:44:04.549612 9857 solver.cpp:571] Iteration 63220, lr = 0.0001 +I0616 12:44:15.964869 9857 solver.cpp:242] Iteration 63240, loss = 0.794273 +I0616 12:44:15.964912 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257927 (* 1 = 0.257927 loss) +I0616 12:44:15.964917 9857 solver.cpp:258] Train net output #1: loss_cls = 0.393474 (* 1 = 0.393474 loss) +I0616 12:44:15.964922 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105581 (* 1 = 0.105581 loss) +I0616 12:44:15.964926 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0496111 (* 1 = 0.0496111 loss) +I0616 12:44:15.964931 9857 solver.cpp:571] Iteration 63240, lr = 0.0001 +I0616 12:44:27.588685 9857 solver.cpp:242] Iteration 63260, loss = 0.228784 +I0616 12:44:27.588727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0708869 (* 1 = 0.0708869 loss) +I0616 12:44:27.588732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144276 (* 1 = 0.144276 loss) +I0616 12:44:27.588737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0208783 (* 1 = 0.0208783 loss) +I0616 12:44:27.588740 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147999 (* 1 = 0.0147999 loss) +I0616 12:44:27.588744 9857 solver.cpp:571] Iteration 63260, lr = 0.0001 +I0616 12:44:39.399513 9857 solver.cpp:242] Iteration 63280, loss = 0.708086 +I0616 12:44:39.399556 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408343 (* 1 = 0.408343 loss) +I0616 12:44:39.399564 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270391 (* 1 = 0.270391 loss) +I0616 12:44:39.399569 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200456 (* 1 = 0.200456 loss) +I0616 12:44:39.399571 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.171917 (* 1 = 0.171917 loss) +I0616 12:44:39.399575 9857 solver.cpp:571] Iteration 63280, lr = 0.0001 +I0616 12:44:50.980729 9857 solver.cpp:242] Iteration 63300, loss = 0.292777 +I0616 12:44:50.980772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1644 (* 1 = 0.1644 loss) +I0616 12:44:50.980777 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191618 (* 1 = 0.191618 loss) +I0616 12:44:50.980782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0473247 (* 1 = 0.0473247 loss) +I0616 12:44:50.980785 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00374446 (* 1 = 0.00374446 loss) +I0616 12:44:50.980792 9857 solver.cpp:571] Iteration 63300, lr = 0.0001 +I0616 12:45:02.560454 9857 solver.cpp:242] Iteration 63320, loss = 0.709506 +I0616 12:45:02.560497 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.362976 (* 1 = 0.362976 loss) +I0616 12:45:02.560503 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370648 (* 1 = 0.370648 loss) +I0616 12:45:02.560508 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162927 (* 1 = 0.162927 loss) +I0616 12:45:02.560511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.212423 (* 1 = 0.212423 loss) +I0616 12:45:02.560518 9857 solver.cpp:571] Iteration 63320, lr = 0.0001 +I0616 12:45:14.202440 9857 solver.cpp:242] Iteration 63340, loss = 0.76831 +I0616 12:45:14.202482 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238952 (* 1 = 0.238952 loss) +I0616 12:45:14.202488 9857 solver.cpp:258] Train net output #1: loss_cls = 0.525232 (* 1 = 0.525232 loss) +I0616 12:45:14.202493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0623005 (* 1 = 0.0623005 loss) +I0616 12:45:14.202497 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0517155 (* 1 = 0.0517155 loss) +I0616 12:45:14.202500 9857 solver.cpp:571] Iteration 63340, lr = 0.0001 +I0616 12:45:25.967625 9857 solver.cpp:242] Iteration 63360, loss = 0.443049 +I0616 12:45:25.967667 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0418389 (* 1 = 0.0418389 loss) +I0616 12:45:25.967674 9857 solver.cpp:258] Train net output #1: loss_cls = 0.09035 (* 1 = 0.09035 loss) +I0616 12:45:25.967677 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0170449 (* 1 = 0.0170449 loss) +I0616 12:45:25.967681 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00285631 (* 1 = 0.00285631 loss) +I0616 12:45:25.967685 9857 solver.cpp:571] Iteration 63360, lr = 0.0001 +I0616 12:45:37.522439 9857 solver.cpp:242] Iteration 63380, loss = 0.330032 +I0616 12:45:37.522480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0744595 (* 1 = 0.0744595 loss) +I0616 12:45:37.522486 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219494 (* 1 = 0.219494 loss) +I0616 12:45:37.522490 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.04531 (* 1 = 0.04531 loss) +I0616 12:45:37.522495 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00291149 (* 1 = 0.00291149 loss) +I0616 12:45:37.522498 9857 solver.cpp:571] Iteration 63380, lr = 0.0001 +speed: 0.605s / iter +I0616 12:45:48.882052 9857 solver.cpp:242] Iteration 63400, loss = 0.537702 +I0616 12:45:48.882096 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140491 (* 1 = 0.140491 loss) +I0616 12:45:48.882102 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155581 (* 1 = 0.155581 loss) +I0616 12:45:48.882107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0471143 (* 1 = 0.0471143 loss) +I0616 12:45:48.882109 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120206 (* 1 = 0.0120206 loss) +I0616 12:45:48.882113 9857 solver.cpp:571] Iteration 63400, lr = 0.0001 +I0616 12:46:00.564055 9857 solver.cpp:242] Iteration 63420, loss = 0.843943 +I0616 12:46:00.564095 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215875 (* 1 = 0.215875 loss) +I0616 12:46:00.564101 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295335 (* 1 = 0.295335 loss) +I0616 12:46:00.564105 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126576 (* 1 = 0.126576 loss) +I0616 12:46:00.564110 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.459419 (* 1 = 0.459419 loss) +I0616 12:46:00.564113 9857 solver.cpp:571] Iteration 63420, lr = 0.0001 +I0616 12:46:12.129408 9857 solver.cpp:242] Iteration 63440, loss = 0.576762 +I0616 12:46:12.129451 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.087496 (* 1 = 0.087496 loss) +I0616 12:46:12.129456 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101263 (* 1 = 0.101263 loss) +I0616 12:46:12.129461 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0767569 (* 1 = 0.0767569 loss) +I0616 12:46:12.129464 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337457 (* 1 = 0.0337457 loss) +I0616 12:46:12.129468 9857 solver.cpp:571] Iteration 63440, lr = 0.0001 +I0616 12:46:23.634598 9857 solver.cpp:242] Iteration 63460, loss = 0.992597 +I0616 12:46:23.634641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260636 (* 1 = 0.260636 loss) +I0616 12:46:23.634647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.282288 (* 1 = 0.282288 loss) +I0616 12:46:23.634651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0706069 (* 1 = 0.0706069 loss) +I0616 12:46:23.634655 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174218 (* 1 = 0.0174218 loss) +I0616 12:46:23.634660 9857 solver.cpp:571] Iteration 63460, lr = 0.0001 +I0616 12:46:35.276051 9857 solver.cpp:242] Iteration 63480, loss = 0.356872 +I0616 12:46:35.276093 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0956106 (* 1 = 0.0956106 loss) +I0616 12:46:35.276098 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255797 (* 1 = 0.255797 loss) +I0616 12:46:35.276103 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0564175 (* 1 = 0.0564175 loss) +I0616 12:46:35.276106 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180387 (* 1 = 0.0180387 loss) +I0616 12:46:35.276109 9857 solver.cpp:571] Iteration 63480, lr = 0.0001 +I0616 12:46:46.558354 9857 solver.cpp:242] Iteration 63500, loss = 0.830251 +I0616 12:46:46.558398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.066151 (* 1 = 0.066151 loss) +I0616 12:46:46.558403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0989739 (* 1 = 0.0989739 loss) +I0616 12:46:46.558408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0198627 (* 1 = 0.0198627 loss) +I0616 12:46:46.558411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0061061 (* 1 = 0.0061061 loss) +I0616 12:46:46.558415 9857 solver.cpp:571] Iteration 63500, lr = 0.0001 +I0616 12:46:57.979480 9857 solver.cpp:242] Iteration 63520, loss = 0.53063 +I0616 12:46:57.979521 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217844 (* 1 = 0.217844 loss) +I0616 12:46:57.979526 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166686 (* 1 = 0.166686 loss) +I0616 12:46:57.979532 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0825457 (* 1 = 0.0825457 loss) +I0616 12:46:57.979534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.216421 (* 1 = 0.216421 loss) +I0616 12:46:57.979538 9857 solver.cpp:571] Iteration 63520, lr = 0.0001 +I0616 12:47:09.611721 9857 solver.cpp:242] Iteration 63540, loss = 0.581212 +I0616 12:47:09.611763 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0591796 (* 1 = 0.0591796 loss) +I0616 12:47:09.611768 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108436 (* 1 = 0.108436 loss) +I0616 12:47:09.611773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274767 (* 1 = 0.0274767 loss) +I0616 12:47:09.611776 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.046604 (* 1 = 0.046604 loss) +I0616 12:47:09.611780 9857 solver.cpp:571] Iteration 63540, lr = 0.0001 +I0616 12:47:21.439647 9857 solver.cpp:242] Iteration 63560, loss = 0.74967 +I0616 12:47:21.439689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345457 (* 1 = 0.345457 loss) +I0616 12:47:21.439694 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292255 (* 1 = 0.292255 loss) +I0616 12:47:21.439698 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106666 (* 1 = 0.106666 loss) +I0616 12:47:21.439702 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360889 (* 1 = 0.0360889 loss) +I0616 12:47:21.439707 9857 solver.cpp:571] Iteration 63560, lr = 0.0001 +I0616 12:47:33.307714 9857 solver.cpp:242] Iteration 63580, loss = 0.354974 +I0616 12:47:33.307756 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0851966 (* 1 = 0.0851966 loss) +I0616 12:47:33.307761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0609962 (* 1 = 0.0609962 loss) +I0616 12:47:33.307765 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00921816 (* 1 = 0.00921816 loss) +I0616 12:47:33.307770 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182327 (* 1 = 0.0182327 loss) +I0616 12:47:33.307773 9857 solver.cpp:571] Iteration 63580, lr = 0.0001 +speed: 0.605s / iter +I0616 12:47:45.155272 9857 solver.cpp:242] Iteration 63600, loss = 0.392895 +I0616 12:47:45.155313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0720031 (* 1 = 0.0720031 loss) +I0616 12:47:45.155318 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165153 (* 1 = 0.165153 loss) +I0616 12:47:45.155323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12305 (* 1 = 0.12305 loss) +I0616 12:47:45.155326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00738422 (* 1 = 0.00738422 loss) +I0616 12:47:45.155330 9857 solver.cpp:571] Iteration 63600, lr = 0.0001 +I0616 12:47:56.828790 9857 solver.cpp:242] Iteration 63620, loss = 1.05783 +I0616 12:47:56.828831 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24796 (* 1 = 0.24796 loss) +I0616 12:47:56.828837 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260173 (* 1 = 0.260173 loss) +I0616 12:47:56.828842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159328 (* 1 = 0.159328 loss) +I0616 12:47:56.828846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00825721 (* 1 = 0.00825721 loss) +I0616 12:47:56.828850 9857 solver.cpp:571] Iteration 63620, lr = 0.0001 +I0616 12:48:08.368881 9857 solver.cpp:242] Iteration 63640, loss = 0.397089 +I0616 12:48:08.368926 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183202 (* 1 = 0.183202 loss) +I0616 12:48:08.368932 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14315 (* 1 = 0.14315 loss) +I0616 12:48:08.368935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172215 (* 1 = 0.172215 loss) +I0616 12:48:08.368939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00226986 (* 1 = 0.00226986 loss) +I0616 12:48:08.368943 9857 solver.cpp:571] Iteration 63640, lr = 0.0001 +I0616 12:48:19.930510 9857 solver.cpp:242] Iteration 63660, loss = 0.990099 +I0616 12:48:19.930552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.42608 (* 1 = 0.42608 loss) +I0616 12:48:19.930557 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332801 (* 1 = 0.332801 loss) +I0616 12:48:19.930562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0183028 (* 1 = 0.0183028 loss) +I0616 12:48:19.930567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278844 (* 1 = 0.0278844 loss) +I0616 12:48:19.930570 9857 solver.cpp:571] Iteration 63660, lr = 0.0001 +I0616 12:48:31.565636 9857 solver.cpp:242] Iteration 63680, loss = 0.686412 +I0616 12:48:31.565680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406174 (* 1 = 0.406174 loss) +I0616 12:48:31.565685 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459326 (* 1 = 0.459326 loss) +I0616 12:48:31.565690 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105819 (* 1 = 0.105819 loss) +I0616 12:48:31.565692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119627 (* 1 = 0.0119627 loss) +I0616 12:48:31.565696 9857 solver.cpp:571] Iteration 63680, lr = 0.0001 +I0616 12:48:42.966979 9857 solver.cpp:242] Iteration 63700, loss = 0.470846 +I0616 12:48:42.967021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0607579 (* 1 = 0.0607579 loss) +I0616 12:48:42.967027 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0771153 (* 1 = 0.0771153 loss) +I0616 12:48:42.967032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0457118 (* 1 = 0.0457118 loss) +I0616 12:48:42.967036 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00799225 (* 1 = 0.00799225 loss) +I0616 12:48:42.967039 9857 solver.cpp:571] Iteration 63700, lr = 0.0001 +I0616 12:48:54.418859 9857 solver.cpp:242] Iteration 63720, loss = 0.401564 +I0616 12:48:54.418901 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168761 (* 1 = 0.168761 loss) +I0616 12:48:54.418906 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215254 (* 1 = 0.215254 loss) +I0616 12:48:54.418911 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176533 (* 1 = 0.0176533 loss) +I0616 12:48:54.418915 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00935925 (* 1 = 0.00935925 loss) +I0616 12:48:54.418918 9857 solver.cpp:571] Iteration 63720, lr = 0.0001 +I0616 12:49:06.151897 9857 solver.cpp:242] Iteration 63740, loss = 0.385681 +I0616 12:49:06.151937 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16678 (* 1 = 0.16678 loss) +I0616 12:49:06.151943 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17573 (* 1 = 0.17573 loss) +I0616 12:49:06.151948 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0417582 (* 1 = 0.0417582 loss) +I0616 12:49:06.151952 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0484443 (* 1 = 0.0484443 loss) +I0616 12:49:06.151955 9857 solver.cpp:571] Iteration 63740, lr = 0.0001 +I0616 12:49:17.823359 9857 solver.cpp:242] Iteration 63760, loss = 0.398423 +I0616 12:49:17.823401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192093 (* 1 = 0.192093 loss) +I0616 12:49:17.823406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169876 (* 1 = 0.169876 loss) +I0616 12:49:17.823411 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11225 (* 1 = 0.11225 loss) +I0616 12:49:17.823415 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0327672 (* 1 = 0.0327672 loss) +I0616 12:49:17.823420 9857 solver.cpp:571] Iteration 63760, lr = 0.0001 +I0616 12:49:29.629169 9857 solver.cpp:242] Iteration 63780, loss = 0.402165 +I0616 12:49:29.629212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116772 (* 1 = 0.116772 loss) +I0616 12:49:29.629218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13146 (* 1 = 0.13146 loss) +I0616 12:49:29.629222 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149973 (* 1 = 0.149973 loss) +I0616 12:49:29.629226 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0166526 (* 1 = 0.0166526 loss) +I0616 12:49:29.629230 9857 solver.cpp:571] Iteration 63780, lr = 0.0001 +speed: 0.605s / iter +I0616 12:49:41.295783 9857 solver.cpp:242] Iteration 63800, loss = 0.986542 +I0616 12:49:41.295826 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313619 (* 1 = 0.313619 loss) +I0616 12:49:41.295831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.658485 (* 1 = 0.658485 loss) +I0616 12:49:41.295835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.31339 (* 1 = 0.31339 loss) +I0616 12:49:41.295840 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.091959 (* 1 = 0.091959 loss) +I0616 12:49:41.295843 9857 solver.cpp:571] Iteration 63800, lr = 0.0001 +I0616 12:49:53.302085 9857 solver.cpp:242] Iteration 63820, loss = 0.309216 +I0616 12:49:53.302127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178594 (* 1 = 0.178594 loss) +I0616 12:49:53.302132 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168673 (* 1 = 0.168673 loss) +I0616 12:49:53.302137 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0668773 (* 1 = 0.0668773 loss) +I0616 12:49:53.302141 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153684 (* 1 = 0.0153684 loss) +I0616 12:49:53.302145 9857 solver.cpp:571] Iteration 63820, lr = 0.0001 +I0616 12:50:04.787714 9857 solver.cpp:242] Iteration 63840, loss = 0.233005 +I0616 12:50:04.787757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0894209 (* 1 = 0.0894209 loss) +I0616 12:50:04.787763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206493 (* 1 = 0.206493 loss) +I0616 12:50:04.787768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0134116 (* 1 = 0.0134116 loss) +I0616 12:50:04.787771 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00324343 (* 1 = 0.00324343 loss) +I0616 12:50:04.787775 9857 solver.cpp:571] Iteration 63840, lr = 0.0001 +I0616 12:50:16.385180 9857 solver.cpp:242] Iteration 63860, loss = 0.632838 +I0616 12:50:16.385222 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182574 (* 1 = 0.182574 loss) +I0616 12:50:16.385228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281324 (* 1 = 0.281324 loss) +I0616 12:50:16.385232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113592 (* 1 = 0.113592 loss) +I0616 12:50:16.385236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0361895 (* 1 = 0.0361895 loss) +I0616 12:50:16.385241 9857 solver.cpp:571] Iteration 63860, lr = 0.0001 +I0616 12:50:28.116462 9857 solver.cpp:242] Iteration 63880, loss = 0.707902 +I0616 12:50:28.116505 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115693 (* 1 = 0.115693 loss) +I0616 12:50:28.116511 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225179 (* 1 = 0.225179 loss) +I0616 12:50:28.116515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.288708 (* 1 = 0.288708 loss) +I0616 12:50:28.116519 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123818 (* 1 = 0.0123818 loss) +I0616 12:50:28.116523 9857 solver.cpp:571] Iteration 63880, lr = 0.0001 +I0616 12:50:39.422839 9857 solver.cpp:242] Iteration 63900, loss = 0.309388 +I0616 12:50:39.422880 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1268 (* 1 = 0.1268 loss) +I0616 12:50:39.422886 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0733802 (* 1 = 0.0733802 loss) +I0616 12:50:39.422890 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228265 (* 1 = 0.0228265 loss) +I0616 12:50:39.422894 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136278 (* 1 = 0.0136278 loss) +I0616 12:50:39.422897 9857 solver.cpp:571] Iteration 63900, lr = 0.0001 +I0616 12:50:51.039109 9857 solver.cpp:242] Iteration 63920, loss = 0.685741 +I0616 12:50:51.039149 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110572 (* 1 = 0.110572 loss) +I0616 12:50:51.039155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214728 (* 1 = 0.214728 loss) +I0616 12:50:51.039158 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0627288 (* 1 = 0.0627288 loss) +I0616 12:50:51.039162 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014327 (* 1 = 0.014327 loss) +I0616 12:50:51.039166 9857 solver.cpp:571] Iteration 63920, lr = 0.0001 +I0616 12:51:02.819034 9857 solver.cpp:242] Iteration 63940, loss = 0.364711 +I0616 12:51:02.819077 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22008 (* 1 = 0.22008 loss) +I0616 12:51:02.819082 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24579 (* 1 = 0.24579 loss) +I0616 12:51:02.819087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0606253 (* 1 = 0.0606253 loss) +I0616 12:51:02.819092 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199082 (* 1 = 0.0199082 loss) +I0616 12:51:02.819094 9857 solver.cpp:571] Iteration 63940, lr = 0.0001 +I0616 12:51:14.555577 9857 solver.cpp:242] Iteration 63960, loss = 0.330815 +I0616 12:51:14.555620 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10611 (* 1 = 0.10611 loss) +I0616 12:51:14.555625 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114857 (* 1 = 0.114857 loss) +I0616 12:51:14.555630 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0528277 (* 1 = 0.0528277 loss) +I0616 12:51:14.555634 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020672 (* 1 = 0.020672 loss) +I0616 12:51:14.555637 9857 solver.cpp:571] Iteration 63960, lr = 0.0001 +I0616 12:51:26.149785 9857 solver.cpp:242] Iteration 63980, loss = 0.307516 +I0616 12:51:26.149822 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116438 (* 1 = 0.116438 loss) +I0616 12:51:26.149828 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118824 (* 1 = 0.118824 loss) +I0616 12:51:26.149833 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0160277 (* 1 = 0.0160277 loss) +I0616 12:51:26.149837 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165589 (* 1 = 0.0165589 loss) +I0616 12:51:26.149842 9857 solver.cpp:571] Iteration 63980, lr = 0.0001 +speed: 0.605s / iter +I0616 12:51:37.853127 9857 solver.cpp:242] Iteration 64000, loss = 0.504204 +I0616 12:51:37.853170 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401974 (* 1 = 0.401974 loss) +I0616 12:51:37.853176 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266273 (* 1 = 0.266273 loss) +I0616 12:51:37.853180 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100859 (* 1 = 0.100859 loss) +I0616 12:51:37.853184 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140905 (* 1 = 0.0140905 loss) +I0616 12:51:37.853188 9857 solver.cpp:571] Iteration 64000, lr = 0.0001 +I0616 12:51:49.383604 9857 solver.cpp:242] Iteration 64020, loss = 0.415932 +I0616 12:51:49.383646 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170851 (* 1 = 0.170851 loss) +I0616 12:51:49.383651 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181873 (* 1 = 0.181873 loss) +I0616 12:51:49.383656 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0151836 (* 1 = 0.0151836 loss) +I0616 12:51:49.383659 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0428621 (* 1 = 0.0428621 loss) +I0616 12:51:49.383663 9857 solver.cpp:571] Iteration 64020, lr = 0.0001 +I0616 12:52:00.897161 9857 solver.cpp:242] Iteration 64040, loss = 0.204683 +I0616 12:52:00.897203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0739747 (* 1 = 0.0739747 loss) +I0616 12:52:00.897208 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103284 (* 1 = 0.103284 loss) +I0616 12:52:00.897213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0175136 (* 1 = 0.0175136 loss) +I0616 12:52:00.897217 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.031907 (* 1 = 0.031907 loss) +I0616 12:52:00.897222 9857 solver.cpp:571] Iteration 64040, lr = 0.0001 +I0616 12:52:12.362301 9857 solver.cpp:242] Iteration 64060, loss = 0.288656 +I0616 12:52:12.362344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0567312 (* 1 = 0.0567312 loss) +I0616 12:52:12.362350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0577827 (* 1 = 0.0577827 loss) +I0616 12:52:12.362354 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.025499 (* 1 = 0.025499 loss) +I0616 12:52:12.362359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112872 (* 1 = 0.0112872 loss) +I0616 12:52:12.362362 9857 solver.cpp:571] Iteration 64060, lr = 0.0001 +I0616 12:52:23.781314 9857 solver.cpp:242] Iteration 64080, loss = 0.370315 +I0616 12:52:23.781357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189973 (* 1 = 0.189973 loss) +I0616 12:52:23.781363 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226042 (* 1 = 0.226042 loss) +I0616 12:52:23.781368 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0511934 (* 1 = 0.0511934 loss) +I0616 12:52:23.781370 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0583781 (* 1 = 0.0583781 loss) +I0616 12:52:23.781374 9857 solver.cpp:571] Iteration 64080, lr = 0.0001 +I0616 12:52:35.355545 9857 solver.cpp:242] Iteration 64100, loss = 0.447839 +I0616 12:52:35.355586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186771 (* 1 = 0.186771 loss) +I0616 12:52:35.355590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158213 (* 1 = 0.158213 loss) +I0616 12:52:35.355595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0211888 (* 1 = 0.0211888 loss) +I0616 12:52:35.355598 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288944 (* 1 = 0.0288944 loss) +I0616 12:52:35.355602 9857 solver.cpp:571] Iteration 64100, lr = 0.0001 +I0616 12:52:46.747411 9857 solver.cpp:242] Iteration 64120, loss = 0.781595 +I0616 12:52:46.747462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16858 (* 1 = 0.16858 loss) +I0616 12:52:46.747472 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201714 (* 1 = 0.201714 loss) +I0616 12:52:46.747483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0689204 (* 1 = 0.0689204 loss) +I0616 12:52:46.747491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0608024 (* 1 = 0.0608024 loss) +I0616 12:52:46.747498 9857 solver.cpp:571] Iteration 64120, lr = 0.0001 +I0616 12:52:58.707942 9857 solver.cpp:242] Iteration 64140, loss = 0.405622 +I0616 12:52:58.707973 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124845 (* 1 = 0.124845 loss) +I0616 12:52:58.707979 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203498 (* 1 = 0.203498 loss) +I0616 12:52:58.707983 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00413841 (* 1 = 0.00413841 loss) +I0616 12:52:58.707988 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127882 (* 1 = 0.0127882 loss) +I0616 12:52:58.707993 9857 solver.cpp:571] Iteration 64140, lr = 0.0001 +I0616 12:53:10.261502 9857 solver.cpp:242] Iteration 64160, loss = 0.550783 +I0616 12:53:10.261548 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205436 (* 1 = 0.205436 loss) +I0616 12:53:10.261554 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317386 (* 1 = 0.317386 loss) +I0616 12:53:10.261557 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0077608 (* 1 = 0.0077608 loss) +I0616 12:53:10.261561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162187 (* 1 = 0.0162187 loss) +I0616 12:53:10.261565 9857 solver.cpp:571] Iteration 64160, lr = 0.0001 +I0616 12:53:22.711930 9857 solver.cpp:242] Iteration 64180, loss = 0.423429 +I0616 12:53:22.711973 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116522 (* 1 = 0.116522 loss) +I0616 12:53:22.711979 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110332 (* 1 = 0.110332 loss) +I0616 12:53:22.711983 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110933 (* 1 = 0.110933 loss) +I0616 12:53:22.711987 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189232 (* 1 = 0.0189232 loss) +I0616 12:53:22.711990 9857 solver.cpp:571] Iteration 64180, lr = 0.0001 +speed: 0.605s / iter +I0616 12:53:35.423159 9857 solver.cpp:242] Iteration 64200, loss = 0.501144 +I0616 12:53:35.423187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0323259 (* 1 = 0.0323259 loss) +I0616 12:53:35.423192 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115372 (* 1 = 0.115372 loss) +I0616 12:53:35.423197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0126945 (* 1 = 0.0126945 loss) +I0616 12:53:35.423200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00970923 (* 1 = 0.00970923 loss) +I0616 12:53:35.423205 9857 solver.cpp:571] Iteration 64200, lr = 0.0001 +I0616 12:53:47.076598 9857 solver.cpp:242] Iteration 64220, loss = 0.319826 +I0616 12:53:47.076640 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0562258 (* 1 = 0.0562258 loss) +I0616 12:53:47.076647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111267 (* 1 = 0.111267 loss) +I0616 12:53:47.076652 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00854538 (* 1 = 0.00854538 loss) +I0616 12:53:47.076655 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00442396 (* 1 = 0.00442396 loss) +I0616 12:53:47.076659 9857 solver.cpp:571] Iteration 64220, lr = 0.0001 +I0616 12:53:58.556293 9857 solver.cpp:242] Iteration 64240, loss = 0.535046 +I0616 12:53:58.556334 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.156341 (* 1 = 0.156341 loss) +I0616 12:53:58.556339 9857 solver.cpp:258] Train net output #1: loss_cls = 0.311733 (* 1 = 0.311733 loss) +I0616 12:53:58.556344 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.192757 (* 1 = 0.192757 loss) +I0616 12:53:58.556347 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00440984 (* 1 = 0.00440984 loss) +I0616 12:53:58.556351 9857 solver.cpp:571] Iteration 64240, lr = 0.0001 +I0616 12:54:10.088755 9857 solver.cpp:242] Iteration 64260, loss = 0.31748 +I0616 12:54:10.088785 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235892 (* 1 = 0.235892 loss) +I0616 12:54:10.088790 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200898 (* 1 = 0.200898 loss) +I0616 12:54:10.088795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0221638 (* 1 = 0.0221638 loss) +I0616 12:54:10.088799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234186 (* 1 = 0.0234186 loss) +I0616 12:54:10.088804 9857 solver.cpp:571] Iteration 64260, lr = 0.0001 +I0616 12:54:24.012177 9857 solver.cpp:242] Iteration 64280, loss = 1.1478 +I0616 12:54:24.012236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341137 (* 1 = 0.341137 loss) +I0616 12:54:24.012243 9857 solver.cpp:258] Train net output #1: loss_cls = 0.514045 (* 1 = 0.514045 loss) +I0616 12:54:24.012246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0343115 (* 1 = 0.0343115 loss) +I0616 12:54:24.012250 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308511 (* 1 = 0.0308511 loss) +I0616 12:54:24.012253 9857 solver.cpp:571] Iteration 64280, lr = 0.0001 +I0616 12:54:38.655485 9857 solver.cpp:242] Iteration 64300, loss = 0.585617 +I0616 12:54:38.655529 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251155 (* 1 = 0.251155 loss) +I0616 12:54:38.655534 9857 solver.cpp:258] Train net output #1: loss_cls = 0.576184 (* 1 = 0.576184 loss) +I0616 12:54:38.655539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0437974 (* 1 = 0.0437974 loss) +I0616 12:54:38.655542 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125284 (* 1 = 0.0125284 loss) +I0616 12:54:38.655545 9857 solver.cpp:571] Iteration 64300, lr = 0.0001 +I0616 12:54:50.085995 9857 solver.cpp:242] Iteration 64320, loss = 0.533867 +I0616 12:54:50.086040 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.325487 (* 1 = 0.325487 loss) +I0616 12:54:50.086045 9857 solver.cpp:258] Train net output #1: loss_cls = 0.277366 (* 1 = 0.277366 loss) +I0616 12:54:50.086050 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.124616 (* 1 = 0.124616 loss) +I0616 12:54:50.086052 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222193 (* 1 = 0.0222193 loss) +I0616 12:54:50.086056 9857 solver.cpp:571] Iteration 64320, lr = 0.0001 +I0616 12:55:01.462790 9857 solver.cpp:242] Iteration 64340, loss = 0.46303 +I0616 12:55:01.462832 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114538 (* 1 = 0.114538 loss) +I0616 12:55:01.462838 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184908 (* 1 = 0.184908 loss) +I0616 12:55:01.462842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0385546 (* 1 = 0.0385546 loss) +I0616 12:55:01.462846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0328753 (* 1 = 0.0328753 loss) +I0616 12:55:01.462851 9857 solver.cpp:571] Iteration 64340, lr = 0.0001 +I0616 12:55:12.996729 9857 solver.cpp:242] Iteration 64360, loss = 0.518391 +I0616 12:55:12.996773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0953769 (* 1 = 0.0953769 loss) +I0616 12:55:12.996778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218175 (* 1 = 0.218175 loss) +I0616 12:55:12.996781 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0556483 (* 1 = 0.0556483 loss) +I0616 12:55:12.996785 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0963583 (* 1 = 0.0963583 loss) +I0616 12:55:12.996789 9857 solver.cpp:571] Iteration 64360, lr = 0.0001 +I0616 12:55:24.352682 9857 solver.cpp:242] Iteration 64380, loss = 0.296212 +I0616 12:55:24.352725 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0644107 (* 1 = 0.0644107 loss) +I0616 12:55:24.352730 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110412 (* 1 = 0.110412 loss) +I0616 12:55:24.352735 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.018283 (* 1 = 0.018283 loss) +I0616 12:55:24.352738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159951 (* 1 = 0.0159951 loss) +I0616 12:55:24.352742 9857 solver.cpp:571] Iteration 64380, lr = 0.0001 +speed: 0.605s / iter +I0616 12:55:35.990114 9857 solver.cpp:242] Iteration 64400, loss = 0.305422 +I0616 12:55:35.990156 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0607781 (* 1 = 0.0607781 loss) +I0616 12:55:35.990162 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138452 (* 1 = 0.138452 loss) +I0616 12:55:35.990166 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0117633 (* 1 = 0.0117633 loss) +I0616 12:55:35.990170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134533 (* 1 = 0.0134533 loss) +I0616 12:55:35.990173 9857 solver.cpp:571] Iteration 64400, lr = 0.0001 +I0616 12:55:47.519490 9857 solver.cpp:242] Iteration 64420, loss = 1.08177 +I0616 12:55:47.519531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.419343 (* 1 = 0.419343 loss) +I0616 12:55:47.519536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.43226 (* 1 = 0.43226 loss) +I0616 12:55:47.519541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0692749 (* 1 = 0.0692749 loss) +I0616 12:55:47.519546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.430997 (* 1 = 0.430997 loss) +I0616 12:55:47.519548 9857 solver.cpp:571] Iteration 64420, lr = 0.0001 +I0616 12:55:59.189170 9857 solver.cpp:242] Iteration 64440, loss = 0.470734 +I0616 12:55:59.189213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117327 (* 1 = 0.117327 loss) +I0616 12:55:59.189218 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13283 (* 1 = 0.13283 loss) +I0616 12:55:59.189221 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0714857 (* 1 = 0.0714857 loss) +I0616 12:55:59.189225 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0462157 (* 1 = 0.0462157 loss) +I0616 12:55:59.189229 9857 solver.cpp:571] Iteration 64440, lr = 0.0001 +I0616 12:56:10.953318 9857 solver.cpp:242] Iteration 64460, loss = 0.900075 +I0616 12:56:10.953361 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291975 (* 1 = 0.291975 loss) +I0616 12:56:10.953366 9857 solver.cpp:258] Train net output #1: loss_cls = 0.311003 (* 1 = 0.311003 loss) +I0616 12:56:10.953371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167964 (* 1 = 0.167964 loss) +I0616 12:56:10.953374 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.046562 (* 1 = 0.046562 loss) +I0616 12:56:10.953378 9857 solver.cpp:571] Iteration 64460, lr = 0.0001 +I0616 12:56:22.479849 9857 solver.cpp:242] Iteration 64480, loss = 0.51349 +I0616 12:56:22.479892 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0610581 (* 1 = 0.0610581 loss) +I0616 12:56:22.479897 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0776749 (* 1 = 0.0776749 loss) +I0616 12:56:22.479902 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0131725 (* 1 = 0.0131725 loss) +I0616 12:56:22.479907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00132879 (* 1 = 0.00132879 loss) +I0616 12:56:22.479909 9857 solver.cpp:571] Iteration 64480, lr = 0.0001 +I0616 12:56:33.905860 9857 solver.cpp:242] Iteration 64500, loss = 0.409602 +I0616 12:56:33.905900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102918 (* 1 = 0.102918 loss) +I0616 12:56:33.905905 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16464 (* 1 = 0.16464 loss) +I0616 12:56:33.905910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.028897 (* 1 = 0.028897 loss) +I0616 12:56:33.905915 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00277791 (* 1 = 0.00277791 loss) +I0616 12:56:33.905917 9857 solver.cpp:571] Iteration 64500, lr = 0.0001 +I0616 12:56:45.522832 9857 solver.cpp:242] Iteration 64520, loss = 0.352614 +I0616 12:56:45.522876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100911 (* 1 = 0.100911 loss) +I0616 12:56:45.522881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121427 (* 1 = 0.121427 loss) +I0616 12:56:45.522884 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0183341 (* 1 = 0.0183341 loss) +I0616 12:56:45.522888 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0121107 (* 1 = 0.0121107 loss) +I0616 12:56:45.522892 9857 solver.cpp:571] Iteration 64520, lr = 0.0001 +I0616 12:56:57.106683 9857 solver.cpp:242] Iteration 64540, loss = 1.01028 +I0616 12:56:57.106725 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.226523 (* 1 = 0.226523 loss) +I0616 12:56:57.106730 9857 solver.cpp:258] Train net output #1: loss_cls = 0.374583 (* 1 = 0.374583 loss) +I0616 12:56:57.106734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.317057 (* 1 = 0.317057 loss) +I0616 12:56:57.106739 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.11289 (* 1 = 0.11289 loss) +I0616 12:56:57.106742 9857 solver.cpp:571] Iteration 64540, lr = 0.0001 +I0616 12:57:08.657346 9857 solver.cpp:242] Iteration 64560, loss = 0.409487 +I0616 12:57:08.657384 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165538 (* 1 = 0.165538 loss) +I0616 12:57:08.657390 9857 solver.cpp:258] Train net output #1: loss_cls = 0.273612 (* 1 = 0.273612 loss) +I0616 12:57:08.657395 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0372295 (* 1 = 0.0372295 loss) +I0616 12:57:08.657399 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0203704 (* 1 = 0.0203704 loss) +I0616 12:57:08.657403 9857 solver.cpp:571] Iteration 64560, lr = 0.0001 +I0616 12:57:20.069795 9857 solver.cpp:242] Iteration 64580, loss = 0.645434 +I0616 12:57:20.069839 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408143 (* 1 = 0.408143 loss) +I0616 12:57:20.069844 9857 solver.cpp:258] Train net output #1: loss_cls = 0.417636 (* 1 = 0.417636 loss) +I0616 12:57:20.069847 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.203517 (* 1 = 0.203517 loss) +I0616 12:57:20.069851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.113148 (* 1 = 0.113148 loss) +I0616 12:57:20.069855 9857 solver.cpp:571] Iteration 64580, lr = 0.0001 +speed: 0.605s / iter +I0616 12:57:31.614328 9857 solver.cpp:242] Iteration 64600, loss = 0.474639 +I0616 12:57:31.614368 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0216498 (* 1 = 0.0216498 loss) +I0616 12:57:31.614374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.227126 (* 1 = 0.227126 loss) +I0616 12:57:31.614379 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0303911 (* 1 = 0.0303911 loss) +I0616 12:57:31.614382 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0597528 (* 1 = 0.0597528 loss) +I0616 12:57:31.614387 9857 solver.cpp:571] Iteration 64600, lr = 0.0001 +I0616 12:57:43.312710 9857 solver.cpp:242] Iteration 64620, loss = 0.258097 +I0616 12:57:43.312752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129004 (* 1 = 0.129004 loss) +I0616 12:57:43.312757 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136518 (* 1 = 0.136518 loss) +I0616 12:57:43.312762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00738378 (* 1 = 0.00738378 loss) +I0616 12:57:43.312765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00941065 (* 1 = 0.00941065 loss) +I0616 12:57:43.312768 9857 solver.cpp:571] Iteration 64620, lr = 0.0001 +I0616 12:57:54.945986 9857 solver.cpp:242] Iteration 64640, loss = 1.06472 +I0616 12:57:54.946027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296196 (* 1 = 0.296196 loss) +I0616 12:57:54.946033 9857 solver.cpp:258] Train net output #1: loss_cls = 0.510195 (* 1 = 0.510195 loss) +I0616 12:57:54.946038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.258159 (* 1 = 0.258159 loss) +I0616 12:57:54.946043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.561383 (* 1 = 0.561383 loss) +I0616 12:57:54.946046 9857 solver.cpp:571] Iteration 64640, lr = 0.0001 +I0616 12:58:06.315215 9857 solver.cpp:242] Iteration 64660, loss = 0.458683 +I0616 12:58:06.315258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0410505 (* 1 = 0.0410505 loss) +I0616 12:58:06.315264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0495425 (* 1 = 0.0495425 loss) +I0616 12:58:06.315268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00397383 (* 1 = 0.00397383 loss) +I0616 12:58:06.315273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00625868 (* 1 = 0.00625868 loss) +I0616 12:58:06.315276 9857 solver.cpp:571] Iteration 64660, lr = 0.0001 +I0616 12:58:17.543490 9857 solver.cpp:242] Iteration 64680, loss = 0.501575 +I0616 12:58:17.543534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159698 (* 1 = 0.159698 loss) +I0616 12:58:17.543539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179337 (* 1 = 0.179337 loss) +I0616 12:58:17.543543 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0790213 (* 1 = 0.0790213 loss) +I0616 12:58:17.543547 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0131956 (* 1 = 0.0131956 loss) +I0616 12:58:17.543550 9857 solver.cpp:571] Iteration 64680, lr = 0.0001 +I0616 12:58:28.994547 9857 solver.cpp:242] Iteration 64700, loss = 0.752882 +I0616 12:58:28.994590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278904 (* 1 = 0.278904 loss) +I0616 12:58:28.994595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.675471 (* 1 = 0.675471 loss) +I0616 12:58:28.994599 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153845 (* 1 = 0.153845 loss) +I0616 12:58:28.994602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.147852 (* 1 = 0.147852 loss) +I0616 12:58:28.994606 9857 solver.cpp:571] Iteration 64700, lr = 0.0001 +I0616 12:58:40.639350 9857 solver.cpp:242] Iteration 64720, loss = 0.36098 +I0616 12:58:40.639394 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159069 (* 1 = 0.159069 loss) +I0616 12:58:40.639399 9857 solver.cpp:258] Train net output #1: loss_cls = 0.106648 (* 1 = 0.106648 loss) +I0616 12:58:40.639403 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0824731 (* 1 = 0.0824731 loss) +I0616 12:58:40.639407 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0293181 (* 1 = 0.0293181 loss) +I0616 12:58:40.639411 9857 solver.cpp:571] Iteration 64720, lr = 0.0001 +I0616 12:58:52.096997 9857 solver.cpp:242] Iteration 64740, loss = 0.222035 +I0616 12:58:52.097039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0562577 (* 1 = 0.0562577 loss) +I0616 12:58:52.097044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0994825 (* 1 = 0.0994825 loss) +I0616 12:58:52.097048 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0275703 (* 1 = 0.0275703 loss) +I0616 12:58:52.097053 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0094411 (* 1 = 0.0094411 loss) +I0616 12:58:52.097056 9857 solver.cpp:571] Iteration 64740, lr = 0.0001 +I0616 12:59:03.615073 9857 solver.cpp:242] Iteration 64760, loss = 0.628477 +I0616 12:59:03.615115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25474 (* 1 = 0.25474 loss) +I0616 12:59:03.615121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32252 (* 1 = 0.32252 loss) +I0616 12:59:03.615126 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141693 (* 1 = 0.141693 loss) +I0616 12:59:03.615130 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.249495 (* 1 = 0.249495 loss) +I0616 12:59:03.615134 9857 solver.cpp:571] Iteration 64760, lr = 0.0001 +I0616 12:59:14.833925 9857 solver.cpp:242] Iteration 64780, loss = 1.04614 +I0616 12:59:14.833966 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336958 (* 1 = 0.336958 loss) +I0616 12:59:14.833972 9857 solver.cpp:258] Train net output #1: loss_cls = 0.480477 (* 1 = 0.480477 loss) +I0616 12:59:14.833976 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0575547 (* 1 = 0.0575547 loss) +I0616 12:59:14.833981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0249421 (* 1 = 0.0249421 loss) +I0616 12:59:14.833984 9857 solver.cpp:571] Iteration 64780, lr = 0.0001 +speed: 0.605s / iter +I0616 12:59:26.341298 9857 solver.cpp:242] Iteration 64800, loss = 0.514187 +I0616 12:59:26.341341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.26598 (* 1 = 0.26598 loss) +I0616 12:59:26.341346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.348125 (* 1 = 0.348125 loss) +I0616 12:59:26.341351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0626902 (* 1 = 0.0626902 loss) +I0616 12:59:26.341354 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0305263 (* 1 = 0.0305263 loss) +I0616 12:59:26.341358 9857 solver.cpp:571] Iteration 64800, lr = 0.0001 +I0616 12:59:37.938161 9857 solver.cpp:242] Iteration 64820, loss = 0.450654 +I0616 12:59:37.938205 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108207 (* 1 = 0.108207 loss) +I0616 12:59:37.938210 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14379 (* 1 = 0.14379 loss) +I0616 12:59:37.938213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0166573 (* 1 = 0.0166573 loss) +I0616 12:59:37.938217 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00745807 (* 1 = 0.00745807 loss) +I0616 12:59:37.938221 9857 solver.cpp:571] Iteration 64820, lr = 0.0001 +I0616 12:59:49.326066 9857 solver.cpp:242] Iteration 64840, loss = 0.279036 +I0616 12:59:49.326108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0307385 (* 1 = 0.0307385 loss) +I0616 12:59:49.326113 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180373 (* 1 = 0.180373 loss) +I0616 12:59:49.326118 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0438806 (* 1 = 0.0438806 loss) +I0616 12:59:49.326122 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.042195 (* 1 = 0.042195 loss) +I0616 12:59:49.326125 9857 solver.cpp:571] Iteration 64840, lr = 0.0001 +I0616 13:00:01.288934 9857 solver.cpp:242] Iteration 64860, loss = 0.336679 +I0616 13:00:01.288977 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17162 (* 1 = 0.17162 loss) +I0616 13:00:01.288983 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219142 (* 1 = 0.219142 loss) +I0616 13:00:01.288987 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111008 (* 1 = 0.111008 loss) +I0616 13:00:01.288991 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0272531 (* 1 = 0.0272531 loss) +I0616 13:00:01.288995 9857 solver.cpp:571] Iteration 64860, lr = 0.0001 +I0616 13:00:12.788741 9857 solver.cpp:242] Iteration 64880, loss = 0.281648 +I0616 13:00:12.788784 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0645727 (* 1 = 0.0645727 loss) +I0616 13:00:12.788789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116449 (* 1 = 0.116449 loss) +I0616 13:00:12.788794 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0123456 (* 1 = 0.0123456 loss) +I0616 13:00:12.788797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00261468 (* 1 = 0.00261468 loss) +I0616 13:00:12.788801 9857 solver.cpp:571] Iteration 64880, lr = 0.0001 +I0616 13:00:24.506789 9857 solver.cpp:242] Iteration 64900, loss = 0.814268 +I0616 13:00:24.506832 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.374735 (* 1 = 0.374735 loss) +I0616 13:00:24.506839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.452845 (* 1 = 0.452845 loss) +I0616 13:00:24.506842 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.234662 (* 1 = 0.234662 loss) +I0616 13:00:24.506846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0723461 (* 1 = 0.0723461 loss) +I0616 13:00:24.506850 9857 solver.cpp:571] Iteration 64900, lr = 0.0001 +I0616 13:00:36.257369 9857 solver.cpp:242] Iteration 64920, loss = 0.227921 +I0616 13:00:36.257411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115943 (* 1 = 0.115943 loss) +I0616 13:00:36.257416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122986 (* 1 = 0.122986 loss) +I0616 13:00:36.257421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0391718 (* 1 = 0.0391718 loss) +I0616 13:00:36.257424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174299 (* 1 = 0.0174299 loss) +I0616 13:00:36.257428 9857 solver.cpp:571] Iteration 64920, lr = 0.0001 +I0616 13:00:47.887076 9857 solver.cpp:242] Iteration 64940, loss = 0.601298 +I0616 13:00:47.887118 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0777607 (* 1 = 0.0777607 loss) +I0616 13:00:47.887125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184091 (* 1 = 0.184091 loss) +I0616 13:00:47.887128 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0341324 (* 1 = 0.0341324 loss) +I0616 13:00:47.887132 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00851117 (* 1 = 0.00851117 loss) +I0616 13:00:47.887136 9857 solver.cpp:571] Iteration 64940, lr = 0.0001 +I0616 13:00:59.612371 9857 solver.cpp:242] Iteration 64960, loss = 0.559342 +I0616 13:00:59.612412 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220303 (* 1 = 0.220303 loss) +I0616 13:00:59.612418 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188957 (* 1 = 0.188957 loss) +I0616 13:00:59.612423 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0566013 (* 1 = 0.0566013 loss) +I0616 13:00:59.612427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011802 (* 1 = 0.011802 loss) +I0616 13:00:59.612431 9857 solver.cpp:571] Iteration 64960, lr = 0.0001 +I0616 13:01:11.165647 9857 solver.cpp:242] Iteration 64980, loss = 0.815799 +I0616 13:01:11.165691 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252056 (* 1 = 0.252056 loss) +I0616 13:01:11.165696 9857 solver.cpp:258] Train net output #1: loss_cls = 0.460978 (* 1 = 0.460978 loss) +I0616 13:01:11.165701 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.15919 (* 1 = 0.15919 loss) +I0616 13:01:11.165705 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0986214 (* 1 = 0.0986214 loss) +I0616 13:01:11.165709 9857 solver.cpp:571] Iteration 64980, lr = 0.0001 +speed: 0.605s / iter +I0616 13:01:22.870616 9857 solver.cpp:242] Iteration 65000, loss = 0.584391 +I0616 13:01:22.870661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0709601 (* 1 = 0.0709601 loss) +I0616 13:01:22.870666 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115381 (* 1 = 0.115381 loss) +I0616 13:01:22.870671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112844 (* 1 = 0.112844 loss) +I0616 13:01:22.870674 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246102 (* 1 = 0.0246102 loss) +I0616 13:01:22.870678 9857 solver.cpp:571] Iteration 65000, lr = 0.0001 +I0616 13:01:34.338536 9857 solver.cpp:242] Iteration 65020, loss = 0.410186 +I0616 13:01:34.338578 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143191 (* 1 = 0.143191 loss) +I0616 13:01:34.338584 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136068 (* 1 = 0.136068 loss) +I0616 13:01:34.338588 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0036122 (* 1 = 0.0036122 loss) +I0616 13:01:34.338593 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0245184 (* 1 = 0.0245184 loss) +I0616 13:01:34.338596 9857 solver.cpp:571] Iteration 65020, lr = 0.0001 +I0616 13:01:45.660933 9857 solver.cpp:242] Iteration 65040, loss = 0.610175 +I0616 13:01:45.660976 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334392 (* 1 = 0.334392 loss) +I0616 13:01:45.660981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328435 (* 1 = 0.328435 loss) +I0616 13:01:45.660986 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0918188 (* 1 = 0.0918188 loss) +I0616 13:01:45.660989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227718 (* 1 = 0.0227718 loss) +I0616 13:01:45.660993 9857 solver.cpp:571] Iteration 65040, lr = 0.0001 +I0616 13:01:57.333446 9857 solver.cpp:242] Iteration 65060, loss = 0.45325 +I0616 13:01:57.333488 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0746079 (* 1 = 0.0746079 loss) +I0616 13:01:57.333494 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0589738 (* 1 = 0.0589738 loss) +I0616 13:01:57.333498 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0889255 (* 1 = 0.0889255 loss) +I0616 13:01:57.333503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111535 (* 1 = 0.0111535 loss) +I0616 13:01:57.333506 9857 solver.cpp:571] Iteration 65060, lr = 0.0001 +I0616 13:02:08.992928 9857 solver.cpp:242] Iteration 65080, loss = 0.622267 +I0616 13:02:08.992972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114134 (* 1 = 0.114134 loss) +I0616 13:02:08.992977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154789 (* 1 = 0.154789 loss) +I0616 13:02:08.992982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0970878 (* 1 = 0.0970878 loss) +I0616 13:02:08.992986 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225177 (* 1 = 0.0225177 loss) +I0616 13:02:08.992990 9857 solver.cpp:571] Iteration 65080, lr = 0.0001 +I0616 13:02:20.418360 9857 solver.cpp:242] Iteration 65100, loss = 0.623602 +I0616 13:02:20.418402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310251 (* 1 = 0.310251 loss) +I0616 13:02:20.418407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.494351 (* 1 = 0.494351 loss) +I0616 13:02:20.418412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103594 (* 1 = 0.103594 loss) +I0616 13:02:20.418416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.182744 (* 1 = 0.182744 loss) +I0616 13:02:20.418419 9857 solver.cpp:571] Iteration 65100, lr = 0.0001 +I0616 13:02:31.977975 9857 solver.cpp:242] Iteration 65120, loss = 0.545134 +I0616 13:02:31.978018 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0704502 (* 1 = 0.0704502 loss) +I0616 13:02:31.978024 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177662 (* 1 = 0.177662 loss) +I0616 13:02:31.978027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0226756 (* 1 = 0.0226756 loss) +I0616 13:02:31.978031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0564656 (* 1 = 0.0564656 loss) +I0616 13:02:31.978035 9857 solver.cpp:571] Iteration 65120, lr = 0.0001 +I0616 13:02:43.613584 9857 solver.cpp:242] Iteration 65140, loss = 0.561055 +I0616 13:02:43.613626 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234572 (* 1 = 0.234572 loss) +I0616 13:02:43.613631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438326 (* 1 = 0.438326 loss) +I0616 13:02:43.613636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0719675 (* 1 = 0.0719675 loss) +I0616 13:02:43.613639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0767173 (* 1 = 0.0767173 loss) +I0616 13:02:43.613643 9857 solver.cpp:571] Iteration 65140, lr = 0.0001 +I0616 13:02:55.043308 9857 solver.cpp:242] Iteration 65160, loss = 0.229133 +I0616 13:02:55.043349 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0474673 (* 1 = 0.0474673 loss) +I0616 13:02:55.043354 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175797 (* 1 = 0.175797 loss) +I0616 13:02:55.043359 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00717034 (* 1 = 0.00717034 loss) +I0616 13:02:55.043362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00459112 (* 1 = 0.00459112 loss) +I0616 13:02:55.043366 9857 solver.cpp:571] Iteration 65160, lr = 0.0001 +I0616 13:03:06.368461 9857 solver.cpp:242] Iteration 65180, loss = 0.49579 +I0616 13:03:06.368504 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242704 (* 1 = 0.242704 loss) +I0616 13:03:06.368510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.284729 (* 1 = 0.284729 loss) +I0616 13:03:06.368513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0324572 (* 1 = 0.0324572 loss) +I0616 13:03:06.368517 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0565323 (* 1 = 0.0565323 loss) +I0616 13:03:06.368521 9857 solver.cpp:571] Iteration 65180, lr = 0.0001 +speed: 0.605s / iter +I0616 13:03:17.951256 9857 solver.cpp:242] Iteration 65200, loss = 0.909097 +I0616 13:03:17.951298 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294211 (* 1 = 0.294211 loss) +I0616 13:03:17.951304 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211982 (* 1 = 0.211982 loss) +I0616 13:03:17.951308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0480975 (* 1 = 0.0480975 loss) +I0616 13:03:17.951313 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0482842 (* 1 = 0.0482842 loss) +I0616 13:03:17.951316 9857 solver.cpp:571] Iteration 65200, lr = 0.0001 +I0616 13:03:29.355342 9857 solver.cpp:242] Iteration 65220, loss = 0.524128 +I0616 13:03:29.355383 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0667461 (* 1 = 0.0667461 loss) +I0616 13:03:29.355389 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148172 (* 1 = 0.148172 loss) +I0616 13:03:29.355393 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0185448 (* 1 = 0.0185448 loss) +I0616 13:03:29.355397 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100528 (* 1 = 0.0100528 loss) +I0616 13:03:29.355401 9857 solver.cpp:571] Iteration 65220, lr = 0.0001 +I0616 13:03:40.831540 9857 solver.cpp:242] Iteration 65240, loss = 1.21535 +I0616 13:03:40.831583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342681 (* 1 = 0.342681 loss) +I0616 13:03:40.831589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.47646 (* 1 = 0.47646 loss) +I0616 13:03:40.831593 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.207122 (* 1 = 0.207122 loss) +I0616 13:03:40.831598 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.204048 (* 1 = 0.204048 loss) +I0616 13:03:40.831601 9857 solver.cpp:571] Iteration 65240, lr = 0.0001 +I0616 13:03:52.436512 9857 solver.cpp:242] Iteration 65260, loss = 0.685168 +I0616 13:03:52.436552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294827 (* 1 = 0.294827 loss) +I0616 13:03:52.436558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.410828 (* 1 = 0.410828 loss) +I0616 13:03:52.436561 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158669 (* 1 = 0.158669 loss) +I0616 13:03:52.436565 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390885 (* 1 = 0.0390885 loss) +I0616 13:03:52.436568 9857 solver.cpp:571] Iteration 65260, lr = 0.0001 +I0616 13:04:03.930379 9857 solver.cpp:242] Iteration 65280, loss = 0.443258 +I0616 13:04:03.930421 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0435269 (* 1 = 0.0435269 loss) +I0616 13:04:03.930426 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16114 (* 1 = 0.16114 loss) +I0616 13:04:03.930430 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0279722 (* 1 = 0.0279722 loss) +I0616 13:04:03.930434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00775217 (* 1 = 0.00775217 loss) +I0616 13:04:03.930438 9857 solver.cpp:571] Iteration 65280, lr = 0.0001 +I0616 13:04:15.442078 9857 solver.cpp:242] Iteration 65300, loss = 0.166945 +I0616 13:04:15.442121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0378681 (* 1 = 0.0378681 loss) +I0616 13:04:15.442126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.082629 (* 1 = 0.082629 loss) +I0616 13:04:15.442131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0194945 (* 1 = 0.0194945 loss) +I0616 13:04:15.442134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00845811 (* 1 = 0.00845811 loss) +I0616 13:04:15.442138 9857 solver.cpp:571] Iteration 65300, lr = 0.0001 +I0616 13:04:26.736809 9857 solver.cpp:242] Iteration 65320, loss = 0.965095 +I0616 13:04:26.736850 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14765 (* 1 = 0.14765 loss) +I0616 13:04:26.736856 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283858 (* 1 = 0.283858 loss) +I0616 13:04:26.736860 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0724589 (* 1 = 0.0724589 loss) +I0616 13:04:26.736865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0335264 (* 1 = 0.0335264 loss) +I0616 13:04:26.736868 9857 solver.cpp:571] Iteration 65320, lr = 0.0001 +I0616 13:04:38.128654 9857 solver.cpp:242] Iteration 65340, loss = 0.420441 +I0616 13:04:38.128695 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281996 (* 1 = 0.281996 loss) +I0616 13:04:38.128701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26923 (* 1 = 0.26923 loss) +I0616 13:04:38.128705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0600669 (* 1 = 0.0600669 loss) +I0616 13:04:38.128710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164764 (* 1 = 0.0164764 loss) +I0616 13:04:38.128713 9857 solver.cpp:571] Iteration 65340, lr = 0.0001 +I0616 13:04:49.653252 9857 solver.cpp:242] Iteration 65360, loss = 0.41408 +I0616 13:04:49.653295 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.090632 (* 1 = 0.090632 loss) +I0616 13:04:49.653301 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145443 (* 1 = 0.145443 loss) +I0616 13:04:49.653304 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00241832 (* 1 = 0.00241832 loss) +I0616 13:04:49.653308 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00599557 (* 1 = 0.00599557 loss) +I0616 13:04:49.653312 9857 solver.cpp:571] Iteration 65360, lr = 0.0001 +I0616 13:05:01.196656 9857 solver.cpp:242] Iteration 65380, loss = 0.378335 +I0616 13:05:01.196699 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129485 (* 1 = 0.129485 loss) +I0616 13:05:01.196704 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12781 (* 1 = 0.12781 loss) +I0616 13:05:01.196709 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555977 (* 1 = 0.0555977 loss) +I0616 13:05:01.196713 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.139649 (* 1 = 0.139649 loss) +I0616 13:05:01.196717 9857 solver.cpp:571] Iteration 65380, lr = 0.0001 +speed: 0.605s / iter +I0616 13:05:12.568933 9857 solver.cpp:242] Iteration 65400, loss = 0.562922 +I0616 13:05:12.568975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274028 (* 1 = 0.274028 loss) +I0616 13:05:12.568981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438811 (* 1 = 0.438811 loss) +I0616 13:05:12.568985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118427 (* 1 = 0.118427 loss) +I0616 13:05:12.568989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.092724 (* 1 = 0.092724 loss) +I0616 13:05:12.568994 9857 solver.cpp:571] Iteration 65400, lr = 0.0001 +I0616 13:05:24.314244 9857 solver.cpp:242] Iteration 65420, loss = 0.345029 +I0616 13:05:24.314286 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188204 (* 1 = 0.188204 loss) +I0616 13:05:24.314291 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245347 (* 1 = 0.245347 loss) +I0616 13:05:24.314296 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119441 (* 1 = 0.0119441 loss) +I0616 13:05:24.314299 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00994505 (* 1 = 0.00994505 loss) +I0616 13:05:24.314303 9857 solver.cpp:571] Iteration 65420, lr = 0.0001 +I0616 13:05:35.973628 9857 solver.cpp:242] Iteration 65440, loss = 0.53701 +I0616 13:05:35.973670 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132248 (* 1 = 0.132248 loss) +I0616 13:05:35.973675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169657 (* 1 = 0.169657 loss) +I0616 13:05:35.973680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00973051 (* 1 = 0.00973051 loss) +I0616 13:05:35.973683 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030167 (* 1 = 0.030167 loss) +I0616 13:05:35.973688 9857 solver.cpp:571] Iteration 65440, lr = 0.0001 +I0616 13:05:47.554066 9857 solver.cpp:242] Iteration 65460, loss = 0.522058 +I0616 13:05:47.554108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0652638 (* 1 = 0.0652638 loss) +I0616 13:05:47.554113 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11443 (* 1 = 0.11443 loss) +I0616 13:05:47.554117 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0107729 (* 1 = 0.0107729 loss) +I0616 13:05:47.554121 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0155518 (* 1 = 0.0155518 loss) +I0616 13:05:47.554126 9857 solver.cpp:571] Iteration 65460, lr = 0.0001 +I0616 13:05:59.342022 9857 solver.cpp:242] Iteration 65480, loss = 0.573783 +I0616 13:05:59.342066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394647 (* 1 = 0.394647 loss) +I0616 13:05:59.342072 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323605 (* 1 = 0.323605 loss) +I0616 13:05:59.342075 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159048 (* 1 = 0.159048 loss) +I0616 13:05:59.342079 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494487 (* 1 = 0.0494487 loss) +I0616 13:05:59.342083 9857 solver.cpp:571] Iteration 65480, lr = 0.0001 +I0616 13:06:10.787817 9857 solver.cpp:242] Iteration 65500, loss = 0.656598 +I0616 13:06:10.787858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27782 (* 1 = 0.27782 loss) +I0616 13:06:10.787864 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301825 (* 1 = 0.301825 loss) +I0616 13:06:10.787869 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555404 (* 1 = 0.0555404 loss) +I0616 13:06:10.787873 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00959547 (* 1 = 0.00959547 loss) +I0616 13:06:10.787878 9857 solver.cpp:571] Iteration 65500, lr = 0.0001 +I0616 13:06:22.112252 9857 solver.cpp:242] Iteration 65520, loss = 0.245899 +I0616 13:06:22.112294 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105008 (* 1 = 0.105008 loss) +I0616 13:06:22.112300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100493 (* 1 = 0.100493 loss) +I0616 13:06:22.112304 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00668009 (* 1 = 0.00668009 loss) +I0616 13:06:22.112308 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184183 (* 1 = 0.0184183 loss) +I0616 13:06:22.112313 9857 solver.cpp:571] Iteration 65520, lr = 0.0001 +I0616 13:06:33.899679 9857 solver.cpp:242] Iteration 65540, loss = 0.753572 +I0616 13:06:33.899719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318587 (* 1 = 0.318587 loss) +I0616 13:06:33.899725 9857 solver.cpp:258] Train net output #1: loss_cls = 0.596317 (* 1 = 0.596317 loss) +I0616 13:06:33.899729 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.211914 (* 1 = 0.211914 loss) +I0616 13:06:33.899734 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.103173 (* 1 = 0.103173 loss) +I0616 13:06:33.899736 9857 solver.cpp:571] Iteration 65540, lr = 0.0001 +I0616 13:06:45.263017 9857 solver.cpp:242] Iteration 65560, loss = 0.347166 +I0616 13:06:45.263059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0744648 (* 1 = 0.0744648 loss) +I0616 13:06:45.263065 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162847 (* 1 = 0.162847 loss) +I0616 13:06:45.263069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00841702 (* 1 = 0.00841702 loss) +I0616 13:06:45.263073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00757364 (* 1 = 0.00757364 loss) +I0616 13:06:45.263077 9857 solver.cpp:571] Iteration 65560, lr = 0.0001 +I0616 13:06:56.991799 9857 solver.cpp:242] Iteration 65580, loss = 0.223997 +I0616 13:06:56.991842 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0733744 (* 1 = 0.0733744 loss) +I0616 13:06:56.991847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10284 (* 1 = 0.10284 loss) +I0616 13:06:56.991852 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0522662 (* 1 = 0.0522662 loss) +I0616 13:06:56.991854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014342 (* 1 = 0.014342 loss) +I0616 13:06:56.991858 9857 solver.cpp:571] Iteration 65580, lr = 0.0001 +speed: 0.605s / iter +I0616 13:07:08.199185 9857 solver.cpp:242] Iteration 65600, loss = 0.431849 +I0616 13:07:08.199225 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158183 (* 1 = 0.158183 loss) +I0616 13:07:08.199231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153263 (* 1 = 0.153263 loss) +I0616 13:07:08.199235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00534972 (* 1 = 0.00534972 loss) +I0616 13:07:08.199239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00396316 (* 1 = 0.00396316 loss) +I0616 13:07:08.199242 9857 solver.cpp:571] Iteration 65600, lr = 0.0001 +I0616 13:07:19.732161 9857 solver.cpp:242] Iteration 65620, loss = 0.382812 +I0616 13:07:19.732203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169053 (* 1 = 0.169053 loss) +I0616 13:07:19.732209 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101764 (* 1 = 0.101764 loss) +I0616 13:07:19.732214 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0800596 (* 1 = 0.0800596 loss) +I0616 13:07:19.732218 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125434 (* 1 = 0.0125434 loss) +I0616 13:07:19.732223 9857 solver.cpp:571] Iteration 65620, lr = 0.0001 +I0616 13:07:31.609315 9857 solver.cpp:242] Iteration 65640, loss = 0.586922 +I0616 13:07:31.609359 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145555 (* 1 = 0.145555 loss) +I0616 13:07:31.609364 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175494 (* 1 = 0.175494 loss) +I0616 13:07:31.609369 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0422936 (* 1 = 0.0422936 loss) +I0616 13:07:31.609372 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103691 (* 1 = 0.0103691 loss) +I0616 13:07:31.609375 9857 solver.cpp:571] Iteration 65640, lr = 0.0001 +I0616 13:07:43.028342 9857 solver.cpp:242] Iteration 65660, loss = 0.62483 +I0616 13:07:43.028383 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199062 (* 1 = 0.199062 loss) +I0616 13:07:43.028388 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179966 (* 1 = 0.179966 loss) +I0616 13:07:43.028393 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0584938 (* 1 = 0.0584938 loss) +I0616 13:07:43.028396 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.112768 (* 1 = 0.112768 loss) +I0616 13:07:43.028399 9857 solver.cpp:571] Iteration 65660, lr = 0.0001 +I0616 13:07:54.344739 9857 solver.cpp:242] Iteration 65680, loss = 0.312571 +I0616 13:07:54.344781 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.099647 (* 1 = 0.099647 loss) +I0616 13:07:54.344787 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173934 (* 1 = 0.173934 loss) +I0616 13:07:54.344791 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0310909 (* 1 = 0.0310909 loss) +I0616 13:07:54.344795 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015886 (* 1 = 0.015886 loss) +I0616 13:07:54.344799 9857 solver.cpp:571] Iteration 65680, lr = 0.0001 +I0616 13:08:05.786182 9857 solver.cpp:242] Iteration 65700, loss = 0.696498 +I0616 13:08:05.786226 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106133 (* 1 = 0.106133 loss) +I0616 13:08:05.786231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129921 (* 1 = 0.129921 loss) +I0616 13:08:05.786236 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0116842 (* 1 = 0.0116842 loss) +I0616 13:08:05.786239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00122535 (* 1 = 0.00122535 loss) +I0616 13:08:05.786243 9857 solver.cpp:571] Iteration 65700, lr = 0.0001 +I0616 13:08:16.779274 9857 solver.cpp:242] Iteration 65720, loss = 0.766475 +I0616 13:08:16.779316 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158961 (* 1 = 0.158961 loss) +I0616 13:08:16.779322 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170861 (* 1 = 0.170861 loss) +I0616 13:08:16.779326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162563 (* 1 = 0.162563 loss) +I0616 13:08:16.779330 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0214262 (* 1 = 0.0214262 loss) +I0616 13:08:16.779333 9857 solver.cpp:571] Iteration 65720, lr = 0.0001 +I0616 13:08:28.151669 9857 solver.cpp:242] Iteration 65740, loss = 0.505518 +I0616 13:08:28.151710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117271 (* 1 = 0.117271 loss) +I0616 13:08:28.151716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152831 (* 1 = 0.152831 loss) +I0616 13:08:28.151721 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0106771 (* 1 = 0.0106771 loss) +I0616 13:08:28.151726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144738 (* 1 = 0.0144738 loss) +I0616 13:08:28.151728 9857 solver.cpp:571] Iteration 65740, lr = 0.0001 +I0616 13:08:39.448380 9857 solver.cpp:242] Iteration 65760, loss = 0.950821 +I0616 13:08:39.448422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114058 (* 1 = 0.114058 loss) +I0616 13:08:39.448428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.341597 (* 1 = 0.341597 loss) +I0616 13:08:39.448433 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0871693 (* 1 = 0.0871693 loss) +I0616 13:08:39.448436 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.528525 (* 1 = 0.528525 loss) +I0616 13:08:39.448441 9857 solver.cpp:571] Iteration 65760, lr = 0.0001 +I0616 13:08:51.047449 9857 solver.cpp:242] Iteration 65780, loss = 0.527238 +I0616 13:08:51.047492 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.319254 (* 1 = 0.319254 loss) +I0616 13:08:51.047497 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288789 (* 1 = 0.288789 loss) +I0616 13:08:51.047500 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0385108 (* 1 = 0.0385108 loss) +I0616 13:08:51.047504 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0365846 (* 1 = 0.0365846 loss) +I0616 13:08:51.047508 9857 solver.cpp:571] Iteration 65780, lr = 0.0001 +speed: 0.605s / iter +I0616 13:09:02.652672 9857 solver.cpp:242] Iteration 65800, loss = 0.479301 +I0616 13:09:02.652714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287962 (* 1 = 0.287962 loss) +I0616 13:09:02.652719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163759 (* 1 = 0.163759 loss) +I0616 13:09:02.652724 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0125262 (* 1 = 0.0125262 loss) +I0616 13:09:02.652726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.054766 (* 1 = 0.054766 loss) +I0616 13:09:02.652730 9857 solver.cpp:571] Iteration 65800, lr = 0.0001 +I0616 13:09:14.006155 9857 solver.cpp:242] Iteration 65820, loss = 0.677334 +I0616 13:09:14.006193 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.274495 (* 1 = 0.274495 loss) +I0616 13:09:14.006199 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254935 (* 1 = 0.254935 loss) +I0616 13:09:14.006203 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0830552 (* 1 = 0.0830552 loss) +I0616 13:09:14.006207 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133254 (* 1 = 0.0133254 loss) +I0616 13:09:14.006211 9857 solver.cpp:571] Iteration 65820, lr = 0.0001 +I0616 13:09:25.367673 9857 solver.cpp:242] Iteration 65840, loss = 0.64987 +I0616 13:09:25.367714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341199 (* 1 = 0.341199 loss) +I0616 13:09:25.367720 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314871 (* 1 = 0.314871 loss) +I0616 13:09:25.367724 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0552855 (* 1 = 0.0552855 loss) +I0616 13:09:25.367728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0561139 (* 1 = 0.0561139 loss) +I0616 13:09:25.367733 9857 solver.cpp:571] Iteration 65840, lr = 0.0001 +I0616 13:09:36.820878 9857 solver.cpp:242] Iteration 65860, loss = 0.560695 +I0616 13:09:36.820920 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231449 (* 1 = 0.231449 loss) +I0616 13:09:36.820926 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195622 (* 1 = 0.195622 loss) +I0616 13:09:36.820930 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129612 (* 1 = 0.129612 loss) +I0616 13:09:36.820935 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.061613 (* 1 = 0.061613 loss) +I0616 13:09:36.820938 9857 solver.cpp:571] Iteration 65860, lr = 0.0001 +I0616 13:09:48.581182 9857 solver.cpp:242] Iteration 65880, loss = 0.415473 +I0616 13:09:48.581223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108556 (* 1 = 0.108556 loss) +I0616 13:09:48.581228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199441 (* 1 = 0.199441 loss) +I0616 13:09:48.581233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0390542 (* 1 = 0.0390542 loss) +I0616 13:09:48.581236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181431 (* 1 = 0.0181431 loss) +I0616 13:09:48.581241 9857 solver.cpp:571] Iteration 65880, lr = 0.0001 +I0616 13:10:00.253465 9857 solver.cpp:242] Iteration 65900, loss = 0.305059 +I0616 13:10:00.253506 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0890165 (* 1 = 0.0890165 loss) +I0616 13:10:00.253511 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156959 (* 1 = 0.156959 loss) +I0616 13:10:00.253516 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0663593 (* 1 = 0.0663593 loss) +I0616 13:10:00.253520 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113817 (* 1 = 0.0113817 loss) +I0616 13:10:00.253523 9857 solver.cpp:571] Iteration 65900, lr = 0.0001 +I0616 13:10:11.822257 9857 solver.cpp:242] Iteration 65920, loss = 0.696029 +I0616 13:10:11.822298 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.288118 (* 1 = 0.288118 loss) +I0616 13:10:11.822302 9857 solver.cpp:258] Train net output #1: loss_cls = 0.42885 (* 1 = 0.42885 loss) +I0616 13:10:11.822306 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.131905 (* 1 = 0.131905 loss) +I0616 13:10:11.822310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.235696 (* 1 = 0.235696 loss) +I0616 13:10:11.822314 9857 solver.cpp:571] Iteration 65920, lr = 0.0001 +I0616 13:10:23.275549 9857 solver.cpp:242] Iteration 65940, loss = 0.864389 +I0616 13:10:23.275590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257546 (* 1 = 0.257546 loss) +I0616 13:10:23.275595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.262436 (* 1 = 0.262436 loss) +I0616 13:10:23.275600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.079737 (* 1 = 0.079737 loss) +I0616 13:10:23.275604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0841889 (* 1 = 0.0841889 loss) +I0616 13:10:23.275607 9857 solver.cpp:571] Iteration 65940, lr = 0.0001 +I0616 13:10:34.721474 9857 solver.cpp:242] Iteration 65960, loss = 0.456267 +I0616 13:10:34.721518 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106195 (* 1 = 0.106195 loss) +I0616 13:10:34.721524 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0853576 (* 1 = 0.0853576 loss) +I0616 13:10:34.721527 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0398289 (* 1 = 0.0398289 loss) +I0616 13:10:34.721531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0155885 (* 1 = 0.0155885 loss) +I0616 13:10:34.721535 9857 solver.cpp:571] Iteration 65960, lr = 0.0001 +I0616 13:10:46.115047 9857 solver.cpp:242] Iteration 65980, loss = 0.49611 +I0616 13:10:46.115088 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0529343 (* 1 = 0.0529343 loss) +I0616 13:10:46.115094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0754299 (* 1 = 0.0754299 loss) +I0616 13:10:46.115098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0279862 (* 1 = 0.0279862 loss) +I0616 13:10:46.115103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00549187 (* 1 = 0.00549187 loss) +I0616 13:10:46.115106 9857 solver.cpp:571] Iteration 65980, lr = 0.0001 +speed: 0.604s / iter +I0616 13:10:57.620167 9857 solver.cpp:242] Iteration 66000, loss = 0.770735 +I0616 13:10:57.620208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.390801 (* 1 = 0.390801 loss) +I0616 13:10:57.620214 9857 solver.cpp:258] Train net output #1: loss_cls = 0.488443 (* 1 = 0.488443 loss) +I0616 13:10:57.620218 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.221655 (* 1 = 0.221655 loss) +I0616 13:10:57.620221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.230246 (* 1 = 0.230246 loss) +I0616 13:10:57.620225 9857 solver.cpp:571] Iteration 66000, lr = 0.0001 +I0616 13:11:08.863996 9857 solver.cpp:242] Iteration 66020, loss = 0.99163 +I0616 13:11:08.864037 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253691 (* 1 = 0.253691 loss) +I0616 13:11:08.864042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328794 (* 1 = 0.328794 loss) +I0616 13:11:08.864047 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0976863 (* 1 = 0.0976863 loss) +I0616 13:11:08.864050 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.100073 (* 1 = 0.100073 loss) +I0616 13:11:08.864053 9857 solver.cpp:571] Iteration 66020, lr = 0.0001 +I0616 13:11:20.255620 9857 solver.cpp:242] Iteration 66040, loss = 0.471349 +I0616 13:11:20.255662 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.15725 (* 1 = 0.15725 loss) +I0616 13:11:20.255668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169679 (* 1 = 0.169679 loss) +I0616 13:11:20.255673 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0221234 (* 1 = 0.0221234 loss) +I0616 13:11:20.255676 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276608 (* 1 = 0.0276608 loss) +I0616 13:11:20.255681 9857 solver.cpp:571] Iteration 66040, lr = 0.0001 +I0616 13:11:31.777844 9857 solver.cpp:242] Iteration 66060, loss = 0.419102 +I0616 13:11:31.777885 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180199 (* 1 = 0.180199 loss) +I0616 13:11:31.777891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175865 (* 1 = 0.175865 loss) +I0616 13:11:31.777895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.226965 (* 1 = 0.226965 loss) +I0616 13:11:31.777899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0578271 (* 1 = 0.0578271 loss) +I0616 13:11:31.777904 9857 solver.cpp:571] Iteration 66060, lr = 0.0001 +I0616 13:11:43.194541 9857 solver.cpp:242] Iteration 66080, loss = 0.3848 +I0616 13:11:43.194583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177207 (* 1 = 0.177207 loss) +I0616 13:11:43.194588 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213551 (* 1 = 0.213551 loss) +I0616 13:11:43.194592 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0142556 (* 1 = 0.0142556 loss) +I0616 13:11:43.194597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00912528 (* 1 = 0.00912528 loss) +I0616 13:11:43.194600 9857 solver.cpp:571] Iteration 66080, lr = 0.0001 +I0616 13:11:54.706562 9857 solver.cpp:242] Iteration 66100, loss = 0.305188 +I0616 13:11:54.706604 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0955373 (* 1 = 0.0955373 loss) +I0616 13:11:54.706609 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0850294 (* 1 = 0.0850294 loss) +I0616 13:11:54.706614 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00965614 (* 1 = 0.00965614 loss) +I0616 13:11:54.706617 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201437 (* 1 = 0.0201437 loss) +I0616 13:11:54.706621 9857 solver.cpp:571] Iteration 66100, lr = 0.0001 +I0616 13:12:06.277211 9857 solver.cpp:242] Iteration 66120, loss = 0.413269 +I0616 13:12:06.277252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174886 (* 1 = 0.174886 loss) +I0616 13:12:06.277257 9857 solver.cpp:258] Train net output #1: loss_cls = 0.299221 (* 1 = 0.299221 loss) +I0616 13:12:06.277262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0353171 (* 1 = 0.0353171 loss) +I0616 13:12:06.277266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255063 (* 1 = 0.0255063 loss) +I0616 13:12:06.277271 9857 solver.cpp:571] Iteration 66120, lr = 0.0001 +I0616 13:12:17.599588 9857 solver.cpp:242] Iteration 66140, loss = 0.187723 +I0616 13:12:17.599632 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0521633 (* 1 = 0.0521633 loss) +I0616 13:12:17.599637 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0991128 (* 1 = 0.0991128 loss) +I0616 13:12:17.599642 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00103726 (* 1 = 0.00103726 loss) +I0616 13:12:17.599645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00529011 (* 1 = 0.00529011 loss) +I0616 13:12:17.599648 9857 solver.cpp:571] Iteration 66140, lr = 0.0001 +I0616 13:12:29.121203 9857 solver.cpp:242] Iteration 66160, loss = 0.643123 +I0616 13:12:29.121245 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242735 (* 1 = 0.242735 loss) +I0616 13:12:29.121250 9857 solver.cpp:258] Train net output #1: loss_cls = 0.373962 (* 1 = 0.373962 loss) +I0616 13:12:29.121255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.254655 (* 1 = 0.254655 loss) +I0616 13:12:29.121259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.153886 (* 1 = 0.153886 loss) +I0616 13:12:29.121263 9857 solver.cpp:571] Iteration 66160, lr = 0.0001 +I0616 13:12:40.793642 9857 solver.cpp:242] Iteration 66180, loss = 0.591012 +I0616 13:12:40.793680 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336932 (* 1 = 0.336932 loss) +I0616 13:12:40.793686 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332307 (* 1 = 0.332307 loss) +I0616 13:12:40.793691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.193207 (* 1 = 0.193207 loss) +I0616 13:12:40.793694 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0915828 (* 1 = 0.0915828 loss) +I0616 13:12:40.793699 9857 solver.cpp:571] Iteration 66180, lr = 0.0001 +speed: 0.604s / iter +I0616 13:12:52.419248 9857 solver.cpp:242] Iteration 66200, loss = 0.280477 +I0616 13:12:52.419291 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0858091 (* 1 = 0.0858091 loss) +I0616 13:12:52.419296 9857 solver.cpp:258] Train net output #1: loss_cls = 0.086341 (* 1 = 0.086341 loss) +I0616 13:12:52.419301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0552857 (* 1 = 0.0552857 loss) +I0616 13:12:52.419304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00474466 (* 1 = 0.00474466 loss) +I0616 13:12:52.419308 9857 solver.cpp:571] Iteration 66200, lr = 0.0001 +I0616 13:13:03.916709 9857 solver.cpp:242] Iteration 66220, loss = 0.370685 +I0616 13:13:03.916750 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222846 (* 1 = 0.222846 loss) +I0616 13:13:03.916756 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20257 (* 1 = 0.20257 loss) +I0616 13:13:03.916760 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0408388 (* 1 = 0.0408388 loss) +I0616 13:13:03.916764 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276711 (* 1 = 0.0276711 loss) +I0616 13:13:03.916769 9857 solver.cpp:571] Iteration 66220, lr = 0.0001 +I0616 13:13:15.519913 9857 solver.cpp:242] Iteration 66240, loss = 0.9286 +I0616 13:13:15.519955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.331688 (* 1 = 0.331688 loss) +I0616 13:13:15.519961 9857 solver.cpp:258] Train net output #1: loss_cls = 0.669835 (* 1 = 0.669835 loss) +I0616 13:13:15.519965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.310381 (* 1 = 0.310381 loss) +I0616 13:13:15.519969 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0914986 (* 1 = 0.0914986 loss) +I0616 13:13:15.519973 9857 solver.cpp:571] Iteration 66240, lr = 0.0001 +I0616 13:13:27.117370 9857 solver.cpp:242] Iteration 66260, loss = 0.508365 +I0616 13:13:27.117411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232321 (* 1 = 0.232321 loss) +I0616 13:13:27.117418 9857 solver.cpp:258] Train net output #1: loss_cls = 0.527271 (* 1 = 0.527271 loss) +I0616 13:13:27.117421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0402466 (* 1 = 0.0402466 loss) +I0616 13:13:27.117425 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0458949 (* 1 = 0.0458949 loss) +I0616 13:13:27.117430 9857 solver.cpp:571] Iteration 66260, lr = 0.0001 +I0616 13:13:38.754650 9857 solver.cpp:242] Iteration 66280, loss = 0.383524 +I0616 13:13:38.754693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3103 (* 1 = 0.3103 loss) +I0616 13:13:38.754700 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158113 (* 1 = 0.158113 loss) +I0616 13:13:38.754704 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0234144 (* 1 = 0.0234144 loss) +I0616 13:13:38.754709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0611631 (* 1 = 0.0611631 loss) +I0616 13:13:38.754712 9857 solver.cpp:571] Iteration 66280, lr = 0.0001 +I0616 13:13:50.310670 9857 solver.cpp:242] Iteration 66300, loss = 0.271231 +I0616 13:13:50.310713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0384941 (* 1 = 0.0384941 loss) +I0616 13:13:50.310717 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0653262 (* 1 = 0.0653262 loss) +I0616 13:13:50.310721 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.009941 (* 1 = 0.009941 loss) +I0616 13:13:50.310725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00269072 (* 1 = 0.00269072 loss) +I0616 13:13:50.310729 9857 solver.cpp:571] Iteration 66300, lr = 0.0001 +I0616 13:14:01.876263 9857 solver.cpp:242] Iteration 66320, loss = 0.61991 +I0616 13:14:01.876305 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0873778 (* 1 = 0.0873778 loss) +I0616 13:14:01.876312 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0821023 (* 1 = 0.0821023 loss) +I0616 13:14:01.876315 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00844782 (* 1 = 0.00844782 loss) +I0616 13:14:01.876318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209623 (* 1 = 0.0209623 loss) +I0616 13:14:01.876322 9857 solver.cpp:571] Iteration 66320, lr = 0.0001 +I0616 13:14:13.153659 9857 solver.cpp:242] Iteration 66340, loss = 0.665732 +I0616 13:14:13.153702 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.408198 (* 1 = 0.408198 loss) +I0616 13:14:13.153707 9857 solver.cpp:258] Train net output #1: loss_cls = 0.370413 (* 1 = 0.370413 loss) +I0616 13:14:13.153712 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126659 (* 1 = 0.126659 loss) +I0616 13:14:13.153715 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.139469 (* 1 = 0.139469 loss) +I0616 13:14:13.153719 9857 solver.cpp:571] Iteration 66340, lr = 0.0001 +I0616 13:14:24.796437 9857 solver.cpp:242] Iteration 66360, loss = 0.566395 +I0616 13:14:24.796478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203982 (* 1 = 0.203982 loss) +I0616 13:14:24.796483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315059 (* 1 = 0.315059 loss) +I0616 13:14:24.796489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0709752 (* 1 = 0.0709752 loss) +I0616 13:14:24.796492 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0506274 (* 1 = 0.0506274 loss) +I0616 13:14:24.796495 9857 solver.cpp:571] Iteration 66360, lr = 0.0001 +I0616 13:14:36.344298 9857 solver.cpp:242] Iteration 66380, loss = 0.784311 +I0616 13:14:36.344341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204975 (* 1 = 0.204975 loss) +I0616 13:14:36.344347 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145136 (* 1 = 0.145136 loss) +I0616 13:14:36.344352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0290375 (* 1 = 0.0290375 loss) +I0616 13:14:36.344354 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0249029 (* 1 = 0.0249029 loss) +I0616 13:14:36.344358 9857 solver.cpp:571] Iteration 66380, lr = 0.0001 +speed: 0.604s / iter +I0616 13:14:47.914088 9857 solver.cpp:242] Iteration 66400, loss = 0.413546 +I0616 13:14:47.914130 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0391084 (* 1 = 0.0391084 loss) +I0616 13:14:47.914136 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107997 (* 1 = 0.107997 loss) +I0616 13:14:47.914141 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212464 (* 1 = 0.0212464 loss) +I0616 13:14:47.914144 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00342751 (* 1 = 0.00342751 loss) +I0616 13:14:47.914149 9857 solver.cpp:571] Iteration 66400, lr = 0.0001 +I0616 13:14:59.431216 9857 solver.cpp:242] Iteration 66420, loss = 0.827674 +I0616 13:14:59.431259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354052 (* 1 = 0.354052 loss) +I0616 13:14:59.431264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235699 (* 1 = 0.235699 loss) +I0616 13:14:59.431269 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190716 (* 1 = 0.0190716 loss) +I0616 13:14:59.431272 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0625725 (* 1 = 0.0625725 loss) +I0616 13:14:59.431277 9857 solver.cpp:571] Iteration 66420, lr = 0.0001 +I0616 13:15:10.930574 9857 solver.cpp:242] Iteration 66440, loss = 0.202437 +I0616 13:15:10.930616 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.043536 (* 1 = 0.043536 loss) +I0616 13:15:10.930622 9857 solver.cpp:258] Train net output #1: loss_cls = 0.133818 (* 1 = 0.133818 loss) +I0616 13:15:10.930626 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00595384 (* 1 = 0.00595384 loss) +I0616 13:15:10.930630 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00443631 (* 1 = 0.00443631 loss) +I0616 13:15:10.930634 9857 solver.cpp:571] Iteration 66440, lr = 0.0001 +I0616 13:15:22.210609 9857 solver.cpp:242] Iteration 66460, loss = 0.629703 +I0616 13:15:22.210651 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176353 (* 1 = 0.176353 loss) +I0616 13:15:22.210657 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18047 (* 1 = 0.18047 loss) +I0616 13:15:22.210661 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0968788 (* 1 = 0.0968788 loss) +I0616 13:15:22.210665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0430027 (* 1 = 0.0430027 loss) +I0616 13:15:22.210669 9857 solver.cpp:571] Iteration 66460, lr = 0.0001 +I0616 13:15:33.730594 9857 solver.cpp:242] Iteration 66480, loss = 0.722344 +I0616 13:15:33.730636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.413527 (* 1 = 0.413527 loss) +I0616 13:15:33.730641 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333197 (* 1 = 0.333197 loss) +I0616 13:15:33.730645 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.252389 (* 1 = 0.252389 loss) +I0616 13:15:33.730649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144308 (* 1 = 0.144308 loss) +I0616 13:15:33.730654 9857 solver.cpp:571] Iteration 66480, lr = 0.0001 +I0616 13:15:45.298691 9857 solver.cpp:242] Iteration 66500, loss = 0.664529 +I0616 13:15:45.298734 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189134 (* 1 = 0.189134 loss) +I0616 13:15:45.298739 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147275 (* 1 = 0.147275 loss) +I0616 13:15:45.298743 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00300011 (* 1 = 0.00300011 loss) +I0616 13:15:45.298748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0021835 (* 1 = 0.0021835 loss) +I0616 13:15:45.298751 9857 solver.cpp:571] Iteration 66500, lr = 0.0001 +I0616 13:15:56.772511 9857 solver.cpp:242] Iteration 66520, loss = 0.872015 +I0616 13:15:56.772552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359508 (* 1 = 0.359508 loss) +I0616 13:15:56.772557 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359287 (* 1 = 0.359287 loss) +I0616 13:15:56.772562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11352 (* 1 = 0.11352 loss) +I0616 13:15:56.772565 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0534356 (* 1 = 0.0534356 loss) +I0616 13:15:56.772569 9857 solver.cpp:571] Iteration 66520, lr = 0.0001 +I0616 13:16:08.195335 9857 solver.cpp:242] Iteration 66540, loss = 0.312461 +I0616 13:16:08.195377 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0737835 (* 1 = 0.0737835 loss) +I0616 13:16:08.195382 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0999782 (* 1 = 0.0999782 loss) +I0616 13:16:08.195387 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0409442 (* 1 = 0.0409442 loss) +I0616 13:16:08.195391 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00656968 (* 1 = 0.00656968 loss) +I0616 13:16:08.195394 9857 solver.cpp:571] Iteration 66540, lr = 0.0001 +I0616 13:16:19.863400 9857 solver.cpp:242] Iteration 66560, loss = 0.781875 +I0616 13:16:19.863443 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270398 (* 1 = 0.270398 loss) +I0616 13:16:19.863448 9857 solver.cpp:258] Train net output #1: loss_cls = 0.331811 (* 1 = 0.331811 loss) +I0616 13:16:19.863453 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106606 (* 1 = 0.106606 loss) +I0616 13:16:19.863456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0757418 (* 1 = 0.0757418 loss) +I0616 13:16:19.863461 9857 solver.cpp:571] Iteration 66560, lr = 0.0001 +I0616 13:16:31.176112 9857 solver.cpp:242] Iteration 66580, loss = 0.541773 +I0616 13:16:31.176154 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144446 (* 1 = 0.144446 loss) +I0616 13:16:31.176161 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162571 (* 1 = 0.162571 loss) +I0616 13:16:31.176164 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0276547 (* 1 = 0.0276547 loss) +I0616 13:16:31.176168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0419078 (* 1 = 0.0419078 loss) +I0616 13:16:31.176172 9857 solver.cpp:571] Iteration 66580, lr = 0.0001 +speed: 0.604s / iter +I0616 13:16:42.760187 9857 solver.cpp:242] Iteration 66600, loss = 0.797976 +I0616 13:16:42.760228 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20806 (* 1 = 0.20806 loss) +I0616 13:16:42.760233 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257529 (* 1 = 0.257529 loss) +I0616 13:16:42.760238 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0705912 (* 1 = 0.0705912 loss) +I0616 13:16:42.760241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0603701 (* 1 = 0.0603701 loss) +I0616 13:16:42.760246 9857 solver.cpp:571] Iteration 66600, lr = 0.0001 +I0616 13:16:54.227144 9857 solver.cpp:242] Iteration 66620, loss = 0.632042 +I0616 13:16:54.227186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.092001 (* 1 = 0.092001 loss) +I0616 13:16:54.227191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260255 (* 1 = 0.260255 loss) +I0616 13:16:54.227196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152043 (* 1 = 0.152043 loss) +I0616 13:16:54.227200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.367982 (* 1 = 0.367982 loss) +I0616 13:16:54.227203 9857 solver.cpp:571] Iteration 66620, lr = 0.0001 +I0616 13:17:05.895565 9857 solver.cpp:242] Iteration 66640, loss = 1.54113 +I0616 13:17:05.895606 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261577 (* 1 = 0.261577 loss) +I0616 13:17:05.895612 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289337 (* 1 = 0.289337 loss) +I0616 13:17:05.895617 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.291868 (* 1 = 0.291868 loss) +I0616 13:17:05.895619 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.262062 (* 1 = 0.262062 loss) +I0616 13:17:05.895624 9857 solver.cpp:571] Iteration 66640, lr = 0.0001 +I0616 13:17:17.517395 9857 solver.cpp:242] Iteration 66660, loss = 0.891791 +I0616 13:17:17.517437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373316 (* 1 = 0.373316 loss) +I0616 13:17:17.517442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342454 (* 1 = 0.342454 loss) +I0616 13:17:17.517447 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14395 (* 1 = 0.14395 loss) +I0616 13:17:17.517451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275638 (* 1 = 0.0275638 loss) +I0616 13:17:17.517454 9857 solver.cpp:571] Iteration 66660, lr = 0.0001 +I0616 13:17:28.897534 9857 solver.cpp:242] Iteration 66680, loss = 0.312899 +I0616 13:17:28.897575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0870833 (* 1 = 0.0870833 loss) +I0616 13:17:28.897581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119624 (* 1 = 0.119624 loss) +I0616 13:17:28.897585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0247003 (* 1 = 0.0247003 loss) +I0616 13:17:28.897588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0217525 (* 1 = 0.0217525 loss) +I0616 13:17:28.897593 9857 solver.cpp:571] Iteration 66680, lr = 0.0001 +I0616 13:17:40.331912 9857 solver.cpp:242] Iteration 66700, loss = 0.567133 +I0616 13:17:40.331954 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.208989 (* 1 = 0.208989 loss) +I0616 13:17:40.331959 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251255 (* 1 = 0.251255 loss) +I0616 13:17:40.331964 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121432 (* 1 = 0.121432 loss) +I0616 13:17:40.331967 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0759116 (* 1 = 0.0759116 loss) +I0616 13:17:40.331974 9857 solver.cpp:571] Iteration 66700, lr = 0.0001 +I0616 13:17:52.164070 9857 solver.cpp:242] Iteration 66720, loss = 0.253023 +I0616 13:17:52.164113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0703276 (* 1 = 0.0703276 loss) +I0616 13:17:52.164119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0694185 (* 1 = 0.0694185 loss) +I0616 13:17:52.164124 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00991701 (* 1 = 0.00991701 loss) +I0616 13:17:52.164129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0187278 (* 1 = 0.0187278 loss) +I0616 13:17:52.164131 9857 solver.cpp:571] Iteration 66720, lr = 0.0001 +I0616 13:18:03.587415 9857 solver.cpp:242] Iteration 66740, loss = 0.37492 +I0616 13:18:03.587458 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160935 (* 1 = 0.160935 loss) +I0616 13:18:03.587463 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206124 (* 1 = 0.206124 loss) +I0616 13:18:03.587467 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.01747 (* 1 = 0.01747 loss) +I0616 13:18:03.587471 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158353 (* 1 = 0.0158353 loss) +I0616 13:18:03.587476 9857 solver.cpp:571] Iteration 66740, lr = 0.0001 +I0616 13:18:15.133612 9857 solver.cpp:242] Iteration 66760, loss = 0.25711 +I0616 13:18:15.133652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0741648 (* 1 = 0.0741648 loss) +I0616 13:18:15.133658 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0949473 (* 1 = 0.0949473 loss) +I0616 13:18:15.133662 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00607091 (* 1 = 0.00607091 loss) +I0616 13:18:15.133666 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00915504 (* 1 = 0.00915504 loss) +I0616 13:18:15.133671 9857 solver.cpp:571] Iteration 66760, lr = 0.0001 +I0616 13:18:26.764457 9857 solver.cpp:242] Iteration 66780, loss = 1.1316 +I0616 13:18:26.764500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379746 (* 1 = 0.379746 loss) +I0616 13:18:26.764505 9857 solver.cpp:258] Train net output #1: loss_cls = 0.471914 (* 1 = 0.471914 loss) +I0616 13:18:26.764509 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20083 (* 1 = 0.20083 loss) +I0616 13:18:26.764513 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.372413 (* 1 = 0.372413 loss) +I0616 13:18:26.764518 9857 solver.cpp:571] Iteration 66780, lr = 0.0001 +speed: 0.604s / iter +I0616 13:18:38.178607 9857 solver.cpp:242] Iteration 66800, loss = 0.447145 +I0616 13:18:38.178648 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131952 (* 1 = 0.131952 loss) +I0616 13:18:38.178668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135805 (* 1 = 0.135805 loss) +I0616 13:18:38.178674 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0019089 (* 1 = 0.0019089 loss) +I0616 13:18:38.178678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000734276 (* 1 = 0.000734276 loss) +I0616 13:18:38.178681 9857 solver.cpp:571] Iteration 66800, lr = 0.0001 +I0616 13:18:49.792737 9857 solver.cpp:242] Iteration 66820, loss = 0.326507 +I0616 13:18:49.792779 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183837 (* 1 = 0.183837 loss) +I0616 13:18:49.792785 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174993 (* 1 = 0.174993 loss) +I0616 13:18:49.792789 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0438148 (* 1 = 0.0438148 loss) +I0616 13:18:49.792793 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275897 (* 1 = 0.0275897 loss) +I0616 13:18:49.792798 9857 solver.cpp:571] Iteration 66820, lr = 0.0001 +I0616 13:19:01.553721 9857 solver.cpp:242] Iteration 66840, loss = 0.723671 +I0616 13:19:01.553762 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203903 (* 1 = 0.203903 loss) +I0616 13:19:01.553768 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260602 (* 1 = 0.260602 loss) +I0616 13:19:01.553772 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0332676 (* 1 = 0.0332676 loss) +I0616 13:19:01.553776 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0361463 (* 1 = 0.0361463 loss) +I0616 13:19:01.553781 9857 solver.cpp:571] Iteration 66840, lr = 0.0001 +I0616 13:19:13.243948 9857 solver.cpp:242] Iteration 66860, loss = 0.291099 +I0616 13:19:13.243991 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0788378 (* 1 = 0.0788378 loss) +I0616 13:19:13.243998 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0744665 (* 1 = 0.0744665 loss) +I0616 13:19:13.244001 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0903066 (* 1 = 0.0903066 loss) +I0616 13:19:13.244005 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0151444 (* 1 = 0.0151444 loss) +I0616 13:19:13.244009 9857 solver.cpp:571] Iteration 66860, lr = 0.0001 +I0616 13:19:24.928537 9857 solver.cpp:242] Iteration 66880, loss = 0.813242 +I0616 13:19:24.928577 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224799 (* 1 = 0.224799 loss) +I0616 13:19:24.928583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295455 (* 1 = 0.295455 loss) +I0616 13:19:24.928587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0786768 (* 1 = 0.0786768 loss) +I0616 13:19:24.928591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288563 (* 1 = 0.0288563 loss) +I0616 13:19:24.928596 9857 solver.cpp:571] Iteration 66880, lr = 0.0001 +I0616 13:19:36.667567 9857 solver.cpp:242] Iteration 66900, loss = 0.451192 +I0616 13:19:36.667608 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263353 (* 1 = 0.263353 loss) +I0616 13:19:36.667614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281345 (* 1 = 0.281345 loss) +I0616 13:19:36.667618 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0894426 (* 1 = 0.0894426 loss) +I0616 13:19:36.667623 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00988373 (* 1 = 0.00988373 loss) +I0616 13:19:36.667625 9857 solver.cpp:571] Iteration 66900, lr = 0.0001 +I0616 13:19:48.199192 9857 solver.cpp:242] Iteration 66920, loss = 0.312802 +I0616 13:19:48.199229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0906611 (* 1 = 0.0906611 loss) +I0616 13:19:48.199235 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122534 (* 1 = 0.122534 loss) +I0616 13:19:48.199239 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.030216 (* 1 = 0.030216 loss) +I0616 13:19:48.199242 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00673559 (* 1 = 0.00673559 loss) +I0616 13:19:48.199246 9857 solver.cpp:571] Iteration 66920, lr = 0.0001 +I0616 13:19:59.612288 9857 solver.cpp:242] Iteration 66940, loss = 0.376881 +I0616 13:19:59.612329 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268881 (* 1 = 0.268881 loss) +I0616 13:19:59.612335 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165435 (* 1 = 0.165435 loss) +I0616 13:19:59.612339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00936192 (* 1 = 0.00936192 loss) +I0616 13:19:59.612344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291569 (* 1 = 0.0291569 loss) +I0616 13:19:59.612347 9857 solver.cpp:571] Iteration 66940, lr = 0.0001 +I0616 13:20:11.371382 9857 solver.cpp:242] Iteration 66960, loss = 0.285383 +I0616 13:20:11.371419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.091484 (* 1 = 0.091484 loss) +I0616 13:20:11.371425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229075 (* 1 = 0.229075 loss) +I0616 13:20:11.371429 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0185438 (* 1 = 0.0185438 loss) +I0616 13:20:11.371433 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00692023 (* 1 = 0.00692023 loss) +I0616 13:20:11.371438 9857 solver.cpp:571] Iteration 66960, lr = 0.0001 +I0616 13:20:22.760534 9857 solver.cpp:242] Iteration 66980, loss = 0.442542 +I0616 13:20:22.760576 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.26871 (* 1 = 0.26871 loss) +I0616 13:20:22.760581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274683 (* 1 = 0.274683 loss) +I0616 13:20:22.760584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0591384 (* 1 = 0.0591384 loss) +I0616 13:20:22.760588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.126812 (* 1 = 0.126812 loss) +I0616 13:20:22.760592 9857 solver.cpp:571] Iteration 66980, lr = 0.0001 +speed: 0.604s / iter +I0616 13:20:34.282728 9857 solver.cpp:242] Iteration 67000, loss = 0.606001 +I0616 13:20:34.282775 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183334 (* 1 = 0.183334 loss) +I0616 13:20:34.282781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250386 (* 1 = 0.250386 loss) +I0616 13:20:34.282786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0338238 (* 1 = 0.0338238 loss) +I0616 13:20:34.282789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0300441 (* 1 = 0.0300441 loss) +I0616 13:20:34.282793 9857 solver.cpp:571] Iteration 67000, lr = 0.0001 +I0616 13:20:45.943758 9857 solver.cpp:242] Iteration 67020, loss = 0.242129 +I0616 13:20:45.943799 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0636342 (* 1 = 0.0636342 loss) +I0616 13:20:45.943805 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123627 (* 1 = 0.123627 loss) +I0616 13:20:45.943809 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0997811 (* 1 = 0.0997811 loss) +I0616 13:20:45.943814 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116518 (* 1 = 0.0116518 loss) +I0616 13:20:45.943816 9857 solver.cpp:571] Iteration 67020, lr = 0.0001 +I0616 13:20:57.683256 9857 solver.cpp:242] Iteration 67040, loss = 0.183138 +I0616 13:20:57.683298 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0678603 (* 1 = 0.0678603 loss) +I0616 13:20:57.683303 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104194 (* 1 = 0.104194 loss) +I0616 13:20:57.683307 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0370548 (* 1 = 0.0370548 loss) +I0616 13:20:57.683311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00264158 (* 1 = 0.00264158 loss) +I0616 13:20:57.683316 9857 solver.cpp:571] Iteration 67040, lr = 0.0001 +I0616 13:21:09.121032 9857 solver.cpp:242] Iteration 67060, loss = 0.319319 +I0616 13:21:09.121073 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107656 (* 1 = 0.107656 loss) +I0616 13:21:09.121078 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200212 (* 1 = 0.200212 loss) +I0616 13:21:09.121083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0474888 (* 1 = 0.0474888 loss) +I0616 13:21:09.121085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115493 (* 1 = 0.0115493 loss) +I0616 13:21:09.121089 9857 solver.cpp:571] Iteration 67060, lr = 0.0001 +I0616 13:21:20.592344 9857 solver.cpp:242] Iteration 67080, loss = 0.318953 +I0616 13:21:20.592386 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0819139 (* 1 = 0.0819139 loss) +I0616 13:21:20.592391 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174308 (* 1 = 0.174308 loss) +I0616 13:21:20.592396 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.062584 (* 1 = 0.062584 loss) +I0616 13:21:20.592399 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00354127 (* 1 = 0.00354127 loss) +I0616 13:21:20.592403 9857 solver.cpp:571] Iteration 67080, lr = 0.0001 +I0616 13:21:32.087007 9857 solver.cpp:242] Iteration 67100, loss = 0.442435 +I0616 13:21:32.087049 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195519 (* 1 = 0.195519 loss) +I0616 13:21:32.087054 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248754 (* 1 = 0.248754 loss) +I0616 13:21:32.087059 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162227 (* 1 = 0.162227 loss) +I0616 13:21:32.087062 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0272436 (* 1 = 0.0272436 loss) +I0616 13:21:32.087066 9857 solver.cpp:571] Iteration 67100, lr = 0.0001 +I0616 13:21:43.644876 9857 solver.cpp:242] Iteration 67120, loss = 0.386767 +I0616 13:21:43.644917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0660074 (* 1 = 0.0660074 loss) +I0616 13:21:43.644923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177512 (* 1 = 0.177512 loss) +I0616 13:21:43.644927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0153144 (* 1 = 0.0153144 loss) +I0616 13:21:43.644932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00946836 (* 1 = 0.00946836 loss) +I0616 13:21:43.644935 9857 solver.cpp:571] Iteration 67120, lr = 0.0001 +I0616 13:21:55.351061 9857 solver.cpp:242] Iteration 67140, loss = 1.13521 +I0616 13:21:55.351102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3989 (* 1 = 0.3989 loss) +I0616 13:21:55.351109 9857 solver.cpp:258] Train net output #1: loss_cls = 0.918302 (* 1 = 0.918302 loss) +I0616 13:21:55.351112 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155275 (* 1 = 0.155275 loss) +I0616 13:21:55.351116 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0339465 (* 1 = 0.0339465 loss) +I0616 13:21:55.351120 9857 solver.cpp:571] Iteration 67140, lr = 0.0001 +I0616 13:22:06.746924 9857 solver.cpp:242] Iteration 67160, loss = 0.389916 +I0616 13:22:06.746968 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0868482 (* 1 = 0.0868482 loss) +I0616 13:22:06.746973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130462 (* 1 = 0.130462 loss) +I0616 13:22:06.746978 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0218453 (* 1 = 0.0218453 loss) +I0616 13:22:06.746981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0044265 (* 1 = 0.0044265 loss) +I0616 13:22:06.746984 9857 solver.cpp:571] Iteration 67160, lr = 0.0001 +I0616 13:22:18.157233 9857 solver.cpp:242] Iteration 67180, loss = 0.557529 +I0616 13:22:18.157275 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185921 (* 1 = 0.185921 loss) +I0616 13:22:18.157280 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246794 (* 1 = 0.246794 loss) +I0616 13:22:18.157285 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.081933 (* 1 = 0.081933 loss) +I0616 13:22:18.157289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.026704 (* 1 = 0.026704 loss) +I0616 13:22:18.157292 9857 solver.cpp:571] Iteration 67180, lr = 0.0001 +speed: 0.604s / iter +I0616 13:22:29.798977 9857 solver.cpp:242] Iteration 67200, loss = 0.508086 +I0616 13:22:29.799021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0387921 (* 1 = 0.0387921 loss) +I0616 13:22:29.799026 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0775431 (* 1 = 0.0775431 loss) +I0616 13:22:29.799029 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0309541 (* 1 = 0.0309541 loss) +I0616 13:22:29.799033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00799707 (* 1 = 0.00799707 loss) +I0616 13:22:29.799039 9857 solver.cpp:571] Iteration 67200, lr = 0.0001 +I0616 13:22:41.638381 9857 solver.cpp:242] Iteration 67220, loss = 0.624623 +I0616 13:22:41.638420 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0739781 (* 1 = 0.0739781 loss) +I0616 13:22:41.638427 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0921908 (* 1 = 0.0921908 loss) +I0616 13:22:41.638430 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0568636 (* 1 = 0.0568636 loss) +I0616 13:22:41.638434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00744235 (* 1 = 0.00744235 loss) +I0616 13:22:41.638437 9857 solver.cpp:571] Iteration 67220, lr = 0.0001 +I0616 13:22:53.198468 9857 solver.cpp:242] Iteration 67240, loss = 0.666453 +I0616 13:22:53.198508 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.08885 (* 1 = 0.08885 loss) +I0616 13:22:53.198516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157936 (* 1 = 0.157936 loss) +I0616 13:22:53.198523 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0239082 (* 1 = 0.0239082 loss) +I0616 13:22:53.198528 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0374564 (* 1 = 0.0374564 loss) +I0616 13:22:53.198534 9857 solver.cpp:571] Iteration 67240, lr = 0.0001 +I0616 13:23:04.809085 9857 solver.cpp:242] Iteration 67260, loss = 0.402867 +I0616 13:23:04.809128 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172288 (* 1 = 0.172288 loss) +I0616 13:23:04.809134 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167736 (* 1 = 0.167736 loss) +I0616 13:23:04.809139 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0330584 (* 1 = 0.0330584 loss) +I0616 13:23:04.809141 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0567903 (* 1 = 0.0567903 loss) +I0616 13:23:04.809145 9857 solver.cpp:571] Iteration 67260, lr = 0.0001 +I0616 13:23:16.434208 9857 solver.cpp:242] Iteration 67280, loss = 0.333724 +I0616 13:23:16.434252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0613191 (* 1 = 0.0613191 loss) +I0616 13:23:16.434258 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143679 (* 1 = 0.143679 loss) +I0616 13:23:16.434262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0533535 (* 1 = 0.0533535 loss) +I0616 13:23:16.434265 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175614 (* 1 = 0.0175614 loss) +I0616 13:23:16.434269 9857 solver.cpp:571] Iteration 67280, lr = 0.0001 +I0616 13:23:27.731071 9857 solver.cpp:242] Iteration 67300, loss = 0.685424 +I0616 13:23:27.731111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.45271 (* 1 = 0.45271 loss) +I0616 13:23:27.731115 9857 solver.cpp:258] Train net output #1: loss_cls = 0.500026 (* 1 = 0.500026 loss) +I0616 13:23:27.731120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.099419 (* 1 = 0.099419 loss) +I0616 13:23:27.731124 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0375711 (* 1 = 0.0375711 loss) +I0616 13:23:27.731128 9857 solver.cpp:571] Iteration 67300, lr = 0.0001 +I0616 13:23:39.312899 9857 solver.cpp:242] Iteration 67320, loss = 0.823422 +I0616 13:23:39.312942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284839 (* 1 = 0.284839 loss) +I0616 13:23:39.312947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4574 (* 1 = 0.4574 loss) +I0616 13:23:39.312952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188801 (* 1 = 0.188801 loss) +I0616 13:23:39.312955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.241317 (* 1 = 0.241317 loss) +I0616 13:23:39.312959 9857 solver.cpp:571] Iteration 67320, lr = 0.0001 +I0616 13:23:50.771914 9857 solver.cpp:242] Iteration 67340, loss = 0.253984 +I0616 13:23:50.771955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0952767 (* 1 = 0.0952767 loss) +I0616 13:23:50.771961 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145385 (* 1 = 0.145385 loss) +I0616 13:23:50.771965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0181384 (* 1 = 0.0181384 loss) +I0616 13:23:50.771970 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00770572 (* 1 = 0.00770572 loss) +I0616 13:23:50.771973 9857 solver.cpp:571] Iteration 67340, lr = 0.0001 +I0616 13:24:02.433373 9857 solver.cpp:242] Iteration 67360, loss = 0.212806 +I0616 13:24:02.433416 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127027 (* 1 = 0.127027 loss) +I0616 13:24:02.433423 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0797418 (* 1 = 0.0797418 loss) +I0616 13:24:02.433426 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0116647 (* 1 = 0.0116647 loss) +I0616 13:24:02.433430 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0217051 (* 1 = 0.0217051 loss) +I0616 13:24:02.433434 9857 solver.cpp:571] Iteration 67360, lr = 0.0001 +I0616 13:24:14.302325 9857 solver.cpp:242] Iteration 67380, loss = 0.439826 +I0616 13:24:14.302367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0494533 (* 1 = 0.0494533 loss) +I0616 13:24:14.302373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119077 (* 1 = 0.119077 loss) +I0616 13:24:14.302377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0376978 (* 1 = 0.0376978 loss) +I0616 13:24:14.302381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00871095 (* 1 = 0.00871095 loss) +I0616 13:24:14.302386 9857 solver.cpp:571] Iteration 67380, lr = 0.0001 +speed: 0.604s / iter +I0616 13:24:25.732715 9857 solver.cpp:242] Iteration 67400, loss = 0.212002 +I0616 13:24:25.732758 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0750097 (* 1 = 0.0750097 loss) +I0616 13:24:25.732763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120324 (* 1 = 0.120324 loss) +I0616 13:24:25.732766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0409086 (* 1 = 0.0409086 loss) +I0616 13:24:25.732770 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255203 (* 1 = 0.0255203 loss) +I0616 13:24:25.732774 9857 solver.cpp:571] Iteration 67400, lr = 0.0001 +I0616 13:24:37.255946 9857 solver.cpp:242] Iteration 67420, loss = 0.607843 +I0616 13:24:37.255987 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200306 (* 1 = 0.200306 loss) +I0616 13:24:37.255992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.308162 (* 1 = 0.308162 loss) +I0616 13:24:37.255996 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224975 (* 1 = 0.224975 loss) +I0616 13:24:37.256000 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.188545 (* 1 = 0.188545 loss) +I0616 13:24:37.256005 9857 solver.cpp:571] Iteration 67420, lr = 0.0001 +I0616 13:24:48.749828 9857 solver.cpp:242] Iteration 67440, loss = 0.516139 +I0616 13:24:48.749871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310571 (* 1 = 0.310571 loss) +I0616 13:24:48.749876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246881 (* 1 = 0.246881 loss) +I0616 13:24:48.749879 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0917489 (* 1 = 0.0917489 loss) +I0616 13:24:48.749883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0873409 (* 1 = 0.0873409 loss) +I0616 13:24:48.749886 9857 solver.cpp:571] Iteration 67440, lr = 0.0001 +I0616 13:24:59.825503 9857 solver.cpp:242] Iteration 67460, loss = 0.448964 +I0616 13:24:59.825546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0915682 (* 1 = 0.0915682 loss) +I0616 13:24:59.825552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0869027 (* 1 = 0.0869027 loss) +I0616 13:24:59.825556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0321214 (* 1 = 0.0321214 loss) +I0616 13:24:59.825561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158398 (* 1 = 0.0158398 loss) +I0616 13:24:59.825564 9857 solver.cpp:571] Iteration 67460, lr = 0.0001 +I0616 13:25:11.241207 9857 solver.cpp:242] Iteration 67480, loss = 0.204195 +I0616 13:25:11.241250 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.050737 (* 1 = 0.050737 loss) +I0616 13:25:11.241256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0628471 (* 1 = 0.0628471 loss) +I0616 13:25:11.241261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0156517 (* 1 = 0.0156517 loss) +I0616 13:25:11.241264 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0055974 (* 1 = 0.0055974 loss) +I0616 13:25:11.241267 9857 solver.cpp:571] Iteration 67480, lr = 0.0001 +I0616 13:25:22.869216 9857 solver.cpp:242] Iteration 67500, loss = 0.893964 +I0616 13:25:22.869257 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228077 (* 1 = 0.228077 loss) +I0616 13:25:22.869263 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388747 (* 1 = 0.388747 loss) +I0616 13:25:22.869267 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246375 (* 1 = 0.246375 loss) +I0616 13:25:22.869271 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.29677 (* 1 = 0.29677 loss) +I0616 13:25:22.869276 9857 solver.cpp:571] Iteration 67500, lr = 0.0001 +I0616 13:25:34.334404 9857 solver.cpp:242] Iteration 67520, loss = 0.651417 +I0616 13:25:34.334446 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.219714 (* 1 = 0.219714 loss) +I0616 13:25:34.334451 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203187 (* 1 = 0.203187 loss) +I0616 13:25:34.334455 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0678387 (* 1 = 0.0678387 loss) +I0616 13:25:34.334460 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0171913 (* 1 = 0.0171913 loss) +I0616 13:25:34.334463 9857 solver.cpp:571] Iteration 67520, lr = 0.0001 +I0616 13:25:45.739816 9857 solver.cpp:242] Iteration 67540, loss = 0.175904 +I0616 13:25:45.739857 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0854975 (* 1 = 0.0854975 loss) +I0616 13:25:45.739863 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0831853 (* 1 = 0.0831853 loss) +I0616 13:25:45.739867 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0378325 (* 1 = 0.0378325 loss) +I0616 13:25:45.739871 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00674864 (* 1 = 0.00674864 loss) +I0616 13:25:45.739874 9857 solver.cpp:571] Iteration 67540, lr = 0.0001 +I0616 13:25:57.053061 9857 solver.cpp:242] Iteration 67560, loss = 0.651221 +I0616 13:25:57.053102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.09731 (* 1 = 0.09731 loss) +I0616 13:25:57.053108 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134257 (* 1 = 0.134257 loss) +I0616 13:25:57.053112 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0087204 (* 1 = 0.0087204 loss) +I0616 13:25:57.053115 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013695 (* 1 = 0.013695 loss) +I0616 13:25:57.053119 9857 solver.cpp:571] Iteration 67560, lr = 0.0001 +I0616 13:26:08.465029 9857 solver.cpp:242] Iteration 67580, loss = 0.353993 +I0616 13:26:08.465068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152624 (* 1 = 0.152624 loss) +I0616 13:26:08.465075 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147007 (* 1 = 0.147007 loss) +I0616 13:26:08.465078 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555051 (* 1 = 0.0555051 loss) +I0616 13:26:08.465082 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0478004 (* 1 = 0.0478004 loss) +I0616 13:26:08.465085 9857 solver.cpp:571] Iteration 67580, lr = 0.0001 +speed: 0.604s / iter +I0616 13:26:20.002724 9857 solver.cpp:242] Iteration 67600, loss = 0.645014 +I0616 13:26:20.002774 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259787 (* 1 = 0.259787 loss) +I0616 13:26:20.002781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266445 (* 1 = 0.266445 loss) +I0616 13:26:20.002785 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.216954 (* 1 = 0.216954 loss) +I0616 13:26:20.002789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.037941 (* 1 = 0.037941 loss) +I0616 13:26:20.002794 9857 solver.cpp:571] Iteration 67600, lr = 0.0001 +I0616 13:26:31.798899 9857 solver.cpp:242] Iteration 67620, loss = 0.647431 +I0616 13:26:31.798941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0643433 (* 1 = 0.0643433 loss) +I0616 13:26:31.798946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180896 (* 1 = 0.180896 loss) +I0616 13:26:31.798951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0319638 (* 1 = 0.0319638 loss) +I0616 13:26:31.798954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00939982 (* 1 = 0.00939982 loss) +I0616 13:26:31.798959 9857 solver.cpp:571] Iteration 67620, lr = 0.0001 +I0616 13:26:43.323081 9857 solver.cpp:242] Iteration 67640, loss = 0.597172 +I0616 13:26:43.323123 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306817 (* 1 = 0.306817 loss) +I0616 13:26:43.323129 9857 solver.cpp:258] Train net output #1: loss_cls = 0.373963 (* 1 = 0.373963 loss) +I0616 13:26:43.323133 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19521 (* 1 = 0.19521 loss) +I0616 13:26:43.323137 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129107 (* 1 = 0.129107 loss) +I0616 13:26:43.323140 9857 solver.cpp:571] Iteration 67640, lr = 0.0001 +I0616 13:26:54.995226 9857 solver.cpp:242] Iteration 67660, loss = 0.394177 +I0616 13:26:54.995267 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210587 (* 1 = 0.210587 loss) +I0616 13:26:54.995273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191592 (* 1 = 0.191592 loss) +I0616 13:26:54.995277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230382 (* 1 = 0.0230382 loss) +I0616 13:26:54.995281 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273118 (* 1 = 0.0273118 loss) +I0616 13:26:54.995285 9857 solver.cpp:571] Iteration 67660, lr = 0.0001 +I0616 13:27:06.505998 9857 solver.cpp:242] Iteration 67680, loss = 0.60112 +I0616 13:27:06.506041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205541 (* 1 = 0.205541 loss) +I0616 13:27:06.506047 9857 solver.cpp:258] Train net output #1: loss_cls = 0.285327 (* 1 = 0.285327 loss) +I0616 13:27:06.506052 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0874978 (* 1 = 0.0874978 loss) +I0616 13:27:06.506054 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337012 (* 1 = 0.0337012 loss) +I0616 13:27:06.506058 9857 solver.cpp:571] Iteration 67680, lr = 0.0001 +I0616 13:27:17.890530 9857 solver.cpp:242] Iteration 67700, loss = 1.13025 +I0616 13:27:17.890573 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.41417 (* 1 = 0.41417 loss) +I0616 13:27:17.890579 9857 solver.cpp:258] Train net output #1: loss_cls = 0.521398 (* 1 = 0.521398 loss) +I0616 13:27:17.890583 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172737 (* 1 = 0.172737 loss) +I0616 13:27:17.890588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.140324 (* 1 = 0.140324 loss) +I0616 13:27:17.890591 9857 solver.cpp:571] Iteration 67700, lr = 0.0001 +I0616 13:27:29.329241 9857 solver.cpp:242] Iteration 67720, loss = 0.613805 +I0616 13:27:29.329282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0842903 (* 1 = 0.0842903 loss) +I0616 13:27:29.329288 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0873454 (* 1 = 0.0873454 loss) +I0616 13:27:29.329291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0447093 (* 1 = 0.0447093 loss) +I0616 13:27:29.329295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0206338 (* 1 = 0.0206338 loss) +I0616 13:27:29.329299 9857 solver.cpp:571] Iteration 67720, lr = 0.0001 +I0616 13:27:40.757655 9857 solver.cpp:242] Iteration 67740, loss = 0.359495 +I0616 13:27:40.757696 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183408 (* 1 = 0.183408 loss) +I0616 13:27:40.757702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17261 (* 1 = 0.17261 loss) +I0616 13:27:40.757706 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0680357 (* 1 = 0.0680357 loss) +I0616 13:27:40.757710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0424963 (* 1 = 0.0424963 loss) +I0616 13:27:40.757714 9857 solver.cpp:571] Iteration 67740, lr = 0.0001 +I0616 13:27:52.186796 9857 solver.cpp:242] Iteration 67760, loss = 0.222841 +I0616 13:27:52.186838 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109561 (* 1 = 0.109561 loss) +I0616 13:27:52.186844 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116881 (* 1 = 0.116881 loss) +I0616 13:27:52.186848 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228293 (* 1 = 0.0228293 loss) +I0616 13:27:52.186852 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00827145 (* 1 = 0.00827145 loss) +I0616 13:27:52.186856 9857 solver.cpp:571] Iteration 67760, lr = 0.0001 +I0616 13:28:03.578058 9857 solver.cpp:242] Iteration 67780, loss = 0.222614 +I0616 13:28:03.578102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.078232 (* 1 = 0.078232 loss) +I0616 13:28:03.578107 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0484388 (* 1 = 0.0484388 loss) +I0616 13:28:03.578111 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0175587 (* 1 = 0.0175587 loss) +I0616 13:28:03.578115 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015062 (* 1 = 0.015062 loss) +I0616 13:28:03.578119 9857 solver.cpp:571] Iteration 67780, lr = 0.0001 +speed: 0.604s / iter +I0616 13:28:15.007565 9857 solver.cpp:242] Iteration 67800, loss = 0.78082 +I0616 13:28:15.007604 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329367 (* 1 = 0.329367 loss) +I0616 13:28:15.007611 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302222 (* 1 = 0.302222 loss) +I0616 13:28:15.007614 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0790621 (* 1 = 0.0790621 loss) +I0616 13:28:15.007618 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0187929 (* 1 = 0.0187929 loss) +I0616 13:28:15.007622 9857 solver.cpp:571] Iteration 67800, lr = 0.0001 +I0616 13:28:26.443866 9857 solver.cpp:242] Iteration 67820, loss = 0.869884 +I0616 13:28:26.443907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0504957 (* 1 = 0.0504957 loss) +I0616 13:28:26.443913 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148286 (* 1 = 0.148286 loss) +I0616 13:28:26.443917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0328894 (* 1 = 0.0328894 loss) +I0616 13:28:26.443922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0733009 (* 1 = 0.0733009 loss) +I0616 13:28:26.443925 9857 solver.cpp:571] Iteration 67820, lr = 0.0001 +I0616 13:28:37.873710 9857 solver.cpp:242] Iteration 67840, loss = 0.243932 +I0616 13:28:37.873752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0605989 (* 1 = 0.0605989 loss) +I0616 13:28:37.873757 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10757 (* 1 = 0.10757 loss) +I0616 13:28:37.873762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116725 (* 1 = 0.116725 loss) +I0616 13:28:37.873766 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00368151 (* 1 = 0.00368151 loss) +I0616 13:28:37.873769 9857 solver.cpp:571] Iteration 67840, lr = 0.0001 +I0616 13:28:49.607538 9857 solver.cpp:242] Iteration 67860, loss = 0.943391 +I0616 13:28:49.607580 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.509417 (* 1 = 0.509417 loss) +I0616 13:28:49.607585 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300977 (* 1 = 0.300977 loss) +I0616 13:28:49.607589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155455 (* 1 = 0.155455 loss) +I0616 13:28:49.607594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00291332 (* 1 = 0.00291332 loss) +I0616 13:28:49.607597 9857 solver.cpp:571] Iteration 67860, lr = 0.0001 +I0616 13:29:00.849272 9857 solver.cpp:242] Iteration 67880, loss = 0.79928 +I0616 13:29:00.849313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281492 (* 1 = 0.281492 loss) +I0616 13:29:00.849319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345393 (* 1 = 0.345393 loss) +I0616 13:29:00.849323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102119 (* 1 = 0.102119 loss) +I0616 13:29:00.849328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276608 (* 1 = 0.0276608 loss) +I0616 13:29:00.849331 9857 solver.cpp:571] Iteration 67880, lr = 0.0001 +I0616 13:29:12.307332 9857 solver.cpp:242] Iteration 67900, loss = 0.351277 +I0616 13:29:12.307375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239872 (* 1 = 0.239872 loss) +I0616 13:29:12.307380 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163414 (* 1 = 0.163414 loss) +I0616 13:29:12.307385 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0492974 (* 1 = 0.0492974 loss) +I0616 13:29:12.307389 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013509 (* 1 = 0.013509 loss) +I0616 13:29:12.307392 9857 solver.cpp:571] Iteration 67900, lr = 0.0001 +I0616 13:29:23.946441 9857 solver.cpp:242] Iteration 67920, loss = 0.505774 +I0616 13:29:23.946483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0832921 (* 1 = 0.0832921 loss) +I0616 13:29:23.946490 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0920313 (* 1 = 0.0920313 loss) +I0616 13:29:23.946494 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00202189 (* 1 = 0.00202189 loss) +I0616 13:29:23.946498 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00171087 (* 1 = 0.00171087 loss) +I0616 13:29:23.946501 9857 solver.cpp:571] Iteration 67920, lr = 0.0001 +I0616 13:29:35.407519 9857 solver.cpp:242] Iteration 67940, loss = 0.858091 +I0616 13:29:35.407559 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158446 (* 1 = 0.158446 loss) +I0616 13:29:35.407564 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171185 (* 1 = 0.171185 loss) +I0616 13:29:35.407568 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0650351 (* 1 = 0.0650351 loss) +I0616 13:29:35.407572 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015905 (* 1 = 0.015905 loss) +I0616 13:29:35.407577 9857 solver.cpp:571] Iteration 67940, lr = 0.0001 +I0616 13:29:47.044445 9857 solver.cpp:242] Iteration 67960, loss = 0.199659 +I0616 13:29:47.044487 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0729998 (* 1 = 0.0729998 loss) +I0616 13:29:47.044492 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0927113 (* 1 = 0.0927113 loss) +I0616 13:29:47.044497 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0039218 (* 1 = 0.0039218 loss) +I0616 13:29:47.044502 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0261092 (* 1 = 0.0261092 loss) +I0616 13:29:47.044504 9857 solver.cpp:571] Iteration 67960, lr = 0.0001 +I0616 13:29:58.344262 9857 solver.cpp:242] Iteration 67980, loss = 0.511903 +I0616 13:29:58.344305 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192324 (* 1 = 0.192324 loss) +I0616 13:29:58.344310 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232726 (* 1 = 0.232726 loss) +I0616 13:29:58.344315 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0699359 (* 1 = 0.0699359 loss) +I0616 13:29:58.344318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273905 (* 1 = 0.0273905 loss) +I0616 13:29:58.344321 9857 solver.cpp:571] Iteration 67980, lr = 0.0001 +speed: 0.604s / iter +I0616 13:30:09.608321 9857 solver.cpp:242] Iteration 68000, loss = 0.368362 +I0616 13:30:09.608362 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237896 (* 1 = 0.237896 loss) +I0616 13:30:09.608368 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265588 (* 1 = 0.265588 loss) +I0616 13:30:09.608372 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0318996 (* 1 = 0.0318996 loss) +I0616 13:30:09.608376 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0393634 (* 1 = 0.0393634 loss) +I0616 13:30:09.608379 9857 solver.cpp:571] Iteration 68000, lr = 0.0001 +I0616 13:30:21.223145 9857 solver.cpp:242] Iteration 68020, loss = 0.396715 +I0616 13:30:21.223186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291359 (* 1 = 0.291359 loss) +I0616 13:30:21.223191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210862 (* 1 = 0.210862 loss) +I0616 13:30:21.223196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.131101 (* 1 = 0.131101 loss) +I0616 13:30:21.223199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0188107 (* 1 = 0.0188107 loss) +I0616 13:30:21.223203 9857 solver.cpp:571] Iteration 68020, lr = 0.0001 +I0616 13:30:32.698796 9857 solver.cpp:242] Iteration 68040, loss = 0.337659 +I0616 13:30:32.698837 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0573591 (* 1 = 0.0573591 loss) +I0616 13:30:32.698843 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111379 (* 1 = 0.111379 loss) +I0616 13:30:32.698848 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190979 (* 1 = 0.0190979 loss) +I0616 13:30:32.698851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201995 (* 1 = 0.0201995 loss) +I0616 13:30:32.698855 9857 solver.cpp:571] Iteration 68040, lr = 0.0001 +I0616 13:30:44.312224 9857 solver.cpp:242] Iteration 68060, loss = 0.696542 +I0616 13:30:44.312266 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238065 (* 1 = 0.238065 loss) +I0616 13:30:44.312273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.356547 (* 1 = 0.356547 loss) +I0616 13:30:44.312276 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0811551 (* 1 = 0.0811551 loss) +I0616 13:30:44.312280 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0926116 (* 1 = 0.0926116 loss) +I0616 13:30:44.312283 9857 solver.cpp:571] Iteration 68060, lr = 0.0001 +I0616 13:30:55.917712 9857 solver.cpp:242] Iteration 68080, loss = 0.758824 +I0616 13:30:55.917755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345359 (* 1 = 0.345359 loss) +I0616 13:30:55.917762 9857 solver.cpp:258] Train net output #1: loss_cls = 0.460906 (* 1 = 0.460906 loss) +I0616 13:30:55.917765 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184694 (* 1 = 0.184694 loss) +I0616 13:30:55.917768 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.148434 (* 1 = 0.148434 loss) +I0616 13:30:55.917773 9857 solver.cpp:571] Iteration 68080, lr = 0.0001 +I0616 13:31:07.509474 9857 solver.cpp:242] Iteration 68100, loss = 0.755037 +I0616 13:31:07.509517 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.442139 (* 1 = 0.442139 loss) +I0616 13:31:07.509523 9857 solver.cpp:258] Train net output #1: loss_cls = 0.484152 (* 1 = 0.484152 loss) +I0616 13:31:07.509527 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.187208 (* 1 = 0.187208 loss) +I0616 13:31:07.509531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0559461 (* 1 = 0.0559461 loss) +I0616 13:31:07.509534 9857 solver.cpp:571] Iteration 68100, lr = 0.0001 +I0616 13:31:19.080797 9857 solver.cpp:242] Iteration 68120, loss = 0.575273 +I0616 13:31:19.080840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303909 (* 1 = 0.303909 loss) +I0616 13:31:19.080845 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252527 (* 1 = 0.252527 loss) +I0616 13:31:19.080849 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0189565 (* 1 = 0.0189565 loss) +I0616 13:31:19.080853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00473586 (* 1 = 0.00473586 loss) +I0616 13:31:19.080857 9857 solver.cpp:571] Iteration 68120, lr = 0.0001 +I0616 13:31:30.606236 9857 solver.cpp:242] Iteration 68140, loss = 0.891911 +I0616 13:31:30.606278 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0524236 (* 1 = 0.0524236 loss) +I0616 13:31:30.606284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0917598 (* 1 = 0.0917598 loss) +I0616 13:31:30.606288 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0227996 (* 1 = 0.0227996 loss) +I0616 13:31:30.606292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247854 (* 1 = 0.0247854 loss) +I0616 13:31:30.606297 9857 solver.cpp:571] Iteration 68140, lr = 0.0001 +I0616 13:31:42.116607 9857 solver.cpp:242] Iteration 68160, loss = 0.608739 +I0616 13:31:42.116649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.454332 (* 1 = 0.454332 loss) +I0616 13:31:42.116655 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358247 (* 1 = 0.358247 loss) +I0616 13:31:42.116659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010157 (* 1 = 0.010157 loss) +I0616 13:31:42.116663 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.10001 (* 1 = 0.10001 loss) +I0616 13:31:42.116667 9857 solver.cpp:571] Iteration 68160, lr = 0.0001 +I0616 13:31:53.459373 9857 solver.cpp:242] Iteration 68180, loss = 0.308901 +I0616 13:31:53.459413 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0801981 (* 1 = 0.0801981 loss) +I0616 13:31:53.459419 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165789 (* 1 = 0.165789 loss) +I0616 13:31:53.459422 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0118858 (* 1 = 0.0118858 loss) +I0616 13:31:53.459426 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159229 (* 1 = 0.0159229 loss) +I0616 13:31:53.459429 9857 solver.cpp:571] Iteration 68180, lr = 0.0001 +speed: 0.604s / iter +I0616 13:32:04.877413 9857 solver.cpp:242] Iteration 68200, loss = 0.183569 +I0616 13:32:04.877454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0360915 (* 1 = 0.0360915 loss) +I0616 13:32:04.877460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0595509 (* 1 = 0.0595509 loss) +I0616 13:32:04.877465 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0177001 (* 1 = 0.0177001 loss) +I0616 13:32:04.877467 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00530607 (* 1 = 0.00530607 loss) +I0616 13:32:04.877471 9857 solver.cpp:571] Iteration 68200, lr = 0.0001 +I0616 13:32:16.321626 9857 solver.cpp:242] Iteration 68220, loss = 0.538337 +I0616 13:32:16.321668 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247234 (* 1 = 0.247234 loss) +I0616 13:32:16.321674 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21874 (* 1 = 0.21874 loss) +I0616 13:32:16.321678 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.148683 (* 1 = 0.148683 loss) +I0616 13:32:16.321682 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0285788 (* 1 = 0.0285788 loss) +I0616 13:32:16.321686 9857 solver.cpp:571] Iteration 68220, lr = 0.0001 +I0616 13:32:27.970079 9857 solver.cpp:242] Iteration 68240, loss = 0.847014 +I0616 13:32:27.970120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254968 (* 1 = 0.254968 loss) +I0616 13:32:27.970127 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236933 (* 1 = 0.236933 loss) +I0616 13:32:27.970131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0471393 (* 1 = 0.0471393 loss) +I0616 13:32:27.970135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159114 (* 1 = 0.159114 loss) +I0616 13:32:27.970139 9857 solver.cpp:571] Iteration 68240, lr = 0.0001 +I0616 13:32:39.568608 9857 solver.cpp:242] Iteration 68260, loss = 0.580201 +I0616 13:32:39.568665 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279113 (* 1 = 0.279113 loss) +I0616 13:32:39.568670 9857 solver.cpp:258] Train net output #1: loss_cls = 0.277178 (* 1 = 0.277178 loss) +I0616 13:32:39.568675 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122007 (* 1 = 0.122007 loss) +I0616 13:32:39.568677 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189819 (* 1 = 0.0189819 loss) +I0616 13:32:39.568681 9857 solver.cpp:571] Iteration 68260, lr = 0.0001 +I0616 13:32:51.109714 9857 solver.cpp:242] Iteration 68280, loss = 0.631134 +I0616 13:32:51.109755 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270995 (* 1 = 0.270995 loss) +I0616 13:32:51.109760 9857 solver.cpp:258] Train net output #1: loss_cls = 0.389684 (* 1 = 0.389684 loss) +I0616 13:32:51.109766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0940763 (* 1 = 0.0940763 loss) +I0616 13:32:51.109769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0670092 (* 1 = 0.0670092 loss) +I0616 13:32:51.109773 9857 solver.cpp:571] Iteration 68280, lr = 0.0001 +I0616 13:33:02.722645 9857 solver.cpp:242] Iteration 68300, loss = 0.767307 +I0616 13:33:02.722687 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218453 (* 1 = 0.218453 loss) +I0616 13:33:02.722693 9857 solver.cpp:258] Train net output #1: loss_cls = 0.33592 (* 1 = 0.33592 loss) +I0616 13:33:02.722697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0575541 (* 1 = 0.0575541 loss) +I0616 13:33:02.722702 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0450361 (* 1 = 0.0450361 loss) +I0616 13:33:02.722705 9857 solver.cpp:571] Iteration 68300, lr = 0.0001 +I0616 13:33:14.075295 9857 solver.cpp:242] Iteration 68320, loss = 0.424828 +I0616 13:33:14.075338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114995 (* 1 = 0.114995 loss) +I0616 13:33:14.075345 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123621 (* 1 = 0.123621 loss) +I0616 13:33:14.075348 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0337493 (* 1 = 0.0337493 loss) +I0616 13:33:14.075352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254717 (* 1 = 0.0254717 loss) +I0616 13:33:14.075358 9857 solver.cpp:571] Iteration 68320, lr = 0.0001 +I0616 13:33:25.677171 9857 solver.cpp:242] Iteration 68340, loss = 0.879984 +I0616 13:33:25.677212 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231049 (* 1 = 0.231049 loss) +I0616 13:33:25.677217 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271969 (* 1 = 0.271969 loss) +I0616 13:33:25.677222 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0875633 (* 1 = 0.0875633 loss) +I0616 13:33:25.677227 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0320579 (* 1 = 0.0320579 loss) +I0616 13:33:25.677229 9857 solver.cpp:571] Iteration 68340, lr = 0.0001 +I0616 13:33:37.194430 9857 solver.cpp:242] Iteration 68360, loss = 0.455407 +I0616 13:33:37.194473 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0631955 (* 1 = 0.0631955 loss) +I0616 13:33:37.194478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10876 (* 1 = 0.10876 loss) +I0616 13:33:37.194483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0181925 (* 1 = 0.0181925 loss) +I0616 13:33:37.194486 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0146633 (* 1 = 0.0146633 loss) +I0616 13:33:37.194490 9857 solver.cpp:571] Iteration 68360, lr = 0.0001 +I0616 13:33:48.814059 9857 solver.cpp:242] Iteration 68380, loss = 0.445879 +I0616 13:33:48.814102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227815 (* 1 = 0.227815 loss) +I0616 13:33:48.814107 9857 solver.cpp:258] Train net output #1: loss_cls = 0.240853 (* 1 = 0.240853 loss) +I0616 13:33:48.814111 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149263 (* 1 = 0.149263 loss) +I0616 13:33:48.814116 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0801518 (* 1 = 0.0801518 loss) +I0616 13:33:48.814119 9857 solver.cpp:571] Iteration 68380, lr = 0.0001 +speed: 0.603s / iter +I0616 13:34:00.350798 9857 solver.cpp:242] Iteration 68400, loss = 0.582333 +I0616 13:34:00.350841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102441 (* 1 = 0.102441 loss) +I0616 13:34:00.350846 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146909 (* 1 = 0.146909 loss) +I0616 13:34:00.350849 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0211933 (* 1 = 0.0211933 loss) +I0616 13:34:00.350853 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00963042 (* 1 = 0.00963042 loss) +I0616 13:34:00.350858 9857 solver.cpp:571] Iteration 68400, lr = 0.0001 +I0616 13:34:11.783917 9857 solver.cpp:242] Iteration 68420, loss = 0.428006 +I0616 13:34:11.783959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.063725 (* 1 = 0.063725 loss) +I0616 13:34:11.783965 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0940669 (* 1 = 0.0940669 loss) +I0616 13:34:11.783969 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0399326 (* 1 = 0.0399326 loss) +I0616 13:34:11.783973 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0124077 (* 1 = 0.0124077 loss) +I0616 13:34:11.783977 9857 solver.cpp:571] Iteration 68420, lr = 0.0001 +I0616 13:34:23.466145 9857 solver.cpp:242] Iteration 68440, loss = 0.383073 +I0616 13:34:23.466187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159826 (* 1 = 0.159826 loss) +I0616 13:34:23.466192 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134314 (* 1 = 0.134314 loss) +I0616 13:34:23.466197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0599164 (* 1 = 0.0599164 loss) +I0616 13:34:23.466200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0091987 (* 1 = 0.0091987 loss) +I0616 13:34:23.466207 9857 solver.cpp:571] Iteration 68440, lr = 0.0001 +I0616 13:34:35.136747 9857 solver.cpp:242] Iteration 68460, loss = 0.518911 +I0616 13:34:35.136788 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132891 (* 1 = 0.132891 loss) +I0616 13:34:35.136795 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125322 (* 1 = 0.125322 loss) +I0616 13:34:35.136800 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.059359 (* 1 = 0.059359 loss) +I0616 13:34:35.136802 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0264253 (* 1 = 0.0264253 loss) +I0616 13:34:35.136806 9857 solver.cpp:571] Iteration 68460, lr = 0.0001 +I0616 13:34:46.551970 9857 solver.cpp:242] Iteration 68480, loss = 0.861766 +I0616 13:34:46.552012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.410706 (* 1 = 0.410706 loss) +I0616 13:34:46.552017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.499731 (* 1 = 0.499731 loss) +I0616 13:34:46.552022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.148042 (* 1 = 0.148042 loss) +I0616 13:34:46.552026 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0632145 (* 1 = 0.0632145 loss) +I0616 13:34:46.552029 9857 solver.cpp:571] Iteration 68480, lr = 0.0001 +I0616 13:34:58.036603 9857 solver.cpp:242] Iteration 68500, loss = 0.838333 +I0616 13:34:58.036645 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0486237 (* 1 = 0.0486237 loss) +I0616 13:34:58.036651 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0583637 (* 1 = 0.0583637 loss) +I0616 13:34:58.036655 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104638 (* 1 = 0.0104638 loss) +I0616 13:34:58.036659 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00437696 (* 1 = 0.00437696 loss) +I0616 13:34:58.036665 9857 solver.cpp:571] Iteration 68500, lr = 0.0001 +I0616 13:35:09.789213 9857 solver.cpp:242] Iteration 68520, loss = 0.578257 +I0616 13:35:09.789254 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327417 (* 1 = 0.327417 loss) +I0616 13:35:09.789260 9857 solver.cpp:258] Train net output #1: loss_cls = 0.30405 (* 1 = 0.30405 loss) +I0616 13:35:09.789264 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147801 (* 1 = 0.147801 loss) +I0616 13:35:09.789268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.135473 (* 1 = 0.135473 loss) +I0616 13:35:09.789273 9857 solver.cpp:571] Iteration 68520, lr = 0.0001 +I0616 13:35:21.193471 9857 solver.cpp:242] Iteration 68540, loss = 0.58256 +I0616 13:35:21.193514 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379147 (* 1 = 0.379147 loss) +I0616 13:35:21.193519 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286545 (* 1 = 0.286545 loss) +I0616 13:35:21.193523 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.180853 (* 1 = 0.180853 loss) +I0616 13:35:21.193527 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0948625 (* 1 = 0.0948625 loss) +I0616 13:35:21.193531 9857 solver.cpp:571] Iteration 68540, lr = 0.0001 +I0616 13:35:32.724640 9857 solver.cpp:242] Iteration 68560, loss = 0.552298 +I0616 13:35:32.724683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215949 (* 1 = 0.215949 loss) +I0616 13:35:32.724687 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326317 (* 1 = 0.326317 loss) +I0616 13:35:32.724691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0650271 (* 1 = 0.0650271 loss) +I0616 13:35:32.724695 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116372 (* 1 = 0.116372 loss) +I0616 13:35:32.724699 9857 solver.cpp:571] Iteration 68560, lr = 0.0001 +I0616 13:35:44.029651 9857 solver.cpp:242] Iteration 68580, loss = 0.357482 +I0616 13:35:44.029695 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.194352 (* 1 = 0.194352 loss) +I0616 13:35:44.029700 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170403 (* 1 = 0.170403 loss) +I0616 13:35:44.029705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0944598 (* 1 = 0.0944598 loss) +I0616 13:35:44.029711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0326766 (* 1 = 0.0326766 loss) +I0616 13:35:44.029716 9857 solver.cpp:571] Iteration 68580, lr = 0.0001 +speed: 0.603s / iter +I0616 13:35:55.530972 9857 solver.cpp:242] Iteration 68600, loss = 0.948251 +I0616 13:35:55.531013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238445 (* 1 = 0.238445 loss) +I0616 13:35:55.531019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223804 (* 1 = 0.223804 loss) +I0616 13:35:55.531023 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0821676 (* 1 = 0.0821676 loss) +I0616 13:35:55.531028 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.223806 (* 1 = 0.223806 loss) +I0616 13:35:55.531030 9857 solver.cpp:571] Iteration 68600, lr = 0.0001 +I0616 13:36:07.357709 9857 solver.cpp:242] Iteration 68620, loss = 0.37699 +I0616 13:36:07.357753 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190118 (* 1 = 0.190118 loss) +I0616 13:36:07.357758 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201696 (* 1 = 0.201696 loss) +I0616 13:36:07.357763 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.160776 (* 1 = 0.160776 loss) +I0616 13:36:07.357766 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0459825 (* 1 = 0.0459825 loss) +I0616 13:36:07.357770 9857 solver.cpp:571] Iteration 68620, lr = 0.0001 +I0616 13:36:18.832042 9857 solver.cpp:242] Iteration 68640, loss = 0.387176 +I0616 13:36:18.832085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0872689 (* 1 = 0.0872689 loss) +I0616 13:36:18.832092 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112991 (* 1 = 0.112991 loss) +I0616 13:36:18.832095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.014297 (* 1 = 0.014297 loss) +I0616 13:36:18.832099 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0119203 (* 1 = 0.0119203 loss) +I0616 13:36:18.832103 9857 solver.cpp:571] Iteration 68640, lr = 0.0001 +I0616 13:36:30.086401 9857 solver.cpp:242] Iteration 68660, loss = 0.540011 +I0616 13:36:30.086444 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114874 (* 1 = 0.114874 loss) +I0616 13:36:30.086449 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19094 (* 1 = 0.19094 loss) +I0616 13:36:30.086453 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0305296 (* 1 = 0.0305296 loss) +I0616 13:36:30.086457 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167539 (* 1 = 0.0167539 loss) +I0616 13:36:30.086462 9857 solver.cpp:571] Iteration 68660, lr = 0.0001 +I0616 13:36:41.591720 9857 solver.cpp:242] Iteration 68680, loss = 0.792653 +I0616 13:36:41.591761 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.449273 (* 1 = 0.449273 loss) +I0616 13:36:41.591768 9857 solver.cpp:258] Train net output #1: loss_cls = 0.641744 (* 1 = 0.641744 loss) +I0616 13:36:41.591771 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.174514 (* 1 = 0.174514 loss) +I0616 13:36:41.591775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0929174 (* 1 = 0.0929174 loss) +I0616 13:36:41.591779 9857 solver.cpp:571] Iteration 68680, lr = 0.0001 +I0616 13:36:53.276301 9857 solver.cpp:242] Iteration 68700, loss = 0.266427 +I0616 13:36:53.276341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0715677 (* 1 = 0.0715677 loss) +I0616 13:36:53.276347 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0818477 (* 1 = 0.0818477 loss) +I0616 13:36:53.276351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141261 (* 1 = 0.0141261 loss) +I0616 13:36:53.276355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00718628 (* 1 = 0.00718628 loss) +I0616 13:36:53.276360 9857 solver.cpp:571] Iteration 68700, lr = 0.0001 +I0616 13:37:05.196044 9857 solver.cpp:242] Iteration 68720, loss = 0.408645 +I0616 13:37:05.196085 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0566288 (* 1 = 0.0566288 loss) +I0616 13:37:05.196091 9857 solver.cpp:258] Train net output #1: loss_cls = 0.088997 (* 1 = 0.088997 loss) +I0616 13:37:05.196095 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00234846 (* 1 = 0.00234846 loss) +I0616 13:37:05.196099 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00712774 (* 1 = 0.00712774 loss) +I0616 13:37:05.196104 9857 solver.cpp:571] Iteration 68720, lr = 0.0001 +I0616 13:37:16.890519 9857 solver.cpp:242] Iteration 68740, loss = 0.642533 +I0616 13:37:16.890563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294017 (* 1 = 0.294017 loss) +I0616 13:37:16.890568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.502139 (* 1 = 0.502139 loss) +I0616 13:37:16.890573 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175125 (* 1 = 0.175125 loss) +I0616 13:37:16.890576 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0518866 (* 1 = 0.0518866 loss) +I0616 13:37:16.890580 9857 solver.cpp:571] Iteration 68740, lr = 0.0001 +I0616 13:37:28.254626 9857 solver.cpp:242] Iteration 68760, loss = 0.835481 +I0616 13:37:28.254668 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0872121 (* 1 = 0.0872121 loss) +I0616 13:37:28.254674 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121927 (* 1 = 0.121927 loss) +I0616 13:37:28.254678 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0352316 (* 1 = 0.0352316 loss) +I0616 13:37:28.254683 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113118 (* 1 = 0.0113118 loss) +I0616 13:37:28.254686 9857 solver.cpp:571] Iteration 68760, lr = 0.0001 +I0616 13:37:39.766327 9857 solver.cpp:242] Iteration 68780, loss = 0.420089 +I0616 13:37:39.766369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0753543 (* 1 = 0.0753543 loss) +I0616 13:37:39.766376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116928 (* 1 = 0.116928 loss) +I0616 13:37:39.766381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109366 (* 1 = 0.109366 loss) +I0616 13:37:39.766383 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147653 (* 1 = 0.0147653 loss) +I0616 13:37:39.766387 9857 solver.cpp:571] Iteration 68780, lr = 0.0001 +speed: 0.603s / iter +I0616 13:37:51.008828 9857 solver.cpp:242] Iteration 68800, loss = 0.58455 +I0616 13:37:51.008872 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125158 (* 1 = 0.125158 loss) +I0616 13:37:51.008877 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268836 (* 1 = 0.268836 loss) +I0616 13:37:51.008882 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0356707 (* 1 = 0.0356707 loss) +I0616 13:37:51.008885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00165258 (* 1 = 0.00165258 loss) +I0616 13:37:51.008888 9857 solver.cpp:571] Iteration 68800, lr = 0.0001 +I0616 13:38:02.406734 9857 solver.cpp:242] Iteration 68820, loss = 0.382335 +I0616 13:38:02.406780 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134387 (* 1 = 0.134387 loss) +I0616 13:38:02.406786 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12409 (* 1 = 0.12409 loss) +I0616 13:38:02.406790 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00347855 (* 1 = 0.00347855 loss) +I0616 13:38:02.406795 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00380204 (* 1 = 0.00380204 loss) +I0616 13:38:02.406798 9857 solver.cpp:571] Iteration 68820, lr = 0.0001 +I0616 13:38:14.042388 9857 solver.cpp:242] Iteration 68840, loss = 0.696296 +I0616 13:38:14.042430 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358037 (* 1 = 0.358037 loss) +I0616 13:38:14.042436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.535406 (* 1 = 0.535406 loss) +I0616 13:38:14.042440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0899694 (* 1 = 0.0899694 loss) +I0616 13:38:14.042445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108843 (* 1 = 0.108843 loss) +I0616 13:38:14.042448 9857 solver.cpp:571] Iteration 68840, lr = 0.0001 +I0616 13:38:25.701647 9857 solver.cpp:242] Iteration 68860, loss = 0.412369 +I0616 13:38:25.701688 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20986 (* 1 = 0.20986 loss) +I0616 13:38:25.701694 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247202 (* 1 = 0.247202 loss) +I0616 13:38:25.701697 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0319569 (* 1 = 0.0319569 loss) +I0616 13:38:25.701701 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494351 (* 1 = 0.0494351 loss) +I0616 13:38:25.701705 9857 solver.cpp:571] Iteration 68860, lr = 0.0001 +I0616 13:38:37.322275 9857 solver.cpp:242] Iteration 68880, loss = 0.521957 +I0616 13:38:37.322317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103744 (* 1 = 0.103744 loss) +I0616 13:38:37.322324 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135812 (* 1 = 0.135812 loss) +I0616 13:38:37.322327 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0302987 (* 1 = 0.0302987 loss) +I0616 13:38:37.322331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0236019 (* 1 = 0.0236019 loss) +I0616 13:38:37.322335 9857 solver.cpp:571] Iteration 68880, lr = 0.0001 +I0616 13:38:48.879194 9857 solver.cpp:242] Iteration 68900, loss = 0.491764 +I0616 13:38:48.879235 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206525 (* 1 = 0.206525 loss) +I0616 13:38:48.879241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.364498 (* 1 = 0.364498 loss) +I0616 13:38:48.879245 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0427037 (* 1 = 0.0427037 loss) +I0616 13:38:48.879250 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0363628 (* 1 = 0.0363628 loss) +I0616 13:38:48.879253 9857 solver.cpp:571] Iteration 68900, lr = 0.0001 +I0616 13:39:00.263309 9857 solver.cpp:242] Iteration 68920, loss = 0.276329 +I0616 13:39:00.263351 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161337 (* 1 = 0.161337 loss) +I0616 13:39:00.263357 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139399 (* 1 = 0.139399 loss) +I0616 13:39:00.263361 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00992807 (* 1 = 0.00992807 loss) +I0616 13:39:00.263365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00788791 (* 1 = 0.00788791 loss) +I0616 13:39:00.263370 9857 solver.cpp:571] Iteration 68920, lr = 0.0001 +I0616 13:39:11.763254 9857 solver.cpp:242] Iteration 68940, loss = 0.241951 +I0616 13:39:11.763296 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.074443 (* 1 = 0.074443 loss) +I0616 13:39:11.763303 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111098 (* 1 = 0.111098 loss) +I0616 13:39:11.763308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00598265 (* 1 = 0.00598265 loss) +I0616 13:39:11.763311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0146036 (* 1 = 0.0146036 loss) +I0616 13:39:11.763314 9857 solver.cpp:571] Iteration 68940, lr = 0.0001 +I0616 13:39:23.244472 9857 solver.cpp:242] Iteration 68960, loss = 1.12739 +I0616 13:39:23.244514 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.383412 (* 1 = 0.383412 loss) +I0616 13:39:23.244520 9857 solver.cpp:258] Train net output #1: loss_cls = 0.827945 (* 1 = 0.827945 loss) +I0616 13:39:23.244524 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.267173 (* 1 = 0.267173 loss) +I0616 13:39:23.244529 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.252636 (* 1 = 0.252636 loss) +I0616 13:39:23.244531 9857 solver.cpp:571] Iteration 68960, lr = 0.0001 +I0616 13:39:34.618810 9857 solver.cpp:242] Iteration 68980, loss = 0.262296 +I0616 13:39:34.618876 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127394 (* 1 = 0.127394 loss) +I0616 13:39:34.618882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177262 (* 1 = 0.177262 loss) +I0616 13:39:34.618886 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00362008 (* 1 = 0.00362008 loss) +I0616 13:39:34.618891 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234044 (* 1 = 0.0234044 loss) +I0616 13:39:34.618893 9857 solver.cpp:571] Iteration 68980, lr = 0.0001 +speed: 0.603s / iter +I0616 13:39:46.164312 9857 solver.cpp:242] Iteration 69000, loss = 0.534223 +I0616 13:39:46.164355 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21381 (* 1 = 0.21381 loss) +I0616 13:39:46.164361 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32005 (* 1 = 0.32005 loss) +I0616 13:39:46.164366 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0818365 (* 1 = 0.0818365 loss) +I0616 13:39:46.164369 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0686665 (* 1 = 0.0686665 loss) +I0616 13:39:46.164373 9857 solver.cpp:571] Iteration 69000, lr = 0.0001 +I0616 13:39:57.741539 9857 solver.cpp:242] Iteration 69020, loss = 0.605423 +I0616 13:39:57.741581 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347608 (* 1 = 0.347608 loss) +I0616 13:39:57.741586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254064 (* 1 = 0.254064 loss) +I0616 13:39:57.741591 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111505 (* 1 = 0.111505 loss) +I0616 13:39:57.741595 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.042089 (* 1 = 0.042089 loss) +I0616 13:39:57.741600 9857 solver.cpp:571] Iteration 69020, lr = 0.0001 +I0616 13:40:09.589866 9857 solver.cpp:242] Iteration 69040, loss = 0.90383 +I0616 13:40:09.589908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265421 (* 1 = 0.265421 loss) +I0616 13:40:09.589915 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432009 (* 1 = 0.432009 loss) +I0616 13:40:09.589918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0657427 (* 1 = 0.0657427 loss) +I0616 13:40:09.589922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.123382 (* 1 = 0.123382 loss) +I0616 13:40:09.589926 9857 solver.cpp:571] Iteration 69040, lr = 0.0001 +I0616 13:40:21.079629 9857 solver.cpp:242] Iteration 69060, loss = 0.901196 +I0616 13:40:21.079671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237203 (* 1 = 0.237203 loss) +I0616 13:40:21.079676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272656 (* 1 = 0.272656 loss) +I0616 13:40:21.079680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119498 (* 1 = 0.119498 loss) +I0616 13:40:21.079684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0262445 (* 1 = 0.0262445 loss) +I0616 13:40:21.079689 9857 solver.cpp:571] Iteration 69060, lr = 0.0001 +I0616 13:40:32.523525 9857 solver.cpp:242] Iteration 69080, loss = 0.335338 +I0616 13:40:32.523567 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0397983 (* 1 = 0.0397983 loss) +I0616 13:40:32.523573 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178065 (* 1 = 0.178065 loss) +I0616 13:40:32.523577 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0528643 (* 1 = 0.0528643 loss) +I0616 13:40:32.523581 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0279049 (* 1 = 0.0279049 loss) +I0616 13:40:32.523586 9857 solver.cpp:571] Iteration 69080, lr = 0.0001 +I0616 13:40:43.906455 9857 solver.cpp:242] Iteration 69100, loss = 0.414966 +I0616 13:40:43.906497 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151067 (* 1 = 0.151067 loss) +I0616 13:40:43.906502 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248622 (* 1 = 0.248622 loss) +I0616 13:40:43.906507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605673 (* 1 = 0.0605673 loss) +I0616 13:40:43.906510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00951354 (* 1 = 0.00951354 loss) +I0616 13:40:43.906514 9857 solver.cpp:571] Iteration 69100, lr = 0.0001 +I0616 13:40:55.264755 9857 solver.cpp:242] Iteration 69120, loss = 0.698622 +I0616 13:40:55.264796 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338486 (* 1 = 0.338486 loss) +I0616 13:40:55.264802 9857 solver.cpp:258] Train net output #1: loss_cls = 0.567793 (* 1 = 0.567793 loss) +I0616 13:40:55.264806 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.222386 (* 1 = 0.222386 loss) +I0616 13:40:55.264811 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0666306 (* 1 = 0.0666306 loss) +I0616 13:40:55.264814 9857 solver.cpp:571] Iteration 69120, lr = 0.0001 +I0616 13:41:06.752856 9857 solver.cpp:242] Iteration 69140, loss = 0.317949 +I0616 13:41:06.752897 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189388 (* 1 = 0.189388 loss) +I0616 13:41:06.752903 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151558 (* 1 = 0.151558 loss) +I0616 13:41:06.752907 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104744 (* 1 = 0.0104744 loss) +I0616 13:41:06.752912 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138245 (* 1 = 0.0138245 loss) +I0616 13:41:06.752915 9857 solver.cpp:571] Iteration 69140, lr = 0.0001 +I0616 13:41:18.026712 9857 solver.cpp:242] Iteration 69160, loss = 0.571242 +I0616 13:41:18.026754 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193918 (* 1 = 0.193918 loss) +I0616 13:41:18.026762 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231703 (* 1 = 0.231703 loss) +I0616 13:41:18.026767 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.064098 (* 1 = 0.064098 loss) +I0616 13:41:18.026770 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0568594 (* 1 = 0.0568594 loss) +I0616 13:41:18.026774 9857 solver.cpp:571] Iteration 69160, lr = 0.0001 +I0616 13:41:29.253954 9857 solver.cpp:242] Iteration 69180, loss = 0.626123 +I0616 13:41:29.253995 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190738 (* 1 = 0.190738 loss) +I0616 13:41:29.254001 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288676 (* 1 = 0.288676 loss) +I0616 13:41:29.254005 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0987463 (* 1 = 0.0987463 loss) +I0616 13:41:29.254009 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.039716 (* 1 = 0.039716 loss) +I0616 13:41:29.254014 9857 solver.cpp:571] Iteration 69180, lr = 0.0001 +speed: 0.603s / iter +I0616 13:41:40.753083 9857 solver.cpp:242] Iteration 69200, loss = 0.222375 +I0616 13:41:40.753124 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0712233 (* 1 = 0.0712233 loss) +I0616 13:41:40.753130 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172245 (* 1 = 0.172245 loss) +I0616 13:41:40.753134 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0106023 (* 1 = 0.0106023 loss) +I0616 13:41:40.753139 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222601 (* 1 = 0.0222601 loss) +I0616 13:41:40.753142 9857 solver.cpp:571] Iteration 69200, lr = 0.0001 +I0616 13:41:51.876540 9857 solver.cpp:242] Iteration 69220, loss = 0.389098 +I0616 13:41:51.876582 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725344 (* 1 = 0.0725344 loss) +I0616 13:41:51.876588 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0606882 (* 1 = 0.0606882 loss) +I0616 13:41:51.876592 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0156549 (* 1 = 0.0156549 loss) +I0616 13:41:51.876596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0680858 (* 1 = 0.0680858 loss) +I0616 13:41:51.876600 9857 solver.cpp:571] Iteration 69220, lr = 0.0001 +I0616 13:42:03.369335 9857 solver.cpp:242] Iteration 69240, loss = 0.519721 +I0616 13:42:03.369379 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0299668 (* 1 = 0.0299668 loss) +I0616 13:42:03.369385 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0684973 (* 1 = 0.0684973 loss) +I0616 13:42:03.369388 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.020834 (* 1 = 0.020834 loss) +I0616 13:42:03.369392 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00912405 (* 1 = 0.00912405 loss) +I0616 13:42:03.369396 9857 solver.cpp:571] Iteration 69240, lr = 0.0001 +I0616 13:42:14.863709 9857 solver.cpp:242] Iteration 69260, loss = 0.521906 +I0616 13:42:14.863750 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0765582 (* 1 = 0.0765582 loss) +I0616 13:42:14.863757 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134109 (* 1 = 0.134109 loss) +I0616 13:42:14.863761 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184801 (* 1 = 0.184801 loss) +I0616 13:42:14.863765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562396 (* 1 = 0.00562396 loss) +I0616 13:42:14.863770 9857 solver.cpp:571] Iteration 69260, lr = 0.0001 +I0616 13:42:26.438469 9857 solver.cpp:242] Iteration 69280, loss = 1.04919 +I0616 13:42:26.438511 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382973 (* 1 = 0.382973 loss) +I0616 13:42:26.438516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28764 (* 1 = 0.28764 loss) +I0616 13:42:26.438521 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0998133 (* 1 = 0.0998133 loss) +I0616 13:42:26.438525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128476 (* 1 = 0.128476 loss) +I0616 13:42:26.438529 9857 solver.cpp:571] Iteration 69280, lr = 0.0001 +I0616 13:42:37.788455 9857 solver.cpp:242] Iteration 69300, loss = 0.45363 +I0616 13:42:37.788499 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187992 (* 1 = 0.187992 loss) +I0616 13:42:37.788504 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163739 (* 1 = 0.163739 loss) +I0616 13:42:37.788509 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0120554 (* 1 = 0.0120554 loss) +I0616 13:42:37.788512 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0244346 (* 1 = 0.0244346 loss) +I0616 13:42:37.788516 9857 solver.cpp:571] Iteration 69300, lr = 0.0001 +I0616 13:42:49.343346 9857 solver.cpp:242] Iteration 69320, loss = 0.216373 +I0616 13:42:49.343389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0915873 (* 1 = 0.0915873 loss) +I0616 13:42:49.343408 9857 solver.cpp:258] Train net output #1: loss_cls = 0.074426 (* 1 = 0.074426 loss) +I0616 13:42:49.343412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00993909 (* 1 = 0.00993909 loss) +I0616 13:42:49.343416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0082054 (* 1 = 0.0082054 loss) +I0616 13:42:49.343420 9857 solver.cpp:571] Iteration 69320, lr = 0.0001 +I0616 13:43:00.971904 9857 solver.cpp:242] Iteration 69340, loss = 0.581234 +I0616 13:43:00.971946 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183992 (* 1 = 0.183992 loss) +I0616 13:43:00.971951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259435 (* 1 = 0.259435 loss) +I0616 13:43:00.971956 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0597522 (* 1 = 0.0597522 loss) +I0616 13:43:00.971959 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113827 (* 1 = 0.0113827 loss) +I0616 13:43:00.971962 9857 solver.cpp:571] Iteration 69340, lr = 0.0001 +I0616 13:43:12.628501 9857 solver.cpp:242] Iteration 69360, loss = 0.497501 +I0616 13:43:12.628552 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149863 (* 1 = 0.149863 loss) +I0616 13:43:12.628558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152187 (* 1 = 0.152187 loss) +I0616 13:43:12.628562 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00841148 (* 1 = 0.00841148 loss) +I0616 13:43:12.628568 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00394889 (* 1 = 0.00394889 loss) +I0616 13:43:12.628574 9857 solver.cpp:571] Iteration 69360, lr = 0.0001 +I0616 13:43:23.745532 9857 solver.cpp:242] Iteration 69380, loss = 0.286282 +I0616 13:43:23.745574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.062944 (* 1 = 0.062944 loss) +I0616 13:43:23.745580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0739881 (* 1 = 0.0739881 loss) +I0616 13:43:23.745584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.021684 (* 1 = 0.021684 loss) +I0616 13:43:23.745589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163524 (* 1 = 0.0163524 loss) +I0616 13:43:23.745592 9857 solver.cpp:571] Iteration 69380, lr = 0.0001 +speed: 0.603s / iter +I0616 13:43:35.335958 9857 solver.cpp:242] Iteration 69400, loss = 0.515026 +I0616 13:43:35.336000 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0766228 (* 1 = 0.0766228 loss) +I0616 13:43:35.336006 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0918933 (* 1 = 0.0918933 loss) +I0616 13:43:35.336010 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00744667 (* 1 = 0.00744667 loss) +I0616 13:43:35.336014 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000951195 (* 1 = 0.000951195 loss) +I0616 13:43:35.336019 9857 solver.cpp:571] Iteration 69400, lr = 0.0001 +I0616 13:43:46.740348 9857 solver.cpp:242] Iteration 69420, loss = 0.39869 +I0616 13:43:46.740391 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101695 (* 1 = 0.101695 loss) +I0616 13:43:46.740396 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283663 (* 1 = 0.283663 loss) +I0616 13:43:46.740401 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144009 (* 1 = 0.144009 loss) +I0616 13:43:46.740404 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110307 (* 1 = 0.110307 loss) +I0616 13:43:46.740409 9857 solver.cpp:571] Iteration 69420, lr = 0.0001 +I0616 13:43:58.108916 9857 solver.cpp:242] Iteration 69440, loss = 0.260022 +I0616 13:43:58.108959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0773321 (* 1 = 0.0773321 loss) +I0616 13:43:58.108964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1823 (* 1 = 0.1823 loss) +I0616 13:43:58.108968 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0248324 (* 1 = 0.0248324 loss) +I0616 13:43:58.108973 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210206 (* 1 = 0.0210206 loss) +I0616 13:43:58.108976 9857 solver.cpp:571] Iteration 69440, lr = 0.0001 +I0616 13:44:09.582715 9857 solver.cpp:242] Iteration 69460, loss = 0.561184 +I0616 13:44:09.582762 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0839357 (* 1 = 0.0839357 loss) +I0616 13:44:09.582767 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101755 (* 1 = 0.101755 loss) +I0616 13:44:09.582772 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00742934 (* 1 = 0.00742934 loss) +I0616 13:44:09.582775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00366883 (* 1 = 0.00366883 loss) +I0616 13:44:09.582779 9857 solver.cpp:571] Iteration 69460, lr = 0.0001 +I0616 13:44:21.054792 9857 solver.cpp:242] Iteration 69480, loss = 0.53285 +I0616 13:44:21.054834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207898 (* 1 = 0.207898 loss) +I0616 13:44:21.054839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.256198 (* 1 = 0.256198 loss) +I0616 13:44:21.054844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0270226 (* 1 = 0.0270226 loss) +I0616 13:44:21.054847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227031 (* 1 = 0.0227031 loss) +I0616 13:44:21.054852 9857 solver.cpp:571] Iteration 69480, lr = 0.0001 +I0616 13:44:32.711594 9857 solver.cpp:242] Iteration 69500, loss = 0.355634 +I0616 13:44:32.711637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0260374 (* 1 = 0.0260374 loss) +I0616 13:44:32.711643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.084049 (* 1 = 0.084049 loss) +I0616 13:44:32.711647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0467695 (* 1 = 0.0467695 loss) +I0616 13:44:32.711652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0488289 (* 1 = 0.0488289 loss) +I0616 13:44:32.711655 9857 solver.cpp:571] Iteration 69500, lr = 0.0001 +I0616 13:44:44.298463 9857 solver.cpp:242] Iteration 69520, loss = 0.538143 +I0616 13:44:44.298506 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0314334 (* 1 = 0.0314334 loss) +I0616 13:44:44.298511 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0651121 (* 1 = 0.0651121 loss) +I0616 13:44:44.298516 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0359782 (* 1 = 0.0359782 loss) +I0616 13:44:44.298519 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0629837 (* 1 = 0.0629837 loss) +I0616 13:44:44.298523 9857 solver.cpp:571] Iteration 69520, lr = 0.0001 +I0616 13:44:55.877755 9857 solver.cpp:242] Iteration 69540, loss = 0.209165 +I0616 13:44:55.877797 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0523787 (* 1 = 0.0523787 loss) +I0616 13:44:55.877804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14882 (* 1 = 0.14882 loss) +I0616 13:44:55.877807 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0109358 (* 1 = 0.0109358 loss) +I0616 13:44:55.877810 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139104 (* 1 = 0.0139104 loss) +I0616 13:44:55.877815 9857 solver.cpp:571] Iteration 69540, lr = 0.0001 +I0616 13:45:07.447635 9857 solver.cpp:242] Iteration 69560, loss = 0.839665 +I0616 13:45:07.447679 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0516392 (* 1 = 0.0516392 loss) +I0616 13:45:07.447685 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0757173 (* 1 = 0.0757173 loss) +I0616 13:45:07.447688 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00504608 (* 1 = 0.00504608 loss) +I0616 13:45:07.447692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0010618 (* 1 = 0.0010618 loss) +I0616 13:45:07.447696 9857 solver.cpp:571] Iteration 69560, lr = 0.0001 +I0616 13:45:18.943682 9857 solver.cpp:242] Iteration 69580, loss = 0.731753 +I0616 13:45:18.943727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186324 (* 1 = 0.186324 loss) +I0616 13:45:18.943732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.324623 (* 1 = 0.324623 loss) +I0616 13:45:18.943737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0650743 (* 1 = 0.0650743 loss) +I0616 13:45:18.943740 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.082597 (* 1 = 0.082597 loss) +I0616 13:45:18.943743 9857 solver.cpp:571] Iteration 69580, lr = 0.0001 +speed: 0.603s / iter +I0616 13:45:30.722383 9857 solver.cpp:242] Iteration 69600, loss = 0.254201 +I0616 13:45:30.722426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0800868 (* 1 = 0.0800868 loss) +I0616 13:45:30.722432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16823 (* 1 = 0.16823 loss) +I0616 13:45:30.722436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00519045 (* 1 = 0.00519045 loss) +I0616 13:45:30.722440 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113696 (* 1 = 0.0113696 loss) +I0616 13:45:30.722445 9857 solver.cpp:571] Iteration 69600, lr = 0.0001 +I0616 13:45:42.100736 9857 solver.cpp:242] Iteration 69620, loss = 0.287095 +I0616 13:45:42.100778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201914 (* 1 = 0.201914 loss) +I0616 13:45:42.100783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125201 (* 1 = 0.125201 loss) +I0616 13:45:42.100788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0256411 (* 1 = 0.0256411 loss) +I0616 13:45:42.100795 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00760496 (* 1 = 0.00760496 loss) +I0616 13:45:42.100803 9857 solver.cpp:571] Iteration 69620, lr = 0.0001 +I0616 13:45:53.685174 9857 solver.cpp:242] Iteration 69640, loss = 0.829083 +I0616 13:45:53.685216 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0627963 (* 1 = 0.0627963 loss) +I0616 13:45:53.685221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0882943 (* 1 = 0.0882943 loss) +I0616 13:45:53.685226 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0103231 (* 1 = 0.0103231 loss) +I0616 13:45:53.685230 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00459108 (* 1 = 0.00459108 loss) +I0616 13:45:53.685233 9857 solver.cpp:571] Iteration 69640, lr = 0.0001 +I0616 13:46:05.326025 9857 solver.cpp:242] Iteration 69660, loss = 0.443064 +I0616 13:46:05.326067 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150363 (* 1 = 0.150363 loss) +I0616 13:46:05.326073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230557 (* 1 = 0.230557 loss) +I0616 13:46:05.326077 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0196339 (* 1 = 0.0196339 loss) +I0616 13:46:05.326081 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.025625 (* 1 = 0.025625 loss) +I0616 13:46:05.326086 9857 solver.cpp:571] Iteration 69660, lr = 0.0001 +I0616 13:46:16.914417 9857 solver.cpp:242] Iteration 69680, loss = 0.177071 +I0616 13:46:16.914458 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0629884 (* 1 = 0.0629884 loss) +I0616 13:46:16.914464 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0977873 (* 1 = 0.0977873 loss) +I0616 13:46:16.914469 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00202975 (* 1 = 0.00202975 loss) +I0616 13:46:16.914472 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0084457 (* 1 = 0.0084457 loss) +I0616 13:46:16.914476 9857 solver.cpp:571] Iteration 69680, lr = 0.0001 +I0616 13:46:27.987437 9857 solver.cpp:242] Iteration 69700, loss = 0.690265 +I0616 13:46:27.987479 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244992 (* 1 = 0.244992 loss) +I0616 13:46:27.987484 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288567 (* 1 = 0.288567 loss) +I0616 13:46:27.987489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110576 (* 1 = 0.110576 loss) +I0616 13:46:27.987493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0691075 (* 1 = 0.0691075 loss) +I0616 13:46:27.987496 9857 solver.cpp:571] Iteration 69700, lr = 0.0001 +I0616 13:46:39.379729 9857 solver.cpp:242] Iteration 69720, loss = 0.289134 +I0616 13:46:39.379772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123032 (* 1 = 0.123032 loss) +I0616 13:46:39.379778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176132 (* 1 = 0.176132 loss) +I0616 13:46:39.379782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0201567 (* 1 = 0.0201567 loss) +I0616 13:46:39.379786 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169944 (* 1 = 0.0169944 loss) +I0616 13:46:39.379789 9857 solver.cpp:571] Iteration 69720, lr = 0.0001 +I0616 13:46:50.851300 9857 solver.cpp:242] Iteration 69740, loss = 1.29185 +I0616 13:46:50.851341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316885 (* 1 = 0.316885 loss) +I0616 13:46:50.851346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.512765 (* 1 = 0.512765 loss) +I0616 13:46:50.851351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0909135 (* 1 = 0.0909135 loss) +I0616 13:46:50.851354 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.2425 (* 1 = 0.2425 loss) +I0616 13:46:50.851358 9857 solver.cpp:571] Iteration 69740, lr = 0.0001 +I0616 13:47:02.657888 9857 solver.cpp:242] Iteration 69760, loss = 0.45235 +I0616 13:47:02.657929 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10325 (* 1 = 0.10325 loss) +I0616 13:47:02.657937 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139917 (* 1 = 0.139917 loss) +I0616 13:47:02.657940 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0320471 (* 1 = 0.0320471 loss) +I0616 13:47:02.657944 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115324 (* 1 = 0.0115324 loss) +I0616 13:47:02.657953 9857 solver.cpp:571] Iteration 69760, lr = 0.0001 +I0616 13:47:14.235002 9857 solver.cpp:242] Iteration 69780, loss = 0.31517 +I0616 13:47:14.235045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107553 (* 1 = 0.107553 loss) +I0616 13:47:14.235051 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173796 (* 1 = 0.173796 loss) +I0616 13:47:14.235056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.020015 (* 1 = 0.020015 loss) +I0616 13:47:14.235059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0172012 (* 1 = 0.0172012 loss) +I0616 13:47:14.235065 9857 solver.cpp:571] Iteration 69780, lr = 0.0001 +speed: 0.603s / iter +I0616 13:47:25.596359 9857 solver.cpp:242] Iteration 69800, loss = 0.817549 +I0616 13:47:25.596401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173455 (* 1 = 0.173455 loss) +I0616 13:47:25.596407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27834 (* 1 = 0.27834 loss) +I0616 13:47:25.596412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0685605 (* 1 = 0.0685605 loss) +I0616 13:47:25.596415 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197714 (* 1 = 0.0197714 loss) +I0616 13:47:25.596420 9857 solver.cpp:571] Iteration 69800, lr = 0.0001 +I0616 13:47:37.190254 9857 solver.cpp:242] Iteration 69820, loss = 0.289946 +I0616 13:47:37.190296 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.082262 (* 1 = 0.082262 loss) +I0616 13:47:37.190301 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102537 (* 1 = 0.102537 loss) +I0616 13:47:37.190305 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00425649 (* 1 = 0.00425649 loss) +I0616 13:47:37.190310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00504886 (* 1 = 0.00504886 loss) +I0616 13:47:37.190313 9857 solver.cpp:571] Iteration 69820, lr = 0.0001 +I0616 13:47:48.512621 9857 solver.cpp:242] Iteration 69840, loss = 0.797327 +I0616 13:47:48.512662 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.419149 (* 1 = 0.419149 loss) +I0616 13:47:48.512668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.611972 (* 1 = 0.611972 loss) +I0616 13:47:48.512672 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200217 (* 1 = 0.200217 loss) +I0616 13:47:48.512676 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0997327 (* 1 = 0.0997327 loss) +I0616 13:47:48.512679 9857 solver.cpp:571] Iteration 69840, lr = 0.0001 +I0616 13:48:00.113512 9857 solver.cpp:242] Iteration 69860, loss = 0.241789 +I0616 13:48:00.113551 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0774415 (* 1 = 0.0774415 loss) +I0616 13:48:00.113557 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114048 (* 1 = 0.114048 loss) +I0616 13:48:00.113561 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00871828 (* 1 = 0.00871828 loss) +I0616 13:48:00.113565 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0250913 (* 1 = 0.0250913 loss) +I0616 13:48:00.113569 9857 solver.cpp:571] Iteration 69860, lr = 0.0001 +I0616 13:48:11.310539 9857 solver.cpp:242] Iteration 69880, loss = 0.528157 +I0616 13:48:11.310582 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0973622 (* 1 = 0.0973622 loss) +I0616 13:48:11.310588 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172255 (* 1 = 0.172255 loss) +I0616 13:48:11.310592 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0741612 (* 1 = 0.0741612 loss) +I0616 13:48:11.310596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.223836 (* 1 = 0.223836 loss) +I0616 13:48:11.310613 9857 solver.cpp:571] Iteration 69880, lr = 0.0001 +I0616 13:48:22.814379 9857 solver.cpp:242] Iteration 69900, loss = 0.507559 +I0616 13:48:22.814424 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134948 (* 1 = 0.134948 loss) +I0616 13:48:22.814430 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217341 (* 1 = 0.217341 loss) +I0616 13:48:22.814435 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.260222 (* 1 = 0.260222 loss) +I0616 13:48:22.814438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00741951 (* 1 = 0.00741951 loss) +I0616 13:48:22.814441 9857 solver.cpp:571] Iteration 69900, lr = 0.0001 +I0616 13:48:34.429076 9857 solver.cpp:242] Iteration 69920, loss = 0.266009 +I0616 13:48:34.429119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10735 (* 1 = 0.10735 loss) +I0616 13:48:34.429124 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188079 (* 1 = 0.188079 loss) +I0616 13:48:34.429129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0283239 (* 1 = 0.0283239 loss) +I0616 13:48:34.429133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00616522 (* 1 = 0.00616522 loss) +I0616 13:48:34.429137 9857 solver.cpp:571] Iteration 69920, lr = 0.0001 +I0616 13:48:46.098309 9857 solver.cpp:242] Iteration 69940, loss = 0.629718 +I0616 13:48:46.098351 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103691 (* 1 = 0.103691 loss) +I0616 13:48:46.098357 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151073 (* 1 = 0.151073 loss) +I0616 13:48:46.098362 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0317282 (* 1 = 0.0317282 loss) +I0616 13:48:46.098366 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0428275 (* 1 = 0.0428275 loss) +I0616 13:48:46.098369 9857 solver.cpp:571] Iteration 69940, lr = 0.0001 +I0616 13:48:57.598203 9857 solver.cpp:242] Iteration 69960, loss = 0.743663 +I0616 13:48:57.598245 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0785795 (* 1 = 0.0785795 loss) +I0616 13:48:57.598251 9857 solver.cpp:258] Train net output #1: loss_cls = 0.079018 (* 1 = 0.079018 loss) +I0616 13:48:57.598255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.022017 (* 1 = 0.022017 loss) +I0616 13:48:57.598259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201579 (* 1 = 0.0201579 loss) +I0616 13:48:57.598263 9857 solver.cpp:571] Iteration 69960, lr = 0.0001 +I0616 13:49:09.421355 9857 solver.cpp:242] Iteration 69980, loss = 0.534577 +I0616 13:49:09.421401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136268 (* 1 = 0.136268 loss) +I0616 13:49:09.421407 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152368 (* 1 = 0.152368 loss) +I0616 13:49:09.421413 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116614 (* 1 = 0.116614 loss) +I0616 13:49:09.421418 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0954876 (* 1 = 0.0954876 loss) +I0616 13:49:09.421427 9857 solver.cpp:571] Iteration 69980, lr = 0.0001 +speed: 0.603s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_70000.caffemodel +I0616 13:49:22.418012 9857 solver.cpp:242] Iteration 70000, loss = 0.242507 +I0616 13:49:22.418056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0629127 (* 1 = 0.0629127 loss) +I0616 13:49:22.418061 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201588 (* 1 = 0.201588 loss) +I0616 13:49:22.418066 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0372992 (* 1 = 0.0372992 loss) +I0616 13:49:22.418069 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00844377 (* 1 = 0.00844377 loss) +I0616 13:49:22.418072 9857 solver.cpp:571] Iteration 70000, lr = 0.0001 +I0616 13:49:33.901269 9857 solver.cpp:242] Iteration 70020, loss = 0.441796 +I0616 13:49:33.901309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201684 (* 1 = 0.201684 loss) +I0616 13:49:33.901314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287241 (* 1 = 0.287241 loss) +I0616 13:49:33.901319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0559586 (* 1 = 0.0559586 loss) +I0616 13:49:33.901322 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407039 (* 1 = 0.0407039 loss) +I0616 13:49:33.901326 9857 solver.cpp:571] Iteration 70020, lr = 0.0001 +I0616 13:49:45.507680 9857 solver.cpp:242] Iteration 70040, loss = 0.352565 +I0616 13:49:45.507722 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0575791 (* 1 = 0.0575791 loss) +I0616 13:49:45.507728 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0830856 (* 1 = 0.0830856 loss) +I0616 13:49:45.507732 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00407886 (* 1 = 0.00407886 loss) +I0616 13:49:45.507736 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00397787 (* 1 = 0.00397787 loss) +I0616 13:49:45.507740 9857 solver.cpp:571] Iteration 70040, lr = 0.0001 +I0616 13:49:56.984637 9857 solver.cpp:242] Iteration 70060, loss = 0.323946 +I0616 13:49:56.984678 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0492028 (* 1 = 0.0492028 loss) +I0616 13:49:56.984683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0684334 (* 1 = 0.0684334 loss) +I0616 13:49:56.984688 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155818 (* 1 = 0.0155818 loss) +I0616 13:49:56.984691 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0071673 (* 1 = 0.0071673 loss) +I0616 13:49:56.984695 9857 solver.cpp:571] Iteration 70060, lr = 0.0001 +I0616 13:50:08.679329 9857 solver.cpp:242] Iteration 70080, loss = 0.680568 +I0616 13:50:08.679371 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100768 (* 1 = 0.100768 loss) +I0616 13:50:08.679376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194651 (* 1 = 0.194651 loss) +I0616 13:50:08.679380 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0173565 (* 1 = 0.0173565 loss) +I0616 13:50:08.679384 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0567483 (* 1 = 0.0567483 loss) +I0616 13:50:08.679388 9857 solver.cpp:571] Iteration 70080, lr = 0.0001 +I0616 13:50:20.152544 9857 solver.cpp:242] Iteration 70100, loss = 0.28633 +I0616 13:50:20.152586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.09032 (* 1 = 0.09032 loss) +I0616 13:50:20.152592 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173427 (* 1 = 0.173427 loss) +I0616 13:50:20.152596 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0968504 (* 1 = 0.0968504 loss) +I0616 13:50:20.152601 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00716634 (* 1 = 0.00716634 loss) +I0616 13:50:20.152603 9857 solver.cpp:571] Iteration 70100, lr = 0.0001 +I0616 13:50:31.670694 9857 solver.cpp:242] Iteration 70120, loss = 0.297681 +I0616 13:50:31.670735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0998524 (* 1 = 0.0998524 loss) +I0616 13:50:31.670740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195475 (* 1 = 0.195475 loss) +I0616 13:50:31.670745 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00380159 (* 1 = 0.00380159 loss) +I0616 13:50:31.670748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00301714 (* 1 = 0.00301714 loss) +I0616 13:50:31.670753 9857 solver.cpp:571] Iteration 70120, lr = 0.0001 +I0616 13:50:43.241793 9857 solver.cpp:242] Iteration 70140, loss = 0.356596 +I0616 13:50:43.241834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228254 (* 1 = 0.228254 loss) +I0616 13:50:43.241839 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221528 (* 1 = 0.221528 loss) +I0616 13:50:43.241844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0467284 (* 1 = 0.0467284 loss) +I0616 13:50:43.241847 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00355216 (* 1 = 0.00355216 loss) +I0616 13:50:43.241852 9857 solver.cpp:571] Iteration 70140, lr = 0.0001 +I0616 13:50:54.878140 9857 solver.cpp:242] Iteration 70160, loss = 0.744425 +I0616 13:50:54.878181 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290953 (* 1 = 0.290953 loss) +I0616 13:50:54.878186 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313958 (* 1 = 0.313958 loss) +I0616 13:50:54.878190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.1027 (* 1 = 0.1027 loss) +I0616 13:50:54.878195 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103736 (* 1 = 0.0103736 loss) +I0616 13:50:54.878198 9857 solver.cpp:571] Iteration 70160, lr = 0.0001 +I0616 13:51:06.439510 9857 solver.cpp:242] Iteration 70180, loss = 0.640075 +I0616 13:51:06.439553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306429 (* 1 = 0.306429 loss) +I0616 13:51:06.439558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432529 (* 1 = 0.432529 loss) +I0616 13:51:06.439563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138133 (* 1 = 0.138133 loss) +I0616 13:51:06.439566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0219759 (* 1 = 0.0219759 loss) +I0616 13:51:06.439570 9857 solver.cpp:571] Iteration 70180, lr = 0.0001 +speed: 0.603s / iter +I0616 13:51:17.806792 9857 solver.cpp:242] Iteration 70200, loss = 0.436206 +I0616 13:51:17.806833 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213529 (* 1 = 0.213529 loss) +I0616 13:51:17.806838 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20054 (* 1 = 0.20054 loss) +I0616 13:51:17.806843 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.05917 (* 1 = 0.05917 loss) +I0616 13:51:17.806846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292874 (* 1 = 0.0292874 loss) +I0616 13:51:17.806850 9857 solver.cpp:571] Iteration 70200, lr = 0.0001 +I0616 13:51:29.283092 9857 solver.cpp:242] Iteration 70220, loss = 0.477121 +I0616 13:51:29.283133 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230581 (* 1 = 0.230581 loss) +I0616 13:51:29.283138 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23085 (* 1 = 0.23085 loss) +I0616 13:51:29.283143 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0302384 (* 1 = 0.0302384 loss) +I0616 13:51:29.283146 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0248714 (* 1 = 0.0248714 loss) +I0616 13:51:29.283150 9857 solver.cpp:571] Iteration 70220, lr = 0.0001 +I0616 13:51:40.957479 9857 solver.cpp:242] Iteration 70240, loss = 0.34374 +I0616 13:51:40.957532 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169281 (* 1 = 0.169281 loss) +I0616 13:51:40.957537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22129 (* 1 = 0.22129 loss) +I0616 13:51:40.957541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.032858 (* 1 = 0.032858 loss) +I0616 13:51:40.957545 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202302 (* 1 = 0.0202302 loss) +I0616 13:51:40.957550 9857 solver.cpp:571] Iteration 70240, lr = 0.0001 +I0616 13:51:52.360729 9857 solver.cpp:242] Iteration 70260, loss = 0.826609 +I0616 13:51:52.360771 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110069 (* 1 = 0.110069 loss) +I0616 13:51:52.360777 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122159 (* 1 = 0.122159 loss) +I0616 13:51:52.360781 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00953457 (* 1 = 0.00953457 loss) +I0616 13:51:52.360786 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0090655 (* 1 = 0.0090655 loss) +I0616 13:51:52.360790 9857 solver.cpp:571] Iteration 70260, lr = 0.0001 +I0616 13:52:03.972380 9857 solver.cpp:242] Iteration 70280, loss = 0.345288 +I0616 13:52:03.972422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22678 (* 1 = 0.22678 loss) +I0616 13:52:03.972429 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184709 (* 1 = 0.184709 loss) +I0616 13:52:03.972432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.048155 (* 1 = 0.048155 loss) +I0616 13:52:03.972436 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.037994 (* 1 = 0.037994 loss) +I0616 13:52:03.972440 9857 solver.cpp:571] Iteration 70280, lr = 0.0001 +I0616 13:52:15.468075 9857 solver.cpp:242] Iteration 70300, loss = 0.522097 +I0616 13:52:15.468117 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23936 (* 1 = 0.23936 loss) +I0616 13:52:15.468123 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251881 (* 1 = 0.251881 loss) +I0616 13:52:15.468127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0427372 (* 1 = 0.0427372 loss) +I0616 13:52:15.468132 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159116 (* 1 = 0.0159116 loss) +I0616 13:52:15.468134 9857 solver.cpp:571] Iteration 70300, lr = 0.0001 +I0616 13:52:26.926723 9857 solver.cpp:242] Iteration 70320, loss = 0.669364 +I0616 13:52:26.926770 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263133 (* 1 = 0.263133 loss) +I0616 13:52:26.926776 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342361 (* 1 = 0.342361 loss) +I0616 13:52:26.926780 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20565 (* 1 = 0.20565 loss) +I0616 13:52:26.926784 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.324499 (* 1 = 0.324499 loss) +I0616 13:52:26.926789 9857 solver.cpp:571] Iteration 70320, lr = 0.0001 +I0616 13:52:38.612784 9857 solver.cpp:242] Iteration 70340, loss = 0.737802 +I0616 13:52:38.612828 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259944 (* 1 = 0.259944 loss) +I0616 13:52:38.612833 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22041 (* 1 = 0.22041 loss) +I0616 13:52:38.612838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.181753 (* 1 = 0.181753 loss) +I0616 13:52:38.612841 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0650356 (* 1 = 0.0650356 loss) +I0616 13:52:38.612844 9857 solver.cpp:571] Iteration 70340, lr = 0.0001 +I0616 13:52:50.110522 9857 solver.cpp:242] Iteration 70360, loss = 0.565295 +I0616 13:52:50.110563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0894395 (* 1 = 0.0894395 loss) +I0616 13:52:50.110569 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210857 (* 1 = 0.210857 loss) +I0616 13:52:50.110574 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139748 (* 1 = 0.0139748 loss) +I0616 13:52:50.110577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00767296 (* 1 = 0.00767296 loss) +I0616 13:52:50.110581 9857 solver.cpp:571] Iteration 70360, lr = 0.0001 +I0616 13:53:01.259423 9857 solver.cpp:242] Iteration 70380, loss = 0.587988 +I0616 13:53:01.259464 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100002 (* 1 = 0.100002 loss) +I0616 13:53:01.259469 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151688 (* 1 = 0.151688 loss) +I0616 13:53:01.259474 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0348969 (* 1 = 0.0348969 loss) +I0616 13:53:01.259477 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00861911 (* 1 = 0.00861911 loss) +I0616 13:53:01.259481 9857 solver.cpp:571] Iteration 70380, lr = 0.0001 +speed: 0.603s / iter +I0616 13:53:12.605118 9857 solver.cpp:242] Iteration 70400, loss = 0.286845 +I0616 13:53:12.605161 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0512254 (* 1 = 0.0512254 loss) +I0616 13:53:12.605167 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0708627 (* 1 = 0.0708627 loss) +I0616 13:53:12.605171 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00773188 (* 1 = 0.00773188 loss) +I0616 13:53:12.605175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0065307 (* 1 = 0.0065307 loss) +I0616 13:53:12.605178 9857 solver.cpp:571] Iteration 70400, lr = 0.0001 +I0616 13:53:24.081079 9857 solver.cpp:242] Iteration 70420, loss = 0.610995 +I0616 13:53:24.081120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28682 (* 1 = 0.28682 loss) +I0616 13:53:24.081126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204771 (* 1 = 0.204771 loss) +I0616 13:53:24.081130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.083983 (* 1 = 0.083983 loss) +I0616 13:53:24.081135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253123 (* 1 = 0.0253123 loss) +I0616 13:53:24.081138 9857 solver.cpp:571] Iteration 70420, lr = 0.0001 +I0616 13:53:35.385345 9857 solver.cpp:242] Iteration 70440, loss = 0.22448 +I0616 13:53:35.385387 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0437641 (* 1 = 0.0437641 loss) +I0616 13:53:35.385393 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12654 (* 1 = 0.12654 loss) +I0616 13:53:35.385397 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0756027 (* 1 = 0.0756027 loss) +I0616 13:53:35.385401 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210638 (* 1 = 0.0210638 loss) +I0616 13:53:35.385404 9857 solver.cpp:571] Iteration 70440, lr = 0.0001 +I0616 13:53:47.046257 9857 solver.cpp:242] Iteration 70460, loss = 0.318487 +I0616 13:53:47.046298 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163948 (* 1 = 0.163948 loss) +I0616 13:53:47.046304 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225857 (* 1 = 0.225857 loss) +I0616 13:53:47.046308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0506062 (* 1 = 0.0506062 loss) +I0616 13:53:47.046314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0324104 (* 1 = 0.0324104 loss) +I0616 13:53:47.046316 9857 solver.cpp:571] Iteration 70460, lr = 0.0001 +I0616 13:53:58.530655 9857 solver.cpp:242] Iteration 70480, loss = 0.252975 +I0616 13:53:58.530697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0743691 (* 1 = 0.0743691 loss) +I0616 13:53:58.530704 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143802 (* 1 = 0.143802 loss) +I0616 13:53:58.530707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00200447 (* 1 = 0.00200447 loss) +I0616 13:53:58.530711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163067 (* 1 = 0.0163067 loss) +I0616 13:53:58.530717 9857 solver.cpp:571] Iteration 70480, lr = 0.0001 +I0616 13:54:09.887311 9857 solver.cpp:242] Iteration 70500, loss = 0.282132 +I0616 13:54:09.887339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.097952 (* 1 = 0.097952 loss) +I0616 13:54:09.887346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161014 (* 1 = 0.161014 loss) +I0616 13:54:09.887349 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.022909 (* 1 = 0.022909 loss) +I0616 13:54:09.887352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0326831 (* 1 = 0.0326831 loss) +I0616 13:54:09.887356 9857 solver.cpp:571] Iteration 70500, lr = 0.0001 +I0616 13:54:21.410131 9857 solver.cpp:242] Iteration 70520, loss = 0.240575 +I0616 13:54:21.410172 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0600676 (* 1 = 0.0600676 loss) +I0616 13:54:21.410178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197588 (* 1 = 0.197588 loss) +I0616 13:54:21.410182 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155948 (* 1 = 0.0155948 loss) +I0616 13:54:21.410187 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00639603 (* 1 = 0.00639603 loss) +I0616 13:54:21.410190 9857 solver.cpp:571] Iteration 70520, lr = 0.0001 +I0616 13:54:32.888000 9857 solver.cpp:242] Iteration 70540, loss = 0.278384 +I0616 13:54:32.888041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0908834 (* 1 = 0.0908834 loss) +I0616 13:54:32.888046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162607 (* 1 = 0.162607 loss) +I0616 13:54:32.888051 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0142053 (* 1 = 0.0142053 loss) +I0616 13:54:32.888053 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103986 (* 1 = 0.0103986 loss) +I0616 13:54:32.888057 9857 solver.cpp:571] Iteration 70540, lr = 0.0001 +I0616 13:54:44.370414 9857 solver.cpp:242] Iteration 70560, loss = 0.808607 +I0616 13:54:44.370455 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320337 (* 1 = 0.320337 loss) +I0616 13:54:44.370460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.357895 (* 1 = 0.357895 loss) +I0616 13:54:44.370465 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0198327 (* 1 = 0.0198327 loss) +I0616 13:54:44.370467 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.115994 (* 1 = 0.115994 loss) +I0616 13:54:44.370471 9857 solver.cpp:571] Iteration 70560, lr = 0.0001 +I0616 13:54:55.951966 9857 solver.cpp:242] Iteration 70580, loss = 0.257132 +I0616 13:54:55.952008 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167984 (* 1 = 0.167984 loss) +I0616 13:54:55.952013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14843 (* 1 = 0.14843 loss) +I0616 13:54:55.952018 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0263357 (* 1 = 0.0263357 loss) +I0616 13:54:55.952021 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00837418 (* 1 = 0.00837418 loss) +I0616 13:54:55.952025 9857 solver.cpp:571] Iteration 70580, lr = 0.0001 +speed: 0.603s / iter +I0616 13:55:07.662963 9857 solver.cpp:242] Iteration 70600, loss = 0.66571 +I0616 13:55:07.663005 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144277 (* 1 = 0.144277 loss) +I0616 13:55:07.663010 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195819 (* 1 = 0.195819 loss) +I0616 13:55:07.663015 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.018326 (* 1 = 0.018326 loss) +I0616 13:55:07.663019 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00830931 (* 1 = 0.00830931 loss) +I0616 13:55:07.663023 9857 solver.cpp:571] Iteration 70600, lr = 0.0001 +I0616 13:55:19.280699 9857 solver.cpp:242] Iteration 70620, loss = 0.407789 +I0616 13:55:19.280741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0621137 (* 1 = 0.0621137 loss) +I0616 13:55:19.280746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0841844 (* 1 = 0.0841844 loss) +I0616 13:55:19.280750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00874993 (* 1 = 0.00874993 loss) +I0616 13:55:19.280755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00389424 (* 1 = 0.00389424 loss) +I0616 13:55:19.280758 9857 solver.cpp:571] Iteration 70620, lr = 0.0001 +I0616 13:55:30.706786 9857 solver.cpp:242] Iteration 70640, loss = 0.616175 +I0616 13:55:30.706827 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176228 (* 1 = 0.176228 loss) +I0616 13:55:30.706833 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289764 (* 1 = 0.289764 loss) +I0616 13:55:30.706837 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0450736 (* 1 = 0.0450736 loss) +I0616 13:55:30.706840 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283139 (* 1 = 0.0283139 loss) +I0616 13:55:30.706845 9857 solver.cpp:571] Iteration 70640, lr = 0.0001 +I0616 13:55:42.392576 9857 solver.cpp:242] Iteration 70660, loss = 0.318119 +I0616 13:55:42.392619 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0528913 (* 1 = 0.0528913 loss) +I0616 13:55:42.392626 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163384 (* 1 = 0.163384 loss) +I0616 13:55:42.392629 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112557 (* 1 = 0.0112557 loss) +I0616 13:55:42.392633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00435866 (* 1 = 0.00435866 loss) +I0616 13:55:42.392637 9857 solver.cpp:571] Iteration 70660, lr = 0.0001 +I0616 13:55:53.848409 9857 solver.cpp:242] Iteration 70680, loss = 0.453615 +I0616 13:55:53.848450 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187675 (* 1 = 0.187675 loss) +I0616 13:55:53.848455 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271473 (* 1 = 0.271473 loss) +I0616 13:55:53.848460 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0889114 (* 1 = 0.0889114 loss) +I0616 13:55:53.848464 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0281133 (* 1 = 0.0281133 loss) +I0616 13:55:53.848467 9857 solver.cpp:571] Iteration 70680, lr = 0.0001 +I0616 13:56:05.306933 9857 solver.cpp:242] Iteration 70700, loss = 0.440322 +I0616 13:56:05.306977 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118174 (* 1 = 0.118174 loss) +I0616 13:56:05.306982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128905 (* 1 = 0.128905 loss) +I0616 13:56:05.306987 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0769846 (* 1 = 0.0769846 loss) +I0616 13:56:05.306990 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00918747 (* 1 = 0.00918747 loss) +I0616 13:56:05.306993 9857 solver.cpp:571] Iteration 70700, lr = 0.0001 +I0616 13:56:16.755596 9857 solver.cpp:242] Iteration 70720, loss = 0.444575 +I0616 13:56:16.755636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143453 (* 1 = 0.143453 loss) +I0616 13:56:16.755641 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191233 (* 1 = 0.191233 loss) +I0616 13:56:16.755645 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0158439 (* 1 = 0.0158439 loss) +I0616 13:56:16.755650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0213076 (* 1 = 0.0213076 loss) +I0616 13:56:16.755653 9857 solver.cpp:571] Iteration 70720, lr = 0.0001 +I0616 13:56:28.407632 9857 solver.cpp:242] Iteration 70740, loss = 0.55538 +I0616 13:56:28.407675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316311 (* 1 = 0.316311 loss) +I0616 13:56:28.407680 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123119 (* 1 = 0.123119 loss) +I0616 13:56:28.407685 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209028 (* 1 = 0.0209028 loss) +I0616 13:56:28.407687 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234302 (* 1 = 0.0234302 loss) +I0616 13:56:28.407691 9857 solver.cpp:571] Iteration 70740, lr = 0.0001 +I0616 13:56:40.176517 9857 solver.cpp:242] Iteration 70760, loss = 0.244784 +I0616 13:56:40.176558 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132857 (* 1 = 0.132857 loss) +I0616 13:56:40.176563 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161438 (* 1 = 0.161438 loss) +I0616 13:56:40.176568 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0168622 (* 1 = 0.0168622 loss) +I0616 13:56:40.176571 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0201338 (* 1 = 0.0201338 loss) +I0616 13:56:40.176575 9857 solver.cpp:571] Iteration 70760, lr = 0.0001 +I0616 13:56:51.546198 9857 solver.cpp:242] Iteration 70780, loss = 0.387719 +I0616 13:56:51.546239 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0537216 (* 1 = 0.0537216 loss) +I0616 13:56:51.546246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114464 (* 1 = 0.114464 loss) +I0616 13:56:51.546249 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0156587 (* 1 = 0.0156587 loss) +I0616 13:56:51.546252 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00392584 (* 1 = 0.00392584 loss) +I0616 13:56:51.546257 9857 solver.cpp:571] Iteration 70780, lr = 0.0001 +speed: 0.603s / iter +I0616 13:57:02.945523 9857 solver.cpp:242] Iteration 70800, loss = 0.717328 +I0616 13:57:02.945566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192225 (* 1 = 0.192225 loss) +I0616 13:57:02.945571 9857 solver.cpp:258] Train net output #1: loss_cls = 0.442824 (* 1 = 0.442824 loss) +I0616 13:57:02.945576 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00991965 (* 1 = 0.00991965 loss) +I0616 13:57:02.945580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00572815 (* 1 = 0.00572815 loss) +I0616 13:57:02.945583 9857 solver.cpp:571] Iteration 70800, lr = 0.0001 +I0616 13:57:14.195399 9857 solver.cpp:242] Iteration 70820, loss = 0.274455 +I0616 13:57:14.195441 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118036 (* 1 = 0.118036 loss) +I0616 13:57:14.195446 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178532 (* 1 = 0.178532 loss) +I0616 13:57:14.195451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0157145 (* 1 = 0.0157145 loss) +I0616 13:57:14.195454 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145829 (* 1 = 0.0145829 loss) +I0616 13:57:14.195457 9857 solver.cpp:571] Iteration 70820, lr = 0.0001 +I0616 13:57:25.656620 9857 solver.cpp:242] Iteration 70840, loss = 0.500637 +I0616 13:57:25.656661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175929 (* 1 = 0.175929 loss) +I0616 13:57:25.656666 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155032 (* 1 = 0.155032 loss) +I0616 13:57:25.656672 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0742663 (* 1 = 0.0742663 loss) +I0616 13:57:25.656674 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165768 (* 1 = 0.0165768 loss) +I0616 13:57:25.656678 9857 solver.cpp:571] Iteration 70840, lr = 0.0001 +I0616 13:57:37.349547 9857 solver.cpp:242] Iteration 70860, loss = 0.826821 +I0616 13:57:37.349591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195197 (* 1 = 0.195197 loss) +I0616 13:57:37.349596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134899 (* 1 = 0.134899 loss) +I0616 13:57:37.349601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0506876 (* 1 = 0.0506876 loss) +I0616 13:57:37.349604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0437095 (* 1 = 0.0437095 loss) +I0616 13:57:37.349607 9857 solver.cpp:571] Iteration 70860, lr = 0.0001 +I0616 13:57:48.518050 9857 solver.cpp:242] Iteration 70880, loss = 0.794094 +I0616 13:57:48.518091 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149891 (* 1 = 0.149891 loss) +I0616 13:57:48.518096 9857 solver.cpp:258] Train net output #1: loss_cls = 0.403182 (* 1 = 0.403182 loss) +I0616 13:57:48.518101 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.035882 (* 1 = 0.035882 loss) +I0616 13:57:48.518105 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0232838 (* 1 = 0.0232838 loss) +I0616 13:57:48.518108 9857 solver.cpp:571] Iteration 70880, lr = 0.0001 +I0616 13:57:59.895370 9857 solver.cpp:242] Iteration 70900, loss = 0.449627 +I0616 13:57:59.895411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.353934 (* 1 = 0.353934 loss) +I0616 13:57:59.895416 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264541 (* 1 = 0.264541 loss) +I0616 13:57:59.895421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0380396 (* 1 = 0.0380396 loss) +I0616 13:57:59.895424 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0604366 (* 1 = 0.0604366 loss) +I0616 13:57:59.895428 9857 solver.cpp:571] Iteration 70900, lr = 0.0001 +I0616 13:58:11.205065 9857 solver.cpp:242] Iteration 70920, loss = 0.295459 +I0616 13:58:11.205106 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0930353 (* 1 = 0.0930353 loss) +I0616 13:58:11.205111 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24206 (* 1 = 0.24206 loss) +I0616 13:58:11.205114 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0633503 (* 1 = 0.0633503 loss) +I0616 13:58:11.205118 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160571 (* 1 = 0.0160571 loss) +I0616 13:58:11.205122 9857 solver.cpp:571] Iteration 70920, lr = 0.0001 +I0616 13:58:22.752436 9857 solver.cpp:242] Iteration 70940, loss = 0.533275 +I0616 13:58:22.752478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30555 (* 1 = 0.30555 loss) +I0616 13:58:22.752483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211321 (* 1 = 0.211321 loss) +I0616 13:58:22.752488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0323472 (* 1 = 0.0323472 loss) +I0616 13:58:22.752491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107888 (* 1 = 0.0107888 loss) +I0616 13:58:22.752496 9857 solver.cpp:571] Iteration 70940, lr = 0.0001 +I0616 13:58:34.362468 9857 solver.cpp:242] Iteration 70960, loss = 0.531954 +I0616 13:58:34.362509 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22293 (* 1 = 0.22293 loss) +I0616 13:58:34.362515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.36881 (* 1 = 0.36881 loss) +I0616 13:58:34.362519 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0617172 (* 1 = 0.0617172 loss) +I0616 13:58:34.362524 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0290461 (* 1 = 0.0290461 loss) +I0616 13:58:34.362526 9857 solver.cpp:571] Iteration 70960, lr = 0.0001 +I0616 13:58:45.821745 9857 solver.cpp:242] Iteration 70980, loss = 0.644047 +I0616 13:58:45.821786 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.198391 (* 1 = 0.198391 loss) +I0616 13:58:45.821791 9857 solver.cpp:258] Train net output #1: loss_cls = 0.436577 (* 1 = 0.436577 loss) +I0616 13:58:45.821795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151243 (* 1 = 0.151243 loss) +I0616 13:58:45.821799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0360988 (* 1 = 0.0360988 loss) +I0616 13:58:45.821804 9857 solver.cpp:571] Iteration 70980, lr = 0.0001 +speed: 0.602s / iter +I0616 13:58:57.127779 9857 solver.cpp:242] Iteration 71000, loss = 0.560632 +I0616 13:58:57.127820 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0959832 (* 1 = 0.0959832 loss) +I0616 13:58:57.127825 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19965 (* 1 = 0.19965 loss) +I0616 13:58:57.127830 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0966314 (* 1 = 0.0966314 loss) +I0616 13:58:57.127833 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135354 (* 1 = 0.0135354 loss) +I0616 13:58:57.127837 9857 solver.cpp:571] Iteration 71000, lr = 0.0001 +I0616 13:59:08.534219 9857 solver.cpp:242] Iteration 71020, loss = 0.324549 +I0616 13:59:08.534260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0632078 (* 1 = 0.0632078 loss) +I0616 13:59:08.534265 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16183 (* 1 = 0.16183 loss) +I0616 13:59:08.534270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0273853 (* 1 = 0.0273853 loss) +I0616 13:59:08.534273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00866661 (* 1 = 0.00866661 loss) +I0616 13:59:08.534277 9857 solver.cpp:571] Iteration 71020, lr = 0.0001 +I0616 13:59:20.137631 9857 solver.cpp:242] Iteration 71040, loss = 0.405719 +I0616 13:59:20.137673 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0807427 (* 1 = 0.0807427 loss) +I0616 13:59:20.137678 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159055 (* 1 = 0.159055 loss) +I0616 13:59:20.137683 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107355 (* 1 = 0.107355 loss) +I0616 13:59:20.137686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00394772 (* 1 = 0.00394772 loss) +I0616 13:59:20.137691 9857 solver.cpp:571] Iteration 71040, lr = 0.0001 +I0616 13:59:31.910549 9857 solver.cpp:242] Iteration 71060, loss = 0.481558 +I0616 13:59:31.910590 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100873 (* 1 = 0.100873 loss) +I0616 13:59:31.910596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132895 (* 1 = 0.132895 loss) +I0616 13:59:31.910600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0261494 (* 1 = 0.0261494 loss) +I0616 13:59:31.910604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140653 (* 1 = 0.0140653 loss) +I0616 13:59:31.910609 9857 solver.cpp:571] Iteration 71060, lr = 0.0001 +I0616 13:59:43.279270 9857 solver.cpp:242] Iteration 71080, loss = 0.350431 +I0616 13:59:43.279310 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101051 (* 1 = 0.101051 loss) +I0616 13:59:43.279317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338698 (* 1 = 0.338698 loss) +I0616 13:59:43.279322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00574775 (* 1 = 0.00574775 loss) +I0616 13:59:43.279326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714359 (* 1 = 0.0714359 loss) +I0616 13:59:43.279330 9857 solver.cpp:571] Iteration 71080, lr = 0.0001 +I0616 13:59:55.011430 9857 solver.cpp:242] Iteration 71100, loss = 0.874977 +I0616 13:59:55.011469 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0753988 (* 1 = 0.0753988 loss) +I0616 13:59:55.011476 9857 solver.cpp:258] Train net output #1: loss_cls = 0.333761 (* 1 = 0.333761 loss) +I0616 13:59:55.011479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11786 (* 1 = 0.11786 loss) +I0616 13:59:55.011483 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0581334 (* 1 = 0.0581334 loss) +I0616 13:59:55.011487 9857 solver.cpp:571] Iteration 71100, lr = 0.0001 +I0616 14:00:06.450641 9857 solver.cpp:242] Iteration 71120, loss = 0.782331 +I0616 14:00:06.450683 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159035 (* 1 = 0.159035 loss) +I0616 14:00:06.450688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173296 (* 1 = 0.173296 loss) +I0616 14:00:06.450693 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0295127 (* 1 = 0.0295127 loss) +I0616 14:00:06.450697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0024801 (* 1 = 0.0024801 loss) +I0616 14:00:06.450700 9857 solver.cpp:571] Iteration 71120, lr = 0.0001 +I0616 14:00:17.846302 9857 solver.cpp:242] Iteration 71140, loss = 0.512575 +I0616 14:00:17.846344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.351081 (* 1 = 0.351081 loss) +I0616 14:00:17.846350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22023 (* 1 = 0.22023 loss) +I0616 14:00:17.846355 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.070625 (* 1 = 0.070625 loss) +I0616 14:00:17.846359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0437176 (* 1 = 0.0437176 loss) +I0616 14:00:17.846362 9857 solver.cpp:571] Iteration 71140, lr = 0.0001 +I0616 14:00:29.299015 9857 solver.cpp:242] Iteration 71160, loss = 0.499054 +I0616 14:00:29.299057 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122975 (* 1 = 0.122975 loss) +I0616 14:00:29.299062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159465 (* 1 = 0.159465 loss) +I0616 14:00:29.299067 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0301336 (* 1 = 0.0301336 loss) +I0616 14:00:29.299070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0218093 (* 1 = 0.0218093 loss) +I0616 14:00:29.299073 9857 solver.cpp:571] Iteration 71160, lr = 0.0001 +I0616 14:00:40.398551 9857 solver.cpp:242] Iteration 71180, loss = 0.544182 +I0616 14:00:40.398594 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190588 (* 1 = 0.190588 loss) +I0616 14:00:40.398600 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197133 (* 1 = 0.197133 loss) +I0616 14:00:40.398604 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.012153 (* 1 = 0.012153 loss) +I0616 14:00:40.398608 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175171 (* 1 = 0.0175171 loss) +I0616 14:00:40.398612 9857 solver.cpp:571] Iteration 71180, lr = 0.0001 +speed: 0.602s / iter +I0616 14:00:51.722823 9857 solver.cpp:242] Iteration 71200, loss = 0.658304 +I0616 14:00:51.722865 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.414861 (* 1 = 0.414861 loss) +I0616 14:00:51.722870 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304346 (* 1 = 0.304346 loss) +I0616 14:00:51.722875 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0907616 (* 1 = 0.0907616 loss) +I0616 14:00:51.722878 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0395923 (* 1 = 0.0395923 loss) +I0616 14:00:51.722883 9857 solver.cpp:571] Iteration 71200, lr = 0.0001 +I0616 14:01:03.196940 9857 solver.cpp:242] Iteration 71220, loss = 0.282447 +I0616 14:01:03.196985 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0566019 (* 1 = 0.0566019 loss) +I0616 14:01:03.196990 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174617 (* 1 = 0.174617 loss) +I0616 14:01:03.196995 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222809 (* 1 = 0.0222809 loss) +I0616 14:01:03.196998 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00809844 (* 1 = 0.00809844 loss) +I0616 14:01:03.197002 9857 solver.cpp:571] Iteration 71220, lr = 0.0001 +I0616 14:01:14.549366 9857 solver.cpp:242] Iteration 71240, loss = 0.303114 +I0616 14:01:14.549407 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0739111 (* 1 = 0.0739111 loss) +I0616 14:01:14.549413 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169245 (* 1 = 0.169245 loss) +I0616 14:01:14.549417 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.033293 (* 1 = 0.033293 loss) +I0616 14:01:14.549420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0178828 (* 1 = 0.0178828 loss) +I0616 14:01:14.549427 9857 solver.cpp:571] Iteration 71240, lr = 0.0001 +I0616 14:01:25.955723 9857 solver.cpp:242] Iteration 71260, loss = 0.496644 +I0616 14:01:25.955765 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352556 (* 1 = 0.352556 loss) +I0616 14:01:25.955770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.335064 (* 1 = 0.335064 loss) +I0616 14:01:25.955773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0737323 (* 1 = 0.0737323 loss) +I0616 14:01:25.955777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014552 (* 1 = 0.014552 loss) +I0616 14:01:25.955781 9857 solver.cpp:571] Iteration 71260, lr = 0.0001 +I0616 14:01:37.482993 9857 solver.cpp:242] Iteration 71280, loss = 0.622326 +I0616 14:01:37.483036 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202794 (* 1 = 0.202794 loss) +I0616 14:01:37.483042 9857 solver.cpp:258] Train net output #1: loss_cls = 0.117708 (* 1 = 0.117708 loss) +I0616 14:01:37.483047 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101509 (* 1 = 0.101509 loss) +I0616 14:01:37.483049 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0931248 (* 1 = 0.0931248 loss) +I0616 14:01:37.483053 9857 solver.cpp:571] Iteration 71280, lr = 0.0001 +I0616 14:01:49.100347 9857 solver.cpp:242] Iteration 71300, loss = 0.323891 +I0616 14:01:49.100389 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.083904 (* 1 = 0.083904 loss) +I0616 14:01:49.100394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0862167 (* 1 = 0.0862167 loss) +I0616 14:01:49.100399 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00780824 (* 1 = 0.00780824 loss) +I0616 14:01:49.100402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136522 (* 1 = 0.0136522 loss) +I0616 14:01:49.100406 9857 solver.cpp:571] Iteration 71300, lr = 0.0001 +I0616 14:02:00.942924 9857 solver.cpp:242] Iteration 71320, loss = 0.862267 +I0616 14:02:00.942966 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326223 (* 1 = 0.326223 loss) +I0616 14:02:00.942972 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332171 (* 1 = 0.332171 loss) +I0616 14:02:00.942976 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.067918 (* 1 = 0.067918 loss) +I0616 14:02:00.942981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.032132 (* 1 = 0.032132 loss) +I0616 14:02:00.942984 9857 solver.cpp:571] Iteration 71320, lr = 0.0001 +I0616 14:02:12.323554 9857 solver.cpp:242] Iteration 71340, loss = 0.583068 +I0616 14:02:12.323592 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204425 (* 1 = 0.204425 loss) +I0616 14:02:12.323597 9857 solver.cpp:258] Train net output #1: loss_cls = 0.427271 (* 1 = 0.427271 loss) +I0616 14:02:12.323601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0639658 (* 1 = 0.0639658 loss) +I0616 14:02:12.323606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0314437 (* 1 = 0.0314437 loss) +I0616 14:02:12.323609 9857 solver.cpp:571] Iteration 71340, lr = 0.0001 +I0616 14:02:23.658748 9857 solver.cpp:242] Iteration 71360, loss = 0.663609 +I0616 14:02:23.658793 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382278 (* 1 = 0.382278 loss) +I0616 14:02:23.658799 9857 solver.cpp:258] Train net output #1: loss_cls = 0.336591 (* 1 = 0.336591 loss) +I0616 14:02:23.658803 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.172137 (* 1 = 0.172137 loss) +I0616 14:02:23.658807 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.082745 (* 1 = 0.082745 loss) +I0616 14:02:23.658810 9857 solver.cpp:571] Iteration 71360, lr = 0.0001 +I0616 14:02:35.177465 9857 solver.cpp:242] Iteration 71380, loss = 0.863364 +I0616 14:02:35.177507 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0884056 (* 1 = 0.0884056 loss) +I0616 14:02:35.177513 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100958 (* 1 = 0.100958 loss) +I0616 14:02:35.177518 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00582841 (* 1 = 0.00582841 loss) +I0616 14:02:35.177522 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00862775 (* 1 = 0.00862775 loss) +I0616 14:02:35.177525 9857 solver.cpp:571] Iteration 71380, lr = 0.0001 +speed: 0.602s / iter +I0616 14:02:46.684092 9857 solver.cpp:242] Iteration 71400, loss = 0.44801 +I0616 14:02:46.684134 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298733 (* 1 = 0.298733 loss) +I0616 14:02:46.684139 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242833 (* 1 = 0.242833 loss) +I0616 14:02:46.684142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0358037 (* 1 = 0.0358037 loss) +I0616 14:02:46.684146 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0188152 (* 1 = 0.0188152 loss) +I0616 14:02:46.684150 9857 solver.cpp:571] Iteration 71400, lr = 0.0001 +I0616 14:02:58.393579 9857 solver.cpp:242] Iteration 71420, loss = 0.315004 +I0616 14:02:58.393620 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131324 (* 1 = 0.131324 loss) +I0616 14:02:58.393626 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12745 (* 1 = 0.12745 loss) +I0616 14:02:58.393630 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0574602 (* 1 = 0.0574602 loss) +I0616 14:02:58.393635 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00858612 (* 1 = 0.00858612 loss) +I0616 14:02:58.393652 9857 solver.cpp:571] Iteration 71420, lr = 0.0001 +I0616 14:03:09.953188 9857 solver.cpp:242] Iteration 71440, loss = 0.692484 +I0616 14:03:09.953229 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199334 (* 1 = 0.199334 loss) +I0616 14:03:09.953235 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200087 (* 1 = 0.200087 loss) +I0616 14:03:09.953239 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.096575 (* 1 = 0.096575 loss) +I0616 14:03:09.953243 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0981527 (* 1 = 0.0981527 loss) +I0616 14:03:09.953248 9857 solver.cpp:571] Iteration 71440, lr = 0.0001 +I0616 14:03:21.485810 9857 solver.cpp:242] Iteration 71460, loss = 0.648732 +I0616 14:03:21.485852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210334 (* 1 = 0.210334 loss) +I0616 14:03:21.485857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257351 (* 1 = 0.257351 loss) +I0616 14:03:21.485860 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0322651 (* 1 = 0.0322651 loss) +I0616 14:03:21.485864 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107755 (* 1 = 0.107755 loss) +I0616 14:03:21.485868 9857 solver.cpp:571] Iteration 71460, lr = 0.0001 +I0616 14:03:32.990255 9857 solver.cpp:242] Iteration 71480, loss = 0.681361 +I0616 14:03:32.990298 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359684 (* 1 = 0.359684 loss) +I0616 14:03:32.990303 9857 solver.cpp:258] Train net output #1: loss_cls = 0.495504 (* 1 = 0.495504 loss) +I0616 14:03:32.990308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0500293 (* 1 = 0.0500293 loss) +I0616 14:03:32.990311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0547391 (* 1 = 0.0547391 loss) +I0616 14:03:32.990315 9857 solver.cpp:571] Iteration 71480, lr = 0.0001 +I0616 14:03:44.568334 9857 solver.cpp:242] Iteration 71500, loss = 0.351756 +I0616 14:03:44.568377 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177248 (* 1 = 0.177248 loss) +I0616 14:03:44.568382 9857 solver.cpp:258] Train net output #1: loss_cls = 0.188731 (* 1 = 0.188731 loss) +I0616 14:03:44.568387 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.049239 (* 1 = 0.049239 loss) +I0616 14:03:44.568390 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334906 (* 1 = 0.0334906 loss) +I0616 14:03:44.568393 9857 solver.cpp:571] Iteration 71500, lr = 0.0001 +I0616 14:03:56.072989 9857 solver.cpp:242] Iteration 71520, loss = 0.339675 +I0616 14:03:56.073030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131748 (* 1 = 0.131748 loss) +I0616 14:03:56.073035 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196319 (* 1 = 0.196319 loss) +I0616 14:03:56.073040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0636166 (* 1 = 0.0636166 loss) +I0616 14:03:56.073043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142747 (* 1 = 0.0142747 loss) +I0616 14:03:56.073047 9857 solver.cpp:571] Iteration 71520, lr = 0.0001 +I0616 14:04:07.490238 9857 solver.cpp:242] Iteration 71540, loss = 0.307548 +I0616 14:04:07.490279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105462 (* 1 = 0.105462 loss) +I0616 14:04:07.490284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215692 (* 1 = 0.215692 loss) +I0616 14:04:07.490289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0771397 (* 1 = 0.0771397 loss) +I0616 14:04:07.490293 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288804 (* 1 = 0.0288804 loss) +I0616 14:04:07.490296 9857 solver.cpp:571] Iteration 71540, lr = 0.0001 +I0616 14:04:19.077433 9857 solver.cpp:242] Iteration 71560, loss = 0.382604 +I0616 14:04:19.077476 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0754188 (* 1 = 0.0754188 loss) +I0616 14:04:19.077482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129983 (* 1 = 0.129983 loss) +I0616 14:04:19.077486 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0148747 (* 1 = 0.0148747 loss) +I0616 14:04:19.077491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117255 (* 1 = 0.0117255 loss) +I0616 14:04:19.077494 9857 solver.cpp:571] Iteration 71560, lr = 0.0001 +I0616 14:04:30.577553 9857 solver.cpp:242] Iteration 71580, loss = 0.274788 +I0616 14:04:30.577594 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0889695 (* 1 = 0.0889695 loss) +I0616 14:04:30.577599 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108037 (* 1 = 0.108037 loss) +I0616 14:04:30.577603 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0491583 (* 1 = 0.0491583 loss) +I0616 14:04:30.577607 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000802021 (* 1 = 0.000802021 loss) +I0616 14:04:30.577610 9857 solver.cpp:571] Iteration 71580, lr = 0.0001 +speed: 0.602s / iter +I0616 14:04:41.974898 9857 solver.cpp:242] Iteration 71600, loss = 0.913135 +I0616 14:04:41.974941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149824 (* 1 = 0.149824 loss) +I0616 14:04:41.974946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.236345 (* 1 = 0.236345 loss) +I0616 14:04:41.974951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175252 (* 1 = 0.175252 loss) +I0616 14:04:41.974954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156941 (* 1 = 0.156941 loss) +I0616 14:04:41.974958 9857 solver.cpp:571] Iteration 71600, lr = 0.0001 +I0616 14:04:53.375710 9857 solver.cpp:242] Iteration 71620, loss = 0.408947 +I0616 14:04:53.375748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0997613 (* 1 = 0.0997613 loss) +I0616 14:04:53.375753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135844 (* 1 = 0.135844 loss) +I0616 14:04:53.375758 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.02973 (* 1 = 0.02973 loss) +I0616 14:04:53.375762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103792 (* 1 = 0.0103792 loss) +I0616 14:04:53.375766 9857 solver.cpp:571] Iteration 71620, lr = 0.0001 +I0616 14:05:04.800442 9857 solver.cpp:242] Iteration 71640, loss = 0.403243 +I0616 14:05:04.800483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186833 (* 1 = 0.186833 loss) +I0616 14:05:04.800489 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200048 (* 1 = 0.200048 loss) +I0616 14:05:04.800493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118756 (* 1 = 0.118756 loss) +I0616 14:05:04.800498 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0372096 (* 1 = 0.0372096 loss) +I0616 14:05:04.800501 9857 solver.cpp:571] Iteration 71640, lr = 0.0001 +I0616 14:05:16.398069 9857 solver.cpp:242] Iteration 71660, loss = 0.48253 +I0616 14:05:16.398113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271659 (* 1 = 0.271659 loss) +I0616 14:05:16.398118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.331119 (* 1 = 0.331119 loss) +I0616 14:05:16.398123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101337 (* 1 = 0.101337 loss) +I0616 14:05:16.398125 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0838018 (* 1 = 0.0838018 loss) +I0616 14:05:16.398129 9857 solver.cpp:571] Iteration 71660, lr = 0.0001 +I0616 14:05:27.822697 9857 solver.cpp:242] Iteration 71680, loss = 0.249895 +I0616 14:05:27.822741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0538799 (* 1 = 0.0538799 loss) +I0616 14:05:27.822746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.098344 (* 1 = 0.098344 loss) +I0616 14:05:27.822751 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0305688 (* 1 = 0.0305688 loss) +I0616 14:05:27.822753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129769 (* 1 = 0.0129769 loss) +I0616 14:05:27.822760 9857 solver.cpp:571] Iteration 71680, lr = 0.0001 +I0616 14:05:39.358877 9857 solver.cpp:242] Iteration 71700, loss = 1.03814 +I0616 14:05:39.358919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.314411 (* 1 = 0.314411 loss) +I0616 14:05:39.358925 9857 solver.cpp:258] Train net output #1: loss_cls = 0.691563 (* 1 = 0.691563 loss) +I0616 14:05:39.358929 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.197274 (* 1 = 0.197274 loss) +I0616 14:05:39.358933 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.716343 (* 1 = 0.716343 loss) +I0616 14:05:39.358937 9857 solver.cpp:571] Iteration 71700, lr = 0.0001 +I0616 14:05:50.789145 9857 solver.cpp:242] Iteration 71720, loss = 0.570449 +I0616 14:05:50.789187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.245815 (* 1 = 0.245815 loss) +I0616 14:05:50.789193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278604 (* 1 = 0.278604 loss) +I0616 14:05:50.789198 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0345251 (* 1 = 0.0345251 loss) +I0616 14:05:50.789201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0527367 (* 1 = 0.0527367 loss) +I0616 14:05:50.789206 9857 solver.cpp:571] Iteration 71720, lr = 0.0001 +I0616 14:06:02.377966 9857 solver.cpp:242] Iteration 71740, loss = 0.580572 +I0616 14:06:02.378008 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203239 (* 1 = 0.203239 loss) +I0616 14:06:02.378013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28502 (* 1 = 0.28502 loss) +I0616 14:06:02.378018 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104747 (* 1 = 0.104747 loss) +I0616 14:06:02.378022 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.025433 (* 1 = 0.025433 loss) +I0616 14:06:02.378026 9857 solver.cpp:571] Iteration 71740, lr = 0.0001 +I0616 14:06:14.116956 9857 solver.cpp:242] Iteration 71760, loss = 0.396218 +I0616 14:06:14.116997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0730835 (* 1 = 0.0730835 loss) +I0616 14:06:14.117002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0832257 (* 1 = 0.0832257 loss) +I0616 14:06:14.117007 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0246764 (* 1 = 0.0246764 loss) +I0616 14:06:14.117012 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010409 (* 1 = 0.010409 loss) +I0616 14:06:14.117014 9857 solver.cpp:571] Iteration 71760, lr = 0.0001 +I0616 14:06:25.379534 9857 solver.cpp:242] Iteration 71780, loss = 0.390394 +I0616 14:06:25.379575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178132 (* 1 = 0.178132 loss) +I0616 14:06:25.379580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21202 (* 1 = 0.21202 loss) +I0616 14:06:25.379585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145454 (* 1 = 0.145454 loss) +I0616 14:06:25.379588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00983826 (* 1 = 0.00983826 loss) +I0616 14:06:25.379591 9857 solver.cpp:571] Iteration 71780, lr = 0.0001 +speed: 0.602s / iter +I0616 14:06:36.591573 9857 solver.cpp:242] Iteration 71800, loss = 0.527822 +I0616 14:06:36.591615 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363224 (* 1 = 0.363224 loss) +I0616 14:06:36.591621 9857 solver.cpp:258] Train net output #1: loss_cls = 0.405612 (* 1 = 0.405612 loss) +I0616 14:06:36.591625 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0470481 (* 1 = 0.0470481 loss) +I0616 14:06:36.591629 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0465119 (* 1 = 0.0465119 loss) +I0616 14:06:36.591634 9857 solver.cpp:571] Iteration 71800, lr = 0.0001 +I0616 14:06:48.359975 9857 solver.cpp:242] Iteration 71820, loss = 0.299124 +I0616 14:06:48.360018 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121152 (* 1 = 0.121152 loss) +I0616 14:06:48.360023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.086068 (* 1 = 0.086068 loss) +I0616 14:06:48.360028 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0857632 (* 1 = 0.0857632 loss) +I0616 14:06:48.360031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.032052 (* 1 = 0.032052 loss) +I0616 14:06:48.360035 9857 solver.cpp:571] Iteration 71820, lr = 0.0001 +I0616 14:06:59.838446 9857 solver.cpp:242] Iteration 71840, loss = 0.601218 +I0616 14:06:59.838487 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0520632 (* 1 = 0.0520632 loss) +I0616 14:06:59.838493 9857 solver.cpp:258] Train net output #1: loss_cls = 0.141694 (* 1 = 0.141694 loss) +I0616 14:06:59.838497 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155005 (* 1 = 0.0155005 loss) +I0616 14:06:59.838501 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101814 (* 1 = 0.0101814 loss) +I0616 14:06:59.838505 9857 solver.cpp:571] Iteration 71840, lr = 0.0001 +I0616 14:07:11.437633 9857 solver.cpp:242] Iteration 71860, loss = 0.779355 +I0616 14:07:11.437674 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128736 (* 1 = 0.128736 loss) +I0616 14:07:11.437680 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22378 (* 1 = 0.22378 loss) +I0616 14:07:11.437685 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0639725 (* 1 = 0.0639725 loss) +I0616 14:07:11.437688 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144283 (* 1 = 0.0144283 loss) +I0616 14:07:11.437692 9857 solver.cpp:571] Iteration 71860, lr = 0.0001 +I0616 14:07:22.916909 9857 solver.cpp:242] Iteration 71880, loss = 0.43966 +I0616 14:07:22.916951 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0551676 (* 1 = 0.0551676 loss) +I0616 14:07:22.916957 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119903 (* 1 = 0.119903 loss) +I0616 14:07:22.916961 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0110333 (* 1 = 0.0110333 loss) +I0616 14:07:22.916965 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00136662 (* 1 = 0.00136662 loss) +I0616 14:07:22.916970 9857 solver.cpp:571] Iteration 71880, lr = 0.0001 +I0616 14:07:34.433341 9857 solver.cpp:242] Iteration 71900, loss = 0.485902 +I0616 14:07:34.433384 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203819 (* 1 = 0.203819 loss) +I0616 14:07:34.433390 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350969 (* 1 = 0.350969 loss) +I0616 14:07:34.433394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0787036 (* 1 = 0.0787036 loss) +I0616 14:07:34.433398 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0723367 (* 1 = 0.0723367 loss) +I0616 14:07:34.433403 9857 solver.cpp:571] Iteration 71900, lr = 0.0001 +I0616 14:07:46.150769 9857 solver.cpp:242] Iteration 71920, loss = 0.421055 +I0616 14:07:46.150812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227059 (* 1 = 0.227059 loss) +I0616 14:07:46.150817 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264473 (* 1 = 0.264473 loss) +I0616 14:07:46.150821 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537144 (* 1 = 0.0537144 loss) +I0616 14:07:46.150825 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14169 (* 1 = 0.14169 loss) +I0616 14:07:46.150830 9857 solver.cpp:571] Iteration 71920, lr = 0.0001 +I0616 14:07:57.718701 9857 solver.cpp:242] Iteration 71940, loss = 0.575741 +I0616 14:07:57.718744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169355 (* 1 = 0.169355 loss) +I0616 14:07:57.718750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168123 (* 1 = 0.168123 loss) +I0616 14:07:57.718755 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163749 (* 1 = 0.163749 loss) +I0616 14:07:57.718765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.53492 (* 1 = 0.53492 loss) +I0616 14:07:57.718768 9857 solver.cpp:571] Iteration 71940, lr = 0.0001 +I0616 14:08:09.027848 9857 solver.cpp:242] Iteration 71960, loss = 0.374778 +I0616 14:08:09.027889 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221108 (* 1 = 0.221108 loss) +I0616 14:08:09.027894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207449 (* 1 = 0.207449 loss) +I0616 14:08:09.027899 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0827602 (* 1 = 0.0827602 loss) +I0616 14:08:09.027902 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0338149 (* 1 = 0.0338149 loss) +I0616 14:08:09.027906 9857 solver.cpp:571] Iteration 71960, lr = 0.0001 +I0616 14:08:20.473129 9857 solver.cpp:242] Iteration 71980, loss = 0.845317 +I0616 14:08:20.473170 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0893744 (* 1 = 0.0893744 loss) +I0616 14:08:20.473176 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252104 (* 1 = 0.252104 loss) +I0616 14:08:20.473179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274001 (* 1 = 0.0274001 loss) +I0616 14:08:20.473183 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163237 (* 1 = 0.0163237 loss) +I0616 14:08:20.473187 9857 solver.cpp:571] Iteration 71980, lr = 0.0001 +speed: 0.602s / iter +I0616 14:08:31.744618 9857 solver.cpp:242] Iteration 72000, loss = 0.516793 +I0616 14:08:31.744660 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.155852 (* 1 = 0.155852 loss) +I0616 14:08:31.744665 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342913 (* 1 = 0.342913 loss) +I0616 14:08:31.744669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00963181 (* 1 = 0.00963181 loss) +I0616 14:08:31.744673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00606649 (* 1 = 0.00606649 loss) +I0616 14:08:31.744678 9857 solver.cpp:571] Iteration 72000, lr = 0.0001 +I0616 14:08:43.106876 9857 solver.cpp:242] Iteration 72020, loss = 0.623016 +I0616 14:08:43.106919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0811827 (* 1 = 0.0811827 loss) +I0616 14:08:43.106925 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0825022 (* 1 = 0.0825022 loss) +I0616 14:08:43.106928 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00946604 (* 1 = 0.00946604 loss) +I0616 14:08:43.106932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00192082 (* 1 = 0.00192082 loss) +I0616 14:08:43.106936 9857 solver.cpp:571] Iteration 72020, lr = 0.0001 +I0616 14:08:54.566968 9857 solver.cpp:242] Iteration 72040, loss = 0.519179 +I0616 14:08:54.567009 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0726199 (* 1 = 0.0726199 loss) +I0616 14:08:54.567014 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180666 (* 1 = 0.180666 loss) +I0616 14:08:54.567018 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0329134 (* 1 = 0.0329134 loss) +I0616 14:08:54.567023 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00760893 (* 1 = 0.00760893 loss) +I0616 14:08:54.567026 9857 solver.cpp:571] Iteration 72040, lr = 0.0001 +I0616 14:09:06.130177 9857 solver.cpp:242] Iteration 72060, loss = 0.421286 +I0616 14:09:06.130218 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0898895 (* 1 = 0.0898895 loss) +I0616 14:09:06.130223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131712 (* 1 = 0.131712 loss) +I0616 14:09:06.130228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.019143 (* 1 = 0.019143 loss) +I0616 14:09:06.130231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251157 (* 1 = 0.0251157 loss) +I0616 14:09:06.130234 9857 solver.cpp:571] Iteration 72060, lr = 0.0001 +I0616 14:09:17.647699 9857 solver.cpp:242] Iteration 72080, loss = 0.805637 +I0616 14:09:17.647740 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.429264 (* 1 = 0.429264 loss) +I0616 14:09:17.647747 9857 solver.cpp:258] Train net output #1: loss_cls = 0.41623 (* 1 = 0.41623 loss) +I0616 14:09:17.647750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.230918 (* 1 = 0.230918 loss) +I0616 14:09:17.647754 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.184674 (* 1 = 0.184674 loss) +I0616 14:09:17.647758 9857 solver.cpp:571] Iteration 72080, lr = 0.0001 +I0616 14:09:29.539813 9857 solver.cpp:242] Iteration 72100, loss = 0.520346 +I0616 14:09:29.539855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134458 (* 1 = 0.134458 loss) +I0616 14:09:29.539860 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134392 (* 1 = 0.134392 loss) +I0616 14:09:29.539865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0215122 (* 1 = 0.0215122 loss) +I0616 14:09:29.539868 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00861218 (* 1 = 0.00861218 loss) +I0616 14:09:29.539872 9857 solver.cpp:571] Iteration 72100, lr = 0.0001 +I0616 14:09:41.165156 9857 solver.cpp:242] Iteration 72120, loss = 0.563548 +I0616 14:09:41.165199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287449 (* 1 = 0.287449 loss) +I0616 14:09:41.165205 9857 solver.cpp:258] Train net output #1: loss_cls = 0.362356 (* 1 = 0.362356 loss) +I0616 14:09:41.165208 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0580536 (* 1 = 0.0580536 loss) +I0616 14:09:41.165212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0243563 (* 1 = 0.0243563 loss) +I0616 14:09:41.165215 9857 solver.cpp:571] Iteration 72120, lr = 0.0001 +I0616 14:09:52.859094 9857 solver.cpp:242] Iteration 72140, loss = 0.440627 +I0616 14:09:52.859136 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.142708 (* 1 = 0.142708 loss) +I0616 14:09:52.859143 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432306 (* 1 = 0.432306 loss) +I0616 14:09:52.859146 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.067207 (* 1 = 0.067207 loss) +I0616 14:09:52.859150 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0354468 (* 1 = 0.0354468 loss) +I0616 14:09:52.859154 9857 solver.cpp:571] Iteration 72140, lr = 0.0001 +I0616 14:10:04.385128 9857 solver.cpp:242] Iteration 72160, loss = 0.778719 +I0616 14:10:04.385169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.376215 (* 1 = 0.376215 loss) +I0616 14:10:04.385174 9857 solver.cpp:258] Train net output #1: loss_cls = 0.493959 (* 1 = 0.493959 loss) +I0616 14:10:04.385179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0991974 (* 1 = 0.0991974 loss) +I0616 14:10:04.385182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0336387 (* 1 = 0.0336387 loss) +I0616 14:10:04.385186 9857 solver.cpp:571] Iteration 72160, lr = 0.0001 +I0616 14:10:15.889039 9857 solver.cpp:242] Iteration 72180, loss = 0.345217 +I0616 14:10:15.889081 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11033 (* 1 = 0.11033 loss) +I0616 14:10:15.889086 9857 solver.cpp:258] Train net output #1: loss_cls = 0.297931 (* 1 = 0.297931 loss) +I0616 14:10:15.889091 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0293326 (* 1 = 0.0293326 loss) +I0616 14:10:15.889094 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0118389 (* 1 = 0.0118389 loss) +I0616 14:10:15.889099 9857 solver.cpp:571] Iteration 72180, lr = 0.0001 +speed: 0.602s / iter +I0616 14:10:27.558367 9857 solver.cpp:242] Iteration 72200, loss = 0.492328 +I0616 14:10:27.558408 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178376 (* 1 = 0.178376 loss) +I0616 14:10:27.558413 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260152 (* 1 = 0.260152 loss) +I0616 14:10:27.558418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151472 (* 1 = 0.151472 loss) +I0616 14:10:27.558421 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.155774 (* 1 = 0.155774 loss) +I0616 14:10:27.558424 9857 solver.cpp:571] Iteration 72200, lr = 0.0001 +I0616 14:10:38.996716 9857 solver.cpp:242] Iteration 72220, loss = 1.02252 +I0616 14:10:38.996757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154624 (* 1 = 0.154624 loss) +I0616 14:10:38.996762 9857 solver.cpp:258] Train net output #1: loss_cls = 0.421153 (* 1 = 0.421153 loss) +I0616 14:10:38.996767 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.32566 (* 1 = 0.32566 loss) +I0616 14:10:38.996769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0968429 (* 1 = 0.0968429 loss) +I0616 14:10:38.996773 9857 solver.cpp:571] Iteration 72220, lr = 0.0001 +I0616 14:10:50.432782 9857 solver.cpp:242] Iteration 72240, loss = 0.386228 +I0616 14:10:50.432824 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206926 (* 1 = 0.206926 loss) +I0616 14:10:50.432829 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153299 (* 1 = 0.153299 loss) +I0616 14:10:50.432834 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0889985 (* 1 = 0.0889985 loss) +I0616 14:10:50.432838 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0464168 (* 1 = 0.0464168 loss) +I0616 14:10:50.432842 9857 solver.cpp:571] Iteration 72240, lr = 0.0001 +I0616 14:11:02.221447 9857 solver.cpp:242] Iteration 72260, loss = 0.881293 +I0616 14:11:02.221490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290297 (* 1 = 0.290297 loss) +I0616 14:11:02.221495 9857 solver.cpp:258] Train net output #1: loss_cls = 0.41518 (* 1 = 0.41518 loss) +I0616 14:11:02.221499 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130555 (* 1 = 0.130555 loss) +I0616 14:11:02.221503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.140569 (* 1 = 0.140569 loss) +I0616 14:11:02.221508 9857 solver.cpp:571] Iteration 72260, lr = 0.0001 +I0616 14:11:13.960165 9857 solver.cpp:242] Iteration 72280, loss = 0.595776 +I0616 14:11:13.960206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0899341 (* 1 = 0.0899341 loss) +I0616 14:11:13.960212 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124446 (* 1 = 0.124446 loss) +I0616 14:11:13.960216 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00711381 (* 1 = 0.00711381 loss) +I0616 14:11:13.960221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00140318 (* 1 = 0.00140318 loss) +I0616 14:11:13.960227 9857 solver.cpp:571] Iteration 72280, lr = 0.0001 +I0616 14:11:25.344960 9857 solver.cpp:242] Iteration 72300, loss = 0.24992 +I0616 14:11:25.345002 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0860144 (* 1 = 0.0860144 loss) +I0616 14:11:25.345007 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159274 (* 1 = 0.159274 loss) +I0616 14:11:25.345012 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0138855 (* 1 = 0.0138855 loss) +I0616 14:11:25.345016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00874063 (* 1 = 0.00874063 loss) +I0616 14:11:25.345019 9857 solver.cpp:571] Iteration 72300, lr = 0.0001 +I0616 14:11:36.980347 9857 solver.cpp:242] Iteration 72320, loss = 0.620215 +I0616 14:11:36.980388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192611 (* 1 = 0.192611 loss) +I0616 14:11:36.980394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207206 (* 1 = 0.207206 loss) +I0616 14:11:36.980398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0267774 (* 1 = 0.0267774 loss) +I0616 14:11:36.980402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.042915 (* 1 = 0.042915 loss) +I0616 14:11:36.980406 9857 solver.cpp:571] Iteration 72320, lr = 0.0001 +I0616 14:11:48.385819 9857 solver.cpp:242] Iteration 72340, loss = 0.403834 +I0616 14:11:48.385862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169991 (* 1 = 0.169991 loss) +I0616 14:11:48.385867 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221721 (* 1 = 0.221721 loss) +I0616 14:11:48.385872 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0529806 (* 1 = 0.0529806 loss) +I0616 14:11:48.385875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260763 (* 1 = 0.0260763 loss) +I0616 14:11:48.385879 9857 solver.cpp:571] Iteration 72340, lr = 0.0001 +I0616 14:11:59.719972 9857 solver.cpp:242] Iteration 72360, loss = 0.547963 +I0616 14:11:59.720015 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1505 (* 1 = 0.1505 loss) +I0616 14:11:59.720021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18291 (* 1 = 0.18291 loss) +I0616 14:11:59.720024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154832 (* 1 = 0.154832 loss) +I0616 14:11:59.720028 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159877 (* 1 = 0.0159877 loss) +I0616 14:11:59.720031 9857 solver.cpp:571] Iteration 72360, lr = 0.0001 +I0616 14:12:11.007560 9857 solver.cpp:242] Iteration 72380, loss = 0.426794 +I0616 14:12:11.007601 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0627567 (* 1 = 0.0627567 loss) +I0616 14:12:11.007606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187338 (* 1 = 0.187338 loss) +I0616 14:12:11.007611 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0210996 (* 1 = 0.0210996 loss) +I0616 14:12:11.007614 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140062 (* 1 = 0.0140062 loss) +I0616 14:12:11.007617 9857 solver.cpp:571] Iteration 72380, lr = 0.0001 +speed: 0.602s / iter +I0616 14:12:22.296921 9857 solver.cpp:242] Iteration 72400, loss = 0.42756 +I0616 14:12:22.296964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164156 (* 1 = 0.164156 loss) +I0616 14:12:22.296969 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225321 (* 1 = 0.225321 loss) +I0616 14:12:22.296974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0108221 (* 1 = 0.0108221 loss) +I0616 14:12:22.296978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0491693 (* 1 = 0.0491693 loss) +I0616 14:12:22.296984 9857 solver.cpp:571] Iteration 72400, lr = 0.0001 +I0616 14:12:33.602648 9857 solver.cpp:242] Iteration 72420, loss = 0.501301 +I0616 14:12:33.602685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0948852 (* 1 = 0.0948852 loss) +I0616 14:12:33.602691 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28645 (* 1 = 0.28645 loss) +I0616 14:12:33.602695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0373635 (* 1 = 0.0373635 loss) +I0616 14:12:33.602699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0698181 (* 1 = 0.0698181 loss) +I0616 14:12:33.602702 9857 solver.cpp:571] Iteration 72420, lr = 0.0001 +I0616 14:12:45.057551 9857 solver.cpp:242] Iteration 72440, loss = 0.330184 +I0616 14:12:45.057591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115245 (* 1 = 0.115245 loss) +I0616 14:12:45.057597 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149088 (* 1 = 0.149088 loss) +I0616 14:12:45.057601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0574024 (* 1 = 0.0574024 loss) +I0616 14:12:45.057605 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136666 (* 1 = 0.0136666 loss) +I0616 14:12:45.057608 9857 solver.cpp:571] Iteration 72440, lr = 0.0001 +I0616 14:12:56.574362 9857 solver.cpp:242] Iteration 72460, loss = 0.360527 +I0616 14:12:56.574404 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0881765 (* 1 = 0.0881765 loss) +I0616 14:12:56.574410 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132157 (* 1 = 0.132157 loss) +I0616 14:12:56.574414 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0453196 (* 1 = 0.0453196 loss) +I0616 14:12:56.574419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00215056 (* 1 = 0.00215056 loss) +I0616 14:12:56.574422 9857 solver.cpp:571] Iteration 72460, lr = 0.0001 +I0616 14:13:08.210322 9857 solver.cpp:242] Iteration 72480, loss = 0.761856 +I0616 14:13:08.210363 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203015 (* 1 = 0.203015 loss) +I0616 14:13:08.210369 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158082 (* 1 = 0.158082 loss) +I0616 14:13:08.210373 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0218871 (* 1 = 0.0218871 loss) +I0616 14:13:08.210377 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0791862 (* 1 = 0.0791862 loss) +I0616 14:13:08.210381 9857 solver.cpp:571] Iteration 72480, lr = 0.0001 +I0616 14:13:20.174619 9857 solver.cpp:242] Iteration 72500, loss = 0.270758 +I0616 14:13:20.174661 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117451 (* 1 = 0.117451 loss) +I0616 14:13:20.174667 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149165 (* 1 = 0.149165 loss) +I0616 14:13:20.174671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0497297 (* 1 = 0.0497297 loss) +I0616 14:13:20.174675 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143973 (* 1 = 0.0143973 loss) +I0616 14:13:20.174679 9857 solver.cpp:571] Iteration 72500, lr = 0.0001 +I0616 14:13:31.562393 9857 solver.cpp:242] Iteration 72520, loss = 0.198686 +I0616 14:13:31.562434 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0879806 (* 1 = 0.0879806 loss) +I0616 14:13:31.562439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116361 (* 1 = 0.116361 loss) +I0616 14:13:31.562444 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00552428 (* 1 = 0.00552428 loss) +I0616 14:13:31.562448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0279072 (* 1 = 0.0279072 loss) +I0616 14:13:31.562451 9857 solver.cpp:571] Iteration 72520, lr = 0.0001 +I0616 14:13:43.202715 9857 solver.cpp:242] Iteration 72540, loss = 0.244442 +I0616 14:13:43.202759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.07445 (* 1 = 0.07445 loss) +I0616 14:13:43.202765 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105231 (* 1 = 0.105231 loss) +I0616 14:13:43.202769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0739854 (* 1 = 0.0739854 loss) +I0616 14:13:43.202774 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0076752 (* 1 = 0.0076752 loss) +I0616 14:13:43.202777 9857 solver.cpp:571] Iteration 72540, lr = 0.0001 +I0616 14:13:54.609702 9857 solver.cpp:242] Iteration 72560, loss = 0.603276 +I0616 14:13:54.609741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371628 (* 1 = 0.371628 loss) +I0616 14:13:54.609747 9857 solver.cpp:258] Train net output #1: loss_cls = 0.426088 (* 1 = 0.426088 loss) +I0616 14:13:54.609751 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0646016 (* 1 = 0.0646016 loss) +I0616 14:13:54.609755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.121069 (* 1 = 0.121069 loss) +I0616 14:13:54.609758 9857 solver.cpp:571] Iteration 72560, lr = 0.0001 +I0616 14:14:06.223866 9857 solver.cpp:242] Iteration 72580, loss = 0.568203 +I0616 14:14:06.223908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13342 (* 1 = 0.13342 loss) +I0616 14:14:06.223914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216691 (* 1 = 0.216691 loss) +I0616 14:14:06.223918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0394437 (* 1 = 0.0394437 loss) +I0616 14:14:06.223922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149447 (* 1 = 0.0149447 loss) +I0616 14:14:06.223927 9857 solver.cpp:571] Iteration 72580, lr = 0.0001 +speed: 0.602s / iter +I0616 14:14:17.502786 9857 solver.cpp:242] Iteration 72600, loss = 0.444927 +I0616 14:14:17.502830 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.119436 (* 1 = 0.119436 loss) +I0616 14:14:17.502835 9857 solver.cpp:258] Train net output #1: loss_cls = 0.450086 (* 1 = 0.450086 loss) +I0616 14:14:17.502838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0176194 (* 1 = 0.0176194 loss) +I0616 14:14:17.502842 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0379289 (* 1 = 0.0379289 loss) +I0616 14:14:17.502846 9857 solver.cpp:571] Iteration 72600, lr = 0.0001 +I0616 14:14:28.952304 9857 solver.cpp:242] Iteration 72620, loss = 0.472397 +I0616 14:14:28.952345 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120678 (* 1 = 0.120678 loss) +I0616 14:14:28.952352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171023 (* 1 = 0.171023 loss) +I0616 14:14:28.952355 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0712744 (* 1 = 0.0712744 loss) +I0616 14:14:28.952359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130702 (* 1 = 0.0130702 loss) +I0616 14:14:28.952363 9857 solver.cpp:571] Iteration 72620, lr = 0.0001 +I0616 14:14:40.410531 9857 solver.cpp:242] Iteration 72640, loss = 0.619184 +I0616 14:14:40.410573 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262026 (* 1 = 0.262026 loss) +I0616 14:14:40.410578 9857 solver.cpp:258] Train net output #1: loss_cls = 0.403871 (* 1 = 0.403871 loss) +I0616 14:14:40.410583 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0854822 (* 1 = 0.0854822 loss) +I0616 14:14:40.410586 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.075365 (* 1 = 0.075365 loss) +I0616 14:14:40.410590 9857 solver.cpp:571] Iteration 72640, lr = 0.0001 +I0616 14:14:51.802237 9857 solver.cpp:242] Iteration 72660, loss = 0.214619 +I0616 14:14:51.802278 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0349056 (* 1 = 0.0349056 loss) +I0616 14:14:51.802284 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0408888 (* 1 = 0.0408888 loss) +I0616 14:14:51.802287 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209258 (* 1 = 0.0209258 loss) +I0616 14:14:51.802291 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135091 (* 1 = 0.0135091 loss) +I0616 14:14:51.802294 9857 solver.cpp:571] Iteration 72660, lr = 0.0001 +I0616 14:15:03.068531 9857 solver.cpp:242] Iteration 72680, loss = 0.366965 +I0616 14:15:03.068572 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144824 (* 1 = 0.144824 loss) +I0616 14:15:03.068578 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204371 (* 1 = 0.204371 loss) +I0616 14:15:03.068583 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0127083 (* 1 = 0.0127083 loss) +I0616 14:15:03.068586 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0408805 (* 1 = 0.0408805 loss) +I0616 14:15:03.068590 9857 solver.cpp:571] Iteration 72680, lr = 0.0001 +I0616 14:15:14.670050 9857 solver.cpp:242] Iteration 72700, loss = 0.403702 +I0616 14:15:14.670092 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204438 (* 1 = 0.204438 loss) +I0616 14:15:14.670099 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208577 (* 1 = 0.208577 loss) +I0616 14:15:14.670102 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114966 (* 1 = 0.114966 loss) +I0616 14:15:14.670106 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0493209 (* 1 = 0.0493209 loss) +I0616 14:15:14.670110 9857 solver.cpp:571] Iteration 72700, lr = 0.0001 +I0616 14:15:26.298769 9857 solver.cpp:242] Iteration 72720, loss = 0.158763 +I0616 14:15:26.298813 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0488474 (* 1 = 0.0488474 loss) +I0616 14:15:26.298820 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0979079 (* 1 = 0.0979079 loss) +I0616 14:15:26.298823 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00315507 (* 1 = 0.00315507 loss) +I0616 14:15:26.298827 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00406718 (* 1 = 0.00406718 loss) +I0616 14:15:26.298831 9857 solver.cpp:571] Iteration 72720, lr = 0.0001 +I0616 14:15:37.834924 9857 solver.cpp:242] Iteration 72740, loss = 0.81305 +I0616 14:15:37.834965 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.219306 (* 1 = 0.219306 loss) +I0616 14:15:37.834971 9857 solver.cpp:258] Train net output #1: loss_cls = 0.501663 (* 1 = 0.501663 loss) +I0616 14:15:37.834975 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0367941 (* 1 = 0.0367941 loss) +I0616 14:15:37.834980 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0284797 (* 1 = 0.0284797 loss) +I0616 14:15:37.834982 9857 solver.cpp:571] Iteration 72740, lr = 0.0001 +I0616 14:15:49.677027 9857 solver.cpp:242] Iteration 72760, loss = 1.56517 +I0616 14:15:49.677068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20536 (* 1 = 0.20536 loss) +I0616 14:15:49.677074 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167313 (* 1 = 0.167313 loss) +I0616 14:15:49.677078 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0273455 (* 1 = 0.0273455 loss) +I0616 14:15:49.677083 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.027663 (* 1 = 0.027663 loss) +I0616 14:15:49.677085 9857 solver.cpp:571] Iteration 72760, lr = 0.0001 +I0616 14:16:01.264040 9857 solver.cpp:242] Iteration 72780, loss = 0.471756 +I0616 14:16:01.264081 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0411328 (* 1 = 0.0411328 loss) +I0616 14:16:01.264087 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180976 (* 1 = 0.180976 loss) +I0616 14:16:01.264091 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0290364 (* 1 = 0.0290364 loss) +I0616 14:16:01.264096 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011246 (* 1 = 0.011246 loss) +I0616 14:16:01.264099 9857 solver.cpp:571] Iteration 72780, lr = 0.0001 +speed: 0.602s / iter +I0616 14:16:12.843386 9857 solver.cpp:242] Iteration 72800, loss = 0.206019 +I0616 14:16:12.843428 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0761105 (* 1 = 0.0761105 loss) +I0616 14:16:12.843433 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120446 (* 1 = 0.120446 loss) +I0616 14:16:12.843437 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0455518 (* 1 = 0.0455518 loss) +I0616 14:16:12.843441 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00916492 (* 1 = 0.00916492 loss) +I0616 14:16:12.843446 9857 solver.cpp:571] Iteration 72800, lr = 0.0001 +I0616 14:16:24.170259 9857 solver.cpp:242] Iteration 72820, loss = 0.185798 +I0616 14:16:24.170300 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.052811 (* 1 = 0.052811 loss) +I0616 14:16:24.170305 9857 solver.cpp:258] Train net output #1: loss_cls = 0.082384 (* 1 = 0.082384 loss) +I0616 14:16:24.170310 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00923546 (* 1 = 0.00923546 loss) +I0616 14:16:24.170313 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0024395 (* 1 = 0.0024395 loss) +I0616 14:16:24.170317 9857 solver.cpp:571] Iteration 72820, lr = 0.0001 +I0616 14:16:35.683091 9857 solver.cpp:242] Iteration 72840, loss = 0.559107 +I0616 14:16:35.683135 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0684023 (* 1 = 0.0684023 loss) +I0616 14:16:35.683140 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142067 (* 1 = 0.142067 loss) +I0616 14:16:35.683145 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0318834 (* 1 = 0.0318834 loss) +I0616 14:16:35.683148 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00416222 (* 1 = 0.00416222 loss) +I0616 14:16:35.683152 9857 solver.cpp:571] Iteration 72840, lr = 0.0001 +I0616 14:16:47.240250 9857 solver.cpp:242] Iteration 72860, loss = 0.377391 +I0616 14:16:47.240289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.058753 (* 1 = 0.058753 loss) +I0616 14:16:47.240295 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119608 (* 1 = 0.119608 loss) +I0616 14:16:47.240299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00563528 (* 1 = 0.00563528 loss) +I0616 14:16:47.240304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0214664 (* 1 = 0.0214664 loss) +I0616 14:16:47.240310 9857 solver.cpp:571] Iteration 72860, lr = 0.0001 +I0616 14:16:58.849270 9857 solver.cpp:242] Iteration 72880, loss = 0.728888 +I0616 14:16:58.849313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260546 (* 1 = 0.260546 loss) +I0616 14:16:58.849319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.456131 (* 1 = 0.456131 loss) +I0616 14:16:58.849324 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0885554 (* 1 = 0.0885554 loss) +I0616 14:16:58.849328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0154028 (* 1 = 0.0154028 loss) +I0616 14:16:58.849331 9857 solver.cpp:571] Iteration 72880, lr = 0.0001 +I0616 14:17:10.397204 9857 solver.cpp:242] Iteration 72900, loss = 0.381237 +I0616 14:17:10.397248 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127605 (* 1 = 0.127605 loss) +I0616 14:17:10.397253 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118888 (* 1 = 0.118888 loss) +I0616 14:17:10.397258 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0286158 (* 1 = 0.0286158 loss) +I0616 14:17:10.397261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00925311 (* 1 = 0.00925311 loss) +I0616 14:17:10.397265 9857 solver.cpp:571] Iteration 72900, lr = 0.0001 +I0616 14:17:22.079301 9857 solver.cpp:242] Iteration 72920, loss = 0.908185 +I0616 14:17:22.079342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.448553 (* 1 = 0.448553 loss) +I0616 14:17:22.079349 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281995 (* 1 = 0.281995 loss) +I0616 14:17:22.079352 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0939099 (* 1 = 0.0939099 loss) +I0616 14:17:22.079356 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0459513 (* 1 = 0.0459513 loss) +I0616 14:17:22.079360 9857 solver.cpp:571] Iteration 72920, lr = 0.0001 +I0616 14:17:33.325515 9857 solver.cpp:242] Iteration 72940, loss = 0.69691 +I0616 14:17:33.325556 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158305 (* 1 = 0.158305 loss) +I0616 14:17:33.325562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223591 (* 1 = 0.223591 loss) +I0616 14:17:33.325567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0274462 (* 1 = 0.0274462 loss) +I0616 14:17:33.325569 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209521 (* 1 = 0.0209521 loss) +I0616 14:17:33.325573 9857 solver.cpp:571] Iteration 72940, lr = 0.0001 +I0616 14:17:44.570865 9857 solver.cpp:242] Iteration 72960, loss = 0.184583 +I0616 14:17:44.570907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0620085 (* 1 = 0.0620085 loss) +I0616 14:17:44.570914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0944407 (* 1 = 0.0944407 loss) +I0616 14:17:44.570917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0325309 (* 1 = 0.0325309 loss) +I0616 14:17:44.570921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00786663 (* 1 = 0.00786663 loss) +I0616 14:17:44.570924 9857 solver.cpp:571] Iteration 72960, lr = 0.0001 +I0616 14:17:55.796912 9857 solver.cpp:242] Iteration 72980, loss = 0.224196 +I0616 14:17:55.796954 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0755099 (* 1 = 0.0755099 loss) +I0616 14:17:55.796960 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0830013 (* 1 = 0.0830013 loss) +I0616 14:17:55.796964 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00675808 (* 1 = 0.00675808 loss) +I0616 14:17:55.796968 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00803261 (* 1 = 0.00803261 loss) +I0616 14:17:55.796972 9857 solver.cpp:571] Iteration 72980, lr = 0.0001 +speed: 0.602s / iter +I0616 14:18:07.243232 9857 solver.cpp:242] Iteration 73000, loss = 0.827577 +I0616 14:18:07.243273 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394868 (* 1 = 0.394868 loss) +I0616 14:18:07.243279 9857 solver.cpp:258] Train net output #1: loss_cls = 0.5645 (* 1 = 0.5645 loss) +I0616 14:18:07.243283 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108924 (* 1 = 0.108924 loss) +I0616 14:18:07.243288 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0425798 (* 1 = 0.0425798 loss) +I0616 14:18:07.243291 9857 solver.cpp:571] Iteration 73000, lr = 0.0001 +I0616 14:18:18.814115 9857 solver.cpp:242] Iteration 73020, loss = 0.713196 +I0616 14:18:18.814154 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.348753 (* 1 = 0.348753 loss) +I0616 14:18:18.814160 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254459 (* 1 = 0.254459 loss) +I0616 14:18:18.814164 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0664255 (* 1 = 0.0664255 loss) +I0616 14:18:18.814168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.143507 (* 1 = 0.143507 loss) +I0616 14:18:18.814172 9857 solver.cpp:571] Iteration 73020, lr = 0.0001 +I0616 14:18:30.304370 9857 solver.cpp:242] Iteration 73040, loss = 0.460445 +I0616 14:18:30.304414 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0546498 (* 1 = 0.0546498 loss) +I0616 14:18:30.304419 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103385 (* 1 = 0.103385 loss) +I0616 14:18:30.304424 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00346198 (* 1 = 0.00346198 loss) +I0616 14:18:30.304427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00797737 (* 1 = 0.00797737 loss) +I0616 14:18:30.304430 9857 solver.cpp:571] Iteration 73040, lr = 0.0001 +I0616 14:18:41.629070 9857 solver.cpp:242] Iteration 73060, loss = 0.515604 +I0616 14:18:41.629112 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312288 (* 1 = 0.312288 loss) +I0616 14:18:41.629118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234509 (* 1 = 0.234509 loss) +I0616 14:18:41.629122 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0825663 (* 1 = 0.0825663 loss) +I0616 14:18:41.629127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.204572 (* 1 = 0.204572 loss) +I0616 14:18:41.629130 9857 solver.cpp:571] Iteration 73060, lr = 0.0001 +I0616 14:18:52.959751 9857 solver.cpp:242] Iteration 73080, loss = 0.461621 +I0616 14:18:52.959795 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127263 (* 1 = 0.127263 loss) +I0616 14:18:52.959800 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112137 (* 1 = 0.112137 loss) +I0616 14:18:52.959805 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0707226 (* 1 = 0.0707226 loss) +I0616 14:18:52.959810 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0403486 (* 1 = 0.0403486 loss) +I0616 14:18:52.959812 9857 solver.cpp:571] Iteration 73080, lr = 0.0001 +I0616 14:19:04.536483 9857 solver.cpp:242] Iteration 73100, loss = 0.944872 +I0616 14:19:04.536523 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.073446 (* 1 = 0.073446 loss) +I0616 14:19:04.536528 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105579 (* 1 = 0.105579 loss) +I0616 14:19:04.536533 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0183653 (* 1 = 0.0183653 loss) +I0616 14:19:04.536537 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0178654 (* 1 = 0.0178654 loss) +I0616 14:19:04.536540 9857 solver.cpp:571] Iteration 73100, lr = 0.0001 +I0616 14:19:16.480940 9857 solver.cpp:242] Iteration 73120, loss = 0.53662 +I0616 14:19:16.480983 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265301 (* 1 = 0.265301 loss) +I0616 14:19:16.480988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.379826 (* 1 = 0.379826 loss) +I0616 14:19:16.480993 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13456 (* 1 = 0.13456 loss) +I0616 14:19:16.480996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170297 (* 1 = 0.0170297 loss) +I0616 14:19:16.481000 9857 solver.cpp:571] Iteration 73120, lr = 0.0001 +I0616 14:19:27.848574 9857 solver.cpp:242] Iteration 73140, loss = 0.426963 +I0616 14:19:27.848616 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0568385 (* 1 = 0.0568385 loss) +I0616 14:19:27.848621 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0978929 (* 1 = 0.0978929 loss) +I0616 14:19:27.848625 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0309375 (* 1 = 0.0309375 loss) +I0616 14:19:27.848629 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.016219 (* 1 = 0.016219 loss) +I0616 14:19:27.848634 9857 solver.cpp:571] Iteration 73140, lr = 0.0001 +I0616 14:19:39.331707 9857 solver.cpp:242] Iteration 73160, loss = 0.309311 +I0616 14:19:39.331748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.058211 (* 1 = 0.058211 loss) +I0616 14:19:39.331753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16352 (* 1 = 0.16352 loss) +I0616 14:19:39.331758 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0105493 (* 1 = 0.0105493 loss) +I0616 14:19:39.331763 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.005004 (* 1 = 0.005004 loss) +I0616 14:19:39.331765 9857 solver.cpp:571] Iteration 73160, lr = 0.0001 +I0616 14:19:51.026567 9857 solver.cpp:242] Iteration 73180, loss = 0.438345 +I0616 14:19:51.026612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336648 (* 1 = 0.336648 loss) +I0616 14:19:51.026618 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237698 (* 1 = 0.237698 loss) +I0616 14:19:51.026621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0610762 (* 1 = 0.0610762 loss) +I0616 14:19:51.026625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207452 (* 1 = 0.0207452 loss) +I0616 14:19:51.026629 9857 solver.cpp:571] Iteration 73180, lr = 0.0001 +speed: 0.602s / iter +I0616 14:20:02.599407 9857 solver.cpp:242] Iteration 73200, loss = 0.424094 +I0616 14:20:02.599450 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0478974 (* 1 = 0.0478974 loss) +I0616 14:20:02.599457 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12138 (* 1 = 0.12138 loss) +I0616 14:20:02.599460 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0184458 (* 1 = 0.0184458 loss) +I0616 14:20:02.599463 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00702413 (* 1 = 0.00702413 loss) +I0616 14:20:02.599467 9857 solver.cpp:571] Iteration 73200, lr = 0.0001 +I0616 14:20:14.205598 9857 solver.cpp:242] Iteration 73220, loss = 0.231138 +I0616 14:20:14.205641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0419993 (* 1 = 0.0419993 loss) +I0616 14:20:14.205647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0655397 (* 1 = 0.0655397 loss) +I0616 14:20:14.205651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0884548 (* 1 = 0.0884548 loss) +I0616 14:20:14.205656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128476 (* 1 = 0.128476 loss) +I0616 14:20:14.205658 9857 solver.cpp:571] Iteration 73220, lr = 0.0001 +I0616 14:20:25.540935 9857 solver.cpp:242] Iteration 73240, loss = 0.760138 +I0616 14:20:25.540976 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.393636 (* 1 = 0.393636 loss) +I0616 14:20:25.540982 9857 solver.cpp:258] Train net output #1: loss_cls = 0.539315 (* 1 = 0.539315 loss) +I0616 14:20:25.540985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.266983 (* 1 = 0.266983 loss) +I0616 14:20:25.540989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14513 (* 1 = 0.14513 loss) +I0616 14:20:25.540992 9857 solver.cpp:571] Iteration 73240, lr = 0.0001 +I0616 14:20:37.124685 9857 solver.cpp:242] Iteration 73260, loss = 0.463774 +I0616 14:20:37.124727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162638 (* 1 = 0.162638 loss) +I0616 14:20:37.124732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178349 (* 1 = 0.178349 loss) +I0616 14:20:37.124737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103941 (* 1 = 0.103941 loss) +I0616 14:20:37.124740 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0355005 (* 1 = 0.0355005 loss) +I0616 14:20:37.124747 9857 solver.cpp:571] Iteration 73260, lr = 0.0001 +I0616 14:20:48.879633 9857 solver.cpp:242] Iteration 73280, loss = 0.297455 +I0616 14:20:48.879676 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0754725 (* 1 = 0.0754725 loss) +I0616 14:20:48.879683 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125306 (* 1 = 0.125306 loss) +I0616 14:20:48.879686 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0337859 (* 1 = 0.0337859 loss) +I0616 14:20:48.879690 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.143115 (* 1 = 0.143115 loss) +I0616 14:20:48.879693 9857 solver.cpp:571] Iteration 73280, lr = 0.0001 +I0616 14:21:00.436817 9857 solver.cpp:242] Iteration 73300, loss = 0.386106 +I0616 14:21:00.436857 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134628 (* 1 = 0.134628 loss) +I0616 14:21:00.436863 9857 solver.cpp:258] Train net output #1: loss_cls = 0.233245 (* 1 = 0.233245 loss) +I0616 14:21:00.436868 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.013 (* 1 = 0.013 loss) +I0616 14:21:00.436872 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0218164 (* 1 = 0.0218164 loss) +I0616 14:21:00.436877 9857 solver.cpp:571] Iteration 73300, lr = 0.0001 +I0616 14:21:12.070049 9857 solver.cpp:242] Iteration 73320, loss = 0.417728 +I0616 14:21:12.070091 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269739 (* 1 = 0.269739 loss) +I0616 14:21:12.070096 9857 solver.cpp:258] Train net output #1: loss_cls = 0.287232 (* 1 = 0.287232 loss) +I0616 14:21:12.070099 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0665002 (* 1 = 0.0665002 loss) +I0616 14:21:12.070103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028418 (* 1 = 0.028418 loss) +I0616 14:21:12.070107 9857 solver.cpp:571] Iteration 73320, lr = 0.0001 +I0616 14:21:23.476683 9857 solver.cpp:242] Iteration 73340, loss = 0.571502 +I0616 14:21:23.476724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270423 (* 1 = 0.270423 loss) +I0616 14:21:23.476730 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217508 (* 1 = 0.217508 loss) +I0616 14:21:23.476734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0352982 (* 1 = 0.0352982 loss) +I0616 14:21:23.476738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033862 (* 1 = 0.033862 loss) +I0616 14:21:23.476742 9857 solver.cpp:571] Iteration 73340, lr = 0.0001 +I0616 14:21:35.102304 9857 solver.cpp:242] Iteration 73360, loss = 0.505502 +I0616 14:21:35.102344 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192152 (* 1 = 0.192152 loss) +I0616 14:21:35.102365 9857 solver.cpp:258] Train net output #1: loss_cls = 0.377613 (* 1 = 0.377613 loss) +I0616 14:21:35.102370 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0336754 (* 1 = 0.0336754 loss) +I0616 14:21:35.102373 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0683431 (* 1 = 0.0683431 loss) +I0616 14:21:35.102377 9857 solver.cpp:571] Iteration 73360, lr = 0.0001 +I0616 14:21:46.589077 9857 solver.cpp:242] Iteration 73380, loss = 0.777182 +I0616 14:21:46.589118 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247096 (* 1 = 0.247096 loss) +I0616 14:21:46.589124 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338624 (* 1 = 0.338624 loss) +I0616 14:21:46.589128 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152649 (* 1 = 0.152649 loss) +I0616 14:21:46.589133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.098561 (* 1 = 0.098561 loss) +I0616 14:21:46.589136 9857 solver.cpp:571] Iteration 73380, lr = 0.0001 +speed: 0.602s / iter +I0616 14:21:58.272053 9857 solver.cpp:242] Iteration 73400, loss = 0.777202 +I0616 14:21:58.272095 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137008 (* 1 = 0.137008 loss) +I0616 14:21:58.272101 9857 solver.cpp:258] Train net output #1: loss_cls = 0.302529 (* 1 = 0.302529 loss) +I0616 14:21:58.272105 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0651643 (* 1 = 0.0651643 loss) +I0616 14:21:58.272110 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0441247 (* 1 = 0.0441247 loss) +I0616 14:21:58.272115 9857 solver.cpp:571] Iteration 73400, lr = 0.0001 +I0616 14:22:09.938268 9857 solver.cpp:242] Iteration 73420, loss = 0.4224 +I0616 14:22:09.938311 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218803 (* 1 = 0.218803 loss) +I0616 14:22:09.938316 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203995 (* 1 = 0.203995 loss) +I0616 14:22:09.938321 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120664 (* 1 = 0.120664 loss) +I0616 14:22:09.938324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00999787 (* 1 = 0.00999787 loss) +I0616 14:22:09.938328 9857 solver.cpp:571] Iteration 73420, lr = 0.0001 +I0616 14:22:21.616930 9857 solver.cpp:242] Iteration 73440, loss = 0.605689 +I0616 14:22:21.616972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287648 (* 1 = 0.287648 loss) +I0616 14:22:21.616977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.428435 (* 1 = 0.428435 loss) +I0616 14:22:21.616981 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.055615 (* 1 = 0.055615 loss) +I0616 14:22:21.616986 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.257068 (* 1 = 0.257068 loss) +I0616 14:22:21.616989 9857 solver.cpp:571] Iteration 73440, lr = 0.0001 +I0616 14:22:33.112237 9857 solver.cpp:242] Iteration 73460, loss = 0.304066 +I0616 14:22:33.112280 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.065969 (* 1 = 0.065969 loss) +I0616 14:22:33.112285 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143365 (* 1 = 0.143365 loss) +I0616 14:22:33.112289 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00258692 (* 1 = 0.00258692 loss) +I0616 14:22:33.112293 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00308261 (* 1 = 0.00308261 loss) +I0616 14:22:33.112298 9857 solver.cpp:571] Iteration 73460, lr = 0.0001 +I0616 14:22:44.519470 9857 solver.cpp:242] Iteration 73480, loss = 0.329355 +I0616 14:22:44.519510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154234 (* 1 = 0.154234 loss) +I0616 14:22:44.519515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226298 (* 1 = 0.226298 loss) +I0616 14:22:44.519520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0929733 (* 1 = 0.0929733 loss) +I0616 14:22:44.519523 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133801 (* 1 = 0.0133801 loss) +I0616 14:22:44.519527 9857 solver.cpp:571] Iteration 73480, lr = 0.0001 +I0616 14:22:56.102684 9857 solver.cpp:242] Iteration 73500, loss = 0.767262 +I0616 14:22:56.102727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.00590627 (* 1 = 0.00590627 loss) +I0616 14:22:56.102732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211448 (* 1 = 0.211448 loss) +I0616 14:22:56.102737 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0598045 (* 1 = 0.0598045 loss) +I0616 14:22:56.102741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0999011 (* 1 = 0.0999011 loss) +I0616 14:22:56.102744 9857 solver.cpp:571] Iteration 73500, lr = 0.0001 +I0616 14:23:07.593670 9857 solver.cpp:242] Iteration 73520, loss = 0.223928 +I0616 14:23:07.593713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0976676 (* 1 = 0.0976676 loss) +I0616 14:23:07.593719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114491 (* 1 = 0.114491 loss) +I0616 14:23:07.593722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0144259 (* 1 = 0.0144259 loss) +I0616 14:23:07.593725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103282 (* 1 = 0.0103282 loss) +I0616 14:23:07.593729 9857 solver.cpp:571] Iteration 73520, lr = 0.0001 +I0616 14:23:19.389317 9857 solver.cpp:242] Iteration 73540, loss = 0.496126 +I0616 14:23:19.389355 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0420007 (* 1 = 0.0420007 loss) +I0616 14:23:19.389361 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119925 (* 1 = 0.119925 loss) +I0616 14:23:19.389365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.012195 (* 1 = 0.012195 loss) +I0616 14:23:19.389369 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145272 (* 1 = 0.0145272 loss) +I0616 14:23:19.389374 9857 solver.cpp:571] Iteration 73540, lr = 0.0001 +I0616 14:23:31.012419 9857 solver.cpp:242] Iteration 73560, loss = 0.600609 +I0616 14:23:31.012461 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343421 (* 1 = 0.343421 loss) +I0616 14:23:31.012466 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394864 (* 1 = 0.394864 loss) +I0616 14:23:31.012470 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.165577 (* 1 = 0.165577 loss) +I0616 14:23:31.012475 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104678 (* 1 = 0.104678 loss) +I0616 14:23:31.012480 9857 solver.cpp:571] Iteration 73560, lr = 0.0001 +I0616 14:23:42.472374 9857 solver.cpp:242] Iteration 73580, loss = 0.263943 +I0616 14:23:42.472417 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0548741 (* 1 = 0.0548741 loss) +I0616 14:23:42.472422 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122455 (* 1 = 0.122455 loss) +I0616 14:23:42.472427 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139599 (* 1 = 0.0139599 loss) +I0616 14:23:42.472431 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0276827 (* 1 = 0.0276827 loss) +I0616 14:23:42.472434 9857 solver.cpp:571] Iteration 73580, lr = 0.0001 +speed: 0.601s / iter +I0616 14:23:53.924862 9857 solver.cpp:242] Iteration 73600, loss = 0.587868 +I0616 14:23:53.924887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.25879 (* 1 = 0.25879 loss) +I0616 14:23:53.924892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366885 (* 1 = 0.366885 loss) +I0616 14:23:53.924897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158975 (* 1 = 0.158975 loss) +I0616 14:23:53.924901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0466753 (* 1 = 0.0466753 loss) +I0616 14:23:53.924906 9857 solver.cpp:571] Iteration 73600, lr = 0.0001 +I0616 14:24:05.493300 9857 solver.cpp:242] Iteration 73620, loss = 0.216858 +I0616 14:24:05.493342 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0363471 (* 1 = 0.0363471 loss) +I0616 14:24:05.493347 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113602 (* 1 = 0.113602 loss) +I0616 14:24:05.493351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0231574 (* 1 = 0.0231574 loss) +I0616 14:24:05.493355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0760169 (* 1 = 0.0760169 loss) +I0616 14:24:05.493360 9857 solver.cpp:571] Iteration 73620, lr = 0.0001 +I0616 14:24:17.040403 9857 solver.cpp:242] Iteration 73640, loss = 0.315639 +I0616 14:24:17.040444 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164027 (* 1 = 0.164027 loss) +I0616 14:24:17.040451 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169264 (* 1 = 0.169264 loss) +I0616 14:24:17.040454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0608535 (* 1 = 0.0608535 loss) +I0616 14:24:17.040458 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0187943 (* 1 = 0.0187943 loss) +I0616 14:24:17.040462 9857 solver.cpp:571] Iteration 73640, lr = 0.0001 +I0616 14:24:28.562319 9857 solver.cpp:242] Iteration 73660, loss = 0.491541 +I0616 14:24:28.562361 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100546 (* 1 = 0.100546 loss) +I0616 14:24:28.562367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162533 (* 1 = 0.162533 loss) +I0616 14:24:28.562371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.017532 (* 1 = 0.017532 loss) +I0616 14:24:28.562376 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0459978 (* 1 = 0.0459978 loss) +I0616 14:24:28.562379 9857 solver.cpp:571] Iteration 73660, lr = 0.0001 +I0616 14:24:40.088690 9857 solver.cpp:242] Iteration 73680, loss = 0.382647 +I0616 14:24:40.088732 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202975 (* 1 = 0.202975 loss) +I0616 14:24:40.088737 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21053 (* 1 = 0.21053 loss) +I0616 14:24:40.088740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0803905 (* 1 = 0.0803905 loss) +I0616 14:24:40.088744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0731973 (* 1 = 0.0731973 loss) +I0616 14:24:40.088748 9857 solver.cpp:571] Iteration 73680, lr = 0.0001 +I0616 14:24:51.624294 9857 solver.cpp:242] Iteration 73700, loss = 0.248583 +I0616 14:24:51.624336 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0913694 (* 1 = 0.0913694 loss) +I0616 14:24:51.624341 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124798 (* 1 = 0.124798 loss) +I0616 14:24:51.624346 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0312019 (* 1 = 0.0312019 loss) +I0616 14:24:51.624349 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00787507 (* 1 = 0.00787507 loss) +I0616 14:24:51.624352 9857 solver.cpp:571] Iteration 73700, lr = 0.0001 +I0616 14:25:03.278772 9857 solver.cpp:242] Iteration 73720, loss = 0.70813 +I0616 14:25:03.278816 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0674329 (* 1 = 0.0674329 loss) +I0616 14:25:03.278822 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0952414 (* 1 = 0.0952414 loss) +I0616 14:25:03.278827 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00238204 (* 1 = 0.00238204 loss) +I0616 14:25:03.278831 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139531 (* 1 = 0.0139531 loss) +I0616 14:25:03.278834 9857 solver.cpp:571] Iteration 73720, lr = 0.0001 +I0616 14:25:14.889811 9857 solver.cpp:242] Iteration 73740, loss = 0.196906 +I0616 14:25:14.889853 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0372675 (* 1 = 0.0372675 loss) +I0616 14:25:14.889858 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110328 (* 1 = 0.110328 loss) +I0616 14:25:14.889863 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.032738 (* 1 = 0.032738 loss) +I0616 14:25:14.889866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00786402 (* 1 = 0.00786402 loss) +I0616 14:25:14.889870 9857 solver.cpp:571] Iteration 73740, lr = 0.0001 +I0616 14:25:26.611505 9857 solver.cpp:242] Iteration 73760, loss = 0.304123 +I0616 14:25:26.611546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137068 (* 1 = 0.137068 loss) +I0616 14:25:26.611552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184979 (* 1 = 0.184979 loss) +I0616 14:25:26.611557 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0419798 (* 1 = 0.0419798 loss) +I0616 14:25:26.611560 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334599 (* 1 = 0.0334599 loss) +I0616 14:25:26.611564 9857 solver.cpp:571] Iteration 73760, lr = 0.0001 +I0616 14:25:38.078415 9857 solver.cpp:242] Iteration 73780, loss = 0.38675 +I0616 14:25:38.078457 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0920553 (* 1 = 0.0920553 loss) +I0616 14:25:38.078464 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121981 (* 1 = 0.121981 loss) +I0616 14:25:38.078467 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114055 (* 1 = 0.0114055 loss) +I0616 14:25:38.078471 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0244342 (* 1 = 0.0244342 loss) +I0616 14:25:38.078474 9857 solver.cpp:571] Iteration 73780, lr = 0.0001 +speed: 0.601s / iter +I0616 14:25:49.517997 9857 solver.cpp:242] Iteration 73800, loss = 0.307382 +I0616 14:25:49.518040 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0893979 (* 1 = 0.0893979 loss) +I0616 14:25:49.518046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0496525 (* 1 = 0.0496525 loss) +I0616 14:25:49.518050 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.02827 (* 1 = 0.02827 loss) +I0616 14:25:49.518054 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0095258 (* 1 = 0.0095258 loss) +I0616 14:25:49.518059 9857 solver.cpp:571] Iteration 73800, lr = 0.0001 +I0616 14:26:01.249362 9857 solver.cpp:242] Iteration 73820, loss = 0.903063 +I0616 14:26:01.249402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302154 (* 1 = 0.302154 loss) +I0616 14:26:01.249408 9857 solver.cpp:258] Train net output #1: loss_cls = 0.386161 (* 1 = 0.386161 loss) +I0616 14:26:01.249413 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112986 (* 1 = 0.112986 loss) +I0616 14:26:01.249416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129085 (* 1 = 0.129085 loss) +I0616 14:26:01.249420 9857 solver.cpp:571] Iteration 73820, lr = 0.0001 +I0616 14:26:12.749661 9857 solver.cpp:242] Iteration 73840, loss = 0.309422 +I0616 14:26:12.749701 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0434228 (* 1 = 0.0434228 loss) +I0616 14:26:12.749707 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0597419 (* 1 = 0.0597419 loss) +I0616 14:26:12.749711 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0121213 (* 1 = 0.0121213 loss) +I0616 14:26:12.749716 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0277633 (* 1 = 0.0277633 loss) +I0616 14:26:12.749719 9857 solver.cpp:571] Iteration 73840, lr = 0.0001 +I0616 14:26:24.426898 9857 solver.cpp:242] Iteration 73860, loss = 0.849724 +I0616 14:26:24.426939 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184293 (* 1 = 0.184293 loss) +I0616 14:26:24.426945 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221713 (* 1 = 0.221713 loss) +I0616 14:26:24.426949 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0377464 (* 1 = 0.0377464 loss) +I0616 14:26:24.426954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00810841 (* 1 = 0.00810841 loss) +I0616 14:26:24.426957 9857 solver.cpp:571] Iteration 73860, lr = 0.0001 +I0616 14:26:36.017853 9857 solver.cpp:242] Iteration 73880, loss = 0.528701 +I0616 14:26:36.017894 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0672618 (* 1 = 0.0672618 loss) +I0616 14:26:36.017899 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112546 (* 1 = 0.112546 loss) +I0616 14:26:36.017904 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0276749 (* 1 = 0.0276749 loss) +I0616 14:26:36.017909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130693 (* 1 = 0.0130693 loss) +I0616 14:26:36.017912 9857 solver.cpp:571] Iteration 73880, lr = 0.0001 +I0616 14:26:47.543789 9857 solver.cpp:242] Iteration 73900, loss = 0.457979 +I0616 14:26:47.543828 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209719 (* 1 = 0.209719 loss) +I0616 14:26:47.543834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21952 (* 1 = 0.21952 loss) +I0616 14:26:47.543838 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.064191 (* 1 = 0.064191 loss) +I0616 14:26:47.543843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175069 (* 1 = 0.0175069 loss) +I0616 14:26:47.543846 9857 solver.cpp:571] Iteration 73900, lr = 0.0001 +I0616 14:26:59.312979 9857 solver.cpp:242] Iteration 73920, loss = 0.329481 +I0616 14:26:59.313020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143038 (* 1 = 0.143038 loss) +I0616 14:26:59.313026 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222609 (* 1 = 0.222609 loss) +I0616 14:26:59.313030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0712247 (* 1 = 0.0712247 loss) +I0616 14:26:59.313035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153254 (* 1 = 0.0153254 loss) +I0616 14:26:59.313038 9857 solver.cpp:571] Iteration 73920, lr = 0.0001 +I0616 14:27:10.592916 9857 solver.cpp:242] Iteration 73940, loss = 0.61014 +I0616 14:27:10.592957 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157822 (* 1 = 0.157822 loss) +I0616 14:27:10.592963 9857 solver.cpp:258] Train net output #1: loss_cls = 0.373035 (* 1 = 0.373035 loss) +I0616 14:27:10.592968 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.156138 (* 1 = 0.156138 loss) +I0616 14:27:10.592972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253343 (* 1 = 0.0253343 loss) +I0616 14:27:10.592975 9857 solver.cpp:571] Iteration 73940, lr = 0.0001 +I0616 14:27:22.110005 9857 solver.cpp:242] Iteration 73960, loss = 0.82784 +I0616 14:27:22.110046 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158691 (* 1 = 0.158691 loss) +I0616 14:27:22.110052 9857 solver.cpp:258] Train net output #1: loss_cls = 0.256275 (* 1 = 0.256275 loss) +I0616 14:27:22.110057 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0785805 (* 1 = 0.0785805 loss) +I0616 14:27:22.110061 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107859 (* 1 = 0.0107859 loss) +I0616 14:27:22.110064 9857 solver.cpp:571] Iteration 73960, lr = 0.0001 +I0616 14:27:33.606750 9857 solver.cpp:242] Iteration 73980, loss = 0.52162 +I0616 14:27:33.606794 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117731 (* 1 = 0.117731 loss) +I0616 14:27:33.606799 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162045 (* 1 = 0.162045 loss) +I0616 14:27:33.606804 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0695249 (* 1 = 0.0695249 loss) +I0616 14:27:33.606807 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0256161 (* 1 = 0.0256161 loss) +I0616 14:27:33.606812 9857 solver.cpp:571] Iteration 73980, lr = 0.0001 +speed: 0.601s / iter +I0616 14:27:45.284528 9857 solver.cpp:242] Iteration 74000, loss = 0.337338 +I0616 14:27:45.284569 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143815 (* 1 = 0.143815 loss) +I0616 14:27:45.284574 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155757 (* 1 = 0.155757 loss) +I0616 14:27:45.284579 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010209 (* 1 = 0.010209 loss) +I0616 14:27:45.284582 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.148918 (* 1 = 0.148918 loss) +I0616 14:27:45.284586 9857 solver.cpp:571] Iteration 74000, lr = 0.0001 +I0616 14:27:56.663931 9857 solver.cpp:242] Iteration 74020, loss = 0.562396 +I0616 14:27:56.663971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125522 (* 1 = 0.125522 loss) +I0616 14:27:56.663977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104565 (* 1 = 0.104565 loss) +I0616 14:27:56.663981 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0889759 (* 1 = 0.0889759 loss) +I0616 14:27:56.663985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165597 (* 1 = 0.0165597 loss) +I0616 14:27:56.663990 9857 solver.cpp:571] Iteration 74020, lr = 0.0001 +I0616 14:28:08.361090 9857 solver.cpp:242] Iteration 74040, loss = 0.63089 +I0616 14:28:08.361134 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247378 (* 1 = 0.247378 loss) +I0616 14:28:08.361138 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402703 (* 1 = 0.402703 loss) +I0616 14:28:08.361143 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0713527 (* 1 = 0.0713527 loss) +I0616 14:28:08.361146 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0542719 (* 1 = 0.0542719 loss) +I0616 14:28:08.361150 9857 solver.cpp:571] Iteration 74040, lr = 0.0001 +I0616 14:28:19.716153 9857 solver.cpp:242] Iteration 74060, loss = 0.488575 +I0616 14:28:19.716197 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341472 (* 1 = 0.341472 loss) +I0616 14:28:19.716202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.320235 (* 1 = 0.320235 loss) +I0616 14:28:19.716207 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0772442 (* 1 = 0.0772442 loss) +I0616 14:28:19.716209 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0534292 (* 1 = 0.0534292 loss) +I0616 14:28:19.716213 9857 solver.cpp:571] Iteration 74060, lr = 0.0001 +I0616 14:28:30.970643 9857 solver.cpp:242] Iteration 74080, loss = 0.70413 +I0616 14:28:30.970685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293264 (* 1 = 0.293264 loss) +I0616 14:28:30.970691 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401008 (* 1 = 0.401008 loss) +I0616 14:28:30.970695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0559164 (* 1 = 0.0559164 loss) +I0616 14:28:30.970700 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0598473 (* 1 = 0.0598473 loss) +I0616 14:28:30.970702 9857 solver.cpp:571] Iteration 74080, lr = 0.0001 +I0616 14:28:42.769748 9857 solver.cpp:242] Iteration 74100, loss = 0.449148 +I0616 14:28:42.769789 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17659 (* 1 = 0.17659 loss) +I0616 14:28:42.769795 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223589 (* 1 = 0.223589 loss) +I0616 14:28:42.769800 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0851703 (* 1 = 0.0851703 loss) +I0616 14:28:42.769804 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306515 (* 1 = 0.0306515 loss) +I0616 14:28:42.769807 9857 solver.cpp:571] Iteration 74100, lr = 0.0001 +I0616 14:28:54.456146 9857 solver.cpp:242] Iteration 74120, loss = 0.202146 +I0616 14:28:54.456189 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0823929 (* 1 = 0.0823929 loss) +I0616 14:28:54.456194 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13538 (* 1 = 0.13538 loss) +I0616 14:28:54.456199 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00681183 (* 1 = 0.00681183 loss) +I0616 14:28:54.456202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00433359 (* 1 = 0.00433359 loss) +I0616 14:28:54.456220 9857 solver.cpp:571] Iteration 74120, lr = 0.0001 +I0616 14:29:05.870764 9857 solver.cpp:242] Iteration 74140, loss = 0.404597 +I0616 14:29:05.870805 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116244 (* 1 = 0.116244 loss) +I0616 14:29:05.870811 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184575 (* 1 = 0.184575 loss) +I0616 14:29:05.870816 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121371 (* 1 = 0.121371 loss) +I0616 14:29:05.870820 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00871363 (* 1 = 0.00871363 loss) +I0616 14:29:05.870823 9857 solver.cpp:571] Iteration 74140, lr = 0.0001 +I0616 14:29:17.783988 9857 solver.cpp:242] Iteration 74160, loss = 0.659988 +I0616 14:29:17.784030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133785 (* 1 = 0.133785 loss) +I0616 14:29:17.784036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260431 (* 1 = 0.260431 loss) +I0616 14:29:17.784040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136262 (* 1 = 0.136262 loss) +I0616 14:29:17.784044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.131122 (* 1 = 0.131122 loss) +I0616 14:29:17.784049 9857 solver.cpp:571] Iteration 74160, lr = 0.0001 +I0616 14:29:29.391710 9857 solver.cpp:242] Iteration 74180, loss = 0.239445 +I0616 14:29:29.391752 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0685313 (* 1 = 0.0685313 loss) +I0616 14:29:29.391757 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107077 (* 1 = 0.107077 loss) +I0616 14:29:29.391762 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.028783 (* 1 = 0.028783 loss) +I0616 14:29:29.391765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0069399 (* 1 = 0.0069399 loss) +I0616 14:29:29.391769 9857 solver.cpp:571] Iteration 74180, lr = 0.0001 +speed: 0.601s / iter +I0616 14:29:40.787286 9857 solver.cpp:242] Iteration 74200, loss = 0.48475 +I0616 14:29:40.787328 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.254661 (* 1 = 0.254661 loss) +I0616 14:29:40.787333 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205663 (* 1 = 0.205663 loss) +I0616 14:29:40.787338 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0603055 (* 1 = 0.0603055 loss) +I0616 14:29:40.787340 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0493166 (* 1 = 0.0493166 loss) +I0616 14:29:40.787344 9857 solver.cpp:571] Iteration 74200, lr = 0.0001 +I0616 14:29:52.154928 9857 solver.cpp:242] Iteration 74220, loss = 0.380928 +I0616 14:29:52.154970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0471882 (* 1 = 0.0471882 loss) +I0616 14:29:52.154976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130457 (* 1 = 0.130457 loss) +I0616 14:29:52.154980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0570915 (* 1 = 0.0570915 loss) +I0616 14:29:52.154984 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00831486 (* 1 = 0.00831486 loss) +I0616 14:29:52.154989 9857 solver.cpp:571] Iteration 74220, lr = 0.0001 +I0616 14:30:03.600263 9857 solver.cpp:242] Iteration 74240, loss = 0.313232 +I0616 14:30:03.600306 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0641446 (* 1 = 0.0641446 loss) +I0616 14:30:03.600311 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0785222 (* 1 = 0.0785222 loss) +I0616 14:30:03.600317 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0930443 (* 1 = 0.0930443 loss) +I0616 14:30:03.600319 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0087554 (* 1 = 0.0087554 loss) +I0616 14:30:03.600323 9857 solver.cpp:571] Iteration 74240, lr = 0.0001 +I0616 14:30:14.897488 9857 solver.cpp:242] Iteration 74260, loss = 0.581316 +I0616 14:30:14.897531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0829051 (* 1 = 0.0829051 loss) +I0616 14:30:14.897536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0877226 (* 1 = 0.0877226 loss) +I0616 14:30:14.897541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0724144 (* 1 = 0.0724144 loss) +I0616 14:30:14.897544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246119 (* 1 = 0.0246119 loss) +I0616 14:30:14.897547 9857 solver.cpp:571] Iteration 74260, lr = 0.0001 +I0616 14:30:26.355715 9857 solver.cpp:242] Iteration 74280, loss = 0.641108 +I0616 14:30:26.355757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21571 (* 1 = 0.21571 loss) +I0616 14:30:26.355763 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317199 (* 1 = 0.317199 loss) +I0616 14:30:26.355767 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.216604 (* 1 = 0.216604 loss) +I0616 14:30:26.355772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.339019 (* 1 = 0.339019 loss) +I0616 14:30:26.355774 9857 solver.cpp:571] Iteration 74280, lr = 0.0001 +I0616 14:30:37.973979 9857 solver.cpp:242] Iteration 74300, loss = 0.81887 +I0616 14:30:37.974022 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127152 (* 1 = 0.127152 loss) +I0616 14:30:37.974027 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0933928 (* 1 = 0.0933928 loss) +I0616 14:30:37.974032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0493891 (* 1 = 0.0493891 loss) +I0616 14:30:37.974035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.2294 (* 1 = 0.2294 loss) +I0616 14:30:37.974040 9857 solver.cpp:571] Iteration 74300, lr = 0.0001 +I0616 14:30:49.426825 9857 solver.cpp:242] Iteration 74320, loss = 0.546833 +I0616 14:30:49.426867 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253978 (* 1 = 0.253978 loss) +I0616 14:30:49.426872 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32918 (* 1 = 0.32918 loss) +I0616 14:30:49.426877 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.185081 (* 1 = 0.185081 loss) +I0616 14:30:49.426882 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0595833 (* 1 = 0.0595833 loss) +I0616 14:30:49.426884 9857 solver.cpp:571] Iteration 74320, lr = 0.0001 +I0616 14:31:01.060005 9857 solver.cpp:242] Iteration 74340, loss = 0.425116 +I0616 14:31:01.060047 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.276522 (* 1 = 0.276522 loss) +I0616 14:31:01.060053 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230968 (* 1 = 0.230968 loss) +I0616 14:31:01.060057 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0584584 (* 1 = 0.0584584 loss) +I0616 14:31:01.060061 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0953547 (* 1 = 0.0953547 loss) +I0616 14:31:01.060065 9857 solver.cpp:571] Iteration 74340, lr = 0.0001 +I0616 14:31:12.500144 9857 solver.cpp:242] Iteration 74360, loss = 0.24355 +I0616 14:31:12.500186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.052154 (* 1 = 0.052154 loss) +I0616 14:31:12.500192 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165059 (* 1 = 0.165059 loss) +I0616 14:31:12.500196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0610636 (* 1 = 0.0610636 loss) +I0616 14:31:12.500200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101971 (* 1 = 0.0101971 loss) +I0616 14:31:12.500203 9857 solver.cpp:571] Iteration 74360, lr = 0.0001 +I0616 14:31:23.863930 9857 solver.cpp:242] Iteration 74380, loss = 0.226314 +I0616 14:31:23.863972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0583187 (* 1 = 0.0583187 loss) +I0616 14:31:23.863978 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0971048 (* 1 = 0.0971048 loss) +I0616 14:31:23.863982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0445721 (* 1 = 0.0445721 loss) +I0616 14:31:23.863986 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0218155 (* 1 = 0.0218155 loss) +I0616 14:31:23.863991 9857 solver.cpp:571] Iteration 74380, lr = 0.0001 +speed: 0.601s / iter +I0616 14:31:35.396913 9857 solver.cpp:242] Iteration 74400, loss = 0.747942 +I0616 14:31:35.396955 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124813 (* 1 = 0.124813 loss) +I0616 14:31:35.396960 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196309 (* 1 = 0.196309 loss) +I0616 14:31:35.396965 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0144176 (* 1 = 0.0144176 loss) +I0616 14:31:35.396970 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116926 (* 1 = 0.0116926 loss) +I0616 14:31:35.396973 9857 solver.cpp:571] Iteration 74400, lr = 0.0001 +I0616 14:31:46.966444 9857 solver.cpp:242] Iteration 74420, loss = 0.838469 +I0616 14:31:46.966486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.194774 (* 1 = 0.194774 loss) +I0616 14:31:46.966491 9857 solver.cpp:258] Train net output #1: loss_cls = 0.284151 (* 1 = 0.284151 loss) +I0616 14:31:46.966495 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.062365 (* 1 = 0.062365 loss) +I0616 14:31:46.966500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.09898 (* 1 = 0.09898 loss) +I0616 14:31:46.966503 9857 solver.cpp:571] Iteration 74420, lr = 0.0001 +I0616 14:31:58.271226 9857 solver.cpp:242] Iteration 74440, loss = 1.00197 +I0616 14:31:58.271270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.220628 (* 1 = 0.220628 loss) +I0616 14:31:58.271275 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278154 (* 1 = 0.278154 loss) +I0616 14:31:58.271278 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161362 (* 1 = 0.161362 loss) +I0616 14:31:58.271282 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.259114 (* 1 = 0.259114 loss) +I0616 14:31:58.271286 9857 solver.cpp:571] Iteration 74440, lr = 0.0001 +I0616 14:32:09.978550 9857 solver.cpp:242] Iteration 74460, loss = 0.611079 +I0616 14:32:09.978591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129151 (* 1 = 0.129151 loss) +I0616 14:32:09.978597 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0819159 (* 1 = 0.0819159 loss) +I0616 14:32:09.978601 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00872702 (* 1 = 0.00872702 loss) +I0616 14:32:09.978605 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0176421 (* 1 = 0.0176421 loss) +I0616 14:32:09.978608 9857 solver.cpp:571] Iteration 74460, lr = 0.0001 +I0616 14:32:21.365350 9857 solver.cpp:242] Iteration 74480, loss = 0.535534 +I0616 14:32:21.365391 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.051709 (* 1 = 0.051709 loss) +I0616 14:32:21.365396 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107313 (* 1 = 0.107313 loss) +I0616 14:32:21.365401 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104678 (* 1 = 0.0104678 loss) +I0616 14:32:21.365404 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00972824 (* 1 = 0.00972824 loss) +I0616 14:32:21.365407 9857 solver.cpp:571] Iteration 74480, lr = 0.0001 +I0616 14:32:32.996902 9857 solver.cpp:242] Iteration 74500, loss = 0.340049 +I0616 14:32:32.996945 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0484241 (* 1 = 0.0484241 loss) +I0616 14:32:32.996950 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0936883 (* 1 = 0.0936883 loss) +I0616 14:32:32.996955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222269 (* 1 = 0.0222269 loss) +I0616 14:32:32.996958 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156709 (* 1 = 0.0156709 loss) +I0616 14:32:32.996963 9857 solver.cpp:571] Iteration 74500, lr = 0.0001 +I0616 14:32:44.571434 9857 solver.cpp:242] Iteration 74520, loss = 0.644499 +I0616 14:32:44.571476 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128191 (* 1 = 0.128191 loss) +I0616 14:32:44.571482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217683 (* 1 = 0.217683 loss) +I0616 14:32:44.571486 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0254884 (* 1 = 0.0254884 loss) +I0616 14:32:44.571491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0495466 (* 1 = 0.0495466 loss) +I0616 14:32:44.571494 9857 solver.cpp:571] Iteration 74520, lr = 0.0001 +I0616 14:32:55.940172 9857 solver.cpp:242] Iteration 74540, loss = 0.427164 +I0616 14:32:55.940214 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165787 (* 1 = 0.165787 loss) +I0616 14:32:55.940219 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176149 (* 1 = 0.176149 loss) +I0616 14:32:55.940223 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00304586 (* 1 = 0.00304586 loss) +I0616 14:32:55.940227 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020815 (* 1 = 0.020815 loss) +I0616 14:32:55.940232 9857 solver.cpp:571] Iteration 74540, lr = 0.0001 +I0616 14:33:07.495435 9857 solver.cpp:242] Iteration 74560, loss = 0.379055 +I0616 14:33:07.495477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145254 (* 1 = 0.145254 loss) +I0616 14:33:07.495483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.365651 (* 1 = 0.365651 loss) +I0616 14:33:07.495487 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.083431 (* 1 = 0.083431 loss) +I0616 14:33:07.495491 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00435297 (* 1 = 0.00435297 loss) +I0616 14:33:07.495496 9857 solver.cpp:571] Iteration 74560, lr = 0.0001 +I0616 14:33:19.069900 9857 solver.cpp:242] Iteration 74580, loss = 0.606847 +I0616 14:33:19.069942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28807 (* 1 = 0.28807 loss) +I0616 14:33:19.069947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378013 (* 1 = 0.378013 loss) +I0616 14:33:19.069952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0612671 (* 1 = 0.0612671 loss) +I0616 14:33:19.069957 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.123566 (* 1 = 0.123566 loss) +I0616 14:33:19.069960 9857 solver.cpp:571] Iteration 74580, lr = 0.0001 +speed: 0.601s / iter +I0616 14:33:30.669397 9857 solver.cpp:242] Iteration 74600, loss = 0.392354 +I0616 14:33:30.669441 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0821818 (* 1 = 0.0821818 loss) +I0616 14:33:30.669446 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187113 (* 1 = 0.187113 loss) +I0616 14:33:30.669451 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0951764 (* 1 = 0.0951764 loss) +I0616 14:33:30.669455 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0137923 (* 1 = 0.0137923 loss) +I0616 14:33:30.669458 9857 solver.cpp:571] Iteration 74600, lr = 0.0001 +I0616 14:33:41.904286 9857 solver.cpp:242] Iteration 74620, loss = 0.707047 +I0616 14:33:41.904327 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0998553 (* 1 = 0.0998553 loss) +I0616 14:33:41.904332 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151725 (* 1 = 0.151725 loss) +I0616 14:33:41.904337 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.05921 (* 1 = 0.05921 loss) +I0616 14:33:41.904340 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00769304 (* 1 = 0.00769304 loss) +I0616 14:33:41.904345 9857 solver.cpp:571] Iteration 74620, lr = 0.0001 +I0616 14:33:53.319145 9857 solver.cpp:242] Iteration 74640, loss = 0.773786 +I0616 14:33:53.319187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164807 (* 1 = 0.164807 loss) +I0616 14:33:53.319192 9857 solver.cpp:258] Train net output #1: loss_cls = 0.297218 (* 1 = 0.297218 loss) +I0616 14:33:53.319196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167452 (* 1 = 0.167452 loss) +I0616 14:33:53.319200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0758069 (* 1 = 0.0758069 loss) +I0616 14:33:53.319205 9857 solver.cpp:571] Iteration 74640, lr = 0.0001 +I0616 14:34:04.885977 9857 solver.cpp:242] Iteration 74660, loss = 0.738787 +I0616 14:34:04.886018 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249201 (* 1 = 0.249201 loss) +I0616 14:34:04.886024 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438616 (* 1 = 0.438616 loss) +I0616 14:34:04.886029 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0850325 (* 1 = 0.0850325 loss) +I0616 14:34:04.886032 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0576695 (* 1 = 0.0576695 loss) +I0616 14:34:04.886036 9857 solver.cpp:571] Iteration 74660, lr = 0.0001 +I0616 14:34:16.345983 9857 solver.cpp:242] Iteration 74680, loss = 0.346429 +I0616 14:34:16.346027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102061 (* 1 = 0.102061 loss) +I0616 14:34:16.346032 9857 solver.cpp:258] Train net output #1: loss_cls = 0.117471 (* 1 = 0.117471 loss) +I0616 14:34:16.346036 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0244175 (* 1 = 0.0244175 loss) +I0616 14:34:16.346040 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275979 (* 1 = 0.0275979 loss) +I0616 14:34:16.346045 9857 solver.cpp:571] Iteration 74680, lr = 0.0001 +I0616 14:34:27.686689 9857 solver.cpp:242] Iteration 74700, loss = 1.14493 +I0616 14:34:27.686733 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343786 (* 1 = 0.343786 loss) +I0616 14:34:27.686738 9857 solver.cpp:258] Train net output #1: loss_cls = 0.979977 (* 1 = 0.979977 loss) +I0616 14:34:27.686743 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.337701 (* 1 = 0.337701 loss) +I0616 14:34:27.686746 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.235596 (* 1 = 0.235596 loss) +I0616 14:34:27.686749 9857 solver.cpp:571] Iteration 74700, lr = 0.0001 +I0616 14:34:38.923209 9857 solver.cpp:242] Iteration 74720, loss = 0.393994 +I0616 14:34:38.923251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238393 (* 1 = 0.238393 loss) +I0616 14:34:38.923257 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19641 (* 1 = 0.19641 loss) +I0616 14:34:38.923261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0324752 (* 1 = 0.0324752 loss) +I0616 14:34:38.923265 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0373216 (* 1 = 0.0373216 loss) +I0616 14:34:38.923269 9857 solver.cpp:571] Iteration 74720, lr = 0.0001 +I0616 14:34:50.686067 9857 solver.cpp:242] Iteration 74740, loss = 0.177215 +I0616 14:34:50.686110 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0754142 (* 1 = 0.0754142 loss) +I0616 14:34:50.686115 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109881 (* 1 = 0.109881 loss) +I0616 14:34:50.686120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00557615 (* 1 = 0.00557615 loss) +I0616 14:34:50.686123 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0059984 (* 1 = 0.0059984 loss) +I0616 14:34:50.686126 9857 solver.cpp:571] Iteration 74740, lr = 0.0001 +I0616 14:35:02.143725 9857 solver.cpp:242] Iteration 74760, loss = 0.480636 +I0616 14:35:02.143767 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0837129 (* 1 = 0.0837129 loss) +I0616 14:35:02.143774 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0878934 (* 1 = 0.0878934 loss) +I0616 14:35:02.143777 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00574373 (* 1 = 0.00574373 loss) +I0616 14:35:02.143781 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00439712 (* 1 = 0.00439712 loss) +I0616 14:35:02.143785 9857 solver.cpp:571] Iteration 74760, lr = 0.0001 +I0616 14:35:13.437676 9857 solver.cpp:242] Iteration 74780, loss = 0.429717 +I0616 14:35:13.437717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215048 (* 1 = 0.215048 loss) +I0616 14:35:13.437723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174912 (* 1 = 0.174912 loss) +I0616 14:35:13.437727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0382515 (* 1 = 0.0382515 loss) +I0616 14:35:13.437731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0651618 (* 1 = 0.0651618 loss) +I0616 14:35:13.437736 9857 solver.cpp:571] Iteration 74780, lr = 0.0001 +speed: 0.601s / iter +I0616 14:35:25.025404 9857 solver.cpp:242] Iteration 74800, loss = 0.303253 +I0616 14:35:25.025444 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10414 (* 1 = 0.10414 loss) +I0616 14:35:25.025449 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13541 (* 1 = 0.13541 loss) +I0616 14:35:25.025454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00858214 (* 1 = 0.00858214 loss) +I0616 14:35:25.025457 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00897871 (* 1 = 0.00897871 loss) +I0616 14:35:25.025461 9857 solver.cpp:571] Iteration 74800, lr = 0.0001 +I0616 14:35:36.524490 9857 solver.cpp:242] Iteration 74820, loss = 0.456179 +I0616 14:35:36.524533 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0465738 (* 1 = 0.0465738 loss) +I0616 14:35:36.524538 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104949 (* 1 = 0.104949 loss) +I0616 14:35:36.524543 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0438634 (* 1 = 0.0438634 loss) +I0616 14:35:36.524546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164722 (* 1 = 0.0164722 loss) +I0616 14:35:36.524550 9857 solver.cpp:571] Iteration 74820, lr = 0.0001 +I0616 14:35:48.083331 9857 solver.cpp:242] Iteration 74840, loss = 0.537128 +I0616 14:35:48.083374 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19904 (* 1 = 0.19904 loss) +I0616 14:35:48.083379 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301143 (* 1 = 0.301143 loss) +I0616 14:35:48.083384 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0670009 (* 1 = 0.0670009 loss) +I0616 14:35:48.083387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157464 (* 1 = 0.0157464 loss) +I0616 14:35:48.083391 9857 solver.cpp:571] Iteration 74840, lr = 0.0001 +I0616 14:35:59.760459 9857 solver.cpp:242] Iteration 74860, loss = 0.515725 +I0616 14:35:59.760501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157712 (* 1 = 0.157712 loss) +I0616 14:35:59.760506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.374947 (* 1 = 0.374947 loss) +I0616 14:35:59.760511 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121407 (* 1 = 0.121407 loss) +I0616 14:35:59.760514 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.231872 (* 1 = 0.231872 loss) +I0616 14:35:59.760519 9857 solver.cpp:571] Iteration 74860, lr = 0.0001 +I0616 14:36:11.373311 9857 solver.cpp:242] Iteration 74880, loss = 0.397937 +I0616 14:36:11.373355 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19109 (* 1 = 0.19109 loss) +I0616 14:36:11.373360 9857 solver.cpp:258] Train net output #1: loss_cls = 0.197905 (* 1 = 0.197905 loss) +I0616 14:36:11.373365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0429879 (* 1 = 0.0429879 loss) +I0616 14:36:11.373369 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0149828 (* 1 = 0.0149828 loss) +I0616 14:36:11.373373 9857 solver.cpp:571] Iteration 74880, lr = 0.0001 +I0616 14:36:22.666641 9857 solver.cpp:242] Iteration 74900, loss = 0.811358 +I0616 14:36:22.666684 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280648 (* 1 = 0.280648 loss) +I0616 14:36:22.666690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.473747 (* 1 = 0.473747 loss) +I0616 14:36:22.666694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.195514 (* 1 = 0.195514 loss) +I0616 14:36:22.666697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.244867 (* 1 = 0.244867 loss) +I0616 14:36:22.666702 9857 solver.cpp:571] Iteration 74900, lr = 0.0001 +I0616 14:36:34.234583 9857 solver.cpp:242] Iteration 74920, loss = 0.495185 +I0616 14:36:34.234627 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165715 (* 1 = 0.165715 loss) +I0616 14:36:34.234632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201658 (* 1 = 0.201658 loss) +I0616 14:36:34.234637 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0566638 (* 1 = 0.0566638 loss) +I0616 14:36:34.234640 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102461 (* 1 = 0.0102461 loss) +I0616 14:36:34.234643 9857 solver.cpp:571] Iteration 74920, lr = 0.0001 +I0616 14:36:45.652534 9857 solver.cpp:242] Iteration 74940, loss = 0.40179 +I0616 14:36:45.652575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.071795 (* 1 = 0.071795 loss) +I0616 14:36:45.652581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189649 (* 1 = 0.189649 loss) +I0616 14:36:45.652585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374117 (* 1 = 0.0374117 loss) +I0616 14:36:45.652590 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00351004 (* 1 = 0.00351004 loss) +I0616 14:36:45.652593 9857 solver.cpp:571] Iteration 74940, lr = 0.0001 +I0616 14:36:57.036144 9857 solver.cpp:242] Iteration 74960, loss = 0.577607 +I0616 14:36:57.036187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.305033 (* 1 = 0.305033 loss) +I0616 14:36:57.036193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366066 (* 1 = 0.366066 loss) +I0616 14:36:57.036197 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0130951 (* 1 = 0.0130951 loss) +I0616 14:36:57.036201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0402867 (* 1 = 0.0402867 loss) +I0616 14:36:57.036206 9857 solver.cpp:571] Iteration 74960, lr = 0.0001 +I0616 14:37:08.646001 9857 solver.cpp:242] Iteration 74980, loss = 0.672466 +I0616 14:37:08.646044 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0555118 (* 1 = 0.0555118 loss) +I0616 14:37:08.646049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128683 (* 1 = 0.128683 loss) +I0616 14:37:08.646054 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0116164 (* 1 = 0.0116164 loss) +I0616 14:37:08.646059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00046868 (* 1 = 0.00046868 loss) +I0616 14:37:08.646061 9857 solver.cpp:571] Iteration 74980, lr = 0.0001 +speed: 0.601s / iter +I0616 14:37:20.016840 9857 solver.cpp:242] Iteration 75000, loss = 0.442259 +I0616 14:37:20.016883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11562 (* 1 = 0.11562 loss) +I0616 14:37:20.016890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126277 (* 1 = 0.126277 loss) +I0616 14:37:20.016893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0303109 (* 1 = 0.0303109 loss) +I0616 14:37:20.016897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0060753 (* 1 = 0.0060753 loss) +I0616 14:37:20.016901 9857 solver.cpp:571] Iteration 75000, lr = 0.0001 +I0616 14:37:31.684041 9857 solver.cpp:242] Iteration 75020, loss = 0.672626 +I0616 14:37:31.684084 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333609 (* 1 = 0.333609 loss) +I0616 14:37:31.684089 9857 solver.cpp:258] Train net output #1: loss_cls = 0.318757 (* 1 = 0.318757 loss) +I0616 14:37:31.684093 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103845 (* 1 = 0.103845 loss) +I0616 14:37:31.684098 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0320546 (* 1 = 0.0320546 loss) +I0616 14:37:31.684101 9857 solver.cpp:571] Iteration 75020, lr = 0.0001 +I0616 14:37:43.220496 9857 solver.cpp:242] Iteration 75040, loss = 0.879638 +I0616 14:37:43.220538 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157356 (* 1 = 0.157356 loss) +I0616 14:37:43.220543 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246312 (* 1 = 0.246312 loss) +I0616 14:37:43.220547 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.192866 (* 1 = 0.192866 loss) +I0616 14:37:43.220551 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.866016 (* 1 = 0.866016 loss) +I0616 14:37:43.220556 9857 solver.cpp:571] Iteration 75040, lr = 0.0001 +I0616 14:37:54.886766 9857 solver.cpp:242] Iteration 75060, loss = 0.331684 +I0616 14:37:54.886807 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244041 (* 1 = 0.244041 loss) +I0616 14:37:54.886812 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181104 (* 1 = 0.181104 loss) +I0616 14:37:54.886817 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0468066 (* 1 = 0.0468066 loss) +I0616 14:37:54.886821 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0229419 (* 1 = 0.0229419 loss) +I0616 14:37:54.886826 9857 solver.cpp:571] Iteration 75060, lr = 0.0001 +I0616 14:38:06.523329 9857 solver.cpp:242] Iteration 75080, loss = 0.466041 +I0616 14:38:06.523373 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107863 (* 1 = 0.107863 loss) +I0616 14:38:06.523378 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0991025 (* 1 = 0.0991025 loss) +I0616 14:38:06.523382 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0201919 (* 1 = 0.0201919 loss) +I0616 14:38:06.523386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0191476 (* 1 = 0.0191476 loss) +I0616 14:38:06.523389 9857 solver.cpp:571] Iteration 75080, lr = 0.0001 +I0616 14:38:17.974697 9857 solver.cpp:242] Iteration 75100, loss = 0.326587 +I0616 14:38:17.974740 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0559512 (* 1 = 0.0559512 loss) +I0616 14:38:17.974745 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0539793 (* 1 = 0.0539793 loss) +I0616 14:38:17.974750 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00705687 (* 1 = 0.00705687 loss) +I0616 14:38:17.974753 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000934667 (* 1 = 0.000934667 loss) +I0616 14:38:17.974763 9857 solver.cpp:571] Iteration 75100, lr = 0.0001 +I0616 14:38:29.473341 9857 solver.cpp:242] Iteration 75120, loss = 0.478509 +I0616 14:38:29.473382 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153789 (* 1 = 0.153789 loss) +I0616 14:38:29.473387 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196018 (* 1 = 0.196018 loss) +I0616 14:38:29.473392 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184204 (* 1 = 0.184204 loss) +I0616 14:38:29.473397 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.101574 (* 1 = 0.101574 loss) +I0616 14:38:29.473399 9857 solver.cpp:571] Iteration 75120, lr = 0.0001 +I0616 14:38:40.942273 9857 solver.cpp:242] Iteration 75140, loss = 0.659934 +I0616 14:38:40.942318 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209398 (* 1 = 0.209398 loss) +I0616 14:38:40.942325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214462 (* 1 = 0.214462 loss) +I0616 14:38:40.942332 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0802861 (* 1 = 0.0802861 loss) +I0616 14:38:40.942337 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.272632 (* 1 = 0.272632 loss) +I0616 14:38:40.942343 9857 solver.cpp:571] Iteration 75140, lr = 0.0001 +I0616 14:38:52.635370 9857 solver.cpp:242] Iteration 75160, loss = 0.322994 +I0616 14:38:52.635411 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0880916 (* 1 = 0.0880916 loss) +I0616 14:38:52.635417 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248034 (* 1 = 0.248034 loss) +I0616 14:38:52.635421 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0617698 (* 1 = 0.0617698 loss) +I0616 14:38:52.635426 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00601936 (* 1 = 0.00601936 loss) +I0616 14:38:52.635429 9857 solver.cpp:571] Iteration 75160, lr = 0.0001 +I0616 14:39:04.146363 9857 solver.cpp:242] Iteration 75180, loss = 0.278034 +I0616 14:39:04.146404 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0635519 (* 1 = 0.0635519 loss) +I0616 14:39:04.146409 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0955639 (* 1 = 0.0955639 loss) +I0616 14:39:04.146414 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0021735 (* 1 = 0.0021735 loss) +I0616 14:39:04.146419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00590303 (* 1 = 0.00590303 loss) +I0616 14:39:04.146421 9857 solver.cpp:571] Iteration 75180, lr = 0.0001 +speed: 0.601s / iter +I0616 14:39:15.446559 9857 solver.cpp:242] Iteration 75200, loss = 0.456513 +I0616 14:39:15.446601 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12736 (* 1 = 0.12736 loss) +I0616 14:39:15.446606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144806 (* 1 = 0.144806 loss) +I0616 14:39:15.446611 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0160399 (* 1 = 0.0160399 loss) +I0616 14:39:15.446615 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483687 (* 1 = 0.0483687 loss) +I0616 14:39:15.446619 9857 solver.cpp:571] Iteration 75200, lr = 0.0001 +I0616 14:39:27.031412 9857 solver.cpp:242] Iteration 75220, loss = 0.283243 +I0616 14:39:27.031452 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116286 (* 1 = 0.116286 loss) +I0616 14:39:27.031458 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213909 (* 1 = 0.213909 loss) +I0616 14:39:27.031462 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0278241 (* 1 = 0.0278241 loss) +I0616 14:39:27.031466 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0303078 (* 1 = 0.0303078 loss) +I0616 14:39:27.031469 9857 solver.cpp:571] Iteration 75220, lr = 0.0001 +I0616 14:39:38.728179 9857 solver.cpp:242] Iteration 75240, loss = 0.201694 +I0616 14:39:38.728217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0707757 (* 1 = 0.0707757 loss) +I0616 14:39:38.728224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12934 (* 1 = 0.12934 loss) +I0616 14:39:38.728227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0404013 (* 1 = 0.0404013 loss) +I0616 14:39:38.728231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00899612 (* 1 = 0.00899612 loss) +I0616 14:39:38.728235 9857 solver.cpp:571] Iteration 75240, lr = 0.0001 +I0616 14:39:50.286278 9857 solver.cpp:242] Iteration 75260, loss = 0.485976 +I0616 14:39:50.286319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169893 (* 1 = 0.169893 loss) +I0616 14:39:50.286325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270871 (* 1 = 0.270871 loss) +I0616 14:39:50.286329 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0788367 (* 1 = 0.0788367 loss) +I0616 14:39:50.286334 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0434621 (* 1 = 0.0434621 loss) +I0616 14:39:50.286336 9857 solver.cpp:571] Iteration 75260, lr = 0.0001 +I0616 14:40:01.862970 9857 solver.cpp:242] Iteration 75280, loss = 0.3124 +I0616 14:40:01.863013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128056 (* 1 = 0.128056 loss) +I0616 14:40:01.863019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128133 (* 1 = 0.128133 loss) +I0616 14:40:01.863024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0266593 (* 1 = 0.0266593 loss) +I0616 14:40:01.863029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000408609 (* 1 = 0.000408609 loss) +I0616 14:40:01.863032 9857 solver.cpp:571] Iteration 75280, lr = 0.0001 +I0616 14:40:13.603790 9857 solver.cpp:242] Iteration 75300, loss = 0.646021 +I0616 14:40:13.603831 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.317992 (* 1 = 0.317992 loss) +I0616 14:40:13.603835 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162709 (* 1 = 0.162709 loss) +I0616 14:40:13.603839 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0573194 (* 1 = 0.0573194 loss) +I0616 14:40:13.603843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388642 (* 1 = 0.0388642 loss) +I0616 14:40:13.603847 9857 solver.cpp:571] Iteration 75300, lr = 0.0001 +I0616 14:40:25.253530 9857 solver.cpp:242] Iteration 75320, loss = 0.770338 +I0616 14:40:25.253571 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.430598 (* 1 = 0.430598 loss) +I0616 14:40:25.253577 9857 solver.cpp:258] Train net output #1: loss_cls = 0.58076 (* 1 = 0.58076 loss) +I0616 14:40:25.253582 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140426 (* 1 = 0.140426 loss) +I0616 14:40:25.253585 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.256458 (* 1 = 0.256458 loss) +I0616 14:40:25.253589 9857 solver.cpp:571] Iteration 75320, lr = 0.0001 +I0616 14:40:36.853968 9857 solver.cpp:242] Iteration 75340, loss = 0.183388 +I0616 14:40:36.854012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0980046 (* 1 = 0.0980046 loss) +I0616 14:40:36.854017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129417 (* 1 = 0.129417 loss) +I0616 14:40:36.854022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00782907 (* 1 = 0.00782907 loss) +I0616 14:40:36.854024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138034 (* 1 = 0.0138034 loss) +I0616 14:40:36.854028 9857 solver.cpp:571] Iteration 75340, lr = 0.0001 +I0616 14:40:48.403023 9857 solver.cpp:242] Iteration 75360, loss = 0.906039 +I0616 14:40:48.403065 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.397235 (* 1 = 0.397235 loss) +I0616 14:40:48.403071 9857 solver.cpp:258] Train net output #1: loss_cls = 0.569732 (* 1 = 0.569732 loss) +I0616 14:40:48.403075 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200494 (* 1 = 0.200494 loss) +I0616 14:40:48.403079 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275759 (* 1 = 0.0275759 loss) +I0616 14:40:48.403084 9857 solver.cpp:571] Iteration 75360, lr = 0.0001 +I0616 14:40:59.978355 9857 solver.cpp:242] Iteration 75380, loss = 0.333201 +I0616 14:40:59.978396 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100171 (* 1 = 0.100171 loss) +I0616 14:40:59.978402 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216752 (* 1 = 0.216752 loss) +I0616 14:40:59.978407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0122144 (* 1 = 0.0122144 loss) +I0616 14:40:59.978410 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132095 (* 1 = 0.0132095 loss) +I0616 14:40:59.978415 9857 solver.cpp:571] Iteration 75380, lr = 0.0001 +speed: 0.601s / iter +I0616 14:41:11.786180 9857 solver.cpp:242] Iteration 75400, loss = 0.251548 +I0616 14:41:11.786223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109233 (* 1 = 0.109233 loss) +I0616 14:41:11.786229 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163019 (* 1 = 0.163019 loss) +I0616 14:41:11.786233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0129482 (* 1 = 0.0129482 loss) +I0616 14:41:11.786237 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00219772 (* 1 = 0.00219772 loss) +I0616 14:41:11.786240 9857 solver.cpp:571] Iteration 75400, lr = 0.0001 +I0616 14:41:23.324942 9857 solver.cpp:242] Iteration 75420, loss = 0.769176 +I0616 14:41:23.324997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363753 (* 1 = 0.363753 loss) +I0616 14:41:23.325002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.537019 (* 1 = 0.537019 loss) +I0616 14:41:23.325006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.362459 (* 1 = 0.362459 loss) +I0616 14:41:23.325011 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.135792 (* 1 = 0.135792 loss) +I0616 14:41:23.325014 9857 solver.cpp:571] Iteration 75420, lr = 0.0001 +I0616 14:41:34.611695 9857 solver.cpp:242] Iteration 75440, loss = 0.610877 +I0616 14:41:34.611737 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292246 (* 1 = 0.292246 loss) +I0616 14:41:34.611742 9857 solver.cpp:258] Train net output #1: loss_cls = 0.475213 (* 1 = 0.475213 loss) +I0616 14:41:34.611747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224978 (* 1 = 0.224978 loss) +I0616 14:41:34.611750 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0527974 (* 1 = 0.0527974 loss) +I0616 14:41:34.611753 9857 solver.cpp:571] Iteration 75440, lr = 0.0001 +I0616 14:41:46.250077 9857 solver.cpp:242] Iteration 75460, loss = 0.720325 +I0616 14:41:46.250120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145583 (* 1 = 0.145583 loss) +I0616 14:41:46.250126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213128 (* 1 = 0.213128 loss) +I0616 14:41:46.250130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0995112 (* 1 = 0.0995112 loss) +I0616 14:41:46.250134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0124504 (* 1 = 0.0124504 loss) +I0616 14:41:46.250139 9857 solver.cpp:571] Iteration 75460, lr = 0.0001 +I0616 14:41:57.923003 9857 solver.cpp:242] Iteration 75480, loss = 0.68118 +I0616 14:41:57.923043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298615 (* 1 = 0.298615 loss) +I0616 14:41:57.923049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.34486 (* 1 = 0.34486 loss) +I0616 14:41:57.923053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0493602 (* 1 = 0.0493602 loss) +I0616 14:41:57.923058 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234681 (* 1 = 0.0234681 loss) +I0616 14:41:57.923061 9857 solver.cpp:571] Iteration 75480, lr = 0.0001 +I0616 14:42:09.429989 9857 solver.cpp:242] Iteration 75500, loss = 0.867968 +I0616 14:42:09.430029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.405683 (* 1 = 0.405683 loss) +I0616 14:42:09.430034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431029 (* 1 = 0.431029 loss) +I0616 14:42:09.430039 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0376249 (* 1 = 0.0376249 loss) +I0616 14:42:09.430043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283102 (* 1 = 0.0283102 loss) +I0616 14:42:09.430047 9857 solver.cpp:571] Iteration 75500, lr = 0.0001 +I0616 14:42:21.019707 9857 solver.cpp:242] Iteration 75520, loss = 0.514318 +I0616 14:42:21.019749 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0553492 (* 1 = 0.0553492 loss) +I0616 14:42:21.019755 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0665113 (* 1 = 0.0665113 loss) +I0616 14:42:21.019759 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0110132 (* 1 = 0.0110132 loss) +I0616 14:42:21.019763 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122056 (* 1 = 0.0122056 loss) +I0616 14:42:21.019767 9857 solver.cpp:571] Iteration 75520, lr = 0.0001 +I0616 14:42:32.609459 9857 solver.cpp:242] Iteration 75540, loss = 0.461882 +I0616 14:42:32.609501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117691 (* 1 = 0.117691 loss) +I0616 14:42:32.609508 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199499 (* 1 = 0.199499 loss) +I0616 14:42:32.609511 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.052069 (* 1 = 0.052069 loss) +I0616 14:42:32.609515 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173652 (* 1 = 0.0173652 loss) +I0616 14:42:32.609519 9857 solver.cpp:571] Iteration 75540, lr = 0.0001 +I0616 14:42:44.386015 9857 solver.cpp:242] Iteration 75560, loss = 0.287558 +I0616 14:42:44.386057 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122556 (* 1 = 0.122556 loss) +I0616 14:42:44.386064 9857 solver.cpp:258] Train net output #1: loss_cls = 0.220112 (* 1 = 0.220112 loss) +I0616 14:42:44.386067 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0250042 (* 1 = 0.0250042 loss) +I0616 14:42:44.386071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0087874 (* 1 = 0.0087874 loss) +I0616 14:42:44.386077 9857 solver.cpp:571] Iteration 75560, lr = 0.0001 +I0616 14:42:55.657513 9857 solver.cpp:242] Iteration 75580, loss = 0.334371 +I0616 14:42:55.657554 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0563211 (* 1 = 0.0563211 loss) +I0616 14:42:55.657560 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0759634 (* 1 = 0.0759634 loss) +I0616 14:42:55.657564 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00140304 (* 1 = 0.00140304 loss) +I0616 14:42:55.657568 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00211921 (* 1 = 0.00211921 loss) +I0616 14:42:55.657572 9857 solver.cpp:571] Iteration 75580, lr = 0.0001 +speed: 0.601s / iter +I0616 14:43:07.332808 9857 solver.cpp:242] Iteration 75600, loss = 0.394828 +I0616 14:43:07.332850 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170343 (* 1 = 0.170343 loss) +I0616 14:43:07.332855 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184575 (* 1 = 0.184575 loss) +I0616 14:43:07.332860 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0284689 (* 1 = 0.0284689 loss) +I0616 14:43:07.332864 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0462379 (* 1 = 0.0462379 loss) +I0616 14:43:07.332867 9857 solver.cpp:571] Iteration 75600, lr = 0.0001 +I0616 14:43:18.784992 9857 solver.cpp:242] Iteration 75620, loss = 0.390776 +I0616 14:43:18.785033 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0746475 (* 1 = 0.0746475 loss) +I0616 14:43:18.785039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120537 (* 1 = 0.120537 loss) +I0616 14:43:18.785043 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0326845 (* 1 = 0.0326845 loss) +I0616 14:43:18.785046 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0874467 (* 1 = 0.0874467 loss) +I0616 14:43:18.785050 9857 solver.cpp:571] Iteration 75620, lr = 0.0001 +I0616 14:43:30.165071 9857 solver.cpp:242] Iteration 75640, loss = 0.220425 +I0616 14:43:30.165113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0606315 (* 1 = 0.0606315 loss) +I0616 14:43:30.165119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123761 (* 1 = 0.123761 loss) +I0616 14:43:30.165124 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00808081 (* 1 = 0.00808081 loss) +I0616 14:43:30.165127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161163 (* 1 = 0.0161163 loss) +I0616 14:43:30.165132 9857 solver.cpp:571] Iteration 75640, lr = 0.0001 +I0616 14:43:41.565605 9857 solver.cpp:242] Iteration 75660, loss = 0.696748 +I0616 14:43:41.565649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250066 (* 1 = 0.250066 loss) +I0616 14:43:41.565654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.339176 (* 1 = 0.339176 loss) +I0616 14:43:41.565659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.171769 (* 1 = 0.171769 loss) +I0616 14:43:41.565662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.291617 (* 1 = 0.291617 loss) +I0616 14:43:41.565665 9857 solver.cpp:571] Iteration 75660, lr = 0.0001 +I0616 14:43:52.923848 9857 solver.cpp:242] Iteration 75680, loss = 1.11679 +I0616 14:43:52.923890 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365796 (* 1 = 0.365796 loss) +I0616 14:43:52.923897 9857 solver.cpp:258] Train net output #1: loss_cls = 0.25249 (* 1 = 0.25249 loss) +I0616 14:43:52.923900 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144481 (* 1 = 0.144481 loss) +I0616 14:43:52.923904 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209711 (* 1 = 0.0209711 loss) +I0616 14:43:52.923908 9857 solver.cpp:571] Iteration 75680, lr = 0.0001 +I0616 14:44:04.489313 9857 solver.cpp:242] Iteration 75700, loss = 0.719135 +I0616 14:44:04.489354 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0881178 (* 1 = 0.0881178 loss) +I0616 14:44:04.489361 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142678 (* 1 = 0.142678 loss) +I0616 14:44:04.489364 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0440811 (* 1 = 0.0440811 loss) +I0616 14:44:04.489368 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0383421 (* 1 = 0.0383421 loss) +I0616 14:44:04.489372 9857 solver.cpp:571] Iteration 75700, lr = 0.0001 +I0616 14:44:15.836658 9857 solver.cpp:242] Iteration 75720, loss = 0.436395 +I0616 14:44:15.836700 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.226895 (* 1 = 0.226895 loss) +I0616 14:44:15.836706 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246298 (* 1 = 0.246298 loss) +I0616 14:44:15.836710 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.011702 (* 1 = 0.011702 loss) +I0616 14:44:15.836714 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00384169 (* 1 = 0.00384169 loss) +I0616 14:44:15.836719 9857 solver.cpp:571] Iteration 75720, lr = 0.0001 +I0616 14:44:27.213980 9857 solver.cpp:242] Iteration 75740, loss = 0.265732 +I0616 14:44:27.214022 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0803713 (* 1 = 0.0803713 loss) +I0616 14:44:27.214027 9857 solver.cpp:258] Train net output #1: loss_cls = 0.133252 (* 1 = 0.133252 loss) +I0616 14:44:27.214032 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190041 (* 1 = 0.0190041 loss) +I0616 14:44:27.214035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00633086 (* 1 = 0.00633086 loss) +I0616 14:44:27.214040 9857 solver.cpp:571] Iteration 75740, lr = 0.0001 +I0616 14:44:38.293108 9857 solver.cpp:242] Iteration 75760, loss = 0.398867 +I0616 14:44:38.293150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163987 (* 1 = 0.163987 loss) +I0616 14:44:38.293155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215497 (* 1 = 0.215497 loss) +I0616 14:44:38.293160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0663133 (* 1 = 0.0663133 loss) +I0616 14:44:38.293164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.114415 (* 1 = 0.114415 loss) +I0616 14:44:38.293167 9857 solver.cpp:571] Iteration 75760, lr = 0.0001 +I0616 14:44:50.127074 9857 solver.cpp:242] Iteration 75780, loss = 0.476996 +I0616 14:44:50.127115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0562496 (* 1 = 0.0562496 loss) +I0616 14:44:50.127121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156036 (* 1 = 0.156036 loss) +I0616 14:44:50.127125 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0281337 (* 1 = 0.0281337 loss) +I0616 14:44:50.127128 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100732 (* 1 = 0.0100732 loss) +I0616 14:44:50.127132 9857 solver.cpp:571] Iteration 75780, lr = 0.0001 +speed: 0.601s / iter +I0616 14:45:01.625093 9857 solver.cpp:242] Iteration 75800, loss = 0.293882 +I0616 14:45:01.625135 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0378129 (* 1 = 0.0378129 loss) +I0616 14:45:01.625141 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0675765 (* 1 = 0.0675765 loss) +I0616 14:45:01.625145 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00393904 (* 1 = 0.00393904 loss) +I0616 14:45:01.625149 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00532525 (* 1 = 0.00532525 loss) +I0616 14:45:01.625152 9857 solver.cpp:571] Iteration 75800, lr = 0.0001 +I0616 14:45:13.378178 9857 solver.cpp:242] Iteration 75820, loss = 0.229835 +I0616 14:45:13.378219 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.062876 (* 1 = 0.062876 loss) +I0616 14:45:13.378226 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120018 (* 1 = 0.120018 loss) +I0616 14:45:13.378229 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0355665 (* 1 = 0.0355665 loss) +I0616 14:45:13.378232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220507 (* 1 = 0.0220507 loss) +I0616 14:45:13.378237 9857 solver.cpp:571] Iteration 75820, lr = 0.0001 +I0616 14:45:24.710897 9857 solver.cpp:242] Iteration 75840, loss = 0.515692 +I0616 14:45:24.710937 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.107414 (* 1 = 0.107414 loss) +I0616 14:45:24.710943 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199999 (* 1 = 0.199999 loss) +I0616 14:45:24.710947 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128664 (* 1 = 0.128664 loss) +I0616 14:45:24.710952 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.336264 (* 1 = 0.336264 loss) +I0616 14:45:24.710954 9857 solver.cpp:571] Iteration 75840, lr = 0.0001 +I0616 14:45:36.330461 9857 solver.cpp:242] Iteration 75860, loss = 0.490735 +I0616 14:45:36.330502 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.218189 (* 1 = 0.218189 loss) +I0616 14:45:36.330507 9857 solver.cpp:258] Train net output #1: loss_cls = 0.369493 (* 1 = 0.369493 loss) +I0616 14:45:36.330513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102504 (* 1 = 0.102504 loss) +I0616 14:45:36.330516 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0327662 (* 1 = 0.0327662 loss) +I0616 14:45:36.330519 9857 solver.cpp:571] Iteration 75860, lr = 0.0001 +I0616 14:45:47.697304 9857 solver.cpp:242] Iteration 75880, loss = 0.304483 +I0616 14:45:47.697346 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0337063 (* 1 = 0.0337063 loss) +I0616 14:45:47.697351 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145965 (* 1 = 0.145965 loss) +I0616 14:45:47.697356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0416743 (* 1 = 0.0416743 loss) +I0616 14:45:47.697360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0176017 (* 1 = 0.0176017 loss) +I0616 14:45:47.697363 9857 solver.cpp:571] Iteration 75880, lr = 0.0001 +I0616 14:45:59.277487 9857 solver.cpp:242] Iteration 75900, loss = 0.425846 +I0616 14:45:59.277529 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186291 (* 1 = 0.186291 loss) +I0616 14:45:59.277535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203658 (* 1 = 0.203658 loss) +I0616 14:45:59.277539 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0886792 (* 1 = 0.0886792 loss) +I0616 14:45:59.277544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0225763 (* 1 = 0.0225763 loss) +I0616 14:45:59.277546 9857 solver.cpp:571] Iteration 75900, lr = 0.0001 +I0616 14:46:10.863026 9857 solver.cpp:242] Iteration 75920, loss = 0.274414 +I0616 14:46:10.863070 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.072969 (* 1 = 0.072969 loss) +I0616 14:46:10.863076 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0932636 (* 1 = 0.0932636 loss) +I0616 14:46:10.863080 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00445255 (* 1 = 0.00445255 loss) +I0616 14:46:10.863085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00415 (* 1 = 0.00415 loss) +I0616 14:46:10.863088 9857 solver.cpp:571] Iteration 75920, lr = 0.0001 +I0616 14:46:22.302789 9857 solver.cpp:242] Iteration 75940, loss = 0.388833 +I0616 14:46:22.302829 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207627 (* 1 = 0.207627 loss) +I0616 14:46:22.302834 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22895 (* 1 = 0.22895 loss) +I0616 14:46:22.302839 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0425258 (* 1 = 0.0425258 loss) +I0616 14:46:22.302844 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0482508 (* 1 = 0.0482508 loss) +I0616 14:46:22.302848 9857 solver.cpp:571] Iteration 75940, lr = 0.0001 +I0616 14:46:34.024332 9857 solver.cpp:242] Iteration 75960, loss = 0.668368 +I0616 14:46:34.024372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.405113 (* 1 = 0.405113 loss) +I0616 14:46:34.024377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.285648 (* 1 = 0.285648 loss) +I0616 14:46:34.024382 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0660617 (* 1 = 0.0660617 loss) +I0616 14:46:34.024386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0577068 (* 1 = 0.0577068 loss) +I0616 14:46:34.024389 9857 solver.cpp:571] Iteration 75960, lr = 0.0001 +I0616 14:46:45.482640 9857 solver.cpp:242] Iteration 75980, loss = 1.06678 +I0616 14:46:45.482678 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.277949 (* 1 = 0.277949 loss) +I0616 14:46:45.482684 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559549 (* 1 = 0.559549 loss) +I0616 14:46:45.482688 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138281 (* 1 = 0.138281 loss) +I0616 14:46:45.482692 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273432 (* 1 = 0.0273432 loss) +I0616 14:46:45.482695 9857 solver.cpp:571] Iteration 75980, lr = 0.0001 +speed: 0.601s / iter +I0616 14:46:57.224236 9857 solver.cpp:242] Iteration 76000, loss = 0.270473 +I0616 14:46:57.224282 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0557991 (* 1 = 0.0557991 loss) +I0616 14:46:57.224288 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150431 (* 1 = 0.150431 loss) +I0616 14:46:57.224294 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.000635141 (* 1 = 0.000635141 loss) +I0616 14:46:57.224300 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100153 (* 1 = 0.0100153 loss) +I0616 14:46:57.224305 9857 solver.cpp:571] Iteration 76000, lr = 0.0001 +I0616 14:47:08.922902 9857 solver.cpp:242] Iteration 76020, loss = 0.400489 +I0616 14:47:08.922945 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241914 (* 1 = 0.241914 loss) +I0616 14:47:08.922950 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254385 (* 1 = 0.254385 loss) +I0616 14:47:08.922955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0344622 (* 1 = 0.0344622 loss) +I0616 14:47:08.922958 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483326 (* 1 = 0.0483326 loss) +I0616 14:47:08.922962 9857 solver.cpp:571] Iteration 76020, lr = 0.0001 +I0616 14:47:20.404362 9857 solver.cpp:242] Iteration 76040, loss = 0.284335 +I0616 14:47:20.404405 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0887881 (* 1 = 0.0887881 loss) +I0616 14:47:20.404412 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145416 (* 1 = 0.145416 loss) +I0616 14:47:20.404415 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0396463 (* 1 = 0.0396463 loss) +I0616 14:47:20.404419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01446 (* 1 = 0.01446 loss) +I0616 14:47:20.404422 9857 solver.cpp:571] Iteration 76040, lr = 0.0001 +I0616 14:47:31.932771 9857 solver.cpp:242] Iteration 76060, loss = 0.456863 +I0616 14:47:31.932814 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0581289 (* 1 = 0.0581289 loss) +I0616 14:47:31.932821 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153056 (* 1 = 0.153056 loss) +I0616 14:47:31.932824 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0313993 (* 1 = 0.0313993 loss) +I0616 14:47:31.932828 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00656037 (* 1 = 0.00656037 loss) +I0616 14:47:31.932834 9857 solver.cpp:571] Iteration 76060, lr = 0.0001 +I0616 14:47:43.617362 9857 solver.cpp:242] Iteration 76080, loss = 0.219553 +I0616 14:47:43.617404 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0549385 (* 1 = 0.0549385 loss) +I0616 14:47:43.617410 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200189 (* 1 = 0.200189 loss) +I0616 14:47:43.617414 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0109981 (* 1 = 0.0109981 loss) +I0616 14:47:43.617419 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00302682 (* 1 = 0.00302682 loss) +I0616 14:47:43.617422 9857 solver.cpp:571] Iteration 76080, lr = 0.0001 +I0616 14:47:54.969542 9857 solver.cpp:242] Iteration 76100, loss = 0.522878 +I0616 14:47:54.969584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296195 (* 1 = 0.296195 loss) +I0616 14:47:54.969590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248101 (* 1 = 0.248101 loss) +I0616 14:47:54.969594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.173322 (* 1 = 0.173322 loss) +I0616 14:47:54.969599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108459 (* 1 = 0.108459 loss) +I0616 14:47:54.969602 9857 solver.cpp:571] Iteration 76100, lr = 0.0001 +I0616 14:48:06.486191 9857 solver.cpp:242] Iteration 76120, loss = 0.262243 +I0616 14:48:06.486233 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0928094 (* 1 = 0.0928094 loss) +I0616 14:48:06.486239 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120294 (* 1 = 0.120294 loss) +I0616 14:48:06.486243 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.07926 (* 1 = 0.07926 loss) +I0616 14:48:06.486248 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00793726 (* 1 = 0.00793726 loss) +I0616 14:48:06.486250 9857 solver.cpp:571] Iteration 76120, lr = 0.0001 +I0616 14:48:17.950158 9857 solver.cpp:242] Iteration 76140, loss = 0.404486 +I0616 14:48:17.950199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126024 (* 1 = 0.126024 loss) +I0616 14:48:17.950206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24191 (* 1 = 0.24191 loss) +I0616 14:48:17.950211 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101442 (* 1 = 0.101442 loss) +I0616 14:48:17.950214 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0452239 (* 1 = 0.0452239 loss) +I0616 14:48:17.950217 9857 solver.cpp:571] Iteration 76140, lr = 0.0001 +I0616 14:48:29.342289 9857 solver.cpp:242] Iteration 76160, loss = 0.284531 +I0616 14:48:29.342345 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12148 (* 1 = 0.12148 loss) +I0616 14:48:29.342351 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221001 (* 1 = 0.221001 loss) +I0616 14:48:29.342355 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0558371 (* 1 = 0.0558371 loss) +I0616 14:48:29.342360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0422473 (* 1 = 0.0422473 loss) +I0616 14:48:29.342363 9857 solver.cpp:571] Iteration 76160, lr = 0.0001 +I0616 14:48:40.979024 9857 solver.cpp:242] Iteration 76180, loss = 0.801395 +I0616 14:48:40.979068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181356 (* 1 = 0.181356 loss) +I0616 14:48:40.979073 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182032 (* 1 = 0.182032 loss) +I0616 14:48:40.979077 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0370576 (* 1 = 0.0370576 loss) +I0616 14:48:40.979081 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0645773 (* 1 = 0.0645773 loss) +I0616 14:48:40.979085 9857 solver.cpp:571] Iteration 76180, lr = 0.0001 +speed: 0.601s / iter +I0616 14:48:52.612900 9857 solver.cpp:242] Iteration 76200, loss = 0.54679 +I0616 14:48:52.612941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0696785 (* 1 = 0.0696785 loss) +I0616 14:48:52.612946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150729 (* 1 = 0.150729 loss) +I0616 14:48:52.612951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0300241 (* 1 = 0.0300241 loss) +I0616 14:48:52.612954 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130595 (* 1 = 0.0130595 loss) +I0616 14:48:52.612958 9857 solver.cpp:571] Iteration 76200, lr = 0.0001 +I0616 14:49:03.916751 9857 solver.cpp:242] Iteration 76220, loss = 0.316636 +I0616 14:49:03.916795 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0788911 (* 1 = 0.0788911 loss) +I0616 14:49:03.916800 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168979 (* 1 = 0.168979 loss) +I0616 14:49:03.916805 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0461185 (* 1 = 0.0461185 loss) +I0616 14:49:03.916808 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00549577 (* 1 = 0.00549577 loss) +I0616 14:49:03.916813 9857 solver.cpp:571] Iteration 76220, lr = 0.0001 +I0616 14:49:15.250710 9857 solver.cpp:242] Iteration 76240, loss = 0.266688 +I0616 14:49:15.250751 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0635995 (* 1 = 0.0635995 loss) +I0616 14:49:15.250761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104162 (* 1 = 0.104162 loss) +I0616 14:49:15.250767 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0390071 (* 1 = 0.0390071 loss) +I0616 14:49:15.250771 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334749 (* 1 = 0.0334749 loss) +I0616 14:49:15.250776 9857 solver.cpp:571] Iteration 76240, lr = 0.0001 +I0616 14:49:26.579653 9857 solver.cpp:242] Iteration 76260, loss = 0.766335 +I0616 14:49:26.579695 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200963 (* 1 = 0.200963 loss) +I0616 14:49:26.579700 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147457 (* 1 = 0.147457 loss) +I0616 14:49:26.579705 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.075274 (* 1 = 0.075274 loss) +I0616 14:49:26.579710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.027008 (* 1 = 0.027008 loss) +I0616 14:49:26.579713 9857 solver.cpp:571] Iteration 76260, lr = 0.0001 +I0616 14:49:38.045876 9857 solver.cpp:242] Iteration 76280, loss = 0.486743 +I0616 14:49:38.045917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168494 (* 1 = 0.168494 loss) +I0616 14:49:38.045922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.376675 (* 1 = 0.376675 loss) +I0616 14:49:38.045927 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00639336 (* 1 = 0.00639336 loss) +I0616 14:49:38.045930 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00113885 (* 1 = 0.00113885 loss) +I0616 14:49:38.045934 9857 solver.cpp:571] Iteration 76280, lr = 0.0001 +I0616 14:49:49.586957 9857 solver.cpp:242] Iteration 76300, loss = 0.369094 +I0616 14:49:49.587002 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0950453 (* 1 = 0.0950453 loss) +I0616 14:49:49.587007 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165326 (* 1 = 0.165326 loss) +I0616 14:49:49.587010 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0782058 (* 1 = 0.0782058 loss) +I0616 14:49:49.587014 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.141517 (* 1 = 0.141517 loss) +I0616 14:49:49.587018 9857 solver.cpp:571] Iteration 76300, lr = 0.0001 +I0616 14:50:01.236516 9857 solver.cpp:242] Iteration 76320, loss = 0.480934 +I0616 14:50:01.236557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197814 (* 1 = 0.197814 loss) +I0616 14:50:01.236562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160381 (* 1 = 0.160381 loss) +I0616 14:50:01.236567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0755597 (* 1 = 0.0755597 loss) +I0616 14:50:01.236570 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00563567 (* 1 = 0.00563567 loss) +I0616 14:50:01.236574 9857 solver.cpp:571] Iteration 76320, lr = 0.0001 +I0616 14:50:12.834678 9857 solver.cpp:242] Iteration 76340, loss = 0.447805 +I0616 14:50:12.834720 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247909 (* 1 = 0.247909 loss) +I0616 14:50:12.834727 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263793 (* 1 = 0.263793 loss) +I0616 14:50:12.834730 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0670871 (* 1 = 0.0670871 loss) +I0616 14:50:12.834734 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226642 (* 1 = 0.0226642 loss) +I0616 14:50:12.834738 9857 solver.cpp:571] Iteration 76340, lr = 0.0001 +I0616 14:50:24.166033 9857 solver.cpp:242] Iteration 76360, loss = 0.159028 +I0616 14:50:24.166076 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0671731 (* 1 = 0.0671731 loss) +I0616 14:50:24.166081 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0794695 (* 1 = 0.0794695 loss) +I0616 14:50:24.166085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0157447 (* 1 = 0.0157447 loss) +I0616 14:50:24.166090 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180746 (* 1 = 0.0180746 loss) +I0616 14:50:24.166093 9857 solver.cpp:571] Iteration 76360, lr = 0.0001 +I0616 14:50:35.529641 9857 solver.cpp:242] Iteration 76380, loss = 0.497904 +I0616 14:50:35.529685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0629803 (* 1 = 0.0629803 loss) +I0616 14:50:35.529690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0780575 (* 1 = 0.0780575 loss) +I0616 14:50:35.529693 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0238779 (* 1 = 0.0238779 loss) +I0616 14:50:35.529697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00273803 (* 1 = 0.00273803 loss) +I0616 14:50:35.529701 9857 solver.cpp:571] Iteration 76380, lr = 0.0001 +speed: 0.601s / iter +I0616 14:50:46.648720 9857 solver.cpp:242] Iteration 76400, loss = 0.76774 +I0616 14:50:46.648761 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200898 (* 1 = 0.200898 loss) +I0616 14:50:46.648767 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167659 (* 1 = 0.167659 loss) +I0616 14:50:46.648772 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151859 (* 1 = 0.151859 loss) +I0616 14:50:46.648775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0372051 (* 1 = 0.0372051 loss) +I0616 14:50:46.648778 9857 solver.cpp:571] Iteration 76400, lr = 0.0001 +I0616 14:50:58.237076 9857 solver.cpp:242] Iteration 76420, loss = 0.875595 +I0616 14:50:58.237118 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.36701 (* 1 = 0.36701 loss) +I0616 14:50:58.237123 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49177 (* 1 = 0.49177 loss) +I0616 14:50:58.237128 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.067094 (* 1 = 0.067094 loss) +I0616 14:50:58.237131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.097249 (* 1 = 0.097249 loss) +I0616 14:50:58.237138 9857 solver.cpp:571] Iteration 76420, lr = 0.0001 +I0616 14:51:09.837378 9857 solver.cpp:242] Iteration 76440, loss = 0.721 +I0616 14:51:09.837420 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282539 (* 1 = 0.282539 loss) +I0616 14:51:09.837425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.459872 (* 1 = 0.459872 loss) +I0616 14:51:09.837430 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0328792 (* 1 = 0.0328792 loss) +I0616 14:51:09.837433 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0725351 (* 1 = 0.0725351 loss) +I0616 14:51:09.837437 9857 solver.cpp:571] Iteration 76440, lr = 0.0001 +I0616 14:51:21.544682 9857 solver.cpp:242] Iteration 76460, loss = 0.565176 +I0616 14:51:21.544723 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236534 (* 1 = 0.236534 loss) +I0616 14:51:21.544729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.424429 (* 1 = 0.424429 loss) +I0616 14:51:21.544734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0336488 (* 1 = 0.0336488 loss) +I0616 14:51:21.544737 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714954 (* 1 = 0.0714954 loss) +I0616 14:51:21.544740 9857 solver.cpp:571] Iteration 76460, lr = 0.0001 +I0616 14:51:33.032765 9857 solver.cpp:242] Iteration 76480, loss = 0.297515 +I0616 14:51:33.032806 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126146 (* 1 = 0.126146 loss) +I0616 14:51:33.032812 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0934959 (* 1 = 0.0934959 loss) +I0616 14:51:33.032816 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.054866 (* 1 = 0.054866 loss) +I0616 14:51:33.032820 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0531291 (* 1 = 0.0531291 loss) +I0616 14:51:33.032825 9857 solver.cpp:571] Iteration 76480, lr = 0.0001 +I0616 14:51:44.690702 9857 solver.cpp:242] Iteration 76500, loss = 0.700015 +I0616 14:51:44.690744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.28204 (* 1 = 0.28204 loss) +I0616 14:51:44.690750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.307899 (* 1 = 0.307899 loss) +I0616 14:51:44.690755 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18225 (* 1 = 0.18225 loss) +I0616 14:51:44.690762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.359439 (* 1 = 0.359439 loss) +I0616 14:51:44.690765 9857 solver.cpp:571] Iteration 76500, lr = 0.0001 +I0616 14:51:56.398571 9857 solver.cpp:242] Iteration 76520, loss = 0.320952 +I0616 14:51:56.398614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146194 (* 1 = 0.146194 loss) +I0616 14:51:56.398619 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213252 (* 1 = 0.213252 loss) +I0616 14:51:56.398623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00981293 (* 1 = 0.00981293 loss) +I0616 14:51:56.398627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0450684 (* 1 = 0.0450684 loss) +I0616 14:51:56.398632 9857 solver.cpp:571] Iteration 76520, lr = 0.0001 +I0616 14:52:07.925902 9857 solver.cpp:242] Iteration 76540, loss = 1.17021 +I0616 14:52:07.925945 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.594074 (* 1 = 0.594074 loss) +I0616 14:52:07.925951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.378249 (* 1 = 0.378249 loss) +I0616 14:52:07.925954 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.243105 (* 1 = 0.243105 loss) +I0616 14:52:07.925958 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0639364 (* 1 = 0.0639364 loss) +I0616 14:52:07.925962 9857 solver.cpp:571] Iteration 76540, lr = 0.0001 +I0616 14:52:19.615970 9857 solver.cpp:242] Iteration 76560, loss = 0.827802 +I0616 14:52:19.616011 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.30025 (* 1 = 0.30025 loss) +I0616 14:52:19.616017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219134 (* 1 = 0.219134 loss) +I0616 14:52:19.616021 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0413758 (* 1 = 0.0413758 loss) +I0616 14:52:19.616025 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0463613 (* 1 = 0.0463613 loss) +I0616 14:52:19.616029 9857 solver.cpp:571] Iteration 76560, lr = 0.0001 +I0616 14:52:31.320543 9857 solver.cpp:242] Iteration 76580, loss = 0.490807 +I0616 14:52:31.320585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.073312 (* 1 = 0.073312 loss) +I0616 14:52:31.320590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111027 (* 1 = 0.111027 loss) +I0616 14:52:31.320593 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147554 (* 1 = 0.147554 loss) +I0616 14:52:31.320597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0654352 (* 1 = 0.0654352 loss) +I0616 14:52:31.320601 9857 solver.cpp:571] Iteration 76580, lr = 0.0001 +speed: 0.600s / iter +I0616 14:52:42.755358 9857 solver.cpp:242] Iteration 76600, loss = 0.301169 +I0616 14:52:42.755401 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0571215 (* 1 = 0.0571215 loss) +I0616 14:52:42.755406 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103494 (* 1 = 0.103494 loss) +I0616 14:52:42.755411 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0134547 (* 1 = 0.0134547 loss) +I0616 14:52:42.755414 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00350677 (* 1 = 0.00350677 loss) +I0616 14:52:42.755419 9857 solver.cpp:571] Iteration 76600, lr = 0.0001 +I0616 14:52:54.304754 9857 solver.cpp:242] Iteration 76620, loss = 0.517742 +I0616 14:52:54.304791 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129207 (* 1 = 0.129207 loss) +I0616 14:52:54.304797 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136984 (* 1 = 0.136984 loss) +I0616 14:52:54.304801 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0627939 (* 1 = 0.0627939 loss) +I0616 14:52:54.304805 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00631849 (* 1 = 0.00631849 loss) +I0616 14:52:54.304810 9857 solver.cpp:571] Iteration 76620, lr = 0.0001 +I0616 14:53:05.738579 9857 solver.cpp:242] Iteration 76640, loss = 0.395258 +I0616 14:53:05.738620 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134835 (* 1 = 0.134835 loss) +I0616 14:53:05.738626 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213272 (* 1 = 0.213272 loss) +I0616 14:53:05.738629 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0250336 (* 1 = 0.0250336 loss) +I0616 14:53:05.738633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0236849 (* 1 = 0.0236849 loss) +I0616 14:53:05.738637 9857 solver.cpp:571] Iteration 76640, lr = 0.0001 +I0616 14:53:17.281072 9857 solver.cpp:242] Iteration 76660, loss = 0.258542 +I0616 14:53:17.281116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136705 (* 1 = 0.136705 loss) +I0616 14:53:17.281121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123582 (* 1 = 0.123582 loss) +I0616 14:53:17.281124 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0196068 (* 1 = 0.0196068 loss) +I0616 14:53:17.281128 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00468301 (* 1 = 0.00468301 loss) +I0616 14:53:17.281132 9857 solver.cpp:571] Iteration 76660, lr = 0.0001 +I0616 14:53:29.010545 9857 solver.cpp:242] Iteration 76680, loss = 0.504805 +I0616 14:53:29.010601 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287683 (* 1 = 0.287683 loss) +I0616 14:53:29.010607 9857 solver.cpp:258] Train net output #1: loss_cls = 0.391348 (* 1 = 0.391348 loss) +I0616 14:53:29.010610 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0251478 (* 1 = 0.0251478 loss) +I0616 14:53:29.010614 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0133221 (* 1 = 0.0133221 loss) +I0616 14:53:29.010617 9857 solver.cpp:571] Iteration 76680, lr = 0.0001 +I0616 14:53:40.502828 9857 solver.cpp:242] Iteration 76700, loss = 0.428665 +I0616 14:53:40.502869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0533127 (* 1 = 0.0533127 loss) +I0616 14:53:40.502876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0899376 (* 1 = 0.0899376 loss) +I0616 14:53:40.502881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0259886 (* 1 = 0.0259886 loss) +I0616 14:53:40.502883 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0089302 (* 1 = 0.0089302 loss) +I0616 14:53:40.502887 9857 solver.cpp:571] Iteration 76700, lr = 0.0001 +I0616 14:53:51.974477 9857 solver.cpp:242] Iteration 76720, loss = 0.363639 +I0616 14:53:51.974519 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0829693 (* 1 = 0.0829693 loss) +I0616 14:53:51.974524 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129079 (* 1 = 0.129079 loss) +I0616 14:53:51.974529 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0790543 (* 1 = 0.0790543 loss) +I0616 14:53:51.974534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0139977 (* 1 = 0.0139977 loss) +I0616 14:53:51.974537 9857 solver.cpp:571] Iteration 76720, lr = 0.0001 +I0616 14:54:03.511947 9857 solver.cpp:242] Iteration 76740, loss = 0.426098 +I0616 14:54:03.511991 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163191 (* 1 = 0.163191 loss) +I0616 14:54:03.511996 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116658 (* 1 = 0.116658 loss) +I0616 14:54:03.512001 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.076112 (* 1 = 0.076112 loss) +I0616 14:54:03.512004 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0272409 (* 1 = 0.0272409 loss) +I0616 14:54:03.512007 9857 solver.cpp:571] Iteration 76740, lr = 0.0001 +I0616 14:54:15.030838 9857 solver.cpp:242] Iteration 76760, loss = 0.446787 +I0616 14:54:15.030879 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0136485 (* 1 = 0.0136485 loss) +I0616 14:54:15.030884 9857 solver.cpp:258] Train net output #1: loss_cls = 0.102883 (* 1 = 0.102883 loss) +I0616 14:54:15.030889 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167355 (* 1 = 0.167355 loss) +I0616 14:54:15.030892 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.112131 (* 1 = 0.112131 loss) +I0616 14:54:15.030896 9857 solver.cpp:571] Iteration 76760, lr = 0.0001 +I0616 14:54:26.216650 9857 solver.cpp:242] Iteration 76780, loss = 0.246714 +I0616 14:54:26.216689 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0538415 (* 1 = 0.0538415 loss) +I0616 14:54:26.216696 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101885 (* 1 = 0.101885 loss) +I0616 14:54:26.216699 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0304289 (* 1 = 0.0304289 loss) +I0616 14:54:26.216703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130269 (* 1 = 0.0130269 loss) +I0616 14:54:26.216706 9857 solver.cpp:571] Iteration 76780, lr = 0.0001 +speed: 0.600s / iter +I0616 14:54:37.766021 9857 solver.cpp:242] Iteration 76800, loss = 0.45392 +I0616 14:54:37.766064 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169428 (* 1 = 0.169428 loss) +I0616 14:54:37.766069 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253768 (* 1 = 0.253768 loss) +I0616 14:54:37.766073 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0489116 (* 1 = 0.0489116 loss) +I0616 14:54:37.766077 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0312355 (* 1 = 0.0312355 loss) +I0616 14:54:37.766080 9857 solver.cpp:571] Iteration 76800, lr = 0.0001 +I0616 14:54:49.402067 9857 solver.cpp:242] Iteration 76820, loss = 0.254879 +I0616 14:54:49.402110 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.069175 (* 1 = 0.069175 loss) +I0616 14:54:49.402117 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151436 (* 1 = 0.151436 loss) +I0616 14:54:49.402122 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0189526 (* 1 = 0.0189526 loss) +I0616 14:54:49.402124 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246724 (* 1 = 0.0246724 loss) +I0616 14:54:49.402128 9857 solver.cpp:571] Iteration 76820, lr = 0.0001 +I0616 14:55:01.014629 9857 solver.cpp:242] Iteration 76840, loss = 0.50132 +I0616 14:55:01.014670 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163506 (* 1 = 0.163506 loss) +I0616 14:55:01.014677 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225627 (* 1 = 0.225627 loss) +I0616 14:55:01.014680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0577389 (* 1 = 0.0577389 loss) +I0616 14:55:01.014684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00809874 (* 1 = 0.00809874 loss) +I0616 14:55:01.014688 9857 solver.cpp:571] Iteration 76840, lr = 0.0001 +I0616 14:55:12.700233 9857 solver.cpp:242] Iteration 76860, loss = 0.445338 +I0616 14:55:12.700276 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234737 (* 1 = 0.234737 loss) +I0616 14:55:12.700281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300855 (* 1 = 0.300855 loss) +I0616 14:55:12.700286 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0386862 (* 1 = 0.0386862 loss) +I0616 14:55:12.700289 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0280015 (* 1 = 0.0280015 loss) +I0616 14:55:12.700294 9857 solver.cpp:571] Iteration 76860, lr = 0.0001 +I0616 14:55:24.337636 9857 solver.cpp:242] Iteration 76880, loss = 0.327197 +I0616 14:55:24.337677 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105897 (* 1 = 0.105897 loss) +I0616 14:55:24.337682 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118747 (* 1 = 0.118747 loss) +I0616 14:55:24.337687 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0520315 (* 1 = 0.0520315 loss) +I0616 14:55:24.337689 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247793 (* 1 = 0.0247793 loss) +I0616 14:55:24.337693 9857 solver.cpp:571] Iteration 76880, lr = 0.0001 +I0616 14:55:35.862321 9857 solver.cpp:242] Iteration 76900, loss = 0.669526 +I0616 14:55:35.862365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199969 (* 1 = 0.199969 loss) +I0616 14:55:35.862370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20739 (* 1 = 0.20739 loss) +I0616 14:55:35.862375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0323838 (* 1 = 0.0323838 loss) +I0616 14:55:35.862378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255734 (* 1 = 0.0255734 loss) +I0616 14:55:35.862382 9857 solver.cpp:571] Iteration 76900, lr = 0.0001 +I0616 14:55:47.412174 9857 solver.cpp:242] Iteration 76920, loss = 0.473764 +I0616 14:55:47.412215 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182144 (* 1 = 0.182144 loss) +I0616 14:55:47.412221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208138 (* 1 = 0.208138 loss) +I0616 14:55:47.412225 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0926102 (* 1 = 0.0926102 loss) +I0616 14:55:47.412230 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145471 (* 1 = 0.0145471 loss) +I0616 14:55:47.412233 9857 solver.cpp:571] Iteration 76920, lr = 0.0001 +I0616 14:55:58.981714 9857 solver.cpp:242] Iteration 76940, loss = 0.564991 +I0616 14:55:58.981757 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215764 (* 1 = 0.215764 loss) +I0616 14:55:58.981762 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31739 (* 1 = 0.31739 loss) +I0616 14:55:58.981766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10351 (* 1 = 0.10351 loss) +I0616 14:55:58.981770 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0095163 (* 1 = 0.0095163 loss) +I0616 14:55:58.981775 9857 solver.cpp:571] Iteration 76940, lr = 0.0001 +I0616 14:56:10.233706 9857 solver.cpp:242] Iteration 76960, loss = 0.515955 +I0616 14:56:10.233748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238995 (* 1 = 0.238995 loss) +I0616 14:56:10.233754 9857 solver.cpp:258] Train net output #1: loss_cls = 0.212548 (* 1 = 0.212548 loss) +I0616 14:56:10.233758 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0763198 (* 1 = 0.0763198 loss) +I0616 14:56:10.233762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292473 (* 1 = 0.0292473 loss) +I0616 14:56:10.233765 9857 solver.cpp:571] Iteration 76960, lr = 0.0001 +I0616 14:56:21.612498 9857 solver.cpp:242] Iteration 76980, loss = 0.780465 +I0616 14:56:21.612540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309949 (* 1 = 0.309949 loss) +I0616 14:56:21.612545 9857 solver.cpp:258] Train net output #1: loss_cls = 0.559322 (* 1 = 0.559322 loss) +I0616 14:56:21.612548 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169532 (* 1 = 0.169532 loss) +I0616 14:56:21.612552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0792962 (* 1 = 0.0792962 loss) +I0616 14:56:21.612556 9857 solver.cpp:571] Iteration 76980, lr = 0.0001 +speed: 0.600s / iter +I0616 14:56:33.153323 9857 solver.cpp:242] Iteration 77000, loss = 0.449593 +I0616 14:56:33.153365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224641 (* 1 = 0.224641 loss) +I0616 14:56:33.153370 9857 solver.cpp:258] Train net output #1: loss_cls = 0.228445 (* 1 = 0.228445 loss) +I0616 14:56:33.153374 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162261 (* 1 = 0.162261 loss) +I0616 14:56:33.153378 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0339228 (* 1 = 0.0339228 loss) +I0616 14:56:33.153383 9857 solver.cpp:571] Iteration 77000, lr = 0.0001 +I0616 14:56:44.405839 9857 solver.cpp:242] Iteration 77020, loss = 0.40516 +I0616 14:56:44.405882 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12205 (* 1 = 0.12205 loss) +I0616 14:56:44.405887 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148675 (* 1 = 0.148675 loss) +I0616 14:56:44.405892 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119979 (* 1 = 0.119979 loss) +I0616 14:56:44.405896 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199637 (* 1 = 0.0199637 loss) +I0616 14:56:44.405900 9857 solver.cpp:571] Iteration 77020, lr = 0.0001 +I0616 14:56:55.905591 9857 solver.cpp:242] Iteration 77040, loss = 0.314278 +I0616 14:56:55.905633 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.226743 (* 1 = 0.226743 loss) +I0616 14:56:55.905638 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231565 (* 1 = 0.231565 loss) +I0616 14:56:55.905642 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0270508 (* 1 = 0.0270508 loss) +I0616 14:56:55.905647 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00927074 (* 1 = 0.00927074 loss) +I0616 14:56:55.905650 9857 solver.cpp:571] Iteration 77040, lr = 0.0001 +I0616 14:57:07.486706 9857 solver.cpp:242] Iteration 77060, loss = 0.135497 +I0616 14:57:07.486748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0507933 (* 1 = 0.0507933 loss) +I0616 14:57:07.486754 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0787798 (* 1 = 0.0787798 loss) +I0616 14:57:07.486764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0328742 (* 1 = 0.0328742 loss) +I0616 14:57:07.486768 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00559489 (* 1 = 0.00559489 loss) +I0616 14:57:07.486773 9857 solver.cpp:571] Iteration 77060, lr = 0.0001 +I0616 14:57:19.106384 9857 solver.cpp:242] Iteration 77080, loss = 0.277964 +I0616 14:57:19.106425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.108896 (* 1 = 0.108896 loss) +I0616 14:57:19.106431 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195654 (* 1 = 0.195654 loss) +I0616 14:57:19.106434 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0634348 (* 1 = 0.0634348 loss) +I0616 14:57:19.106438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114263 (* 1 = 0.0114263 loss) +I0616 14:57:19.106442 9857 solver.cpp:571] Iteration 77080, lr = 0.0001 +I0616 14:57:30.538589 9857 solver.cpp:242] Iteration 77100, loss = 0.17187 +I0616 14:57:30.538631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0624748 (* 1 = 0.0624748 loss) +I0616 14:57:30.538637 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0931168 (* 1 = 0.0931168 loss) +I0616 14:57:30.538641 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0225111 (* 1 = 0.0225111 loss) +I0616 14:57:30.538645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181913 (* 1 = 0.0181913 loss) +I0616 14:57:30.538650 9857 solver.cpp:571] Iteration 77100, lr = 0.0001 +I0616 14:57:41.998096 9857 solver.cpp:242] Iteration 77120, loss = 0.206598 +I0616 14:57:41.998138 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0746964 (* 1 = 0.0746964 loss) +I0616 14:57:41.998144 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0986672 (* 1 = 0.0986672 loss) +I0616 14:57:41.998148 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.047023 (* 1 = 0.047023 loss) +I0616 14:57:41.998152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00408495 (* 1 = 0.00408495 loss) +I0616 14:57:41.998155 9857 solver.cpp:571] Iteration 77120, lr = 0.0001 +I0616 14:57:53.811843 9857 solver.cpp:242] Iteration 77140, loss = 0.179928 +I0616 14:57:53.811883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0733935 (* 1 = 0.0733935 loss) +I0616 14:57:53.811888 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105503 (* 1 = 0.105503 loss) +I0616 14:57:53.811893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00857938 (* 1 = 0.00857938 loss) +I0616 14:57:53.811897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169687 (* 1 = 0.0169687 loss) +I0616 14:57:53.811900 9857 solver.cpp:571] Iteration 77140, lr = 0.0001 +I0616 14:58:05.355275 9857 solver.cpp:242] Iteration 77160, loss = 0.450322 +I0616 14:58:05.355317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191558 (* 1 = 0.191558 loss) +I0616 14:58:05.355324 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184939 (* 1 = 0.184939 loss) +I0616 14:58:05.355327 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0800178 (* 1 = 0.0800178 loss) +I0616 14:58:05.355331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.089006 (* 1 = 0.089006 loss) +I0616 14:58:05.355334 9857 solver.cpp:571] Iteration 77160, lr = 0.0001 +I0616 14:58:16.885432 9857 solver.cpp:242] Iteration 77180, loss = 0.392857 +I0616 14:58:16.885474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0773959 (* 1 = 0.0773959 loss) +I0616 14:58:16.885480 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114739 (* 1 = 0.114739 loss) +I0616 14:58:16.885484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0474792 (* 1 = 0.0474792 loss) +I0616 14:58:16.885488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.131914 (* 1 = 0.131914 loss) +I0616 14:58:16.885491 9857 solver.cpp:571] Iteration 77180, lr = 0.0001 +speed: 0.600s / iter +I0616 14:58:28.300825 9857 solver.cpp:242] Iteration 77200, loss = 0.389439 +I0616 14:58:28.300866 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253835 (* 1 = 0.253835 loss) +I0616 14:58:28.300873 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305331 (* 1 = 0.305331 loss) +I0616 14:58:28.300878 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0584322 (* 1 = 0.0584322 loss) +I0616 14:58:28.300880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00828553 (* 1 = 0.00828553 loss) +I0616 14:58:28.300884 9857 solver.cpp:571] Iteration 77200, lr = 0.0001 +I0616 14:58:39.979212 9857 solver.cpp:242] Iteration 77220, loss = 0.286759 +I0616 14:58:39.979252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0879754 (* 1 = 0.0879754 loss) +I0616 14:58:39.979259 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155299 (* 1 = 0.155299 loss) +I0616 14:58:39.979264 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00795982 (* 1 = 0.00795982 loss) +I0616 14:58:39.979267 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0618374 (* 1 = 0.0618374 loss) +I0616 14:58:39.979270 9857 solver.cpp:571] Iteration 77220, lr = 0.0001 +I0616 14:58:51.456472 9857 solver.cpp:242] Iteration 77240, loss = 0.671376 +I0616 14:58:51.456516 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365368 (* 1 = 0.365368 loss) +I0616 14:58:51.456521 9857 solver.cpp:258] Train net output #1: loss_cls = 0.448813 (* 1 = 0.448813 loss) +I0616 14:58:51.456526 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121804 (* 1 = 0.121804 loss) +I0616 14:58:51.456529 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0688045 (* 1 = 0.0688045 loss) +I0616 14:58:51.456532 9857 solver.cpp:571] Iteration 77240, lr = 0.0001 +I0616 14:59:02.797211 9857 solver.cpp:242] Iteration 77260, loss = 0.968809 +I0616 14:59:02.797250 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.416805 (* 1 = 0.416805 loss) +I0616 14:59:02.797255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.195965 (* 1 = 0.195965 loss) +I0616 14:59:02.797258 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114699 (* 1 = 0.114699 loss) +I0616 14:59:02.797262 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014103 (* 1 = 0.014103 loss) +I0616 14:59:02.797266 9857 solver.cpp:571] Iteration 77260, lr = 0.0001 +I0616 14:59:14.315948 9857 solver.cpp:242] Iteration 77280, loss = 0.956122 +I0616 14:59:14.315991 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193037 (* 1 = 0.193037 loss) +I0616 14:59:14.315996 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288433 (* 1 = 0.288433 loss) +I0616 14:59:14.316000 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0447317 (* 1 = 0.0447317 loss) +I0616 14:59:14.316004 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.385942 (* 1 = 0.385942 loss) +I0616 14:59:14.316009 9857 solver.cpp:571] Iteration 77280, lr = 0.0001 +I0616 14:59:25.817610 9857 solver.cpp:242] Iteration 77300, loss = 0.45791 +I0616 14:59:25.817652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0812405 (* 1 = 0.0812405 loss) +I0616 14:59:25.817658 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199429 (* 1 = 0.199429 loss) +I0616 14:59:25.817662 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230643 (* 1 = 0.0230643 loss) +I0616 14:59:25.817667 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0733285 (* 1 = 0.0733285 loss) +I0616 14:59:25.817669 9857 solver.cpp:571] Iteration 77300, lr = 0.0001 +I0616 14:59:37.128659 9857 solver.cpp:242] Iteration 77320, loss = 0.266793 +I0616 14:59:37.128700 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0974994 (* 1 = 0.0974994 loss) +I0616 14:59:37.128706 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209612 (* 1 = 0.209612 loss) +I0616 14:59:37.128710 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155725 (* 1 = 0.0155725 loss) +I0616 14:59:37.128715 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00676792 (* 1 = 0.00676792 loss) +I0616 14:59:37.128717 9857 solver.cpp:571] Iteration 77320, lr = 0.0001 +I0616 14:59:48.482853 9857 solver.cpp:242] Iteration 77340, loss = 0.883928 +I0616 14:59:48.482897 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.072652 (* 1 = 0.072652 loss) +I0616 14:59:48.482903 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16055 (* 1 = 0.16055 loss) +I0616 14:59:48.482908 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0092457 (* 1 = 0.0092457 loss) +I0616 14:59:48.482911 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00553039 (* 1 = 0.00553039 loss) +I0616 14:59:48.482916 9857 solver.cpp:571] Iteration 77340, lr = 0.0001 +I0616 15:00:00.171833 9857 solver.cpp:242] Iteration 77360, loss = 0.495746 +I0616 15:00:00.171874 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167607 (* 1 = 0.167607 loss) +I0616 15:00:00.171880 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203614 (* 1 = 0.203614 loss) +I0616 15:00:00.171883 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00460997 (* 1 = 0.00460997 loss) +I0616 15:00:00.171887 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00486704 (* 1 = 0.00486704 loss) +I0616 15:00:00.171891 9857 solver.cpp:571] Iteration 77360, lr = 0.0001 +I0616 15:00:11.775007 9857 solver.cpp:242] Iteration 77380, loss = 0.510458 +I0616 15:00:11.775048 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.085739 (* 1 = 0.085739 loss) +I0616 15:00:11.775053 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139917 (* 1 = 0.139917 loss) +I0616 15:00:11.775058 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0125065 (* 1 = 0.0125065 loss) +I0616 15:00:11.775061 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00609711 (* 1 = 0.00609711 loss) +I0616 15:00:11.775065 9857 solver.cpp:571] Iteration 77380, lr = 0.0001 +speed: 0.600s / iter +I0616 15:00:23.087669 9857 solver.cpp:242] Iteration 77400, loss = 0.313709 +I0616 15:00:23.087713 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0977929 (* 1 = 0.0977929 loss) +I0616 15:00:23.087718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127929 (* 1 = 0.127929 loss) +I0616 15:00:23.087723 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0317483 (* 1 = 0.0317483 loss) +I0616 15:00:23.087726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162481 (* 1 = 0.0162481 loss) +I0616 15:00:23.087730 9857 solver.cpp:571] Iteration 77400, lr = 0.0001 +I0616 15:00:34.680240 9857 solver.cpp:242] Iteration 77420, loss = 0.423525 +I0616 15:00:34.680284 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320832 (* 1 = 0.320832 loss) +I0616 15:00:34.680289 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22428 (* 1 = 0.22428 loss) +I0616 15:00:34.680294 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.092255 (* 1 = 0.092255 loss) +I0616 15:00:34.680297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142959 (* 1 = 0.0142959 loss) +I0616 15:00:34.680301 9857 solver.cpp:571] Iteration 77420, lr = 0.0001 +I0616 15:00:46.076422 9857 solver.cpp:242] Iteration 77440, loss = 0.286149 +I0616 15:00:46.076462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0691765 (* 1 = 0.0691765 loss) +I0616 15:00:46.076467 9857 solver.cpp:258] Train net output #1: loss_cls = 0.165474 (* 1 = 0.165474 loss) +I0616 15:00:46.076472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0272572 (* 1 = 0.0272572 loss) +I0616 15:00:46.076475 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0043901 (* 1 = 0.0043901 loss) +I0616 15:00:46.076478 9857 solver.cpp:571] Iteration 77440, lr = 0.0001 +I0616 15:00:57.453466 9857 solver.cpp:242] Iteration 77460, loss = 0.605391 +I0616 15:00:57.453510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266336 (* 1 = 0.266336 loss) +I0616 15:00:57.453516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251529 (* 1 = 0.251529 loss) +I0616 15:00:57.453519 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0714012 (* 1 = 0.0714012 loss) +I0616 15:00:57.453522 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0765257 (* 1 = 0.0765257 loss) +I0616 15:00:57.453526 9857 solver.cpp:571] Iteration 77460, lr = 0.0001 +I0616 15:01:08.774595 9857 solver.cpp:242] Iteration 77480, loss = 0.265656 +I0616 15:01:08.774636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.109005 (* 1 = 0.109005 loss) +I0616 15:01:08.774642 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153475 (* 1 = 0.153475 loss) +I0616 15:01:08.774646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0147489 (* 1 = 0.0147489 loss) +I0616 15:01:08.774651 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157209 (* 1 = 0.0157209 loss) +I0616 15:01:08.774654 9857 solver.cpp:571] Iteration 77480, lr = 0.0001 +I0616 15:01:20.147079 9857 solver.cpp:242] Iteration 77500, loss = 0.626426 +I0616 15:01:20.147120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124591 (* 1 = 0.124591 loss) +I0616 15:01:20.147126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148107 (* 1 = 0.148107 loss) +I0616 15:01:20.147130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0160937 (* 1 = 0.0160937 loss) +I0616 15:01:20.147135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260682 (* 1 = 0.0260682 loss) +I0616 15:01:20.147137 9857 solver.cpp:571] Iteration 77500, lr = 0.0001 +I0616 15:01:31.685734 9857 solver.cpp:242] Iteration 77520, loss = 0.58522 +I0616 15:01:31.685776 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135476 (* 1 = 0.135476 loss) +I0616 15:01:31.685781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144054 (* 1 = 0.144054 loss) +I0616 15:01:31.685786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0519027 (* 1 = 0.0519027 loss) +I0616 15:01:31.685789 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0284497 (* 1 = 0.0284497 loss) +I0616 15:01:31.685793 9857 solver.cpp:571] Iteration 77520, lr = 0.0001 +I0616 15:01:43.301537 9857 solver.cpp:242] Iteration 77540, loss = 0.675456 +I0616 15:01:43.301579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256132 (* 1 = 0.256132 loss) +I0616 15:01:43.301584 9857 solver.cpp:258] Train net output #1: loss_cls = 0.300808 (* 1 = 0.300808 loss) +I0616 15:01:43.301589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166919 (* 1 = 0.166919 loss) +I0616 15:01:43.301592 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0344534 (* 1 = 0.0344534 loss) +I0616 15:01:43.301596 9857 solver.cpp:571] Iteration 77540, lr = 0.0001 +I0616 15:01:54.829385 9857 solver.cpp:242] Iteration 77560, loss = 0.611701 +I0616 15:01:54.829422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257713 (* 1 = 0.257713 loss) +I0616 15:01:54.829428 9857 solver.cpp:258] Train net output #1: loss_cls = 0.293843 (* 1 = 0.293843 loss) +I0616 15:01:54.829432 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0283279 (* 1 = 0.0283279 loss) +I0616 15:01:54.829437 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.150715 (* 1 = 0.150715 loss) +I0616 15:01:54.829440 9857 solver.cpp:571] Iteration 77560, lr = 0.0001 +I0616 15:02:06.627743 9857 solver.cpp:242] Iteration 77580, loss = 0.49368 +I0616 15:02:06.627785 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0685577 (* 1 = 0.0685577 loss) +I0616 15:02:06.627791 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184506 (* 1 = 0.184506 loss) +I0616 15:02:06.627795 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0069582 (* 1 = 0.0069582 loss) +I0616 15:02:06.627799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562016 (* 1 = 0.00562016 loss) +I0616 15:02:06.627804 9857 solver.cpp:571] Iteration 77580, lr = 0.0001 +speed: 0.600s / iter +I0616 15:02:18.390921 9857 solver.cpp:242] Iteration 77600, loss = 0.887123 +I0616 15:02:18.390964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285318 (* 1 = 0.285318 loss) +I0616 15:02:18.390969 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250317 (* 1 = 0.250317 loss) +I0616 15:02:18.390974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.296378 (* 1 = 0.296378 loss) +I0616 15:02:18.390977 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.561578 (* 1 = 0.561578 loss) +I0616 15:02:18.390981 9857 solver.cpp:571] Iteration 77600, lr = 0.0001 +I0616 15:02:30.011261 9857 solver.cpp:242] Iteration 77620, loss = 0.305764 +I0616 15:02:30.011302 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0950822 (* 1 = 0.0950822 loss) +I0616 15:02:30.011308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143874 (* 1 = 0.143874 loss) +I0616 15:02:30.011312 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0210402 (* 1 = 0.0210402 loss) +I0616 15:02:30.011317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.108208 (* 1 = 0.108208 loss) +I0616 15:02:30.011319 9857 solver.cpp:571] Iteration 77620, lr = 0.0001 +I0616 15:02:41.127002 9857 solver.cpp:242] Iteration 77640, loss = 0.357379 +I0616 15:02:41.127043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101473 (* 1 = 0.101473 loss) +I0616 15:02:41.127048 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11127 (* 1 = 0.11127 loss) +I0616 15:02:41.127053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135852 (* 1 = 0.135852 loss) +I0616 15:02:41.127058 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0196121 (* 1 = 0.0196121 loss) +I0616 15:02:41.127060 9857 solver.cpp:571] Iteration 77640, lr = 0.0001 +I0616 15:02:52.486567 9857 solver.cpp:242] Iteration 77660, loss = 0.263297 +I0616 15:02:52.486609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0323894 (* 1 = 0.0323894 loss) +I0616 15:02:52.486615 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149458 (* 1 = 0.149458 loss) +I0616 15:02:52.486619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00618289 (* 1 = 0.00618289 loss) +I0616 15:02:52.486624 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00502045 (* 1 = 0.00502045 loss) +I0616 15:02:52.486627 9857 solver.cpp:571] Iteration 77660, lr = 0.0001 +I0616 15:03:03.704500 9857 solver.cpp:242] Iteration 77680, loss = 0.660723 +I0616 15:03:03.704542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.403838 (* 1 = 0.403838 loss) +I0616 15:03:03.704547 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344283 (* 1 = 0.344283 loss) +I0616 15:03:03.704552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120535 (* 1 = 0.120535 loss) +I0616 15:03:03.704556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0374262 (* 1 = 0.0374262 loss) +I0616 15:03:03.704560 9857 solver.cpp:571] Iteration 77680, lr = 0.0001 +I0616 15:03:15.145680 9857 solver.cpp:242] Iteration 77700, loss = 0.694033 +I0616 15:03:15.145721 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.239955 (* 1 = 0.239955 loss) +I0616 15:03:15.145727 9857 solver.cpp:258] Train net output #1: loss_cls = 0.673287 (* 1 = 0.673287 loss) +I0616 15:03:15.145731 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0556616 (* 1 = 0.0556616 loss) +I0616 15:03:15.145735 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117502 (* 1 = 0.0117502 loss) +I0616 15:03:15.145738 9857 solver.cpp:571] Iteration 77700, lr = 0.0001 +I0616 15:03:26.647954 9857 solver.cpp:242] Iteration 77720, loss = 0.267171 +I0616 15:03:26.647996 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118257 (* 1 = 0.118257 loss) +I0616 15:03:26.648001 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217108 (* 1 = 0.217108 loss) +I0616 15:03:26.648006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00596028 (* 1 = 0.00596028 loss) +I0616 15:03:26.648010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00208876 (* 1 = 0.00208876 loss) +I0616 15:03:26.648015 9857 solver.cpp:571] Iteration 77720, lr = 0.0001 +I0616 15:03:38.195746 9857 solver.cpp:242] Iteration 77740, loss = 0.206039 +I0616 15:03:38.195790 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0415944 (* 1 = 0.0415944 loss) +I0616 15:03:38.195794 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0889836 (* 1 = 0.0889836 loss) +I0616 15:03:38.195799 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141295 (* 1 = 0.0141295 loss) +I0616 15:03:38.195803 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0202174 (* 1 = 0.0202174 loss) +I0616 15:03:38.195806 9857 solver.cpp:571] Iteration 77740, lr = 0.0001 +I0616 15:03:49.679842 9857 solver.cpp:242] Iteration 77760, loss = 0.635365 +I0616 15:03:49.679883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105285 (* 1 = 0.105285 loss) +I0616 15:03:49.679889 9857 solver.cpp:258] Train net output #1: loss_cls = 0.193741 (* 1 = 0.193741 loss) +I0616 15:03:49.679893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00469727 (* 1 = 0.00469727 loss) +I0616 15:03:49.679898 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156585 (* 1 = 0.0156585 loss) +I0616 15:03:49.679901 9857 solver.cpp:571] Iteration 77760, lr = 0.0001 +I0616 15:04:01.417244 9857 solver.cpp:242] Iteration 77780, loss = 0.373708 +I0616 15:04:01.417286 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0658522 (* 1 = 0.0658522 loss) +I0616 15:04:01.417292 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146139 (* 1 = 0.146139 loss) +I0616 15:04:01.417296 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110268 (* 1 = 0.110268 loss) +I0616 15:04:01.417300 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.024669 (* 1 = 0.024669 loss) +I0616 15:04:01.417304 9857 solver.cpp:571] Iteration 77780, lr = 0.0001 +speed: 0.600s / iter +I0616 15:04:12.973098 9857 solver.cpp:242] Iteration 77800, loss = 0.189805 +I0616 15:04:12.973141 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100489 (* 1 = 0.100489 loss) +I0616 15:04:12.973146 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111968 (* 1 = 0.111968 loss) +I0616 15:04:12.973150 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0164652 (* 1 = 0.0164652 loss) +I0616 15:04:12.973155 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00344207 (* 1 = 0.00344207 loss) +I0616 15:04:12.973158 9857 solver.cpp:571] Iteration 77800, lr = 0.0001 +I0616 15:04:24.349180 9857 solver.cpp:242] Iteration 77820, loss = 0.740954 +I0616 15:04:24.349222 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.223581 (* 1 = 0.223581 loss) +I0616 15:04:24.349228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252116 (* 1 = 0.252116 loss) +I0616 15:04:24.349232 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.38459 (* 1 = 0.38459 loss) +I0616 15:04:24.349236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.164114 (* 1 = 0.164114 loss) +I0616 15:04:24.349254 9857 solver.cpp:571] Iteration 77820, lr = 0.0001 +I0616 15:04:35.759600 9857 solver.cpp:242] Iteration 77840, loss = 0.389115 +I0616 15:04:35.759644 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0650686 (* 1 = 0.0650686 loss) +I0616 15:04:35.759649 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115608 (* 1 = 0.115608 loss) +I0616 15:04:35.759654 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00634397 (* 1 = 0.00634397 loss) +I0616 15:04:35.759657 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00476353 (* 1 = 0.00476353 loss) +I0616 15:04:35.759661 9857 solver.cpp:571] Iteration 77840, lr = 0.0001 +I0616 15:04:47.119446 9857 solver.cpp:242] Iteration 77860, loss = 0.458822 +I0616 15:04:47.119487 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0747624 (* 1 = 0.0747624 loss) +I0616 15:04:47.119493 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0953013 (* 1 = 0.0953013 loss) +I0616 15:04:47.119496 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0439927 (* 1 = 0.0439927 loss) +I0616 15:04:47.119500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00611026 (* 1 = 0.00611026 loss) +I0616 15:04:47.119504 9857 solver.cpp:571] Iteration 77860, lr = 0.0001 +I0616 15:04:58.492321 9857 solver.cpp:242] Iteration 77880, loss = 0.346369 +I0616 15:04:58.492363 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169617 (* 1 = 0.169617 loss) +I0616 15:04:58.492369 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148224 (* 1 = 0.148224 loss) +I0616 15:04:58.492373 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0482298 (* 1 = 0.0482298 loss) +I0616 15:04:58.492377 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00870207 (* 1 = 0.00870207 loss) +I0616 15:04:58.492382 9857 solver.cpp:571] Iteration 77880, lr = 0.0001 +I0616 15:05:10.292558 9857 solver.cpp:242] Iteration 77900, loss = 0.437677 +I0616 15:05:10.292598 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201779 (* 1 = 0.201779 loss) +I0616 15:05:10.292604 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252909 (* 1 = 0.252909 loss) +I0616 15:05:10.292608 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10718 (* 1 = 0.10718 loss) +I0616 15:05:10.292613 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325691 (* 1 = 0.0325691 loss) +I0616 15:05:10.292616 9857 solver.cpp:571] Iteration 77900, lr = 0.0001 +I0616 15:05:21.816226 9857 solver.cpp:242] Iteration 77920, loss = 0.862828 +I0616 15:05:21.816267 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.243811 (* 1 = 0.243811 loss) +I0616 15:05:21.816273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283208 (* 1 = 0.283208 loss) +I0616 15:05:21.816277 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0143462 (* 1 = 0.0143462 loss) +I0616 15:05:21.816282 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135426 (* 1 = 0.0135426 loss) +I0616 15:05:21.816285 9857 solver.cpp:571] Iteration 77920, lr = 0.0001 +I0616 15:05:33.425771 9857 solver.cpp:242] Iteration 77940, loss = 0.543076 +I0616 15:05:33.425813 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.348113 (* 1 = 0.348113 loss) +I0616 15:05:33.425818 9857 solver.cpp:258] Train net output #1: loss_cls = 0.326485 (* 1 = 0.326485 loss) +I0616 15:05:33.425823 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0507799 (* 1 = 0.0507799 loss) +I0616 15:05:33.425827 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0614127 (* 1 = 0.0614127 loss) +I0616 15:05:33.425830 9857 solver.cpp:571] Iteration 77940, lr = 0.0001 +I0616 15:05:45.062806 9857 solver.cpp:242] Iteration 77960, loss = 0.476863 +I0616 15:05:45.062849 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10986 (* 1 = 0.10986 loss) +I0616 15:05:45.062854 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169159 (* 1 = 0.169159 loss) +I0616 15:05:45.062857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0269664 (* 1 = 0.0269664 loss) +I0616 15:05:45.062861 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00294694 (* 1 = 0.00294694 loss) +I0616 15:05:45.062865 9857 solver.cpp:571] Iteration 77960, lr = 0.0001 +I0616 15:05:56.609344 9857 solver.cpp:242] Iteration 77980, loss = 0.447358 +I0616 15:05:56.609385 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.080696 (* 1 = 0.080696 loss) +I0616 15:05:56.609392 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108741 (* 1 = 0.108741 loss) +I0616 15:05:56.609396 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0275027 (* 1 = 0.0275027 loss) +I0616 15:05:56.609400 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159298 (* 1 = 0.0159298 loss) +I0616 15:05:56.609405 9857 solver.cpp:571] Iteration 77980, lr = 0.0001 +speed: 0.600s / iter +I0616 15:06:08.108748 9857 solver.cpp:242] Iteration 78000, loss = 0.307556 +I0616 15:06:08.108790 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103633 (* 1 = 0.103633 loss) +I0616 15:06:08.108795 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127332 (* 1 = 0.127332 loss) +I0616 15:06:08.108799 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0844235 (* 1 = 0.0844235 loss) +I0616 15:06:08.108803 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00545393 (* 1 = 0.00545393 loss) +I0616 15:06:08.108808 9857 solver.cpp:571] Iteration 78000, lr = 0.0001 +I0616 15:06:19.680994 9857 solver.cpp:242] Iteration 78020, loss = 0.197781 +I0616 15:06:19.681035 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0764025 (* 1 = 0.0764025 loss) +I0616 15:06:19.681041 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110039 (* 1 = 0.110039 loss) +I0616 15:06:19.681044 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00606098 (* 1 = 0.00606098 loss) +I0616 15:06:19.681048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00869538 (* 1 = 0.00869538 loss) +I0616 15:06:19.681052 9857 solver.cpp:571] Iteration 78020, lr = 0.0001 +I0616 15:06:31.314622 9857 solver.cpp:242] Iteration 78040, loss = 0.52365 +I0616 15:06:31.314663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235793 (* 1 = 0.235793 loss) +I0616 15:06:31.314671 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28303 (* 1 = 0.28303 loss) +I0616 15:06:31.314674 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0599627 (* 1 = 0.0599627 loss) +I0616 15:06:31.314678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323279 (* 1 = 0.0323279 loss) +I0616 15:06:31.314682 9857 solver.cpp:571] Iteration 78040, lr = 0.0001 +I0616 15:06:42.603147 9857 solver.cpp:242] Iteration 78060, loss = 0.453357 +I0616 15:06:42.603188 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242986 (* 1 = 0.242986 loss) +I0616 15:06:42.603193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161348 (* 1 = 0.161348 loss) +I0616 15:06:42.603198 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0535557 (* 1 = 0.0535557 loss) +I0616 15:06:42.603201 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0409254 (* 1 = 0.0409254 loss) +I0616 15:06:42.603205 9857 solver.cpp:571] Iteration 78060, lr = 0.0001 +I0616 15:06:53.911491 9857 solver.cpp:242] Iteration 78080, loss = 0.794055 +I0616 15:06:53.911531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.221638 (* 1 = 0.221638 loss) +I0616 15:06:53.911536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4502 (* 1 = 0.4502 loss) +I0616 15:06:53.911541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0668542 (* 1 = 0.0668542 loss) +I0616 15:06:53.911545 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0814123 (* 1 = 0.0814123 loss) +I0616 15:06:53.911550 9857 solver.cpp:571] Iteration 78080, lr = 0.0001 +I0616 15:07:05.621110 9857 solver.cpp:242] Iteration 78100, loss = 0.827546 +I0616 15:07:05.621150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358386 (* 1 = 0.358386 loss) +I0616 15:07:05.621155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399581 (* 1 = 0.399581 loss) +I0616 15:07:05.621158 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138878 (* 1 = 0.138878 loss) +I0616 15:07:05.621162 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0523676 (* 1 = 0.0523676 loss) +I0616 15:07:05.621166 9857 solver.cpp:571] Iteration 78100, lr = 0.0001 +I0616 15:07:17.195511 9857 solver.cpp:242] Iteration 78120, loss = 0.64485 +I0616 15:07:17.195554 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206959 (* 1 = 0.206959 loss) +I0616 15:07:17.195560 9857 solver.cpp:258] Train net output #1: loss_cls = 0.270776 (* 1 = 0.270776 loss) +I0616 15:07:17.195564 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155693 (* 1 = 0.155693 loss) +I0616 15:07:17.195567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0204531 (* 1 = 0.0204531 loss) +I0616 15:07:17.195571 9857 solver.cpp:571] Iteration 78120, lr = 0.0001 +I0616 15:07:28.572113 9857 solver.cpp:242] Iteration 78140, loss = 0.543216 +I0616 15:07:28.572154 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172029 (* 1 = 0.172029 loss) +I0616 15:07:28.572160 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244434 (* 1 = 0.244434 loss) +I0616 15:07:28.572165 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0560098 (* 1 = 0.0560098 loss) +I0616 15:07:28.572168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.054386 (* 1 = 0.054386 loss) +I0616 15:07:28.572172 9857 solver.cpp:571] Iteration 78140, lr = 0.0001 +I0616 15:07:40.139560 9857 solver.cpp:242] Iteration 78160, loss = 0.789092 +I0616 15:07:40.139602 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225918 (* 1 = 0.225918 loss) +I0616 15:07:40.139608 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340201 (* 1 = 0.340201 loss) +I0616 15:07:40.139612 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.047437 (* 1 = 0.047437 loss) +I0616 15:07:40.139616 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226158 (* 1 = 0.0226158 loss) +I0616 15:07:40.139621 9857 solver.cpp:571] Iteration 78160, lr = 0.0001 +I0616 15:07:51.560621 9857 solver.cpp:242] Iteration 78180, loss = 0.210682 +I0616 15:07:51.560663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0631328 (* 1 = 0.0631328 loss) +I0616 15:07:51.560668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0900013 (* 1 = 0.0900013 loss) +I0616 15:07:51.560673 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112083 (* 1 = 0.0112083 loss) +I0616 15:07:51.560678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00511736 (* 1 = 0.00511736 loss) +I0616 15:07:51.560681 9857 solver.cpp:571] Iteration 78180, lr = 0.0001 +speed: 0.600s / iter +I0616 15:08:02.963794 9857 solver.cpp:242] Iteration 78200, loss = 0.628964 +I0616 15:08:02.963837 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280402 (* 1 = 0.280402 loss) +I0616 15:08:02.963842 9857 solver.cpp:258] Train net output #1: loss_cls = 0.534326 (* 1 = 0.534326 loss) +I0616 15:08:02.963846 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.173996 (* 1 = 0.173996 loss) +I0616 15:08:02.963850 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0874063 (* 1 = 0.0874063 loss) +I0616 15:08:02.963855 9857 solver.cpp:571] Iteration 78200, lr = 0.0001 +I0616 15:08:14.290969 9857 solver.cpp:242] Iteration 78220, loss = 1.20092 +I0616 15:08:14.291012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.418363 (* 1 = 0.418363 loss) +I0616 15:08:14.291016 9857 solver.cpp:258] Train net output #1: loss_cls = 0.389153 (* 1 = 0.389153 loss) +I0616 15:08:14.291020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.279907 (* 1 = 0.279907 loss) +I0616 15:08:14.291024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.214312 (* 1 = 0.214312 loss) +I0616 15:08:14.291028 9857 solver.cpp:571] Iteration 78220, lr = 0.0001 +I0616 15:08:25.532088 9857 solver.cpp:242] Iteration 78240, loss = 0.304414 +I0616 15:08:25.532130 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237969 (* 1 = 0.237969 loss) +I0616 15:08:25.532135 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138923 (* 1 = 0.138923 loss) +I0616 15:08:25.532140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0106887 (* 1 = 0.0106887 loss) +I0616 15:08:25.532143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.017022 (* 1 = 0.017022 loss) +I0616 15:08:25.532147 9857 solver.cpp:571] Iteration 78240, lr = 0.0001 +I0616 15:08:37.306936 9857 solver.cpp:242] Iteration 78260, loss = 0.52389 +I0616 15:08:37.306978 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.110104 (* 1 = 0.110104 loss) +I0616 15:08:37.306984 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10873 (* 1 = 0.10873 loss) +I0616 15:08:37.306989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0504997 (* 1 = 0.0504997 loss) +I0616 15:08:37.306993 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562917 (* 1 = 0.00562917 loss) +I0616 15:08:37.306998 9857 solver.cpp:571] Iteration 78260, lr = 0.0001 +I0616 15:08:48.614387 9857 solver.cpp:242] Iteration 78280, loss = 0.46188 +I0616 15:08:48.614429 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102028 (* 1 = 0.102028 loss) +I0616 15:08:48.614435 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322577 (* 1 = 0.322577 loss) +I0616 15:08:48.614439 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0547537 (* 1 = 0.0547537 loss) +I0616 15:08:48.614444 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00834541 (* 1 = 0.00834541 loss) +I0616 15:08:48.614446 9857 solver.cpp:571] Iteration 78280, lr = 0.0001 +I0616 15:09:00.208128 9857 solver.cpp:242] Iteration 78300, loss = 0.675772 +I0616 15:09:00.208170 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241562 (* 1 = 0.241562 loss) +I0616 15:09:00.208175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266276 (* 1 = 0.266276 loss) +I0616 15:09:00.208179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169794 (* 1 = 0.169794 loss) +I0616 15:09:00.208184 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.026417 (* 1 = 0.026417 loss) +I0616 15:09:00.208187 9857 solver.cpp:571] Iteration 78300, lr = 0.0001 +I0616 15:09:11.830935 9857 solver.cpp:242] Iteration 78320, loss = 0.588906 +I0616 15:09:11.830976 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10832 (* 1 = 0.10832 loss) +I0616 15:09:11.830981 9857 solver.cpp:258] Train net output #1: loss_cls = 0.103487 (* 1 = 0.103487 loss) +I0616 15:09:11.830986 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163724 (* 1 = 0.0163724 loss) +I0616 15:09:11.830989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0589049 (* 1 = 0.0589049 loss) +I0616 15:09:11.830993 9857 solver.cpp:571] Iteration 78320, lr = 0.0001 +I0616 15:09:23.322643 9857 solver.cpp:242] Iteration 78340, loss = 0.412374 +I0616 15:09:23.322685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204847 (* 1 = 0.204847 loss) +I0616 15:09:23.322690 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204744 (* 1 = 0.204744 loss) +I0616 15:09:23.322695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0684616 (* 1 = 0.0684616 loss) +I0616 15:09:23.322698 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167123 (* 1 = 0.0167123 loss) +I0616 15:09:23.322701 9857 solver.cpp:571] Iteration 78340, lr = 0.0001 +I0616 15:09:34.996225 9857 solver.cpp:242] Iteration 78360, loss = 0.814263 +I0616 15:09:34.996268 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133333 (* 1 = 0.133333 loss) +I0616 15:09:34.996273 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138789 (* 1 = 0.138789 loss) +I0616 15:09:34.996278 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0105461 (* 1 = 0.0105461 loss) +I0616 15:09:34.996281 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100048 (* 1 = 0.0100048 loss) +I0616 15:09:34.996285 9857 solver.cpp:571] Iteration 78360, lr = 0.0001 +I0616 15:09:46.402464 9857 solver.cpp:242] Iteration 78380, loss = 0.299881 +I0616 15:09:46.402505 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157878 (* 1 = 0.157878 loss) +I0616 15:09:46.402511 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19953 (* 1 = 0.19953 loss) +I0616 15:09:46.402515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0771098 (* 1 = 0.0771098 loss) +I0616 15:09:46.402519 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00904402 (* 1 = 0.00904402 loss) +I0616 15:09:46.402523 9857 solver.cpp:571] Iteration 78380, lr = 0.0001 +speed: 0.600s / iter +I0616 15:09:58.032977 9857 solver.cpp:242] Iteration 78400, loss = 0.319988 +I0616 15:09:58.033020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165543 (* 1 = 0.165543 loss) +I0616 15:09:58.033025 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186981 (* 1 = 0.186981 loss) +I0616 15:09:58.033030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0259064 (* 1 = 0.0259064 loss) +I0616 15:09:58.033032 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00660162 (* 1 = 0.00660162 loss) +I0616 15:09:58.033036 9857 solver.cpp:571] Iteration 78400, lr = 0.0001 +I0616 15:10:09.497323 9857 solver.cpp:242] Iteration 78420, loss = 0.271581 +I0616 15:10:09.497365 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.098615 (* 1 = 0.098615 loss) +I0616 15:10:09.497371 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146262 (* 1 = 0.146262 loss) +I0616 15:10:09.497375 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0217307 (* 1 = 0.0217307 loss) +I0616 15:10:09.497380 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103443 (* 1 = 0.0103443 loss) +I0616 15:10:09.497397 9857 solver.cpp:571] Iteration 78420, lr = 0.0001 +I0616 15:10:20.902765 9857 solver.cpp:242] Iteration 78440, loss = 0.268454 +I0616 15:10:20.902807 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118597 (* 1 = 0.118597 loss) +I0616 15:10:20.902813 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1564 (* 1 = 0.1564 loss) +I0616 15:10:20.902817 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0796593 (* 1 = 0.0796593 loss) +I0616 15:10:20.902822 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0545934 (* 1 = 0.0545934 loss) +I0616 15:10:20.902824 9857 solver.cpp:571] Iteration 78440, lr = 0.0001 +I0616 15:10:32.580654 9857 solver.cpp:242] Iteration 78460, loss = 0.370044 +I0616 15:10:32.580696 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260807 (* 1 = 0.260807 loss) +I0616 15:10:32.580701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178943 (* 1 = 0.178943 loss) +I0616 15:10:32.580706 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0641144 (* 1 = 0.0641144 loss) +I0616 15:10:32.580709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181425 (* 1 = 0.0181425 loss) +I0616 15:10:32.580713 9857 solver.cpp:571] Iteration 78460, lr = 0.0001 +I0616 15:10:44.065649 9857 solver.cpp:242] Iteration 78480, loss = 0.729608 +I0616 15:10:44.065691 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327316 (* 1 = 0.327316 loss) +I0616 15:10:44.065697 9857 solver.cpp:258] Train net output #1: loss_cls = 0.290237 (* 1 = 0.290237 loss) +I0616 15:10:44.065701 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0236152 (* 1 = 0.0236152 loss) +I0616 15:10:44.065706 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174461 (* 1 = 0.0174461 loss) +I0616 15:10:44.065709 9857 solver.cpp:571] Iteration 78480, lr = 0.0001 +I0616 15:10:55.268812 9857 solver.cpp:242] Iteration 78500, loss = 0.474779 +I0616 15:10:55.268852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0728964 (* 1 = 0.0728964 loss) +I0616 15:10:55.268858 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0640422 (* 1 = 0.0640422 loss) +I0616 15:10:55.268862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228736 (* 1 = 0.0228736 loss) +I0616 15:10:55.268867 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292688 (* 1 = 0.0292688 loss) +I0616 15:10:55.268870 9857 solver.cpp:571] Iteration 78500, lr = 0.0001 +I0616 15:11:06.599596 9857 solver.cpp:242] Iteration 78520, loss = 0.377115 +I0616 15:11:06.599637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0589586 (* 1 = 0.0589586 loss) +I0616 15:11:06.599643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0669485 (* 1 = 0.0669485 loss) +I0616 15:11:06.599647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123414 (* 1 = 0.123414 loss) +I0616 15:11:06.599650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0260641 (* 1 = 0.0260641 loss) +I0616 15:11:06.599654 9857 solver.cpp:571] Iteration 78520, lr = 0.0001 +I0616 15:11:18.132052 9857 solver.cpp:242] Iteration 78540, loss = 0.396599 +I0616 15:11:18.132093 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0619293 (* 1 = 0.0619293 loss) +I0616 15:11:18.132099 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140352 (* 1 = 0.140352 loss) +I0616 15:11:18.132104 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00248357 (* 1 = 0.00248357 loss) +I0616 15:11:18.132107 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00605832 (* 1 = 0.00605832 loss) +I0616 15:11:18.132112 9857 solver.cpp:571] Iteration 78540, lr = 0.0001 +I0616 15:11:29.924639 9857 solver.cpp:242] Iteration 78560, loss = 0.979052 +I0616 15:11:29.924682 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379687 (* 1 = 0.379687 loss) +I0616 15:11:29.924688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.428564 (* 1 = 0.428564 loss) +I0616 15:11:29.924692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.224878 (* 1 = 0.224878 loss) +I0616 15:11:29.924696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.120958 (* 1 = 0.120958 loss) +I0616 15:11:29.924700 9857 solver.cpp:571] Iteration 78560, lr = 0.0001 +I0616 15:11:41.642968 9857 solver.cpp:242] Iteration 78580, loss = 0.480718 +I0616 15:11:41.643012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241056 (* 1 = 0.241056 loss) +I0616 15:11:41.643016 9857 solver.cpp:258] Train net output #1: loss_cls = 0.412529 (* 1 = 0.412529 loss) +I0616 15:11:41.643021 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0778717 (* 1 = 0.0778717 loss) +I0616 15:11:41.643024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0340995 (* 1 = 0.0340995 loss) +I0616 15:11:41.643028 9857 solver.cpp:571] Iteration 78580, lr = 0.0001 +speed: 0.600s / iter +I0616 15:11:53.065603 9857 solver.cpp:242] Iteration 78600, loss = 0.562369 +I0616 15:11:53.065644 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241022 (* 1 = 0.241022 loss) +I0616 15:11:53.065650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209992 (* 1 = 0.209992 loss) +I0616 15:11:53.065670 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0289336 (* 1 = 0.0289336 loss) +I0616 15:11:53.065673 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129435 (* 1 = 0.0129435 loss) +I0616 15:11:53.065676 9857 solver.cpp:571] Iteration 78600, lr = 0.0001 +I0616 15:12:04.745789 9857 solver.cpp:242] Iteration 78620, loss = 0.854436 +I0616 15:12:04.745831 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203896 (* 1 = 0.203896 loss) +I0616 15:12:04.745836 9857 solver.cpp:258] Train net output #1: loss_cls = 0.574507 (* 1 = 0.574507 loss) +I0616 15:12:04.745841 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.292145 (* 1 = 0.292145 loss) +I0616 15:12:04.745844 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.42899 (* 1 = 0.42899 loss) +I0616 15:12:04.745848 9857 solver.cpp:571] Iteration 78620, lr = 0.0001 +I0616 15:12:16.195951 9857 solver.cpp:242] Iteration 78640, loss = 0.253537 +I0616 15:12:16.195994 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0441404 (* 1 = 0.0441404 loss) +I0616 15:12:16.196001 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149358 (* 1 = 0.149358 loss) +I0616 15:12:16.196004 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0324707 (* 1 = 0.0324707 loss) +I0616 15:12:16.196007 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00817541 (* 1 = 0.00817541 loss) +I0616 15:12:16.196012 9857 solver.cpp:571] Iteration 78640, lr = 0.0001 +I0616 15:12:27.511806 9857 solver.cpp:242] Iteration 78660, loss = 0.558593 +I0616 15:12:27.511847 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29433 (* 1 = 0.29433 loss) +I0616 15:12:27.511853 9857 solver.cpp:258] Train net output #1: loss_cls = 0.395029 (* 1 = 0.395029 loss) +I0616 15:12:27.511857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0762292 (* 1 = 0.0762292 loss) +I0616 15:12:27.511860 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0206441 (* 1 = 0.0206441 loss) +I0616 15:12:27.511864 9857 solver.cpp:571] Iteration 78660, lr = 0.0001 +I0616 15:12:39.156437 9857 solver.cpp:242] Iteration 78680, loss = 0.331458 +I0616 15:12:39.156481 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18392 (* 1 = 0.18392 loss) +I0616 15:12:39.156486 9857 solver.cpp:258] Train net output #1: loss_cls = 0.137997 (* 1 = 0.137997 loss) +I0616 15:12:39.156489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0796075 (* 1 = 0.0796075 loss) +I0616 15:12:39.156493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0177804 (* 1 = 0.0177804 loss) +I0616 15:12:39.156497 9857 solver.cpp:571] Iteration 78680, lr = 0.0001 +I0616 15:12:50.760356 9857 solver.cpp:242] Iteration 78700, loss = 0.379286 +I0616 15:12:50.760397 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154914 (* 1 = 0.154914 loss) +I0616 15:12:50.760403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21149 (* 1 = 0.21149 loss) +I0616 15:12:50.760407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0860637 (* 1 = 0.0860637 loss) +I0616 15:12:50.760411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0840363 (* 1 = 0.0840363 loss) +I0616 15:12:50.760416 9857 solver.cpp:571] Iteration 78700, lr = 0.0001 +I0616 15:13:02.198451 9857 solver.cpp:242] Iteration 78720, loss = 0.432686 +I0616 15:13:02.198493 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0969347 (* 1 = 0.0969347 loss) +I0616 15:13:02.198499 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167889 (* 1 = 0.167889 loss) +I0616 15:13:02.198503 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287856 (* 1 = 0.0287856 loss) +I0616 15:13:02.198508 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00805083 (* 1 = 0.00805083 loss) +I0616 15:13:02.198513 9857 solver.cpp:571] Iteration 78720, lr = 0.0001 +I0616 15:13:13.657479 9857 solver.cpp:242] Iteration 78740, loss = 0.691332 +I0616 15:13:13.657521 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222978 (* 1 = 0.222978 loss) +I0616 15:13:13.657526 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259785 (* 1 = 0.259785 loss) +I0616 15:13:13.657531 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120649 (* 1 = 0.120649 loss) +I0616 15:13:13.657534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0238819 (* 1 = 0.0238819 loss) +I0616 15:13:13.657538 9857 solver.cpp:571] Iteration 78740, lr = 0.0001 +I0616 15:13:25.128969 9857 solver.cpp:242] Iteration 78760, loss = 0.348888 +I0616 15:13:25.129010 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255175 (* 1 = 0.255175 loss) +I0616 15:13:25.129015 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156208 (* 1 = 0.156208 loss) +I0616 15:13:25.129020 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0253875 (* 1 = 0.0253875 loss) +I0616 15:13:25.129024 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0281869 (* 1 = 0.0281869 loss) +I0616 15:13:25.129027 9857 solver.cpp:571] Iteration 78760, lr = 0.0001 +I0616 15:13:36.778120 9857 solver.cpp:242] Iteration 78780, loss = 0.801159 +I0616 15:13:36.778162 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306951 (* 1 = 0.306951 loss) +I0616 15:13:36.778167 9857 solver.cpp:258] Train net output #1: loss_cls = 0.414952 (* 1 = 0.414952 loss) +I0616 15:13:36.778172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125791 (* 1 = 0.125791 loss) +I0616 15:13:36.778175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107331 (* 1 = 0.107331 loss) +I0616 15:13:36.778178 9857 solver.cpp:571] Iteration 78780, lr = 0.0001 +speed: 0.600s / iter +I0616 15:13:48.346808 9857 solver.cpp:242] Iteration 78800, loss = 1.20664 +I0616 15:13:48.346851 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.435753 (* 1 = 0.435753 loss) +I0616 15:13:48.346858 9857 solver.cpp:258] Train net output #1: loss_cls = 0.488589 (* 1 = 0.488589 loss) +I0616 15:13:48.346861 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.247045 (* 1 = 0.247045 loss) +I0616 15:13:48.346865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0954431 (* 1 = 0.0954431 loss) +I0616 15:13:48.346869 9857 solver.cpp:571] Iteration 78800, lr = 0.0001 +I0616 15:13:59.753265 9857 solver.cpp:242] Iteration 78820, loss = 0.591656 +I0616 15:13:59.753307 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0992698 (* 1 = 0.0992698 loss) +I0616 15:13:59.753312 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159342 (* 1 = 0.159342 loss) +I0616 15:13:59.753316 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0541456 (* 1 = 0.0541456 loss) +I0616 15:13:59.753320 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.017407 (* 1 = 0.017407 loss) +I0616 15:13:59.753324 9857 solver.cpp:571] Iteration 78820, lr = 0.0001 +I0616 15:14:11.583490 9857 solver.cpp:242] Iteration 78840, loss = 0.496145 +I0616 15:14:11.583534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130166 (* 1 = 0.130166 loss) +I0616 15:14:11.583539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16835 (* 1 = 0.16835 loss) +I0616 15:14:11.583542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0113864 (* 1 = 0.0113864 loss) +I0616 15:14:11.583546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0707741 (* 1 = 0.0707741 loss) +I0616 15:14:11.583550 9857 solver.cpp:571] Iteration 78840, lr = 0.0001 +I0616 15:14:23.109997 9857 solver.cpp:242] Iteration 78860, loss = 0.821644 +I0616 15:14:23.110039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280728 (* 1 = 0.280728 loss) +I0616 15:14:23.110044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321264 (* 1 = 0.321264 loss) +I0616 15:14:23.110049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200861 (* 1 = 0.200861 loss) +I0616 15:14:23.110054 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.215857 (* 1 = 0.215857 loss) +I0616 15:14:23.110057 9857 solver.cpp:571] Iteration 78860, lr = 0.0001 +I0616 15:14:34.450227 9857 solver.cpp:242] Iteration 78880, loss = 0.517591 +I0616 15:14:34.450269 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308052 (* 1 = 0.308052 loss) +I0616 15:14:34.450275 9857 solver.cpp:258] Train net output #1: loss_cls = 0.410559 (* 1 = 0.410559 loss) +I0616 15:14:34.450279 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0886223 (* 1 = 0.0886223 loss) +I0616 15:14:34.450284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183926 (* 1 = 0.0183926 loss) +I0616 15:14:34.450287 9857 solver.cpp:571] Iteration 78880, lr = 0.0001 +I0616 15:14:46.156931 9857 solver.cpp:242] Iteration 78900, loss = 0.8561 +I0616 15:14:46.156975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261257 (* 1 = 0.261257 loss) +I0616 15:14:46.156980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174901 (* 1 = 0.174901 loss) +I0616 15:14:46.156985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0855209 (* 1 = 0.0855209 loss) +I0616 15:14:46.156988 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174314 (* 1 = 0.0174314 loss) +I0616 15:14:46.156992 9857 solver.cpp:571] Iteration 78900, lr = 0.0001 +I0616 15:14:57.603330 9857 solver.cpp:242] Iteration 78920, loss = 0.844655 +I0616 15:14:57.603373 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209057 (* 1 = 0.209057 loss) +I0616 15:14:57.603379 9857 solver.cpp:258] Train net output #1: loss_cls = 0.403149 (* 1 = 0.403149 loss) +I0616 15:14:57.603382 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.134483 (* 1 = 0.134483 loss) +I0616 15:14:57.603386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.365145 (* 1 = 0.365145 loss) +I0616 15:14:57.603390 9857 solver.cpp:571] Iteration 78920, lr = 0.0001 +I0616 15:15:09.500113 9857 solver.cpp:242] Iteration 78940, loss = 0.439324 +I0616 15:15:09.500155 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.054271 (* 1 = 0.054271 loss) +I0616 15:15:09.500160 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0939381 (* 1 = 0.0939381 loss) +I0616 15:15:09.500165 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0160865 (* 1 = 0.0160865 loss) +I0616 15:15:09.500169 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00886472 (* 1 = 0.00886472 loss) +I0616 15:15:09.500172 9857 solver.cpp:571] Iteration 78940, lr = 0.0001 +I0616 15:15:21.054810 9857 solver.cpp:242] Iteration 78960, loss = 0.377319 +I0616 15:15:21.054852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.178933 (* 1 = 0.178933 loss) +I0616 15:15:21.054857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154617 (* 1 = 0.154617 loss) +I0616 15:15:21.054862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0392127 (* 1 = 0.0392127 loss) +I0616 15:15:21.054867 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207714 (* 1 = 0.0207714 loss) +I0616 15:15:21.054869 9857 solver.cpp:571] Iteration 78960, lr = 0.0001 +I0616 15:15:32.522861 9857 solver.cpp:242] Iteration 78980, loss = 0.540994 +I0616 15:15:32.522903 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32877 (* 1 = 0.32877 loss) +I0616 15:15:32.522909 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24523 (* 1 = 0.24523 loss) +I0616 15:15:32.522913 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13589 (* 1 = 0.13589 loss) +I0616 15:15:32.522917 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494254 (* 1 = 0.0494254 loss) +I0616 15:15:32.522920 9857 solver.cpp:571] Iteration 78980, lr = 0.0001 +speed: 0.600s / iter +I0616 15:15:43.602335 9857 solver.cpp:242] Iteration 79000, loss = 0.956514 +I0616 15:15:43.602375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300056 (* 1 = 0.300056 loss) +I0616 15:15:43.602380 9857 solver.cpp:258] Train net output #1: loss_cls = 0.476181 (* 1 = 0.476181 loss) +I0616 15:15:43.602385 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161128 (* 1 = 0.161128 loss) +I0616 15:15:43.602388 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0411992 (* 1 = 0.0411992 loss) +I0616 15:15:43.602392 9857 solver.cpp:571] Iteration 79000, lr = 0.0001 +I0616 15:15:55.235472 9857 solver.cpp:242] Iteration 79020, loss = 0.451708 +I0616 15:15:55.235512 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0647001 (* 1 = 0.0647001 loss) +I0616 15:15:55.235517 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146856 (* 1 = 0.146856 loss) +I0616 15:15:55.235522 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0108965 (* 1 = 0.0108965 loss) +I0616 15:15:55.235525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00533718 (* 1 = 0.00533718 loss) +I0616 15:15:55.235529 9857 solver.cpp:571] Iteration 79020, lr = 0.0001 +I0616 15:16:06.980129 9857 solver.cpp:242] Iteration 79040, loss = 0.682622 +I0616 15:16:06.980171 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.07975 (* 1 = 0.07975 loss) +I0616 15:16:06.980176 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0666016 (* 1 = 0.0666016 loss) +I0616 15:16:06.980181 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100052 (* 1 = 0.100052 loss) +I0616 15:16:06.980185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.020609 (* 1 = 0.020609 loss) +I0616 15:16:06.980188 9857 solver.cpp:571] Iteration 79040, lr = 0.0001 +I0616 15:16:18.381692 9857 solver.cpp:242] Iteration 79060, loss = 0.209094 +I0616 15:16:18.381721 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11647 (* 1 = 0.11647 loss) +I0616 15:16:18.381726 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113957 (* 1 = 0.113957 loss) +I0616 15:16:18.381731 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00355146 (* 1 = 0.00355146 loss) +I0616 15:16:18.381734 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00574327 (* 1 = 0.00574327 loss) +I0616 15:16:18.381738 9857 solver.cpp:571] Iteration 79060, lr = 0.0001 +I0616 15:16:30.006489 9857 solver.cpp:242] Iteration 79080, loss = 0.463163 +I0616 15:16:30.006530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202173 (* 1 = 0.202173 loss) +I0616 15:16:30.006536 9857 solver.cpp:258] Train net output #1: loss_cls = 0.504125 (* 1 = 0.504125 loss) +I0616 15:16:30.006541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0513027 (* 1 = 0.0513027 loss) +I0616 15:16:30.006543 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100301 (* 1 = 0.0100301 loss) +I0616 15:16:30.006547 9857 solver.cpp:571] Iteration 79080, lr = 0.0001 +I0616 15:16:41.319810 9857 solver.cpp:242] Iteration 79100, loss = 0.332644 +I0616 15:16:41.319854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.198038 (* 1 = 0.198038 loss) +I0616 15:16:41.319859 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210719 (* 1 = 0.210719 loss) +I0616 15:16:41.319862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0477715 (* 1 = 0.0477715 loss) +I0616 15:16:41.319866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0204344 (* 1 = 0.0204344 loss) +I0616 15:16:41.319870 9857 solver.cpp:571] Iteration 79100, lr = 0.0001 +I0616 15:16:52.825769 9857 solver.cpp:242] Iteration 79120, loss = 0.802165 +I0616 15:16:52.825812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241482 (* 1 = 0.241482 loss) +I0616 15:16:52.825819 9857 solver.cpp:258] Train net output #1: loss_cls = 0.317929 (* 1 = 0.317929 loss) +I0616 15:16:52.825822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0764774 (* 1 = 0.0764774 loss) +I0616 15:16:52.825826 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0928315 (* 1 = 0.0928315 loss) +I0616 15:16:52.825830 9857 solver.cpp:571] Iteration 79120, lr = 0.0001 +I0616 15:17:04.348042 9857 solver.cpp:242] Iteration 79140, loss = 0.392638 +I0616 15:17:04.348086 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.156058 (* 1 = 0.156058 loss) +I0616 15:17:04.348093 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105308 (* 1 = 0.105308 loss) +I0616 15:17:04.348098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00280212 (* 1 = 0.00280212 loss) +I0616 15:17:04.348100 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00420495 (* 1 = 0.00420495 loss) +I0616 15:17:04.348104 9857 solver.cpp:571] Iteration 79140, lr = 0.0001 +I0616 15:17:15.912657 9857 solver.cpp:242] Iteration 79160, loss = 0.24323 +I0616 15:17:15.912698 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104853 (* 1 = 0.104853 loss) +I0616 15:17:15.912704 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21052 (* 1 = 0.21052 loss) +I0616 15:17:15.912708 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.022394 (* 1 = 0.022394 loss) +I0616 15:17:15.912713 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0118157 (* 1 = 0.0118157 loss) +I0616 15:17:15.912715 9857 solver.cpp:571] Iteration 79160, lr = 0.0001 +I0616 15:17:27.346746 9857 solver.cpp:242] Iteration 79180, loss = 0.220542 +I0616 15:17:27.346793 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0944493 (* 1 = 0.0944493 loss) +I0616 15:17:27.346801 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156845 (* 1 = 0.156845 loss) +I0616 15:17:27.346806 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0364664 (* 1 = 0.0364664 loss) +I0616 15:17:27.346809 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114585 (* 1 = 0.0114585 loss) +I0616 15:17:27.346812 9857 solver.cpp:571] Iteration 79180, lr = 0.0001 +speed: 0.600s / iter +I0616 15:17:38.536625 9857 solver.cpp:242] Iteration 79200, loss = 0.443843 +I0616 15:17:38.536669 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0535153 (* 1 = 0.0535153 loss) +I0616 15:17:38.536675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145586 (* 1 = 0.145586 loss) +I0616 15:17:38.536679 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0420148 (* 1 = 0.0420148 loss) +I0616 15:17:38.536684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00937516 (* 1 = 0.00937516 loss) +I0616 15:17:38.536686 9857 solver.cpp:571] Iteration 79200, lr = 0.0001 +I0616 15:17:50.174257 9857 solver.cpp:242] Iteration 79220, loss = 0.619672 +I0616 15:17:50.174299 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128031 (* 1 = 0.128031 loss) +I0616 15:17:50.174304 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226204 (* 1 = 0.226204 loss) +I0616 15:17:50.174309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00967078 (* 1 = 0.00967078 loss) +I0616 15:17:50.174314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0407904 (* 1 = 0.0407904 loss) +I0616 15:17:50.174317 9857 solver.cpp:571] Iteration 79220, lr = 0.0001 +I0616 15:18:01.317035 9857 solver.cpp:242] Iteration 79240, loss = 0.416581 +I0616 15:18:01.317075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0869139 (* 1 = 0.0869139 loss) +I0616 15:18:01.317080 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14326 (* 1 = 0.14326 loss) +I0616 15:18:01.317085 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00755112 (* 1 = 0.00755112 loss) +I0616 15:18:01.317088 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00682708 (* 1 = 0.00682708 loss) +I0616 15:18:01.317092 9857 solver.cpp:571] Iteration 79240, lr = 0.0001 +I0616 15:18:12.838505 9857 solver.cpp:242] Iteration 79260, loss = 0.492306 +I0616 15:18:12.838549 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172957 (* 1 = 0.172957 loss) +I0616 15:18:12.838554 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190054 (* 1 = 0.190054 loss) +I0616 15:18:12.838558 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101885 (* 1 = 0.101885 loss) +I0616 15:18:12.838562 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0826541 (* 1 = 0.0826541 loss) +I0616 15:18:12.838567 9857 solver.cpp:571] Iteration 79260, lr = 0.0001 +I0616 15:18:24.390422 9857 solver.cpp:242] Iteration 79280, loss = 1.08015 +I0616 15:18:24.390463 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352367 (* 1 = 0.352367 loss) +I0616 15:18:24.390468 9857 solver.cpp:258] Train net output #1: loss_cls = 0.582826 (* 1 = 0.582826 loss) +I0616 15:18:24.390472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.180841 (* 1 = 0.180841 loss) +I0616 15:18:24.390476 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.441113 (* 1 = 0.441113 loss) +I0616 15:18:24.390480 9857 solver.cpp:571] Iteration 79280, lr = 0.0001 +I0616 15:18:35.652858 9857 solver.cpp:242] Iteration 79300, loss = 0.565123 +I0616 15:18:35.652900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0890314 (* 1 = 0.0890314 loss) +I0616 15:18:35.652905 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226332 (* 1 = 0.226332 loss) +I0616 15:18:35.652909 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00292942 (* 1 = 0.00292942 loss) +I0616 15:18:35.652914 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0267101 (* 1 = 0.0267101 loss) +I0616 15:18:35.652917 9857 solver.cpp:571] Iteration 79300, lr = 0.0001 +I0616 15:18:47.151546 9857 solver.cpp:242] Iteration 79320, loss = 0.47936 +I0616 15:18:47.151588 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144775 (* 1 = 0.144775 loss) +I0616 15:18:47.151593 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255276 (* 1 = 0.255276 loss) +I0616 15:18:47.151598 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0609586 (* 1 = 0.0609586 loss) +I0616 15:18:47.151602 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0176719 (* 1 = 0.0176719 loss) +I0616 15:18:47.151605 9857 solver.cpp:571] Iteration 79320, lr = 0.0001 +I0616 15:18:58.651866 9857 solver.cpp:242] Iteration 79340, loss = 0.755004 +I0616 15:18:58.651908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17548 (* 1 = 0.17548 loss) +I0616 15:18:58.651913 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170666 (* 1 = 0.170666 loss) +I0616 15:18:58.651917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.266165 (* 1 = 0.266165 loss) +I0616 15:18:58.651921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.48598 (* 1 = 0.48598 loss) +I0616 15:18:58.651926 9857 solver.cpp:571] Iteration 79340, lr = 0.0001 +I0616 15:19:10.296802 9857 solver.cpp:242] Iteration 79360, loss = 0.602372 +I0616 15:19:10.296844 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0657462 (* 1 = 0.0657462 loss) +I0616 15:19:10.296849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.194489 (* 1 = 0.194489 loss) +I0616 15:19:10.296854 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.008553 (* 1 = 0.008553 loss) +I0616 15:19:10.296857 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00185352 (* 1 = 0.00185352 loss) +I0616 15:19:10.296860 9857 solver.cpp:571] Iteration 79360, lr = 0.0001 +I0616 15:19:21.801925 9857 solver.cpp:242] Iteration 79380, loss = 1.24197 +I0616 15:19:21.801969 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32249 (* 1 = 0.32249 loss) +I0616 15:19:21.801973 9857 solver.cpp:258] Train net output #1: loss_cls = 0.456171 (* 1 = 0.456171 loss) +I0616 15:19:21.801977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18149 (* 1 = 0.18149 loss) +I0616 15:19:21.801981 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.482038 (* 1 = 0.482038 loss) +I0616 15:19:21.801985 9857 solver.cpp:571] Iteration 79380, lr = 0.0001 +speed: 0.600s / iter +I0616 15:19:33.488145 9857 solver.cpp:242] Iteration 79400, loss = 0.558342 +I0616 15:19:33.488188 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.078276 (* 1 = 0.078276 loss) +I0616 15:19:33.488193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200586 (* 1 = 0.200586 loss) +I0616 15:19:33.488196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150761 (* 1 = 0.150761 loss) +I0616 15:19:33.488200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.525027 (* 1 = 0.525027 loss) +I0616 15:19:33.488205 9857 solver.cpp:571] Iteration 79400, lr = 0.0001 +I0616 15:19:45.352669 9857 solver.cpp:242] Iteration 79420, loss = 1.18236 +I0616 15:19:45.352712 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187115 (* 1 = 0.187115 loss) +I0616 15:19:45.352718 9857 solver.cpp:258] Train net output #1: loss_cls = 0.387491 (* 1 = 0.387491 loss) +I0616 15:19:45.352722 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.283592 (* 1 = 0.283592 loss) +I0616 15:19:45.352726 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.568874 (* 1 = 0.568874 loss) +I0616 15:19:45.352730 9857 solver.cpp:571] Iteration 79420, lr = 0.0001 +I0616 15:19:56.780426 9857 solver.cpp:242] Iteration 79440, loss = 0.666449 +I0616 15:19:56.780467 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.335692 (* 1 = 0.335692 loss) +I0616 15:19:56.780472 9857 solver.cpp:258] Train net output #1: loss_cls = 0.330321 (* 1 = 0.330321 loss) +I0616 15:19:56.780477 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537634 (* 1 = 0.0537634 loss) +I0616 15:19:56.780480 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0473975 (* 1 = 0.0473975 loss) +I0616 15:19:56.780485 9857 solver.cpp:571] Iteration 79440, lr = 0.0001 +I0616 15:20:08.445215 9857 solver.cpp:242] Iteration 79460, loss = 0.530092 +I0616 15:20:08.445255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290776 (* 1 = 0.290776 loss) +I0616 15:20:08.445261 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276795 (* 1 = 0.276795 loss) +I0616 15:20:08.445266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0320647 (* 1 = 0.0320647 loss) +I0616 15:20:08.445268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160971 (* 1 = 0.0160971 loss) +I0616 15:20:08.445272 9857 solver.cpp:571] Iteration 79460, lr = 0.0001 +I0616 15:20:19.889684 9857 solver.cpp:242] Iteration 79480, loss = 0.369663 +I0616 15:20:19.889722 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146357 (* 1 = 0.146357 loss) +I0616 15:20:19.889729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169157 (* 1 = 0.169157 loss) +I0616 15:20:19.889732 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00826444 (* 1 = 0.00826444 loss) +I0616 15:20:19.889736 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120664 (* 1 = 0.0120664 loss) +I0616 15:20:19.889739 9857 solver.cpp:571] Iteration 79480, lr = 0.0001 +I0616 15:20:31.481246 9857 solver.cpp:242] Iteration 79500, loss = 0.579291 +I0616 15:20:31.481289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222152 (* 1 = 0.222152 loss) +I0616 15:20:31.481294 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216586 (* 1 = 0.216586 loss) +I0616 15:20:31.481299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0790834 (* 1 = 0.0790834 loss) +I0616 15:20:31.481302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0861419 (* 1 = 0.0861419 loss) +I0616 15:20:31.481307 9857 solver.cpp:571] Iteration 79500, lr = 0.0001 +I0616 15:20:42.863557 9857 solver.cpp:242] Iteration 79520, loss = 0.363207 +I0616 15:20:42.863600 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0558923 (* 1 = 0.0558923 loss) +I0616 15:20:42.863605 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0926841 (* 1 = 0.0926841 loss) +I0616 15:20:42.863610 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00704496 (* 1 = 0.00704496 loss) +I0616 15:20:42.863613 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00314538 (* 1 = 0.00314538 loss) +I0616 15:20:42.863616 9857 solver.cpp:571] Iteration 79520, lr = 0.0001 +I0616 15:20:54.331604 9857 solver.cpp:242] Iteration 79540, loss = 0.714224 +I0616 15:20:54.331643 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112766 (* 1 = 0.112766 loss) +I0616 15:20:54.331650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121228 (* 1 = 0.121228 loss) +I0616 15:20:54.331653 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0333921 (* 1 = 0.0333921 loss) +I0616 15:20:54.331657 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170923 (* 1 = 0.0170923 loss) +I0616 15:20:54.331661 9857 solver.cpp:571] Iteration 79540, lr = 0.0001 +I0616 15:21:06.058110 9857 solver.cpp:242] Iteration 79560, loss = 0.285226 +I0616 15:21:06.058152 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0754682 (* 1 = 0.0754682 loss) +I0616 15:21:06.058158 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147256 (* 1 = 0.147256 loss) +I0616 15:21:06.058162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.027814 (* 1 = 0.027814 loss) +I0616 15:21:06.058166 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00845233 (* 1 = 0.00845233 loss) +I0616 15:21:06.058169 9857 solver.cpp:571] Iteration 79560, lr = 0.0001 +I0616 15:21:17.421299 9857 solver.cpp:242] Iteration 79580, loss = 0.513844 +I0616 15:21:17.421340 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184579 (* 1 = 0.184579 loss) +I0616 15:21:17.421346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185005 (* 1 = 0.185005 loss) +I0616 15:21:17.421350 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0786864 (* 1 = 0.0786864 loss) +I0616 15:21:17.421355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189467 (* 1 = 0.0189467 loss) +I0616 15:21:17.421357 9857 solver.cpp:571] Iteration 79580, lr = 0.0001 +speed: 0.600s / iter +I0616 15:21:28.595504 9857 solver.cpp:242] Iteration 79600, loss = 0.558238 +I0616 15:21:28.595546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214824 (* 1 = 0.214824 loss) +I0616 15:21:28.595552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.357515 (* 1 = 0.357515 loss) +I0616 15:21:28.595556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168863 (* 1 = 0.168863 loss) +I0616 15:21:28.595561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.062339 (* 1 = 0.062339 loss) +I0616 15:21:28.595564 9857 solver.cpp:571] Iteration 79600, lr = 0.0001 +I0616 15:21:40.248373 9857 solver.cpp:242] Iteration 79620, loss = 0.785242 +I0616 15:21:40.248415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.074953 (* 1 = 0.074953 loss) +I0616 15:21:40.248420 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182132 (* 1 = 0.182132 loss) +I0616 15:21:40.248425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0261491 (* 1 = 0.0261491 loss) +I0616 15:21:40.248428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00427244 (* 1 = 0.00427244 loss) +I0616 15:21:40.248432 9857 solver.cpp:571] Iteration 79620, lr = 0.0001 +I0616 15:21:51.646520 9857 solver.cpp:242] Iteration 79640, loss = 0.875355 +I0616 15:21:51.646576 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136705 (* 1 = 0.136705 loss) +I0616 15:21:51.646584 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184953 (* 1 = 0.184953 loss) +I0616 15:21:51.646587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0962347 (* 1 = 0.0962347 loss) +I0616 15:21:51.646591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313723 (* 1 = 0.0313723 loss) +I0616 15:21:51.646595 9857 solver.cpp:571] Iteration 79640, lr = 0.0001 +I0616 15:22:02.915899 9857 solver.cpp:242] Iteration 79660, loss = 0.337563 +I0616 15:22:02.915941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.059478 (* 1 = 0.059478 loss) +I0616 15:22:02.915946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169802 (* 1 = 0.169802 loss) +I0616 15:22:02.915951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00810759 (* 1 = 0.00810759 loss) +I0616 15:22:02.915956 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198199 (* 1 = 0.0198199 loss) +I0616 15:22:02.915959 9857 solver.cpp:571] Iteration 79660, lr = 0.0001 +I0616 15:22:14.614745 9857 solver.cpp:242] Iteration 79680, loss = 0.443276 +I0616 15:22:14.614791 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0412214 (* 1 = 0.0412214 loss) +I0616 15:22:14.614797 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12268 (* 1 = 0.12268 loss) +I0616 15:22:14.614802 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00862947 (* 1 = 0.00862947 loss) +I0616 15:22:14.614806 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00992476 (* 1 = 0.00992476 loss) +I0616 15:22:14.614809 9857 solver.cpp:571] Iteration 79680, lr = 0.0001 +I0616 15:22:26.209126 9857 solver.cpp:242] Iteration 79700, loss = 0.671416 +I0616 15:22:26.209168 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.189718 (* 1 = 0.189718 loss) +I0616 15:22:26.209174 9857 solver.cpp:258] Train net output #1: loss_cls = 0.474105 (* 1 = 0.474105 loss) +I0616 15:22:26.209178 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0883726 (* 1 = 0.0883726 loss) +I0616 15:22:26.209182 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0271225 (* 1 = 0.0271225 loss) +I0616 15:22:26.209187 9857 solver.cpp:571] Iteration 79700, lr = 0.0001 +I0616 15:22:37.500221 9857 solver.cpp:242] Iteration 79720, loss = 0.228005 +I0616 15:22:37.500262 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0777274 (* 1 = 0.0777274 loss) +I0616 15:22:37.500268 9857 solver.cpp:258] Train net output #1: loss_cls = 0.129975 (* 1 = 0.129975 loss) +I0616 15:22:37.500272 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0510394 (* 1 = 0.0510394 loss) +I0616 15:22:37.500277 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00636439 (* 1 = 0.00636439 loss) +I0616 15:22:37.500279 9857 solver.cpp:571] Iteration 79720, lr = 0.0001 +I0616 15:22:49.409061 9857 solver.cpp:242] Iteration 79740, loss = 0.842316 +I0616 15:22:49.409102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.420269 (* 1 = 0.420269 loss) +I0616 15:22:49.409108 9857 solver.cpp:258] Train net output #1: loss_cls = 0.360265 (* 1 = 0.360265 loss) +I0616 15:22:49.409112 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0928596 (* 1 = 0.0928596 loss) +I0616 15:22:49.409116 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159837 (* 1 = 0.159837 loss) +I0616 15:22:49.409121 9857 solver.cpp:571] Iteration 79740, lr = 0.0001 +I0616 15:23:00.667297 9857 solver.cpp:242] Iteration 79760, loss = 0.397996 +I0616 15:23:00.667337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0484924 (* 1 = 0.0484924 loss) +I0616 15:23:00.667342 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118288 (* 1 = 0.118288 loss) +I0616 15:23:00.667346 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0110467 (* 1 = 0.0110467 loss) +I0616 15:23:00.667351 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174388 (* 1 = 0.0174388 loss) +I0616 15:23:00.667354 9857 solver.cpp:571] Iteration 79760, lr = 0.0001 +I0616 15:23:12.360800 9857 solver.cpp:242] Iteration 79780, loss = 0.3998 +I0616 15:23:12.360842 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0982562 (* 1 = 0.0982562 loss) +I0616 15:23:12.360847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208062 (* 1 = 0.208062 loss) +I0616 15:23:12.360852 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139234 (* 1 = 0.0139234 loss) +I0616 15:23:12.360855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0263449 (* 1 = 0.0263449 loss) +I0616 15:23:12.360860 9857 solver.cpp:571] Iteration 79780, lr = 0.0001 +speed: 0.599s / iter +I0616 15:23:23.791616 9857 solver.cpp:242] Iteration 79800, loss = 0.611801 +I0616 15:23:23.791657 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0804073 (* 1 = 0.0804073 loss) +I0616 15:23:23.791664 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0794963 (* 1 = 0.0794963 loss) +I0616 15:23:23.791668 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0254772 (* 1 = 0.0254772 loss) +I0616 15:23:23.791671 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00912261 (* 1 = 0.00912261 loss) +I0616 15:23:23.791676 9857 solver.cpp:571] Iteration 79800, lr = 0.0001 +I0616 15:23:35.500107 9857 solver.cpp:242] Iteration 79820, loss = 0.733495 +I0616 15:23:35.500147 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256729 (* 1 = 0.256729 loss) +I0616 15:23:35.500154 9857 solver.cpp:258] Train net output #1: loss_cls = 0.318541 (* 1 = 0.318541 loss) +I0616 15:23:35.500156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.374087 (* 1 = 0.374087 loss) +I0616 15:23:35.500160 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0760527 (* 1 = 0.0760527 loss) +I0616 15:23:35.500164 9857 solver.cpp:571] Iteration 79820, lr = 0.0001 +I0616 15:23:46.905843 9857 solver.cpp:242] Iteration 79840, loss = 0.467432 +I0616 15:23:46.905885 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211298 (* 1 = 0.211298 loss) +I0616 15:23:46.905890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210643 (* 1 = 0.210643 loss) +I0616 15:23:46.905895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0657466 (* 1 = 0.0657466 loss) +I0616 15:23:46.905900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.109692 (* 1 = 0.109692 loss) +I0616 15:23:46.905902 9857 solver.cpp:571] Iteration 79840, lr = 0.0001 +I0616 15:23:58.483304 9857 solver.cpp:242] Iteration 79860, loss = 0.428156 +I0616 15:23:58.483345 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104627 (* 1 = 0.104627 loss) +I0616 15:23:58.483350 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253683 (* 1 = 0.253683 loss) +I0616 15:23:58.483355 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.096629 (* 1 = 0.096629 loss) +I0616 15:23:58.483358 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.141986 (* 1 = 0.141986 loss) +I0616 15:23:58.483362 9857 solver.cpp:571] Iteration 79860, lr = 0.0001 +I0616 15:24:09.753701 9857 solver.cpp:242] Iteration 79880, loss = 0.376597 +I0616 15:24:09.753742 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0582332 (* 1 = 0.0582332 loss) +I0616 15:24:09.753748 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0628275 (* 1 = 0.0628275 loss) +I0616 15:24:09.753752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0144727 (* 1 = 0.0144727 loss) +I0616 15:24:09.753756 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175542 (* 1 = 0.0175542 loss) +I0616 15:24:09.753760 9857 solver.cpp:571] Iteration 79880, lr = 0.0001 +I0616 15:24:21.466034 9857 solver.cpp:242] Iteration 79900, loss = 1.00109 +I0616 15:24:21.466078 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.536272 (* 1 = 0.536272 loss) +I0616 15:24:21.466084 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340784 (* 1 = 0.340784 loss) +I0616 15:24:21.466087 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114259 (* 1 = 0.114259 loss) +I0616 15:24:21.466091 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.158248 (* 1 = 0.158248 loss) +I0616 15:24:21.466095 9857 solver.cpp:571] Iteration 79900, lr = 0.0001 +I0616 15:24:32.931638 9857 solver.cpp:242] Iteration 79920, loss = 0.481456 +I0616 15:24:32.931682 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144971 (* 1 = 0.144971 loss) +I0616 15:24:32.931687 9857 solver.cpp:258] Train net output #1: loss_cls = 0.278917 (* 1 = 0.278917 loss) +I0616 15:24:32.931692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114762 (* 1 = 0.114762 loss) +I0616 15:24:32.931696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0215904 (* 1 = 0.0215904 loss) +I0616 15:24:32.931699 9857 solver.cpp:571] Iteration 79920, lr = 0.0001 +I0616 15:24:44.282045 9857 solver.cpp:242] Iteration 79940, loss = 0.464475 +I0616 15:24:44.282088 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0897825 (* 1 = 0.0897825 loss) +I0616 15:24:44.282094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0798208 (* 1 = 0.0798208 loss) +I0616 15:24:44.282097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0178802 (* 1 = 0.0178802 loss) +I0616 15:24:44.282101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00253241 (* 1 = 0.00253241 loss) +I0616 15:24:44.282105 9857 solver.cpp:571] Iteration 79940, lr = 0.0001 +I0616 15:24:55.693598 9857 solver.cpp:242] Iteration 79960, loss = 0.357321 +I0616 15:24:55.693639 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183985 (* 1 = 0.183985 loss) +I0616 15:24:55.693645 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22368 (* 1 = 0.22368 loss) +I0616 15:24:55.693650 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0522141 (* 1 = 0.0522141 loss) +I0616 15:24:55.693652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.016607 (* 1 = 0.016607 loss) +I0616 15:24:55.693656 9857 solver.cpp:571] Iteration 79960, lr = 0.0001 +I0616 15:25:07.263778 9857 solver.cpp:242] Iteration 79980, loss = 0.31582 +I0616 15:25:07.263819 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0570914 (* 1 = 0.0570914 loss) +I0616 15:25:07.263825 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104283 (* 1 = 0.104283 loss) +I0616 15:25:07.263829 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00707169 (* 1 = 0.00707169 loss) +I0616 15:25:07.263833 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00377407 (* 1 = 0.00377407 loss) +I0616 15:25:07.263838 9857 solver.cpp:571] Iteration 79980, lr = 0.0001 +speed: 0.599s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_80000.caffemodel +I0616 15:25:20.535270 9857 solver.cpp:242] Iteration 80000, loss = 0.773928 +I0616 15:25:20.535313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132196 (* 1 = 0.132196 loss) +I0616 15:25:20.535318 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145398 (* 1 = 0.145398 loss) +I0616 15:25:20.535323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0167787 (* 1 = 0.0167787 loss) +I0616 15:25:20.535326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127073 (* 1 = 0.0127073 loss) +I0616 15:25:20.535331 9857 solver.cpp:571] Iteration 80000, lr = 0.0001 +I0616 15:25:32.016319 9857 solver.cpp:242] Iteration 80020, loss = 0.334255 +I0616 15:25:32.016361 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0868234 (* 1 = 0.0868234 loss) +I0616 15:25:32.016367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178291 (* 1 = 0.178291 loss) +I0616 15:25:32.016371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0599321 (* 1 = 0.0599321 loss) +I0616 15:25:32.016376 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00403762 (* 1 = 0.00403762 loss) +I0616 15:25:32.016379 9857 solver.cpp:571] Iteration 80020, lr = 0.0001 +I0616 15:25:43.676640 9857 solver.cpp:242] Iteration 80040, loss = 0.557634 +I0616 15:25:43.676681 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296794 (* 1 = 0.296794 loss) +I0616 15:25:43.676687 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279726 (* 1 = 0.279726 loss) +I0616 15:25:43.676692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0264629 (* 1 = 0.0264629 loss) +I0616 15:25:43.676694 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319262 (* 1 = 0.0319262 loss) +I0616 15:25:43.676698 9857 solver.cpp:571] Iteration 80040, lr = 0.0001 +I0616 15:25:55.232688 9857 solver.cpp:242] Iteration 80060, loss = 0.254437 +I0616 15:25:55.232729 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0738836 (* 1 = 0.0738836 loss) +I0616 15:25:55.232735 9857 solver.cpp:258] Train net output #1: loss_cls = 0.075251 (* 1 = 0.075251 loss) +I0616 15:25:55.232739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0476352 (* 1 = 0.0476352 loss) +I0616 15:25:55.232743 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00345736 (* 1 = 0.00345736 loss) +I0616 15:25:55.232746 9857 solver.cpp:571] Iteration 80060, lr = 0.0001 +I0616 15:26:06.790060 9857 solver.cpp:242] Iteration 80080, loss = 0.918705 +I0616 15:26:06.790102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21026 (* 1 = 0.21026 loss) +I0616 15:26:06.790108 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120928 (* 1 = 0.120928 loss) +I0616 15:26:06.790112 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0519562 (* 1 = 0.0519562 loss) +I0616 15:26:06.790117 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0224847 (* 1 = 0.0224847 loss) +I0616 15:26:06.790120 9857 solver.cpp:571] Iteration 80080, lr = 0.0001 +I0616 15:26:18.099189 9857 solver.cpp:242] Iteration 80100, loss = 0.701232 +I0616 15:26:18.099231 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22378 (* 1 = 0.22378 loss) +I0616 15:26:18.099236 9857 solver.cpp:258] Train net output #1: loss_cls = 0.305524 (* 1 = 0.305524 loss) +I0616 15:26:18.099239 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158264 (* 1 = 0.158264 loss) +I0616 15:26:18.099243 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.430788 (* 1 = 0.430788 loss) +I0616 15:26:18.099247 9857 solver.cpp:571] Iteration 80100, lr = 0.0001 +I0616 15:26:29.539078 9857 solver.cpp:242] Iteration 80120, loss = 1.33801 +I0616 15:26:29.539119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172246 (* 1 = 0.172246 loss) +I0616 15:26:29.539124 9857 solver.cpp:258] Train net output #1: loss_cls = 0.215283 (* 1 = 0.215283 loss) +I0616 15:26:29.539129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0878515 (* 1 = 0.0878515 loss) +I0616 15:26:29.539134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.34761 (* 1 = 0.34761 loss) +I0616 15:26:29.539136 9857 solver.cpp:571] Iteration 80120, lr = 0.0001 +I0616 15:26:41.302122 9857 solver.cpp:242] Iteration 80140, loss = 0.213575 +I0616 15:26:41.302163 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0619667 (* 1 = 0.0619667 loss) +I0616 15:26:41.302170 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0917864 (* 1 = 0.0917864 loss) +I0616 15:26:41.302173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0311463 (* 1 = 0.0311463 loss) +I0616 15:26:41.302177 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0438504 (* 1 = 0.0438504 loss) +I0616 15:26:41.302182 9857 solver.cpp:571] Iteration 80140, lr = 0.0001 +I0616 15:26:52.763814 9857 solver.cpp:242] Iteration 80160, loss = 0.698384 +I0616 15:26:52.763854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298403 (* 1 = 0.298403 loss) +I0616 15:26:52.763860 9857 solver.cpp:258] Train net output #1: loss_cls = 0.352548 (* 1 = 0.352548 loss) +I0616 15:26:52.763864 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0631863 (* 1 = 0.0631863 loss) +I0616 15:26:52.763869 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0299343 (* 1 = 0.0299343 loss) +I0616 15:26:52.763871 9857 solver.cpp:571] Iteration 80160, lr = 0.0001 +I0616 15:27:04.108834 9857 solver.cpp:242] Iteration 80180, loss = 0.78316 +I0616 15:27:04.108875 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.398255 (* 1 = 0.398255 loss) +I0616 15:27:04.108881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.466074 (* 1 = 0.466074 loss) +I0616 15:27:04.108886 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.21908 (* 1 = 0.21908 loss) +I0616 15:27:04.108889 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.150771 (* 1 = 0.150771 loss) +I0616 15:27:04.108892 9857 solver.cpp:571] Iteration 80180, lr = 0.0001 +speed: 0.599s / iter +I0616 15:27:15.643667 9857 solver.cpp:242] Iteration 80200, loss = 0.236258 +I0616 15:27:15.643709 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0873643 (* 1 = 0.0873643 loss) +I0616 15:27:15.643714 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10537 (* 1 = 0.10537 loss) +I0616 15:27:15.643719 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0140425 (* 1 = 0.0140425 loss) +I0616 15:27:15.643723 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0451705 (* 1 = 0.0451705 loss) +I0616 15:27:15.643726 9857 solver.cpp:571] Iteration 80200, lr = 0.0001 +I0616 15:27:27.261108 9857 solver.cpp:242] Iteration 80220, loss = 0.3616 +I0616 15:27:27.261150 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169485 (* 1 = 0.169485 loss) +I0616 15:27:27.261155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295488 (* 1 = 0.295488 loss) +I0616 15:27:27.261160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0156001 (* 1 = 0.0156001 loss) +I0616 15:27:27.261164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0483538 (* 1 = 0.0483538 loss) +I0616 15:27:27.261168 9857 solver.cpp:571] Iteration 80220, lr = 0.0001 +I0616 15:27:38.855409 9857 solver.cpp:242] Iteration 80240, loss = 0.254379 +I0616 15:27:38.855451 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123891 (* 1 = 0.123891 loss) +I0616 15:27:38.855456 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154338 (* 1 = 0.154338 loss) +I0616 15:27:38.855461 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00835148 (* 1 = 0.00835148 loss) +I0616 15:27:38.855464 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00538163 (* 1 = 0.00538163 loss) +I0616 15:27:38.855482 9857 solver.cpp:571] Iteration 80240, lr = 0.0001 +I0616 15:27:50.341847 9857 solver.cpp:242] Iteration 80260, loss = 0.418487 +I0616 15:27:50.341889 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117547 (* 1 = 0.117547 loss) +I0616 15:27:50.341894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0924842 (* 1 = 0.0924842 loss) +I0616 15:27:50.341899 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0384872 (* 1 = 0.0384872 loss) +I0616 15:27:50.341902 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0230919 (* 1 = 0.0230919 loss) +I0616 15:27:50.341907 9857 solver.cpp:571] Iteration 80260, lr = 0.0001 +I0616 15:28:01.780655 9857 solver.cpp:242] Iteration 80280, loss = 0.308385 +I0616 15:28:01.780697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163937 (* 1 = 0.163937 loss) +I0616 15:28:01.780702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203525 (* 1 = 0.203525 loss) +I0616 15:28:01.780707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0297171 (* 1 = 0.0297171 loss) +I0616 15:28:01.780710 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253665 (* 1 = 0.0253665 loss) +I0616 15:28:01.780714 9857 solver.cpp:571] Iteration 80280, lr = 0.0001 +I0616 15:28:12.924233 9857 solver.cpp:242] Iteration 80300, loss = 0.609324 +I0616 15:28:12.924275 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287456 (* 1 = 0.287456 loss) +I0616 15:28:12.924280 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248128 (* 1 = 0.248128 loss) +I0616 15:28:12.924284 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0273565 (* 1 = 0.0273565 loss) +I0616 15:28:12.924288 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0185615 (* 1 = 0.0185615 loss) +I0616 15:28:12.924291 9857 solver.cpp:571] Iteration 80300, lr = 0.0001 +I0616 15:28:24.558229 9857 solver.cpp:242] Iteration 80320, loss = 0.355334 +I0616 15:28:24.558270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0717065 (* 1 = 0.0717065 loss) +I0616 15:28:24.558276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17106 (* 1 = 0.17106 loss) +I0616 15:28:24.558281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0690501 (* 1 = 0.0690501 loss) +I0616 15:28:24.558284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0457656 (* 1 = 0.0457656 loss) +I0616 15:28:24.558289 9857 solver.cpp:571] Iteration 80320, lr = 0.0001 +I0616 15:28:35.979856 9857 solver.cpp:242] Iteration 80340, loss = 0.265874 +I0616 15:28:35.979894 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0811592 (* 1 = 0.0811592 loss) +I0616 15:28:35.979900 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130391 (* 1 = 0.130391 loss) +I0616 15:28:35.979904 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00426664 (* 1 = 0.00426664 loss) +I0616 15:28:35.979907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134842 (* 1 = 0.0134842 loss) +I0616 15:28:35.979912 9857 solver.cpp:571] Iteration 80340, lr = 0.0001 +I0616 15:28:47.631799 9857 solver.cpp:242] Iteration 80360, loss = 0.243083 +I0616 15:28:47.631841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0564275 (* 1 = 0.0564275 loss) +I0616 15:28:47.631847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0818505 (* 1 = 0.0818505 loss) +I0616 15:28:47.631851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0158659 (* 1 = 0.0158659 loss) +I0616 15:28:47.631855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00242301 (* 1 = 0.00242301 loss) +I0616 15:28:47.631858 9857 solver.cpp:571] Iteration 80360, lr = 0.0001 +I0616 15:28:59.224521 9857 solver.cpp:242] Iteration 80380, loss = 0.410129 +I0616 15:28:59.224565 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225764 (* 1 = 0.225764 loss) +I0616 15:28:59.224570 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301906 (* 1 = 0.301906 loss) +I0616 15:28:59.224575 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374528 (* 1 = 0.0374528 loss) +I0616 15:28:59.224578 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.02757 (* 1 = 0.02757 loss) +I0616 15:28:59.224581 9857 solver.cpp:571] Iteration 80380, lr = 0.0001 +speed: 0.599s / iter +I0616 15:29:10.663084 9857 solver.cpp:242] Iteration 80400, loss = 0.687377 +I0616 15:29:10.663125 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0822305 (* 1 = 0.0822305 loss) +I0616 15:29:10.663130 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145923 (* 1 = 0.145923 loss) +I0616 15:29:10.663136 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0430528 (* 1 = 0.0430528 loss) +I0616 15:29:10.663139 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0144531 (* 1 = 0.0144531 loss) +I0616 15:29:10.663142 9857 solver.cpp:571] Iteration 80400, lr = 0.0001 +I0616 15:29:21.967957 9857 solver.cpp:242] Iteration 80420, loss = 0.473014 +I0616 15:29:21.967998 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.282656 (* 1 = 0.282656 loss) +I0616 15:29:21.968003 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304402 (* 1 = 0.304402 loss) +I0616 15:29:21.968008 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0791097 (* 1 = 0.0791097 loss) +I0616 15:29:21.968011 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323245 (* 1 = 0.0323245 loss) +I0616 15:29:21.968015 9857 solver.cpp:571] Iteration 80420, lr = 0.0001 +I0616 15:29:33.387892 9857 solver.cpp:242] Iteration 80440, loss = 1.0267 +I0616 15:29:33.387933 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315869 (* 1 = 0.315869 loss) +I0616 15:29:33.387938 9857 solver.cpp:258] Train net output #1: loss_cls = 1.03904 (* 1 = 1.03904 loss) +I0616 15:29:33.387943 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0470251 (* 1 = 0.0470251 loss) +I0616 15:29:33.387946 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0384503 (* 1 = 0.0384503 loss) +I0616 15:29:33.387950 9857 solver.cpp:571] Iteration 80440, lr = 0.0001 +I0616 15:29:44.867054 9857 solver.cpp:242] Iteration 80460, loss = 0.506194 +I0616 15:29:44.867097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266504 (* 1 = 0.266504 loss) +I0616 15:29:44.867103 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29878 (* 1 = 0.29878 loss) +I0616 15:29:44.867107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0917874 (* 1 = 0.0917874 loss) +I0616 15:29:44.867111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0150092 (* 1 = 0.0150092 loss) +I0616 15:29:44.867115 9857 solver.cpp:571] Iteration 80460, lr = 0.0001 +I0616 15:29:56.661613 9857 solver.cpp:242] Iteration 80480, loss = 0.554955 +I0616 15:29:56.661654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166961 (* 1 = 0.166961 loss) +I0616 15:29:56.661660 9857 solver.cpp:258] Train net output #1: loss_cls = 0.2837 (* 1 = 0.2837 loss) +I0616 15:29:56.661664 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0796024 (* 1 = 0.0796024 loss) +I0616 15:29:56.661669 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00933324 (* 1 = 0.00933324 loss) +I0616 15:29:56.661672 9857 solver.cpp:571] Iteration 80480, lr = 0.0001 +I0616 15:30:08.083392 9857 solver.cpp:242] Iteration 80500, loss = 0.388908 +I0616 15:30:08.083434 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244962 (* 1 = 0.244962 loss) +I0616 15:30:08.083439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24427 (* 1 = 0.24427 loss) +I0616 15:30:08.083444 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0461861 (* 1 = 0.0461861 loss) +I0616 15:30:08.083448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01865 (* 1 = 0.01865 loss) +I0616 15:30:08.083451 9857 solver.cpp:571] Iteration 80500, lr = 0.0001 +I0616 15:30:19.826382 9857 solver.cpp:242] Iteration 80520, loss = 0.447622 +I0616 15:30:19.826426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137808 (* 1 = 0.137808 loss) +I0616 15:30:19.826431 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154105 (* 1 = 0.154105 loss) +I0616 15:30:19.826436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.242211 (* 1 = 0.242211 loss) +I0616 15:30:19.826439 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00604925 (* 1 = 0.00604925 loss) +I0616 15:30:19.826442 9857 solver.cpp:571] Iteration 80520, lr = 0.0001 +I0616 15:30:31.322134 9857 solver.cpp:242] Iteration 80540, loss = 0.468366 +I0616 15:30:31.322176 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160039 (* 1 = 0.160039 loss) +I0616 15:30:31.322182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178529 (* 1 = 0.178529 loss) +I0616 15:30:31.322186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109051 (* 1 = 0.109051 loss) +I0616 15:30:31.322190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207374 (* 1 = 0.0207374 loss) +I0616 15:30:31.322194 9857 solver.cpp:571] Iteration 80540, lr = 0.0001 +I0616 15:30:42.744554 9857 solver.cpp:242] Iteration 80560, loss = 0.305761 +I0616 15:30:42.744596 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.083621 (* 1 = 0.083621 loss) +I0616 15:30:42.744601 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108012 (* 1 = 0.108012 loss) +I0616 15:30:42.744606 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00199739 (* 1 = 0.00199739 loss) +I0616 15:30:42.744609 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0476348 (* 1 = 0.0476348 loss) +I0616 15:30:42.744612 9857 solver.cpp:571] Iteration 80560, lr = 0.0001 +I0616 15:30:54.131330 9857 solver.cpp:242] Iteration 80580, loss = 0.384366 +I0616 15:30:54.131372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0729239 (* 1 = 0.0729239 loss) +I0616 15:30:54.131377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110307 (* 1 = 0.110307 loss) +I0616 15:30:54.131381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0222346 (* 1 = 0.0222346 loss) +I0616 15:30:54.131386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0211421 (* 1 = 0.0211421 loss) +I0616 15:30:54.131389 9857 solver.cpp:571] Iteration 80580, lr = 0.0001 +speed: 0.599s / iter +I0616 15:31:05.929930 9857 solver.cpp:242] Iteration 80600, loss = 0.728336 +I0616 15:31:05.929970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214099 (* 1 = 0.214099 loss) +I0616 15:31:05.929975 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242575 (* 1 = 0.242575 loss) +I0616 15:31:05.929980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.044081 (* 1 = 0.044081 loss) +I0616 15:31:05.929983 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0251584 (* 1 = 0.0251584 loss) +I0616 15:31:05.929987 9857 solver.cpp:571] Iteration 80600, lr = 0.0001 +I0616 15:31:17.593525 9857 solver.cpp:242] Iteration 80620, loss = 0.281684 +I0616 15:31:17.593569 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0920368 (* 1 = 0.0920368 loss) +I0616 15:31:17.593575 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148471 (* 1 = 0.148471 loss) +I0616 15:31:17.593578 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.050028 (* 1 = 0.050028 loss) +I0616 15:31:17.593582 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0212351 (* 1 = 0.0212351 loss) +I0616 15:31:17.593585 9857 solver.cpp:571] Iteration 80620, lr = 0.0001 +I0616 15:31:28.963485 9857 solver.cpp:242] Iteration 80640, loss = 0.354399 +I0616 15:31:28.963527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0832624 (* 1 = 0.0832624 loss) +I0616 15:31:28.963532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144578 (* 1 = 0.144578 loss) +I0616 15:31:28.963537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0102319 (* 1 = 0.0102319 loss) +I0616 15:31:28.963541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117455 (* 1 = 0.0117455 loss) +I0616 15:31:28.963544 9857 solver.cpp:571] Iteration 80640, lr = 0.0001 +I0616 15:31:40.563272 9857 solver.cpp:242] Iteration 80660, loss = 0.485121 +I0616 15:31:40.563313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.078768 (* 1 = 0.078768 loss) +I0616 15:31:40.563319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200638 (* 1 = 0.200638 loss) +I0616 15:31:40.563323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00295421 (* 1 = 0.00295421 loss) +I0616 15:31:40.563328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0179535 (* 1 = 0.0179535 loss) +I0616 15:31:40.563331 9857 solver.cpp:571] Iteration 80660, lr = 0.0001 +I0616 15:31:51.980520 9857 solver.cpp:242] Iteration 80680, loss = 0.457172 +I0616 15:31:51.980562 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138767 (* 1 = 0.138767 loss) +I0616 15:31:51.980568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178775 (* 1 = 0.178775 loss) +I0616 15:31:51.980573 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0236959 (* 1 = 0.0236959 loss) +I0616 15:31:51.980576 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223696 (* 1 = 0.0223696 loss) +I0616 15:31:51.980581 9857 solver.cpp:571] Iteration 80680, lr = 0.0001 +I0616 15:32:03.513712 9857 solver.cpp:242] Iteration 80700, loss = 0.776033 +I0616 15:32:03.513756 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345736 (* 1 = 0.345736 loss) +I0616 15:32:03.513761 9857 solver.cpp:258] Train net output #1: loss_cls = 0.555246 (* 1 = 0.555246 loss) +I0616 15:32:03.513766 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.17463 (* 1 = 0.17463 loss) +I0616 15:32:03.513769 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.201341 (* 1 = 0.201341 loss) +I0616 15:32:03.513772 9857 solver.cpp:571] Iteration 80700, lr = 0.0001 +I0616 15:32:15.018121 9857 solver.cpp:242] Iteration 80720, loss = 0.601063 +I0616 15:32:15.018160 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164892 (* 1 = 0.164892 loss) +I0616 15:32:15.018167 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126056 (* 1 = 0.126056 loss) +I0616 15:32:15.018172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0713769 (* 1 = 0.0713769 loss) +I0616 15:32:15.018175 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0452133 (* 1 = 0.0452133 loss) +I0616 15:32:15.018178 9857 solver.cpp:571] Iteration 80720, lr = 0.0001 +I0616 15:32:26.530020 9857 solver.cpp:242] Iteration 80740, loss = 0.308755 +I0616 15:32:26.530061 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0865031 (* 1 = 0.0865031 loss) +I0616 15:32:26.530067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118207 (* 1 = 0.118207 loss) +I0616 15:32:26.530071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0818087 (* 1 = 0.0818087 loss) +I0616 15:32:26.530074 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0228832 (* 1 = 0.0228832 loss) +I0616 15:32:26.530078 9857 solver.cpp:571] Iteration 80740, lr = 0.0001 +I0616 15:32:38.085367 9857 solver.cpp:242] Iteration 80760, loss = 0.564595 +I0616 15:32:38.085409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166416 (* 1 = 0.166416 loss) +I0616 15:32:38.085414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160689 (* 1 = 0.160689 loss) +I0616 15:32:38.085418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0488458 (* 1 = 0.0488458 loss) +I0616 15:32:38.085422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182198 (* 1 = 0.0182198 loss) +I0616 15:32:38.085425 9857 solver.cpp:571] Iteration 80760, lr = 0.0001 +I0616 15:32:49.553279 9857 solver.cpp:242] Iteration 80780, loss = 0.717633 +I0616 15:32:49.553321 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284532 (* 1 = 0.284532 loss) +I0616 15:32:49.553328 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160488 (* 1 = 0.160488 loss) +I0616 15:32:49.553331 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0463497 (* 1 = 0.0463497 loss) +I0616 15:32:49.553335 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0544485 (* 1 = 0.0544485 loss) +I0616 15:32:49.553339 9857 solver.cpp:571] Iteration 80780, lr = 0.0001 +speed: 0.599s / iter +I0616 15:33:01.185261 9857 solver.cpp:242] Iteration 80800, loss = 0.7274 +I0616 15:33:01.185304 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123443 (* 1 = 0.123443 loss) +I0616 15:33:01.185325 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17192 (* 1 = 0.17192 loss) +I0616 15:33:01.185329 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0376968 (* 1 = 0.0376968 loss) +I0616 15:33:01.185333 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0064231 (* 1 = 0.0064231 loss) +I0616 15:33:01.185336 9857 solver.cpp:571] Iteration 80800, lr = 0.0001 +I0616 15:33:12.739531 9857 solver.cpp:242] Iteration 80820, loss = 0.209928 +I0616 15:33:12.739573 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0784945 (* 1 = 0.0784945 loss) +I0616 15:33:12.739578 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156504 (* 1 = 0.156504 loss) +I0616 15:33:12.739583 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0238408 (* 1 = 0.0238408 loss) +I0616 15:33:12.739585 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136392 (* 1 = 0.0136392 loss) +I0616 15:33:12.739589 9857 solver.cpp:571] Iteration 80820, lr = 0.0001 +I0616 15:33:24.335067 9857 solver.cpp:242] Iteration 80840, loss = 0.340832 +I0616 15:33:24.335111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16699 (* 1 = 0.16699 loss) +I0616 15:33:24.335116 9857 solver.cpp:258] Train net output #1: loss_cls = 0.237572 (* 1 = 0.237572 loss) +I0616 15:33:24.335121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0063278 (* 1 = 0.0063278 loss) +I0616 15:33:24.335125 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252249 (* 1 = 0.0252249 loss) +I0616 15:33:24.335129 9857 solver.cpp:571] Iteration 80840, lr = 0.0001 +I0616 15:33:35.940654 9857 solver.cpp:242] Iteration 80860, loss = 0.680788 +I0616 15:33:35.940696 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0613897 (* 1 = 0.0613897 loss) +I0616 15:33:35.940701 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108116 (* 1 = 0.108116 loss) +I0616 15:33:35.940706 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0336578 (* 1 = 0.0336578 loss) +I0616 15:33:35.940709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00199299 (* 1 = 0.00199299 loss) +I0616 15:33:35.940714 9857 solver.cpp:571] Iteration 80860, lr = 0.0001 +I0616 15:33:47.313391 9857 solver.cpp:242] Iteration 80880, loss = 0.264583 +I0616 15:33:47.313432 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136738 (* 1 = 0.136738 loss) +I0616 15:33:47.313438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118939 (* 1 = 0.118939 loss) +I0616 15:33:47.313442 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0180959 (* 1 = 0.0180959 loss) +I0616 15:33:47.313446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0171137 (* 1 = 0.0171137 loss) +I0616 15:33:47.313449 9857 solver.cpp:571] Iteration 80880, lr = 0.0001 +I0616 15:33:58.810205 9857 solver.cpp:242] Iteration 80900, loss = 0.44176 +I0616 15:33:58.810248 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.169418 (* 1 = 0.169418 loss) +I0616 15:33:58.810255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.24327 (* 1 = 0.24327 loss) +I0616 15:33:58.810258 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058769 (* 1 = 0.058769 loss) +I0616 15:33:58.810262 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.139185 (* 1 = 0.139185 loss) +I0616 15:33:58.810266 9857 solver.cpp:571] Iteration 80900, lr = 0.0001 +I0616 15:34:10.371114 9857 solver.cpp:242] Iteration 80920, loss = 0.3163 +I0616 15:34:10.371157 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0528238 (* 1 = 0.0528238 loss) +I0616 15:34:10.371162 9857 solver.cpp:258] Train net output #1: loss_cls = 0.080714 (* 1 = 0.080714 loss) +I0616 15:34:10.371167 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00693894 (* 1 = 0.00693894 loss) +I0616 15:34:10.371170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00409407 (* 1 = 0.00409407 loss) +I0616 15:34:10.371175 9857 solver.cpp:571] Iteration 80920, lr = 0.0001 +I0616 15:34:21.655016 9857 solver.cpp:242] Iteration 80940, loss = 0.607454 +I0616 15:34:21.655059 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0815937 (* 1 = 0.0815937 loss) +I0616 15:34:21.655066 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0859481 (* 1 = 0.0859481 loss) +I0616 15:34:21.655069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.196278 (* 1 = 0.196278 loss) +I0616 15:34:21.655072 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0382807 (* 1 = 0.0382807 loss) +I0616 15:34:21.655076 9857 solver.cpp:571] Iteration 80940, lr = 0.0001 +I0616 15:34:33.328268 9857 solver.cpp:242] Iteration 80960, loss = 0.504417 +I0616 15:34:33.328308 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725262 (* 1 = 0.0725262 loss) +I0616 15:34:33.328315 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112746 (* 1 = 0.112746 loss) +I0616 15:34:33.328318 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0418213 (* 1 = 0.0418213 loss) +I0616 15:34:33.328321 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0116085 (* 1 = 0.0116085 loss) +I0616 15:34:33.328325 9857 solver.cpp:571] Iteration 80960, lr = 0.0001 +I0616 15:34:44.909250 9857 solver.cpp:242] Iteration 80980, loss = 0.503961 +I0616 15:34:44.909291 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0963298 (* 1 = 0.0963298 loss) +I0616 15:34:44.909296 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288931 (* 1 = 0.288931 loss) +I0616 15:34:44.909301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.131788 (* 1 = 0.131788 loss) +I0616 15:34:44.909304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198828 (* 1 = 0.0198828 loss) +I0616 15:34:44.909307 9857 solver.cpp:571] Iteration 80980, lr = 0.0001 +speed: 0.599s / iter +I0616 15:34:56.438570 9857 solver.cpp:242] Iteration 81000, loss = 0.768403 +I0616 15:34:56.438611 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252641 (* 1 = 0.252641 loss) +I0616 15:34:56.438617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.427183 (* 1 = 0.427183 loss) +I0616 15:34:56.438621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246247 (* 1 = 0.246247 loss) +I0616 15:34:56.438626 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.466379 (* 1 = 0.466379 loss) +I0616 15:34:56.438628 9857 solver.cpp:571] Iteration 81000, lr = 0.0001 +I0616 15:35:07.673756 9857 solver.cpp:242] Iteration 81020, loss = 0.838159 +I0616 15:35:07.673799 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0566842 (* 1 = 0.0566842 loss) +I0616 15:35:07.673804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105604 (* 1 = 0.105604 loss) +I0616 15:35:07.673809 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00386411 (* 1 = 0.00386411 loss) +I0616 15:35:07.673812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00976383 (* 1 = 0.00976383 loss) +I0616 15:35:07.673815 9857 solver.cpp:571] Iteration 81020, lr = 0.0001 +I0616 15:35:19.071871 9857 solver.cpp:242] Iteration 81040, loss = 2.02573 +I0616 15:35:19.071912 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177343 (* 1 = 0.177343 loss) +I0616 15:35:19.071916 9857 solver.cpp:258] Train net output #1: loss_cls = 0.415195 (* 1 = 0.415195 loss) +I0616 15:35:19.071920 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.512365 (* 1 = 0.512365 loss) +I0616 15:35:19.071924 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 1.90014 (* 1 = 1.90014 loss) +I0616 15:35:19.071929 9857 solver.cpp:571] Iteration 81040, lr = 0.0001 +I0616 15:35:30.837873 9857 solver.cpp:242] Iteration 81060, loss = 0.392157 +I0616 15:35:30.837915 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222684 (* 1 = 0.222684 loss) +I0616 15:35:30.837920 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180443 (* 1 = 0.180443 loss) +I0616 15:35:30.837925 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0576205 (* 1 = 0.0576205 loss) +I0616 15:35:30.837929 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0724656 (* 1 = 0.0724656 loss) +I0616 15:35:30.837932 9857 solver.cpp:571] Iteration 81060, lr = 0.0001 +I0616 15:35:41.962656 9857 solver.cpp:242] Iteration 81080, loss = 0.868842 +I0616 15:35:41.962698 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.250238 (* 1 = 0.250238 loss) +I0616 15:35:41.962704 9857 solver.cpp:258] Train net output #1: loss_cls = 0.363237 (* 1 = 0.363237 loss) +I0616 15:35:41.962709 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0901393 (* 1 = 0.0901393 loss) +I0616 15:35:41.962713 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.088661 (* 1 = 0.088661 loss) +I0616 15:35:41.962716 9857 solver.cpp:571] Iteration 81080, lr = 0.0001 +I0616 15:35:53.515846 9857 solver.cpp:242] Iteration 81100, loss = 0.244001 +I0616 15:35:53.515887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.051678 (* 1 = 0.051678 loss) +I0616 15:35:53.515892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187898 (* 1 = 0.187898 loss) +I0616 15:35:53.515897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0279344 (* 1 = 0.0279344 loss) +I0616 15:35:53.515900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157047 (* 1 = 0.0157047 loss) +I0616 15:35:53.515904 9857 solver.cpp:571] Iteration 81100, lr = 0.0001 +I0616 15:36:05.082942 9857 solver.cpp:242] Iteration 81120, loss = 0.842802 +I0616 15:36:05.082981 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174287 (* 1 = 0.174287 loss) +I0616 15:36:05.082988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242821 (* 1 = 0.242821 loss) +I0616 15:36:05.082991 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0912398 (* 1 = 0.0912398 loss) +I0616 15:36:05.082995 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.157466 (* 1 = 0.157466 loss) +I0616 15:36:05.083000 9857 solver.cpp:571] Iteration 81120, lr = 0.0001 +I0616 15:36:16.562882 9857 solver.cpp:242] Iteration 81140, loss = 0.525428 +I0616 15:36:16.562925 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.156387 (* 1 = 0.156387 loss) +I0616 15:36:16.562932 9857 solver.cpp:258] Train net output #1: loss_cls = 0.535836 (* 1 = 0.535836 loss) +I0616 15:36:16.562935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146723 (* 1 = 0.146723 loss) +I0616 15:36:16.562939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0792181 (* 1 = 0.0792181 loss) +I0616 15:36:16.562942 9857 solver.cpp:571] Iteration 81140, lr = 0.0001 +I0616 15:36:27.877167 9857 solver.cpp:242] Iteration 81160, loss = 0.480997 +I0616 15:36:27.877209 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20739 (* 1 = 0.20739 loss) +I0616 15:36:27.877215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139848 (* 1 = 0.139848 loss) +I0616 15:36:27.877219 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0786364 (* 1 = 0.0786364 loss) +I0616 15:36:27.877223 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00443761 (* 1 = 0.00443761 loss) +I0616 15:36:27.877226 9857 solver.cpp:571] Iteration 81160, lr = 0.0001 +I0616 15:36:39.536012 9857 solver.cpp:242] Iteration 81180, loss = 0.728904 +I0616 15:36:39.536054 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164476 (* 1 = 0.164476 loss) +I0616 15:36:39.536059 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371986 (* 1 = 0.371986 loss) +I0616 15:36:39.536063 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0598401 (* 1 = 0.0598401 loss) +I0616 15:36:39.536067 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234455 (* 1 = 0.0234455 loss) +I0616 15:36:39.536072 9857 solver.cpp:571] Iteration 81180, lr = 0.0001 +speed: 0.599s / iter +I0616 15:36:51.130277 9857 solver.cpp:242] Iteration 81200, loss = 0.577491 +I0616 15:36:51.130319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345975 (* 1 = 0.345975 loss) +I0616 15:36:51.130326 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257326 (* 1 = 0.257326 loss) +I0616 15:36:51.130329 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.170387 (* 1 = 0.170387 loss) +I0616 15:36:51.130332 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.157652 (* 1 = 0.157652 loss) +I0616 15:36:51.130336 9857 solver.cpp:571] Iteration 81200, lr = 0.0001 +I0616 15:37:02.638042 9857 solver.cpp:242] Iteration 81220, loss = 0.397513 +I0616 15:37:02.638083 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0724391 (* 1 = 0.0724391 loss) +I0616 15:37:02.638089 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121917 (* 1 = 0.121917 loss) +I0616 15:37:02.638093 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0282156 (* 1 = 0.0282156 loss) +I0616 15:37:02.638098 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0352672 (* 1 = 0.0352672 loss) +I0616 15:37:02.638100 9857 solver.cpp:571] Iteration 81220, lr = 0.0001 +I0616 15:37:14.200070 9857 solver.cpp:242] Iteration 81240, loss = 0.540995 +I0616 15:37:14.200112 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105134 (* 1 = 0.105134 loss) +I0616 15:37:14.200119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177441 (* 1 = 0.177441 loss) +I0616 15:37:14.200122 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126219 (* 1 = 0.126219 loss) +I0616 15:37:14.200126 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00476639 (* 1 = 0.00476639 loss) +I0616 15:37:14.200131 9857 solver.cpp:571] Iteration 81240, lr = 0.0001 +I0616 15:37:25.793396 9857 solver.cpp:242] Iteration 81260, loss = 0.50536 +I0616 15:37:25.793438 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162068 (* 1 = 0.162068 loss) +I0616 15:37:25.793444 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438235 (* 1 = 0.438235 loss) +I0616 15:37:25.793449 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114495 (* 1 = 0.114495 loss) +I0616 15:37:25.793453 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0175782 (* 1 = 0.0175782 loss) +I0616 15:37:25.793457 9857 solver.cpp:571] Iteration 81260, lr = 0.0001 +I0616 15:37:37.328657 9857 solver.cpp:242] Iteration 81280, loss = 0.676347 +I0616 15:37:37.328699 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0339007 (* 1 = 0.0339007 loss) +I0616 15:37:37.328704 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0790174 (* 1 = 0.0790174 loss) +I0616 15:37:37.328708 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00522415 (* 1 = 0.00522415 loss) +I0616 15:37:37.328712 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00134372 (* 1 = 0.00134372 loss) +I0616 15:37:37.328716 9857 solver.cpp:571] Iteration 81280, lr = 0.0001 +I0616 15:37:48.900918 9857 solver.cpp:242] Iteration 81300, loss = 0.182665 +I0616 15:37:48.900960 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0505682 (* 1 = 0.0505682 loss) +I0616 15:37:48.900966 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0777632 (* 1 = 0.0777632 loss) +I0616 15:37:48.900970 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0180309 (* 1 = 0.0180309 loss) +I0616 15:37:48.900974 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0486763 (* 1 = 0.0486763 loss) +I0616 15:37:48.900977 9857 solver.cpp:571] Iteration 81300, lr = 0.0001 +I0616 15:38:00.388521 9857 solver.cpp:242] Iteration 81320, loss = 0.275784 +I0616 15:38:00.388564 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0667026 (* 1 = 0.0667026 loss) +I0616 15:38:00.388569 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128069 (* 1 = 0.128069 loss) +I0616 15:38:00.388574 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0241357 (* 1 = 0.0241357 loss) +I0616 15:38:00.388577 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160303 (* 1 = 0.0160303 loss) +I0616 15:38:00.388581 9857 solver.cpp:571] Iteration 81320, lr = 0.0001 +I0616 15:38:12.004367 9857 solver.cpp:242] Iteration 81340, loss = 0.341353 +I0616 15:38:12.004410 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112089 (* 1 = 0.112089 loss) +I0616 15:38:12.004415 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249132 (* 1 = 0.249132 loss) +I0616 15:38:12.004420 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0207008 (* 1 = 0.0207008 loss) +I0616 15:38:12.004422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00898719 (* 1 = 0.00898719 loss) +I0616 15:38:12.004426 9857 solver.cpp:571] Iteration 81340, lr = 0.0001 +I0616 15:38:23.430752 9857 solver.cpp:242] Iteration 81360, loss = 0.317904 +I0616 15:38:23.430795 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0599662 (* 1 = 0.0599662 loss) +I0616 15:38:23.430801 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156337 (* 1 = 0.156337 loss) +I0616 15:38:23.430805 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0135939 (* 1 = 0.0135939 loss) +I0616 15:38:23.430809 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216122 (* 1 = 0.0216122 loss) +I0616 15:38:23.430812 9857 solver.cpp:571] Iteration 81360, lr = 0.0001 +I0616 15:38:34.835561 9857 solver.cpp:242] Iteration 81380, loss = 0.467566 +I0616 15:38:34.835602 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.073267 (* 1 = 0.073267 loss) +I0616 15:38:34.835608 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150214 (* 1 = 0.150214 loss) +I0616 15:38:34.835611 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374214 (* 1 = 0.0374214 loss) +I0616 15:38:34.835615 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00560084 (* 1 = 0.00560084 loss) +I0616 15:38:34.835619 9857 solver.cpp:571] Iteration 81380, lr = 0.0001 +speed: 0.599s / iter +I0616 15:38:46.415385 9857 solver.cpp:242] Iteration 81400, loss = 0.485662 +I0616 15:38:46.415426 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263407 (* 1 = 0.263407 loss) +I0616 15:38:46.415432 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206678 (* 1 = 0.206678 loss) +I0616 15:38:46.415436 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0558947 (* 1 = 0.0558947 loss) +I0616 15:38:46.415439 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107225 (* 1 = 0.107225 loss) +I0616 15:38:46.415443 9857 solver.cpp:571] Iteration 81400, lr = 0.0001 +I0616 15:38:57.959869 9857 solver.cpp:242] Iteration 81420, loss = 0.648702 +I0616 15:38:57.959897 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159504 (* 1 = 0.159504 loss) +I0616 15:38:57.959903 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265613 (* 1 = 0.265613 loss) +I0616 15:38:57.959908 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0187182 (* 1 = 0.0187182 loss) +I0616 15:38:57.959911 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0465849 (* 1 = 0.0465849 loss) +I0616 15:38:57.959914 9857 solver.cpp:571] Iteration 81420, lr = 0.0001 +I0616 15:39:09.572444 9857 solver.cpp:242] Iteration 81440, loss = 0.708709 +I0616 15:39:09.572486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193488 (* 1 = 0.193488 loss) +I0616 15:39:09.572491 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413761 (* 1 = 0.413761 loss) +I0616 15:39:09.572495 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.154308 (* 1 = 0.154308 loss) +I0616 15:39:09.572499 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.135977 (* 1 = 0.135977 loss) +I0616 15:39:09.572504 9857 solver.cpp:571] Iteration 81440, lr = 0.0001 +I0616 15:39:21.205344 9857 solver.cpp:242] Iteration 81460, loss = 0.51706 +I0616 15:39:21.205386 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242661 (* 1 = 0.242661 loss) +I0616 15:39:21.205391 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255448 (* 1 = 0.255448 loss) +I0616 15:39:21.205396 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0447588 (* 1 = 0.0447588 loss) +I0616 15:39:21.205399 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159952 (* 1 = 0.159952 loss) +I0616 15:39:21.205404 9857 solver.cpp:571] Iteration 81460, lr = 0.0001 +I0616 15:39:32.824532 9857 solver.cpp:242] Iteration 81480, loss = 0.565145 +I0616 15:39:32.824574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0425423 (* 1 = 0.0425423 loss) +I0616 15:39:32.824579 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118299 (* 1 = 0.118299 loss) +I0616 15:39:32.824584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0635759 (* 1 = 0.0635759 loss) +I0616 15:39:32.824589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00988485 (* 1 = 0.00988485 loss) +I0616 15:39:32.824591 9857 solver.cpp:571] Iteration 81480, lr = 0.0001 +I0616 15:39:44.492693 9857 solver.cpp:242] Iteration 81500, loss = 0.474702 +I0616 15:39:44.492735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161375 (* 1 = 0.161375 loss) +I0616 15:39:44.492740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254204 (* 1 = 0.254204 loss) +I0616 15:39:44.492744 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0860831 (* 1 = 0.0860831 loss) +I0616 15:39:44.492748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0600938 (* 1 = 0.0600938 loss) +I0616 15:39:44.492751 9857 solver.cpp:571] Iteration 81500, lr = 0.0001 +I0616 15:39:56.074033 9857 solver.cpp:242] Iteration 81520, loss = 0.421192 +I0616 15:39:56.074074 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0920258 (* 1 = 0.0920258 loss) +I0616 15:39:56.074080 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211913 (* 1 = 0.211913 loss) +I0616 15:39:56.074084 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0117286 (* 1 = 0.0117286 loss) +I0616 15:39:56.074089 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308026 (* 1 = 0.0308026 loss) +I0616 15:39:56.074092 9857 solver.cpp:571] Iteration 81520, lr = 0.0001 +I0616 15:40:07.698112 9857 solver.cpp:242] Iteration 81540, loss = 0.207222 +I0616 15:40:07.698154 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0461334 (* 1 = 0.0461334 loss) +I0616 15:40:07.698159 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12759 (* 1 = 0.12759 loss) +I0616 15:40:07.698163 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0269435 (* 1 = 0.0269435 loss) +I0616 15:40:07.698168 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104884 (* 1 = 0.0104884 loss) +I0616 15:40:07.698171 9857 solver.cpp:571] Iteration 81540, lr = 0.0001 +I0616 15:40:19.459458 9857 solver.cpp:242] Iteration 81560, loss = 0.369871 +I0616 15:40:19.459501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212587 (* 1 = 0.212587 loss) +I0616 15:40:19.459506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128172 (* 1 = 0.128172 loss) +I0616 15:40:19.459511 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0353356 (* 1 = 0.0353356 loss) +I0616 15:40:19.459513 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0151248 (* 1 = 0.0151248 loss) +I0616 15:40:19.459517 9857 solver.cpp:571] Iteration 81560, lr = 0.0001 +I0616 15:40:30.922288 9857 solver.cpp:242] Iteration 81580, loss = 0.28839 +I0616 15:40:30.922332 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149076 (* 1 = 0.149076 loss) +I0616 15:40:30.922336 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160167 (* 1 = 0.160167 loss) +I0616 15:40:30.922341 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0232208 (* 1 = 0.0232208 loss) +I0616 15:40:30.922344 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0188404 (* 1 = 0.0188404 loss) +I0616 15:40:30.922348 9857 solver.cpp:571] Iteration 81580, lr = 0.0001 +speed: 0.599s / iter +I0616 15:40:42.386309 9857 solver.cpp:242] Iteration 81600, loss = 0.580718 +I0616 15:40:42.386353 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.363938 (* 1 = 0.363938 loss) +I0616 15:40:42.386358 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351866 (* 1 = 0.351866 loss) +I0616 15:40:42.386363 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.148905 (* 1 = 0.148905 loss) +I0616 15:40:42.386366 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.119934 (* 1 = 0.119934 loss) +I0616 15:40:42.386369 9857 solver.cpp:571] Iteration 81600, lr = 0.0001 +I0616 15:40:53.979280 9857 solver.cpp:242] Iteration 81620, loss = 0.248456 +I0616 15:40:53.979321 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0775921 (* 1 = 0.0775921 loss) +I0616 15:40:53.979326 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119653 (* 1 = 0.119653 loss) +I0616 15:40:53.979331 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0252123 (* 1 = 0.0252123 loss) +I0616 15:40:53.979334 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0257155 (* 1 = 0.0257155 loss) +I0616 15:40:53.979338 9857 solver.cpp:571] Iteration 81620, lr = 0.0001 +I0616 15:41:05.357555 9857 solver.cpp:242] Iteration 81640, loss = 0.276343 +I0616 15:41:05.357599 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0987779 (* 1 = 0.0987779 loss) +I0616 15:41:05.357604 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108689 (* 1 = 0.108689 loss) +I0616 15:41:05.357607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0645253 (* 1 = 0.0645253 loss) +I0616 15:41:05.357611 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0799199 (* 1 = 0.0799199 loss) +I0616 15:41:05.357614 9857 solver.cpp:571] Iteration 81640, lr = 0.0001 +I0616 15:41:16.915755 9857 solver.cpp:242] Iteration 81660, loss = 0.56267 +I0616 15:41:16.915797 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.252967 (* 1 = 0.252967 loss) +I0616 15:41:16.915802 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251105 (* 1 = 0.251105 loss) +I0616 15:41:16.915807 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0966256 (* 1 = 0.0966256 loss) +I0616 15:41:16.915812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0298052 (* 1 = 0.0298052 loss) +I0616 15:41:16.915814 9857 solver.cpp:571] Iteration 81660, lr = 0.0001 +I0616 15:41:28.508108 9857 solver.cpp:242] Iteration 81680, loss = 0.292761 +I0616 15:41:28.508149 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163348 (* 1 = 0.163348 loss) +I0616 15:41:28.508155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166902 (* 1 = 0.166902 loss) +I0616 15:41:28.508159 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0426399 (* 1 = 0.0426399 loss) +I0616 15:41:28.508163 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0341469 (* 1 = 0.0341469 loss) +I0616 15:41:28.508167 9857 solver.cpp:571] Iteration 81680, lr = 0.0001 +I0616 15:41:39.696993 9857 solver.cpp:242] Iteration 81700, loss = 0.638144 +I0616 15:41:39.697036 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.507401 (* 1 = 0.507401 loss) +I0616 15:41:39.697041 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322058 (* 1 = 0.322058 loss) +I0616 15:41:39.697046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152765 (* 1 = 0.152765 loss) +I0616 15:41:39.697048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00555024 (* 1 = 0.00555024 loss) +I0616 15:41:39.697052 9857 solver.cpp:571] Iteration 81700, lr = 0.0001 +I0616 15:41:51.369289 9857 solver.cpp:242] Iteration 81720, loss = 1.11403 +I0616 15:41:51.369333 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336934 (* 1 = 0.336934 loss) +I0616 15:41:51.369339 9857 solver.cpp:258] Train net output #1: loss_cls = 0.405687 (* 1 = 0.405687 loss) +I0616 15:41:51.369343 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12665 (* 1 = 0.12665 loss) +I0616 15:41:51.369348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.410277 (* 1 = 0.410277 loss) +I0616 15:41:51.369351 9857 solver.cpp:571] Iteration 81720, lr = 0.0001 +I0616 15:42:02.901943 9857 solver.cpp:242] Iteration 81740, loss = 0.280308 +I0616 15:42:02.901985 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0761306 (* 1 = 0.0761306 loss) +I0616 15:42:02.901990 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0984788 (* 1 = 0.0984788 loss) +I0616 15:42:02.901995 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.056222 (* 1 = 0.056222 loss) +I0616 15:42:02.901998 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00539887 (* 1 = 0.00539887 loss) +I0616 15:42:02.902003 9857 solver.cpp:571] Iteration 81740, lr = 0.0001 +I0616 15:42:14.214900 9857 solver.cpp:242] Iteration 81760, loss = 1.02632 +I0616 15:42:14.214941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.328859 (* 1 = 0.328859 loss) +I0616 15:42:14.214947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309469 (* 1 = 0.309469 loss) +I0616 15:42:14.214951 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.177685 (* 1 = 0.177685 loss) +I0616 15:42:14.214956 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0558897 (* 1 = 0.0558897 loss) +I0616 15:42:14.214959 9857 solver.cpp:571] Iteration 81760, lr = 0.0001 +I0616 15:42:25.668594 9857 solver.cpp:242] Iteration 81780, loss = 0.590695 +I0616 15:42:25.668637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296574 (* 1 = 0.296574 loss) +I0616 15:42:25.668642 9857 solver.cpp:258] Train net output #1: loss_cls = 0.31885 (* 1 = 0.31885 loss) +I0616 15:42:25.668647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.230362 (* 1 = 0.230362 loss) +I0616 15:42:25.668650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231754 (* 1 = 0.0231754 loss) +I0616 15:42:25.668654 9857 solver.cpp:571] Iteration 81780, lr = 0.0001 +speed: 0.599s / iter +I0616 15:42:37.304209 9857 solver.cpp:242] Iteration 81800, loss = 0.537699 +I0616 15:42:37.304251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184133 (* 1 = 0.184133 loss) +I0616 15:42:37.304257 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113111 (* 1 = 0.113111 loss) +I0616 15:42:37.304262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0873083 (* 1 = 0.0873083 loss) +I0616 15:42:37.304266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.345094 (* 1 = 0.345094 loss) +I0616 15:42:37.304270 9857 solver.cpp:571] Iteration 81800, lr = 0.0001 +I0616 15:42:48.870268 9857 solver.cpp:242] Iteration 81820, loss = 0.188336 +I0616 15:42:48.870311 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0489322 (* 1 = 0.0489322 loss) +I0616 15:42:48.870316 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100864 (* 1 = 0.100864 loss) +I0616 15:42:48.870321 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0316985 (* 1 = 0.0316985 loss) +I0616 15:42:48.870324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00616166 (* 1 = 0.00616166 loss) +I0616 15:42:48.870328 9857 solver.cpp:571] Iteration 81820, lr = 0.0001 +I0616 15:43:00.243350 9857 solver.cpp:242] Iteration 81840, loss = 0.186394 +I0616 15:43:00.243392 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0870828 (* 1 = 0.0870828 loss) +I0616 15:43:00.243397 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0988565 (* 1 = 0.0988565 loss) +I0616 15:43:00.243402 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0094342 (* 1 = 0.0094342 loss) +I0616 15:43:00.243405 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00430402 (* 1 = 0.00430402 loss) +I0616 15:43:00.243409 9857 solver.cpp:571] Iteration 81840, lr = 0.0001 +I0616 15:43:11.703639 9857 solver.cpp:242] Iteration 81860, loss = 0.972486 +I0616 15:43:11.703682 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270541 (* 1 = 0.270541 loss) +I0616 15:43:11.703687 9857 solver.cpp:258] Train net output #1: loss_cls = 0.554311 (* 1 = 0.554311 loss) +I0616 15:43:11.703691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184846 (* 1 = 0.184846 loss) +I0616 15:43:11.703696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.132727 (* 1 = 0.132727 loss) +I0616 15:43:11.703698 9857 solver.cpp:571] Iteration 81860, lr = 0.0001 +I0616 15:43:23.537372 9857 solver.cpp:242] Iteration 81880, loss = 0.504149 +I0616 15:43:23.537415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143298 (* 1 = 0.143298 loss) +I0616 15:43:23.537420 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138132 (* 1 = 0.138132 loss) +I0616 15:43:23.537425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0826261 (* 1 = 0.0826261 loss) +I0616 15:43:23.537427 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390834 (* 1 = 0.0390834 loss) +I0616 15:43:23.537431 9857 solver.cpp:571] Iteration 81880, lr = 0.0001 +I0616 15:43:35.063256 9857 solver.cpp:242] Iteration 81900, loss = 1.14228 +I0616 15:43:35.063297 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.464002 (* 1 = 0.464002 loss) +I0616 15:43:35.063302 9857 solver.cpp:258] Train net output #1: loss_cls = 0.65778 (* 1 = 0.65778 loss) +I0616 15:43:35.063308 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.231822 (* 1 = 0.231822 loss) +I0616 15:43:35.063310 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.384075 (* 1 = 0.384075 loss) +I0616 15:43:35.063314 9857 solver.cpp:571] Iteration 81900, lr = 0.0001 +I0616 15:43:46.682715 9857 solver.cpp:242] Iteration 81920, loss = 0.648675 +I0616 15:43:46.682760 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.256664 (* 1 = 0.256664 loss) +I0616 15:43:46.682766 9857 solver.cpp:258] Train net output #1: loss_cls = 0.398814 (* 1 = 0.398814 loss) +I0616 15:43:46.682770 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0964368 (* 1 = 0.0964368 loss) +I0616 15:43:46.682775 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0606681 (* 1 = 0.0606681 loss) +I0616 15:43:46.682777 9857 solver.cpp:571] Iteration 81920, lr = 0.0001 +I0616 15:43:58.218086 9857 solver.cpp:242] Iteration 81940, loss = 0.447424 +I0616 15:43:58.218127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.284344 (* 1 = 0.284344 loss) +I0616 15:43:58.218132 9857 solver.cpp:258] Train net output #1: loss_cls = 0.316462 (* 1 = 0.316462 loss) +I0616 15:43:58.218137 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110716 (* 1 = 0.110716 loss) +I0616 15:43:58.218140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0238216 (* 1 = 0.0238216 loss) +I0616 15:43:58.218144 9857 solver.cpp:571] Iteration 81940, lr = 0.0001 +I0616 15:44:09.700456 9857 solver.cpp:242] Iteration 81960, loss = 0.388344 +I0616 15:44:09.700497 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175526 (* 1 = 0.175526 loss) +I0616 15:44:09.700502 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267712 (* 1 = 0.267712 loss) +I0616 15:44:09.700507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.107211 (* 1 = 0.107211 loss) +I0616 15:44:09.700510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0597196 (* 1 = 0.0597196 loss) +I0616 15:44:09.700515 9857 solver.cpp:571] Iteration 81960, lr = 0.0001 +I0616 15:44:20.741106 9857 solver.cpp:242] Iteration 81980, loss = 0.33611 +I0616 15:44:20.741148 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0849226 (* 1 = 0.0849226 loss) +I0616 15:44:20.741153 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209701 (* 1 = 0.209701 loss) +I0616 15:44:20.741158 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.184557 (* 1 = 0.184557 loss) +I0616 15:44:20.741161 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00568812 (* 1 = 0.00568812 loss) +I0616 15:44:20.741165 9857 solver.cpp:571] Iteration 81980, lr = 0.0001 +speed: 0.599s / iter +I0616 15:44:32.035511 9857 solver.cpp:242] Iteration 82000, loss = 0.717835 +I0616 15:44:32.035553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21253 (* 1 = 0.21253 loss) +I0616 15:44:32.035559 9857 solver.cpp:258] Train net output #1: loss_cls = 0.402319 (* 1 = 0.402319 loss) +I0616 15:44:32.035563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.137436 (* 1 = 0.137436 loss) +I0616 15:44:32.035567 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388421 (* 1 = 0.0388421 loss) +I0616 15:44:32.035570 9857 solver.cpp:571] Iteration 82000, lr = 0.0001 +I0616 15:44:43.458230 9857 solver.cpp:242] Iteration 82020, loss = 0.15902 +I0616 15:44:43.458271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0457811 (* 1 = 0.0457811 loss) +I0616 15:44:43.458276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0552413 (* 1 = 0.0552413 loss) +I0616 15:44:43.458281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0047827 (* 1 = 0.0047827 loss) +I0616 15:44:43.458283 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00216388 (* 1 = 0.00216388 loss) +I0616 15:44:43.458287 9857 solver.cpp:571] Iteration 82020, lr = 0.0001 +I0616 15:44:54.903827 9857 solver.cpp:242] Iteration 82040, loss = 0.71816 +I0616 15:44:54.903867 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271831 (* 1 = 0.271831 loss) +I0616 15:44:54.903873 9857 solver.cpp:258] Train net output #1: loss_cls = 0.420705 (* 1 = 0.420705 loss) +I0616 15:44:54.903877 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.199234 (* 1 = 0.199234 loss) +I0616 15:44:54.903882 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0836178 (* 1 = 0.0836178 loss) +I0616 15:44:54.903884 9857 solver.cpp:571] Iteration 82040, lr = 0.0001 +I0616 15:45:06.432190 9857 solver.cpp:242] Iteration 82060, loss = 0.307195 +I0616 15:45:06.432231 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0888048 (* 1 = 0.0888048 loss) +I0616 15:45:06.432237 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107111 (* 1 = 0.107111 loss) +I0616 15:45:06.432241 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00666232 (* 1 = 0.00666232 loss) +I0616 15:45:06.432245 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0368951 (* 1 = 0.0368951 loss) +I0616 15:45:06.432250 9857 solver.cpp:571] Iteration 82060, lr = 0.0001 +I0616 15:45:18.143663 9857 solver.cpp:242] Iteration 82080, loss = 0.369804 +I0616 15:45:18.143707 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0815172 (* 1 = 0.0815172 loss) +I0616 15:45:18.143712 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123347 (* 1 = 0.123347 loss) +I0616 15:45:18.143717 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0602771 (* 1 = 0.0602771 loss) +I0616 15:45:18.143719 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00423739 (* 1 = 0.00423739 loss) +I0616 15:45:18.143723 9857 solver.cpp:571] Iteration 82080, lr = 0.0001 +I0616 15:45:29.697613 9857 solver.cpp:242] Iteration 82100, loss = 0.458815 +I0616 15:45:29.697656 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0301479 (* 1 = 0.0301479 loss) +I0616 15:45:29.697662 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0734635 (* 1 = 0.0734635 loss) +I0616 15:45:29.697666 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0107835 (* 1 = 0.0107835 loss) +I0616 15:45:29.697670 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00898379 (* 1 = 0.00898379 loss) +I0616 15:45:29.697674 9857 solver.cpp:571] Iteration 82100, lr = 0.0001 +I0616 15:45:40.844955 9857 solver.cpp:242] Iteration 82120, loss = 0.333944 +I0616 15:45:40.844997 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0996589 (* 1 = 0.0996589 loss) +I0616 15:45:40.845002 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163824 (* 1 = 0.163824 loss) +I0616 15:45:40.845006 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0401813 (* 1 = 0.0401813 loss) +I0616 15:45:40.845010 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102216 (* 1 = 0.0102216 loss) +I0616 15:45:40.845015 9857 solver.cpp:571] Iteration 82120, lr = 0.0001 +I0616 15:45:52.261380 9857 solver.cpp:242] Iteration 82140, loss = 0.600329 +I0616 15:45:52.261422 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149907 (* 1 = 0.149907 loss) +I0616 15:45:52.261427 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14336 (* 1 = 0.14336 loss) +I0616 15:45:52.261431 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0310937 (* 1 = 0.0310937 loss) +I0616 15:45:52.261435 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00586468 (* 1 = 0.00586468 loss) +I0616 15:45:52.261440 9857 solver.cpp:571] Iteration 82140, lr = 0.0001 +I0616 15:46:03.956446 9857 solver.cpp:242] Iteration 82160, loss = 0.468906 +I0616 15:46:03.956490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0695634 (* 1 = 0.0695634 loss) +I0616 15:46:03.956496 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119557 (* 1 = 0.119557 loss) +I0616 15:46:03.956499 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0159585 (* 1 = 0.0159585 loss) +I0616 15:46:03.956503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122175 (* 1 = 0.0122175 loss) +I0616 15:46:03.956506 9857 solver.cpp:571] Iteration 82160, lr = 0.0001 +I0616 15:46:15.074470 9857 solver.cpp:242] Iteration 82180, loss = 0.970142 +I0616 15:46:15.074511 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358732 (* 1 = 0.358732 loss) +I0616 15:46:15.074515 9857 solver.cpp:258] Train net output #1: loss_cls = 0.938723 (* 1 = 0.938723 loss) +I0616 15:46:15.074520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.113733 (* 1 = 0.113733 loss) +I0616 15:46:15.074523 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0557035 (* 1 = 0.0557035 loss) +I0616 15:46:15.074527 9857 solver.cpp:571] Iteration 82180, lr = 0.0001 +speed: 0.599s / iter +I0616 15:46:26.596815 9857 solver.cpp:242] Iteration 82200, loss = 0.38701 +I0616 15:46:26.596855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0974272 (* 1 = 0.0974272 loss) +I0616 15:46:26.596861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124032 (* 1 = 0.124032 loss) +I0616 15:46:26.596865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00708637 (* 1 = 0.00708637 loss) +I0616 15:46:26.596869 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00641064 (* 1 = 0.00641064 loss) +I0616 15:46:26.596873 9857 solver.cpp:571] Iteration 82200, lr = 0.0001 +I0616 15:46:38.331974 9857 solver.cpp:242] Iteration 82220, loss = 0.622314 +I0616 15:46:38.332017 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.388827 (* 1 = 0.388827 loss) +I0616 15:46:38.332023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204917 (* 1 = 0.204917 loss) +I0616 15:46:38.332028 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0530103 (* 1 = 0.0530103 loss) +I0616 15:46:38.332031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.134981 (* 1 = 0.134981 loss) +I0616 15:46:38.332036 9857 solver.cpp:571] Iteration 82220, lr = 0.0001 +I0616 15:46:49.747678 9857 solver.cpp:242] Iteration 82240, loss = 0.384771 +I0616 15:46:49.747720 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0809841 (* 1 = 0.0809841 loss) +I0616 15:46:49.747727 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208876 (* 1 = 0.208876 loss) +I0616 15:46:49.747731 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0549379 (* 1 = 0.0549379 loss) +I0616 15:46:49.747735 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.138671 (* 1 = 0.138671 loss) +I0616 15:46:49.747738 9857 solver.cpp:571] Iteration 82240, lr = 0.0001 +I0616 15:47:01.284680 9857 solver.cpp:242] Iteration 82260, loss = 0.321339 +I0616 15:47:01.284719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173037 (* 1 = 0.173037 loss) +I0616 15:47:01.284724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234378 (* 1 = 0.234378 loss) +I0616 15:47:01.284729 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0555062 (* 1 = 0.0555062 loss) +I0616 15:47:01.284732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00766253 (* 1 = 0.00766253 loss) +I0616 15:47:01.284736 9857 solver.cpp:571] Iteration 82260, lr = 0.0001 +I0616 15:47:12.838582 9857 solver.cpp:242] Iteration 82280, loss = 0.224765 +I0616 15:47:12.838624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0651929 (* 1 = 0.0651929 loss) +I0616 15:47:12.838630 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0800542 (* 1 = 0.0800542 loss) +I0616 15:47:12.838635 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00676716 (* 1 = 0.00676716 loss) +I0616 15:47:12.838639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00138754 (* 1 = 0.00138754 loss) +I0616 15:47:12.838642 9857 solver.cpp:571] Iteration 82280, lr = 0.0001 +I0616 15:47:24.312162 9857 solver.cpp:242] Iteration 82300, loss = 0.442522 +I0616 15:47:24.312203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269981 (* 1 = 0.269981 loss) +I0616 15:47:24.312234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244196 (* 1 = 0.244196 loss) +I0616 15:47:24.312239 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0280619 (* 1 = 0.0280619 loss) +I0616 15:47:24.312258 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00881742 (* 1 = 0.00881742 loss) +I0616 15:47:24.312261 9857 solver.cpp:571] Iteration 82300, lr = 0.0001 +I0616 15:47:36.011461 9857 solver.cpp:242] Iteration 82320, loss = 0.446026 +I0616 15:47:36.011503 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.213462 (* 1 = 0.213462 loss) +I0616 15:47:36.011508 9857 solver.cpp:258] Train net output #1: loss_cls = 0.25687 (* 1 = 0.25687 loss) +I0616 15:47:36.011513 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0394758 (* 1 = 0.0394758 loss) +I0616 15:47:36.011517 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0418798 (* 1 = 0.0418798 loss) +I0616 15:47:36.011520 9857 solver.cpp:571] Iteration 82320, lr = 0.0001 +I0616 15:47:47.659965 9857 solver.cpp:242] Iteration 82340, loss = 0.322878 +I0616 15:47:47.660006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.152931 (* 1 = 0.152931 loss) +I0616 15:47:47.660012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.239439 (* 1 = 0.239439 loss) +I0616 15:47:47.660015 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0346869 (* 1 = 0.0346869 loss) +I0616 15:47:47.660019 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117355 (* 1 = 0.0117355 loss) +I0616 15:47:47.660023 9857 solver.cpp:571] Iteration 82340, lr = 0.0001 +I0616 15:47:59.191311 9857 solver.cpp:242] Iteration 82360, loss = 0.700628 +I0616 15:47:59.191354 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251876 (* 1 = 0.251876 loss) +I0616 15:47:59.191360 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20938 (* 1 = 0.20938 loss) +I0616 15:47:59.191365 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0606468 (* 1 = 0.0606468 loss) +I0616 15:47:59.191368 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0381701 (* 1 = 0.0381701 loss) +I0616 15:47:59.191371 9857 solver.cpp:571] Iteration 82360, lr = 0.0001 +I0616 15:48:10.648260 9857 solver.cpp:242] Iteration 82380, loss = 0.574802 +I0616 15:48:10.648301 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125098 (* 1 = 0.125098 loss) +I0616 15:48:10.648308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16374 (* 1 = 0.16374 loss) +I0616 15:48:10.648311 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00819857 (* 1 = 0.00819857 loss) +I0616 15:48:10.648315 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014063 (* 1 = 0.014063 loss) +I0616 15:48:10.648319 9857 solver.cpp:571] Iteration 82380, lr = 0.0001 +speed: 0.599s / iter +I0616 15:48:21.869261 9857 solver.cpp:242] Iteration 82400, loss = 0.71181 +I0616 15:48:21.869303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262667 (* 1 = 0.262667 loss) +I0616 15:48:21.869309 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252642 (* 1 = 0.252642 loss) +I0616 15:48:21.869314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0534409 (* 1 = 0.0534409 loss) +I0616 15:48:21.869318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0911897 (* 1 = 0.0911897 loss) +I0616 15:48:21.869321 9857 solver.cpp:571] Iteration 82400, lr = 0.0001 +I0616 15:48:33.468976 9857 solver.cpp:242] Iteration 82420, loss = 0.9291 +I0616 15:48:33.469018 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336608 (* 1 = 0.336608 loss) +I0616 15:48:33.469024 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244631 (* 1 = 0.244631 loss) +I0616 15:48:33.469028 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0730408 (* 1 = 0.0730408 loss) +I0616 15:48:33.469032 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0587853 (* 1 = 0.0587853 loss) +I0616 15:48:33.469035 9857 solver.cpp:571] Iteration 82420, lr = 0.0001 +I0616 15:48:45.127120 9857 solver.cpp:242] Iteration 82440, loss = 0.374399 +I0616 15:48:45.127161 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0942113 (* 1 = 0.0942113 loss) +I0616 15:48:45.127166 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144818 (* 1 = 0.144818 loss) +I0616 15:48:45.127171 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0216875 (* 1 = 0.0216875 loss) +I0616 15:48:45.127174 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00454229 (* 1 = 0.00454229 loss) +I0616 15:48:45.127179 9857 solver.cpp:571] Iteration 82440, lr = 0.0001 +I0616 15:48:56.578531 9857 solver.cpp:242] Iteration 82460, loss = 0.182903 +I0616 15:48:56.578574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0754988 (* 1 = 0.0754988 loss) +I0616 15:48:56.578580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.066248 (* 1 = 0.066248 loss) +I0616 15:48:56.578584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0605241 (* 1 = 0.0605241 loss) +I0616 15:48:56.578588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00561928 (* 1 = 0.00561928 loss) +I0616 15:48:56.578591 9857 solver.cpp:571] Iteration 82460, lr = 0.0001 +I0616 15:49:08.151175 9857 solver.cpp:242] Iteration 82480, loss = 0.558121 +I0616 15:49:08.151218 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.238488 (* 1 = 0.238488 loss) +I0616 15:49:08.151223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404297 (* 1 = 0.404297 loss) +I0616 15:49:08.151227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149828 (* 1 = 0.149828 loss) +I0616 15:49:08.151231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.175654 (* 1 = 0.175654 loss) +I0616 15:49:08.151234 9857 solver.cpp:571] Iteration 82480, lr = 0.0001 +I0616 15:49:19.516501 9857 solver.cpp:242] Iteration 82500, loss = 0.723218 +I0616 15:49:19.516546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101173 (* 1 = 0.101173 loss) +I0616 15:49:19.516551 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126479 (* 1 = 0.126479 loss) +I0616 15:49:19.516554 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125323 (* 1 = 0.125323 loss) +I0616 15:49:19.516558 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.621476 (* 1 = 0.621476 loss) +I0616 15:49:19.516561 9857 solver.cpp:571] Iteration 82500, lr = 0.0001 +I0616 15:49:31.181996 9857 solver.cpp:242] Iteration 82520, loss = 0.166976 +I0616 15:49:31.182039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0727962 (* 1 = 0.0727962 loss) +I0616 15:49:31.182044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0794274 (* 1 = 0.0794274 loss) +I0616 15:49:31.182049 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0106416 (* 1 = 0.0106416 loss) +I0616 15:49:31.182052 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183893 (* 1 = 0.0183893 loss) +I0616 15:49:31.182055 9857 solver.cpp:571] Iteration 82520, lr = 0.0001 +I0616 15:49:42.683784 9857 solver.cpp:242] Iteration 82540, loss = 0.776861 +I0616 15:49:42.683825 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19829 (* 1 = 0.19829 loss) +I0616 15:49:42.683831 9857 solver.cpp:258] Train net output #1: loss_cls = 0.385552 (* 1 = 0.385552 loss) +I0616 15:49:42.683835 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110427 (* 1 = 0.110427 loss) +I0616 15:49:42.683840 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0874393 (* 1 = 0.0874393 loss) +I0616 15:49:42.683842 9857 solver.cpp:571] Iteration 82540, lr = 0.0001 +I0616 15:49:54.060438 9857 solver.cpp:242] Iteration 82560, loss = 0.402198 +I0616 15:49:54.060479 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0890108 (* 1 = 0.0890108 loss) +I0616 15:49:54.060485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180125 (* 1 = 0.180125 loss) +I0616 15:49:54.060489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104091 (* 1 = 0.0104091 loss) +I0616 15:49:54.060493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255426 (* 1 = 0.0255426 loss) +I0616 15:49:54.060497 9857 solver.cpp:571] Iteration 82560, lr = 0.0001 +I0616 15:50:05.541339 9857 solver.cpp:242] Iteration 82580, loss = 0.743473 +I0616 15:50:05.541383 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.411414 (* 1 = 0.411414 loss) +I0616 15:50:05.541388 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358506 (* 1 = 0.358506 loss) +I0616 15:50:05.541393 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14963 (* 1 = 0.14963 loss) +I0616 15:50:05.541398 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0707866 (* 1 = 0.0707866 loss) +I0616 15:50:05.541400 9857 solver.cpp:571] Iteration 82580, lr = 0.0001 +speed: 0.599s / iter +I0616 15:50:17.007784 9857 solver.cpp:242] Iteration 82600, loss = 0.64408 +I0616 15:50:17.007825 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.425122 (* 1 = 0.425122 loss) +I0616 15:50:17.007830 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245771 (* 1 = 0.245771 loss) +I0616 15:50:17.007834 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0180572 (* 1 = 0.0180572 loss) +I0616 15:50:17.007838 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136648 (* 1 = 0.0136648 loss) +I0616 15:50:17.007843 9857 solver.cpp:571] Iteration 82600, lr = 0.0001 +I0616 15:50:28.594154 9857 solver.cpp:242] Iteration 82620, loss = 0.622358 +I0616 15:50:28.594195 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0443504 (* 1 = 0.0443504 loss) +I0616 15:50:28.594202 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0683485 (* 1 = 0.0683485 loss) +I0616 15:50:28.594205 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0391421 (* 1 = 0.0391421 loss) +I0616 15:50:28.594208 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0384789 (* 1 = 0.0384789 loss) +I0616 15:50:28.594213 9857 solver.cpp:571] Iteration 82620, lr = 0.0001 +I0616 15:50:40.015992 9857 solver.cpp:242] Iteration 82640, loss = 0.450489 +I0616 15:50:40.016034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.265193 (* 1 = 0.265193 loss) +I0616 15:50:40.016041 9857 solver.cpp:258] Train net output #1: loss_cls = 0.489124 (* 1 = 0.489124 loss) +I0616 15:50:40.016046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.028337 (* 1 = 0.028337 loss) +I0616 15:50:40.016048 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00995237 (* 1 = 0.00995237 loss) +I0616 15:50:40.016052 9857 solver.cpp:571] Iteration 82640, lr = 0.0001 +I0616 15:50:51.523310 9857 solver.cpp:242] Iteration 82660, loss = 0.554103 +I0616 15:50:51.523353 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149708 (* 1 = 0.149708 loss) +I0616 15:50:51.523360 9857 solver.cpp:258] Train net output #1: loss_cls = 0.319944 (* 1 = 0.319944 loss) +I0616 15:50:51.523363 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179902 (* 1 = 0.179902 loss) +I0616 15:50:51.523366 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0575697 (* 1 = 0.0575697 loss) +I0616 15:50:51.523370 9857 solver.cpp:571] Iteration 82660, lr = 0.0001 +I0616 15:51:03.049607 9857 solver.cpp:242] Iteration 82680, loss = 0.174431 +I0616 15:51:03.049649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0448014 (* 1 = 0.0448014 loss) +I0616 15:51:03.049655 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0663334 (* 1 = 0.0663334 loss) +I0616 15:51:03.049660 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0219979 (* 1 = 0.0219979 loss) +I0616 15:51:03.049664 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00530385 (* 1 = 0.00530385 loss) +I0616 15:51:03.049667 9857 solver.cpp:571] Iteration 82680, lr = 0.0001 +I0616 15:51:14.433532 9857 solver.cpp:242] Iteration 82700, loss = 0.63768 +I0616 15:51:14.433575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327227 (* 1 = 0.327227 loss) +I0616 15:51:14.433581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332377 (* 1 = 0.332377 loss) +I0616 15:51:14.433586 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.179829 (* 1 = 0.179829 loss) +I0616 15:51:14.433589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0257147 (* 1 = 0.0257147 loss) +I0616 15:51:14.433593 9857 solver.cpp:571] Iteration 82700, lr = 0.0001 +I0616 15:51:26.292991 9857 solver.cpp:242] Iteration 82720, loss = 0.509831 +I0616 15:51:26.293032 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236114 (* 1 = 0.236114 loss) +I0616 15:51:26.293037 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22734 (* 1 = 0.22734 loss) +I0616 15:51:26.293041 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0843611 (* 1 = 0.0843611 loss) +I0616 15:51:26.293045 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00777975 (* 1 = 0.00777975 loss) +I0616 15:51:26.293050 9857 solver.cpp:571] Iteration 82720, lr = 0.0001 +I0616 15:51:37.905958 9857 solver.cpp:242] Iteration 82740, loss = 0.287224 +I0616 15:51:37.905999 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135414 (* 1 = 0.135414 loss) +I0616 15:51:37.906005 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0696836 (* 1 = 0.0696836 loss) +I0616 15:51:37.906009 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0500672 (* 1 = 0.0500672 loss) +I0616 15:51:37.906013 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0808285 (* 1 = 0.0808285 loss) +I0616 15:51:37.906016 9857 solver.cpp:571] Iteration 82740, lr = 0.0001 +I0616 15:51:49.528537 9857 solver.cpp:242] Iteration 82760, loss = 0.406128 +I0616 15:51:49.528579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0698152 (* 1 = 0.0698152 loss) +I0616 15:51:49.528585 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168762 (* 1 = 0.168762 loss) +I0616 15:51:49.528589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0559648 (* 1 = 0.0559648 loss) +I0616 15:51:49.528594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170739 (* 1 = 0.0170739 loss) +I0616 15:51:49.528597 9857 solver.cpp:571] Iteration 82760, lr = 0.0001 +I0616 15:52:01.263092 9857 solver.cpp:242] Iteration 82780, loss = 0.463951 +I0616 15:52:01.263131 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230079 (* 1 = 0.230079 loss) +I0616 15:52:01.263136 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260664 (* 1 = 0.260664 loss) +I0616 15:52:01.263141 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123039 (* 1 = 0.123039 loss) +I0616 15:52:01.263144 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0737719 (* 1 = 0.0737719 loss) +I0616 15:52:01.263149 9857 solver.cpp:571] Iteration 82780, lr = 0.0001 +speed: 0.599s / iter +I0616 15:52:13.175950 9857 solver.cpp:242] Iteration 82800, loss = 0.383739 +I0616 15:52:13.175990 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16163 (* 1 = 0.16163 loss) +I0616 15:52:13.175997 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232845 (* 1 = 0.232845 loss) +I0616 15:52:13.176000 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0796788 (* 1 = 0.0796788 loss) +I0616 15:52:13.176003 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167521 (* 1 = 0.0167521 loss) +I0616 15:52:13.176007 9857 solver.cpp:571] Iteration 82800, lr = 0.0001 +I0616 15:52:24.813217 9857 solver.cpp:242] Iteration 82820, loss = 0.380294 +I0616 15:52:24.813258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147928 (* 1 = 0.147928 loss) +I0616 15:52:24.813264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105704 (* 1 = 0.105704 loss) +I0616 15:52:24.813268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141057 (* 1 = 0.0141057 loss) +I0616 15:52:24.813271 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0193103 (* 1 = 0.0193103 loss) +I0616 15:52:24.813276 9857 solver.cpp:571] Iteration 82820, lr = 0.0001 +I0616 15:52:36.487336 9857 solver.cpp:242] Iteration 82840, loss = 0.302855 +I0616 15:52:36.487380 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0870837 (* 1 = 0.0870837 loss) +I0616 15:52:36.487385 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0837644 (* 1 = 0.0837644 loss) +I0616 15:52:36.487390 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.035139 (* 1 = 0.035139 loss) +I0616 15:52:36.487393 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00631502 (* 1 = 0.00631502 loss) +I0616 15:52:36.487396 9857 solver.cpp:571] Iteration 82840, lr = 0.0001 +I0616 15:52:47.912883 9857 solver.cpp:242] Iteration 82860, loss = 0.504303 +I0616 15:52:47.912924 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271428 (* 1 = 0.271428 loss) +I0616 15:52:47.912930 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229615 (* 1 = 0.229615 loss) +I0616 15:52:47.912933 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0255574 (* 1 = 0.0255574 loss) +I0616 15:52:47.912937 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0176716 (* 1 = 0.0176716 loss) +I0616 15:52:47.912941 9857 solver.cpp:571] Iteration 82860, lr = 0.0001 +I0616 15:52:59.218287 9857 solver.cpp:242] Iteration 82880, loss = 0.439064 +I0616 15:52:59.218329 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.248668 (* 1 = 0.248668 loss) +I0616 15:52:59.218335 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192839 (* 1 = 0.192839 loss) +I0616 15:52:59.218339 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0610489 (* 1 = 0.0610489 loss) +I0616 15:52:59.218343 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0267165 (* 1 = 0.0267165 loss) +I0616 15:52:59.218346 9857 solver.cpp:571] Iteration 82880, lr = 0.0001 +I0616 15:53:10.716899 9857 solver.cpp:242] Iteration 82900, loss = 0.247086 +I0616 15:53:10.716939 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0737093 (* 1 = 0.0737093 loss) +I0616 15:53:10.716958 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181008 (* 1 = 0.181008 loss) +I0616 15:53:10.716964 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0391168 (* 1 = 0.0391168 loss) +I0616 15:53:10.716967 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00106364 (* 1 = 0.00106364 loss) +I0616 15:53:10.716971 9857 solver.cpp:571] Iteration 82900, lr = 0.0001 +I0616 15:53:21.934061 9857 solver.cpp:242] Iteration 82920, loss = 0.565049 +I0616 15:53:21.934114 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406111 (* 1 = 0.406111 loss) +I0616 15:53:21.934121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221852 (* 1 = 0.221852 loss) +I0616 15:53:21.934125 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.111459 (* 1 = 0.111459 loss) +I0616 15:53:21.934129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0477174 (* 1 = 0.0477174 loss) +I0616 15:53:21.934134 9857 solver.cpp:571] Iteration 82920, lr = 0.0001 +I0616 15:53:33.216027 9857 solver.cpp:242] Iteration 82940, loss = 0.455417 +I0616 15:53:33.216068 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264628 (* 1 = 0.264628 loss) +I0616 15:53:33.216074 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309095 (* 1 = 0.309095 loss) +I0616 15:53:33.216078 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0907136 (* 1 = 0.0907136 loss) +I0616 15:53:33.216081 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0349801 (* 1 = 0.0349801 loss) +I0616 15:53:33.216085 9857 solver.cpp:571] Iteration 82940, lr = 0.0001 +I0616 15:53:44.863041 9857 solver.cpp:242] Iteration 82960, loss = 0.775464 +I0616 15:53:44.863082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.235716 (* 1 = 0.235716 loss) +I0616 15:53:44.863088 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388445 (* 1 = 0.388445 loss) +I0616 15:53:44.863092 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120084 (* 1 = 0.120084 loss) +I0616 15:53:44.863096 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.231398 (* 1 = 0.231398 loss) +I0616 15:53:44.863100 9857 solver.cpp:571] Iteration 82960, lr = 0.0001 +I0616 15:53:56.341462 9857 solver.cpp:242] Iteration 82980, loss = 1.01154 +I0616 15:53:56.341500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399253 (* 1 = 0.399253 loss) +I0616 15:53:56.341506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.742687 (* 1 = 0.742687 loss) +I0616 15:53:56.341509 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.196478 (* 1 = 0.196478 loss) +I0616 15:53:56.341512 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0776125 (* 1 = 0.0776125 loss) +I0616 15:53:56.341517 9857 solver.cpp:571] Iteration 82980, lr = 0.0001 +speed: 0.599s / iter +I0616 15:54:08.011126 9857 solver.cpp:242] Iteration 83000, loss = 0.323851 +I0616 15:54:08.011169 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0496757 (* 1 = 0.0496757 loss) +I0616 15:54:08.011175 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0967324 (* 1 = 0.0967324 loss) +I0616 15:54:08.011179 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0537754 (* 1 = 0.0537754 loss) +I0616 15:54:08.011183 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00702521 (* 1 = 0.00702521 loss) +I0616 15:54:08.011186 9857 solver.cpp:571] Iteration 83000, lr = 0.0001 +I0616 15:54:19.612972 9857 solver.cpp:242] Iteration 83020, loss = 0.833142 +I0616 15:54:19.613013 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32282 (* 1 = 0.32282 loss) +I0616 15:54:19.613019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351722 (* 1 = 0.351722 loss) +I0616 15:54:19.613023 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.268108 (* 1 = 0.268108 loss) +I0616 15:54:19.613028 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.173524 (* 1 = 0.173524 loss) +I0616 15:54:19.613030 9857 solver.cpp:571] Iteration 83020, lr = 0.0001 +I0616 15:54:31.191571 9857 solver.cpp:242] Iteration 83040, loss = 0.370399 +I0616 15:54:31.191612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134172 (* 1 = 0.134172 loss) +I0616 15:54:31.191618 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251192 (* 1 = 0.251192 loss) +I0616 15:54:31.191623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0150674 (* 1 = 0.0150674 loss) +I0616 15:54:31.191627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0110135 (* 1 = 0.0110135 loss) +I0616 15:54:31.191630 9857 solver.cpp:571] Iteration 83040, lr = 0.0001 +I0616 15:54:42.681613 9857 solver.cpp:242] Iteration 83060, loss = 1.09942 +I0616 15:54:42.681655 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.458631 (* 1 = 0.458631 loss) +I0616 15:54:42.681661 9857 solver.cpp:258] Train net output #1: loss_cls = 0.599841 (* 1 = 0.599841 loss) +I0616 15:54:42.681665 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0405448 (* 1 = 0.0405448 loss) +I0616 15:54:42.681669 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0748418 (* 1 = 0.0748418 loss) +I0616 15:54:42.681674 9857 solver.cpp:571] Iteration 83060, lr = 0.0001 +I0616 15:54:54.178932 9857 solver.cpp:242] Iteration 83080, loss = 0.359088 +I0616 15:54:54.178975 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143542 (* 1 = 0.143542 loss) +I0616 15:54:54.178980 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0920491 (* 1 = 0.0920491 loss) +I0616 15:54:54.178985 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.098115 (* 1 = 0.098115 loss) +I0616 15:54:54.178989 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0190135 (* 1 = 0.0190135 loss) +I0616 15:54:54.178993 9857 solver.cpp:571] Iteration 83080, lr = 0.0001 +I0616 15:55:05.537273 9857 solver.cpp:242] Iteration 83100, loss = 0.255657 +I0616 15:55:05.537317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0788917 (* 1 = 0.0788917 loss) +I0616 15:55:05.537323 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0860383 (* 1 = 0.0860383 loss) +I0616 15:55:05.537328 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0188247 (* 1 = 0.0188247 loss) +I0616 15:55:05.537331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0832844 (* 1 = 0.0832844 loss) +I0616 15:55:05.537334 9857 solver.cpp:571] Iteration 83100, lr = 0.0001 +I0616 15:55:16.997107 9857 solver.cpp:242] Iteration 83120, loss = 0.573032 +I0616 15:55:16.997149 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.345414 (* 1 = 0.345414 loss) +I0616 15:55:16.997155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176916 (* 1 = 0.176916 loss) +I0616 15:55:16.997160 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139408 (* 1 = 0.0139408 loss) +I0616 15:55:16.997164 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0677608 (* 1 = 0.0677608 loss) +I0616 15:55:16.997169 9857 solver.cpp:571] Iteration 83120, lr = 0.0001 +I0616 15:55:28.460042 9857 solver.cpp:242] Iteration 83140, loss = 0.84085 +I0616 15:55:28.460084 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.247331 (* 1 = 0.247331 loss) +I0616 15:55:28.460090 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28594 (* 1 = 0.28594 loss) +I0616 15:55:28.460094 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0423382 (* 1 = 0.0423382 loss) +I0616 15:55:28.460098 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0462211 (* 1 = 0.0462211 loss) +I0616 15:55:28.460101 9857 solver.cpp:571] Iteration 83140, lr = 0.0001 +I0616 15:55:40.171505 9857 solver.cpp:242] Iteration 83160, loss = 0.371998 +I0616 15:55:40.171548 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0910082 (* 1 = 0.0910082 loss) +I0616 15:55:40.171555 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136327 (* 1 = 0.136327 loss) +I0616 15:55:40.171558 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.011193 (* 1 = 0.011193 loss) +I0616 15:55:40.171562 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0368284 (* 1 = 0.0368284 loss) +I0616 15:55:40.171566 9857 solver.cpp:571] Iteration 83160, lr = 0.0001 +I0616 15:55:51.557842 9857 solver.cpp:242] Iteration 83180, loss = 0.259331 +I0616 15:55:51.557883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0425494 (* 1 = 0.0425494 loss) +I0616 15:55:51.557889 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0860397 (* 1 = 0.0860397 loss) +I0616 15:55:51.557893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00510564 (* 1 = 0.00510564 loss) +I0616 15:55:51.557896 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0164971 (* 1 = 0.0164971 loss) +I0616 15:55:51.557901 9857 solver.cpp:571] Iteration 83180, lr = 0.0001 +speed: 0.599s / iter +I0616 15:56:03.040431 9857 solver.cpp:242] Iteration 83200, loss = 0.601929 +I0616 15:56:03.040474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17851 (* 1 = 0.17851 loss) +I0616 15:56:03.040479 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275317 (* 1 = 0.275317 loss) +I0616 15:56:03.040484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0454999 (* 1 = 0.0454999 loss) +I0616 15:56:03.040488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00169329 (* 1 = 0.00169329 loss) +I0616 15:56:03.040491 9857 solver.cpp:571] Iteration 83200, lr = 0.0001 +I0616 15:56:14.492187 9857 solver.cpp:242] Iteration 83220, loss = 0.567891 +I0616 15:56:14.492228 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197155 (* 1 = 0.197155 loss) +I0616 15:56:14.492234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310903 (* 1 = 0.310903 loss) +I0616 15:56:14.492238 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0995771 (* 1 = 0.0995771 loss) +I0616 15:56:14.492241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198492 (* 1 = 0.0198492 loss) +I0616 15:56:14.492246 9857 solver.cpp:571] Iteration 83220, lr = 0.0001 +I0616 15:56:25.946173 9857 solver.cpp:242] Iteration 83240, loss = 0.782079 +I0616 15:56:25.946210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.399759 (* 1 = 0.399759 loss) +I0616 15:56:25.946215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.41675 (* 1 = 0.41675 loss) +I0616 15:56:25.946219 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.227845 (* 1 = 0.227845 loss) +I0616 15:56:25.946223 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0617998 (* 1 = 0.0617998 loss) +I0616 15:56:25.946228 9857 solver.cpp:571] Iteration 83240, lr = 0.0001 +I0616 15:56:37.559139 9857 solver.cpp:242] Iteration 83260, loss = 0.365885 +I0616 15:56:37.559180 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0748207 (* 1 = 0.0748207 loss) +I0616 15:56:37.559186 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1678 (* 1 = 0.1678 loss) +I0616 15:56:37.559191 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0050376 (* 1 = 0.0050376 loss) +I0616 15:56:37.559195 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104227 (* 1 = 0.0104227 loss) +I0616 15:56:37.559198 9857 solver.cpp:571] Iteration 83260, lr = 0.0001 +I0616 15:56:49.205986 9857 solver.cpp:242] Iteration 83280, loss = 0.448243 +I0616 15:56:49.206028 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204765 (* 1 = 0.204765 loss) +I0616 15:56:49.206034 9857 solver.cpp:258] Train net output #1: loss_cls = 0.282819 (* 1 = 0.282819 loss) +I0616 15:56:49.206038 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0944763 (* 1 = 0.0944763 loss) +I0616 15:56:49.206043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0323568 (* 1 = 0.0323568 loss) +I0616 15:56:49.206046 9857 solver.cpp:571] Iteration 83280, lr = 0.0001 +I0616 15:57:00.688262 9857 solver.cpp:242] Iteration 83300, loss = 0.687989 +I0616 15:57:00.688304 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725228 (* 1 = 0.0725228 loss) +I0616 15:57:00.688309 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160176 (* 1 = 0.160176 loss) +I0616 15:57:00.688314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.018243 (* 1 = 0.018243 loss) +I0616 15:57:00.688318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0258946 (* 1 = 0.0258946 loss) +I0616 15:57:00.688321 9857 solver.cpp:571] Iteration 83300, lr = 0.0001 +I0616 15:57:12.220664 9857 solver.cpp:242] Iteration 83320, loss = 0.177316 +I0616 15:57:12.220705 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0633579 (* 1 = 0.0633579 loss) +I0616 15:57:12.220711 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0710252 (* 1 = 0.0710252 loss) +I0616 15:57:12.220716 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00671052 (* 1 = 0.00671052 loss) +I0616 15:57:12.220720 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0049635 (* 1 = 0.0049635 loss) +I0616 15:57:12.220723 9857 solver.cpp:571] Iteration 83320, lr = 0.0001 +I0616 15:57:23.843190 9857 solver.cpp:242] Iteration 83340, loss = 0.747124 +I0616 15:57:23.843232 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0722747 (* 1 = 0.0722747 loss) +I0616 15:57:23.843238 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114523 (* 1 = 0.114523 loss) +I0616 15:57:23.843242 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0853685 (* 1 = 0.0853685 loss) +I0616 15:57:23.843246 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00462518 (* 1 = 0.00462518 loss) +I0616 15:57:23.843250 9857 solver.cpp:571] Iteration 83340, lr = 0.0001 +I0616 15:57:35.246963 9857 solver.cpp:242] Iteration 83360, loss = 0.708847 +I0616 15:57:35.247005 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112248 (* 1 = 0.112248 loss) +I0616 15:57:35.247011 9857 solver.cpp:258] Train net output #1: loss_cls = 0.199818 (* 1 = 0.199818 loss) +I0616 15:57:35.247015 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0184144 (* 1 = 0.0184144 loss) +I0616 15:57:35.247020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.021223 (* 1 = 0.021223 loss) +I0616 15:57:35.247023 9857 solver.cpp:571] Iteration 83360, lr = 0.0001 +I0616 15:57:46.749835 9857 solver.cpp:242] Iteration 83380, loss = 0.355401 +I0616 15:57:46.749877 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0543232 (* 1 = 0.0543232 loss) +I0616 15:57:46.749882 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0911271 (* 1 = 0.0911271 loss) +I0616 15:57:46.749887 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.000525202 (* 1 = 0.000525202 loss) +I0616 15:57:46.749891 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00967607 (* 1 = 0.00967607 loss) +I0616 15:57:46.749894 9857 solver.cpp:571] Iteration 83380, lr = 0.0001 +speed: 0.598s / iter +I0616 15:57:58.098516 9857 solver.cpp:242] Iteration 83400, loss = 0.229839 +I0616 15:57:58.098556 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0406909 (* 1 = 0.0406909 loss) +I0616 15:57:58.098562 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0805556 (* 1 = 0.0805556 loss) +I0616 15:57:58.098567 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00628978 (* 1 = 0.00628978 loss) +I0616 15:57:58.098570 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291895 (* 1 = 0.0291895 loss) +I0616 15:57:58.098574 9857 solver.cpp:571] Iteration 83400, lr = 0.0001 +I0616 15:58:09.479980 9857 solver.cpp:242] Iteration 83420, loss = 0.641448 +I0616 15:58:09.480023 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0914496 (* 1 = 0.0914496 loss) +I0616 15:58:09.480028 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217128 (* 1 = 0.217128 loss) +I0616 15:58:09.480033 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0181614 (* 1 = 0.0181614 loss) +I0616 15:58:09.480036 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0314755 (* 1 = 0.0314755 loss) +I0616 15:58:09.480041 9857 solver.cpp:571] Iteration 83420, lr = 0.0001 +I0616 15:58:20.928431 9857 solver.cpp:242] Iteration 83440, loss = 0.512258 +I0616 15:58:20.928472 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163906 (* 1 = 0.163906 loss) +I0616 15:58:20.928478 9857 solver.cpp:258] Train net output #1: loss_cls = 0.304144 (* 1 = 0.304144 loss) +I0616 15:58:20.928483 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0960626 (* 1 = 0.0960626 loss) +I0616 15:58:20.928486 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101688 (* 1 = 0.0101688 loss) +I0616 15:58:20.928489 9857 solver.cpp:571] Iteration 83440, lr = 0.0001 +I0616 15:58:32.432859 9857 solver.cpp:242] Iteration 83460, loss = 0.217207 +I0616 15:58:32.432900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0828409 (* 1 = 0.0828409 loss) +I0616 15:58:32.432906 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0795127 (* 1 = 0.0795127 loss) +I0616 15:58:32.432910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00176016 (* 1 = 0.00176016 loss) +I0616 15:58:32.432914 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0169081 (* 1 = 0.0169081 loss) +I0616 15:58:32.432919 9857 solver.cpp:571] Iteration 83460, lr = 0.0001 +I0616 15:58:43.857622 9857 solver.cpp:242] Iteration 83480, loss = 0.385283 +I0616 15:58:43.857663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141124 (* 1 = 0.141124 loss) +I0616 15:58:43.857669 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234728 (* 1 = 0.234728 loss) +I0616 15:58:43.857673 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0429074 (* 1 = 0.0429074 loss) +I0616 15:58:43.857677 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0314274 (* 1 = 0.0314274 loss) +I0616 15:58:43.857681 9857 solver.cpp:571] Iteration 83480, lr = 0.0001 +I0616 15:58:55.359704 9857 solver.cpp:242] Iteration 83500, loss = 0.594941 +I0616 15:58:55.359743 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.392 (* 1 = 0.392 loss) +I0616 15:58:55.359750 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344281 (* 1 = 0.344281 loss) +I0616 15:58:55.359753 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114781 (* 1 = 0.114781 loss) +I0616 15:58:55.359757 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0628159 (* 1 = 0.0628159 loss) +I0616 15:58:55.359761 9857 solver.cpp:571] Iteration 83500, lr = 0.0001 +I0616 15:59:06.855221 9857 solver.cpp:242] Iteration 83520, loss = 0.418531 +I0616 15:59:06.855262 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0617894 (* 1 = 0.0617894 loss) +I0616 15:59:06.855268 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113335 (* 1 = 0.113335 loss) +I0616 15:59:06.855271 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0148816 (* 1 = 0.0148816 loss) +I0616 15:59:06.855275 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00355753 (* 1 = 0.00355753 loss) +I0616 15:59:06.855278 9857 solver.cpp:571] Iteration 83520, lr = 0.0001 +I0616 15:59:18.284291 9857 solver.cpp:242] Iteration 83540, loss = 0.396656 +I0616 15:59:18.284332 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0689703 (* 1 = 0.0689703 loss) +I0616 15:59:18.284338 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150627 (* 1 = 0.150627 loss) +I0616 15:59:18.284343 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0440093 (* 1 = 0.0440093 loss) +I0616 15:59:18.284346 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00698986 (* 1 = 0.00698986 loss) +I0616 15:59:18.284350 9857 solver.cpp:571] Iteration 83540, lr = 0.0001 +I0616 15:59:29.925675 9857 solver.cpp:242] Iteration 83560, loss = 0.387243 +I0616 15:59:29.925719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0915071 (* 1 = 0.0915071 loss) +I0616 15:59:29.925724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131272 (* 1 = 0.131272 loss) +I0616 15:59:29.925727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0351076 (* 1 = 0.0351076 loss) +I0616 15:59:29.925731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0347251 (* 1 = 0.0347251 loss) +I0616 15:59:29.925735 9857 solver.cpp:571] Iteration 83560, lr = 0.0001 +I0616 15:59:41.356008 9857 solver.cpp:242] Iteration 83580, loss = 0.251836 +I0616 15:59:41.356050 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113238 (* 1 = 0.113238 loss) +I0616 15:59:41.356055 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0737378 (* 1 = 0.0737378 loss) +I0616 15:59:41.356060 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0223674 (* 1 = 0.0223674 loss) +I0616 15:59:41.356063 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0296281 (* 1 = 0.0296281 loss) +I0616 15:59:41.356067 9857 solver.cpp:571] Iteration 83580, lr = 0.0001 +speed: 0.598s / iter +I0616 15:59:52.758749 9857 solver.cpp:242] Iteration 83600, loss = 0.400507 +I0616 15:59:52.758795 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111142 (* 1 = 0.111142 loss) +I0616 15:59:52.758800 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29732 (* 1 = 0.29732 loss) +I0616 15:59:52.758803 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0607128 (* 1 = 0.0607128 loss) +I0616 15:59:52.758807 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00300006 (* 1 = 0.00300006 loss) +I0616 15:59:52.758811 9857 solver.cpp:571] Iteration 83600, lr = 0.0001 +I0616 16:00:04.287513 9857 solver.cpp:242] Iteration 83620, loss = 1.11108 +I0616 16:00:04.287555 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.312569 (* 1 = 0.312569 loss) +I0616 16:00:04.287561 9857 solver.cpp:258] Train net output #1: loss_cls = 0.394066 (* 1 = 0.394066 loss) +I0616 16:00:04.287565 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.225347 (* 1 = 0.225347 loss) +I0616 16:00:04.287569 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.124876 (* 1 = 0.124876 loss) +I0616 16:00:04.287574 9857 solver.cpp:571] Iteration 83620, lr = 0.0001 +I0616 16:00:15.723225 9857 solver.cpp:242] Iteration 83640, loss = 0.217856 +I0616 16:00:15.723268 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0627624 (* 1 = 0.0627624 loss) +I0616 16:00:15.723274 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154504 (* 1 = 0.154504 loss) +I0616 16:00:15.723278 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0606402 (* 1 = 0.0606402 loss) +I0616 16:00:15.723283 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00806218 (* 1 = 0.00806218 loss) +I0616 16:00:15.723285 9857 solver.cpp:571] Iteration 83640, lr = 0.0001 +I0616 16:00:27.104343 9857 solver.cpp:242] Iteration 83660, loss = 1.10611 +I0616 16:00:27.104385 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342489 (* 1 = 0.342489 loss) +I0616 16:00:27.104392 9857 solver.cpp:258] Train net output #1: loss_cls = 0.341331 (* 1 = 0.341331 loss) +I0616 16:00:27.104395 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219471 (* 1 = 0.219471 loss) +I0616 16:00:27.104399 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.633493 (* 1 = 0.633493 loss) +I0616 16:00:27.104403 9857 solver.cpp:571] Iteration 83660, lr = 0.0001 +I0616 16:00:38.654598 9857 solver.cpp:242] Iteration 83680, loss = 0.458472 +I0616 16:00:38.654640 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192692 (* 1 = 0.192692 loss) +I0616 16:00:38.654646 9857 solver.cpp:258] Train net output #1: loss_cls = 0.280719 (* 1 = 0.280719 loss) +I0616 16:00:38.654650 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0651112 (* 1 = 0.0651112 loss) +I0616 16:00:38.654654 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0470955 (* 1 = 0.0470955 loss) +I0616 16:00:38.654657 9857 solver.cpp:571] Iteration 83680, lr = 0.0001 +I0616 16:00:50.086175 9857 solver.cpp:242] Iteration 83700, loss = 0.79604 +I0616 16:00:50.086217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228237 (* 1 = 0.228237 loss) +I0616 16:00:50.086223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.420974 (* 1 = 0.420974 loss) +I0616 16:00:50.086227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.150334 (* 1 = 0.150334 loss) +I0616 16:00:50.086231 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.431439 (* 1 = 0.431439 loss) +I0616 16:00:50.086236 9857 solver.cpp:571] Iteration 83700, lr = 0.0001 +I0616 16:01:01.579944 9857 solver.cpp:242] Iteration 83720, loss = 0.457828 +I0616 16:01:01.579985 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0785023 (* 1 = 0.0785023 loss) +I0616 16:01:01.579991 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160368 (* 1 = 0.160368 loss) +I0616 16:01:01.579995 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00469656 (* 1 = 0.00469656 loss) +I0616 16:01:01.579999 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104031 (* 1 = 0.0104031 loss) +I0616 16:01:01.580003 9857 solver.cpp:571] Iteration 83720, lr = 0.0001 +I0616 16:01:13.019139 9857 solver.cpp:242] Iteration 83740, loss = 0.606372 +I0616 16:01:13.019179 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145794 (* 1 = 0.145794 loss) +I0616 16:01:13.019184 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166718 (* 1 = 0.166718 loss) +I0616 16:01:13.019188 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0228433 (* 1 = 0.0228433 loss) +I0616 16:01:13.019192 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0339057 (* 1 = 0.0339057 loss) +I0616 16:01:13.019197 9857 solver.cpp:571] Iteration 83740, lr = 0.0001 +I0616 16:01:24.526567 9857 solver.cpp:242] Iteration 83760, loss = 0.307422 +I0616 16:01:24.526609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0616041 (* 1 = 0.0616041 loss) +I0616 16:01:24.526614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0469752 (* 1 = 0.0469752 loss) +I0616 16:01:24.526619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0335707 (* 1 = 0.0335707 loss) +I0616 16:01:24.526623 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0171719 (* 1 = 0.0171719 loss) +I0616 16:01:24.526629 9857 solver.cpp:571] Iteration 83760, lr = 0.0001 +I0616 16:01:36.023000 9857 solver.cpp:242] Iteration 83780, loss = 1.09904 +I0616 16:01:36.023042 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0640192 (* 1 = 0.0640192 loss) +I0616 16:01:36.023048 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124708 (* 1 = 0.124708 loss) +I0616 16:01:36.023052 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0473597 (* 1 = 0.0473597 loss) +I0616 16:01:36.023056 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0148819 (* 1 = 0.0148819 loss) +I0616 16:01:36.023059 9857 solver.cpp:571] Iteration 83780, lr = 0.0001 +speed: 0.598s / iter +I0616 16:01:47.270660 9857 solver.cpp:242] Iteration 83800, loss = 0.76378 +I0616 16:01:47.270704 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.157207 (* 1 = 0.157207 loss) +I0616 16:01:47.270709 9857 solver.cpp:258] Train net output #1: loss_cls = 0.141871 (* 1 = 0.141871 loss) +I0616 16:01:47.270714 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0633858 (* 1 = 0.0633858 loss) +I0616 16:01:47.270716 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0288037 (* 1 = 0.0288037 loss) +I0616 16:01:47.270720 9857 solver.cpp:571] Iteration 83800, lr = 0.0001 +I0616 16:01:59.107846 9857 solver.cpp:242] Iteration 83820, loss = 0.410987 +I0616 16:01:59.107887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0603265 (* 1 = 0.0603265 loss) +I0616 16:01:59.107892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115564 (* 1 = 0.115564 loss) +I0616 16:01:59.107895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128986 (* 1 = 0.128986 loss) +I0616 16:01:59.107899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00168638 (* 1 = 0.00168638 loss) +I0616 16:01:59.107903 9857 solver.cpp:571] Iteration 83820, lr = 0.0001 +I0616 16:02:10.518594 9857 solver.cpp:242] Iteration 83840, loss = 0.199636 +I0616 16:02:10.518633 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0618215 (* 1 = 0.0618215 loss) +I0616 16:02:10.518640 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0939768 (* 1 = 0.0939768 loss) +I0616 16:02:10.518643 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00449434 (* 1 = 0.00449434 loss) +I0616 16:02:10.518647 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00107633 (* 1 = 0.00107633 loss) +I0616 16:02:10.518651 9857 solver.cpp:571] Iteration 83840, lr = 0.0001 +I0616 16:02:22.025538 9857 solver.cpp:242] Iteration 83860, loss = 0.424531 +I0616 16:02:22.025580 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292753 (* 1 = 0.292753 loss) +I0616 16:02:22.025586 9857 solver.cpp:258] Train net output #1: loss_cls = 0.321263 (* 1 = 0.321263 loss) +I0616 16:02:22.025590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0698276 (* 1 = 0.0698276 loss) +I0616 16:02:22.025594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0321318 (* 1 = 0.0321318 loss) +I0616 16:02:22.025598 9857 solver.cpp:571] Iteration 83860, lr = 0.0001 +I0616 16:02:33.577381 9857 solver.cpp:242] Iteration 83880, loss = 0.437757 +I0616 16:02:33.577425 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146422 (* 1 = 0.146422 loss) +I0616 16:02:33.577430 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245128 (* 1 = 0.245128 loss) +I0616 16:02:33.577435 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0439733 (* 1 = 0.0439733 loss) +I0616 16:02:33.577438 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0247717 (* 1 = 0.0247717 loss) +I0616 16:02:33.577442 9857 solver.cpp:571] Iteration 83880, lr = 0.0001 +I0616 16:02:45.137970 9857 solver.cpp:242] Iteration 83900, loss = 0.3548 +I0616 16:02:45.138012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118896 (* 1 = 0.118896 loss) +I0616 16:02:45.138017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269296 (* 1 = 0.269296 loss) +I0616 16:02:45.138021 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0358435 (* 1 = 0.0358435 loss) +I0616 16:02:45.138025 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00524792 (* 1 = 0.00524792 loss) +I0616 16:02:45.138030 9857 solver.cpp:571] Iteration 83900, lr = 0.0001 +I0616 16:02:56.587333 9857 solver.cpp:242] Iteration 83920, loss = 0.758385 +I0616 16:02:56.587375 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0868933 (* 1 = 0.0868933 loss) +I0616 16:02:56.587380 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15404 (* 1 = 0.15404 loss) +I0616 16:02:56.587385 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0942726 (* 1 = 0.0942726 loss) +I0616 16:02:56.587388 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00241826 (* 1 = 0.00241826 loss) +I0616 16:02:56.587393 9857 solver.cpp:571] Iteration 83920, lr = 0.0001 +I0616 16:03:08.375576 9857 solver.cpp:242] Iteration 83940, loss = 0.756772 +I0616 16:03:08.375619 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209462 (* 1 = 0.209462 loss) +I0616 16:03:08.375624 9857 solver.cpp:258] Train net output #1: loss_cls = 0.337285 (* 1 = 0.337285 loss) +I0616 16:03:08.375629 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162029 (* 1 = 0.162029 loss) +I0616 16:03:08.375633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.159313 (* 1 = 0.159313 loss) +I0616 16:03:08.375636 9857 solver.cpp:571] Iteration 83940, lr = 0.0001 +I0616 16:03:19.969538 9857 solver.cpp:242] Iteration 83960, loss = 0.583092 +I0616 16:03:19.969579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0946056 (* 1 = 0.0946056 loss) +I0616 16:03:19.969584 9857 solver.cpp:258] Train net output #1: loss_cls = 0.228342 (* 1 = 0.228342 loss) +I0616 16:03:19.969589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0854026 (* 1 = 0.0854026 loss) +I0616 16:03:19.969594 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00989961 (* 1 = 0.00989961 loss) +I0616 16:03:19.969597 9857 solver.cpp:571] Iteration 83960, lr = 0.0001 +I0616 16:03:31.434079 9857 solver.cpp:242] Iteration 83980, loss = 0.270366 +I0616 16:03:31.434121 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144096 (* 1 = 0.144096 loss) +I0616 16:03:31.434126 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139094 (* 1 = 0.139094 loss) +I0616 16:03:31.434131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0044333 (* 1 = 0.0044333 loss) +I0616 16:03:31.434134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0447751 (* 1 = 0.0447751 loss) +I0616 16:03:31.434139 9857 solver.cpp:571] Iteration 83980, lr = 0.0001 +speed: 0.598s / iter +I0616 16:03:43.172767 9857 solver.cpp:242] Iteration 84000, loss = 0.356773 +I0616 16:03:43.172807 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237176 (* 1 = 0.237176 loss) +I0616 16:03:43.172813 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157395 (* 1 = 0.157395 loss) +I0616 16:03:43.172817 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139981 (* 1 = 0.0139981 loss) +I0616 16:03:43.172821 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0475814 (* 1 = 0.0475814 loss) +I0616 16:03:43.172824 9857 solver.cpp:571] Iteration 84000, lr = 0.0001 +I0616 16:03:54.791075 9857 solver.cpp:242] Iteration 84020, loss = 0.518002 +I0616 16:03:54.791116 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.262621 (* 1 = 0.262621 loss) +I0616 16:03:54.791122 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286323 (* 1 = 0.286323 loss) +I0616 16:03:54.791126 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0746863 (* 1 = 0.0746863 loss) +I0616 16:03:54.791131 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0339252 (* 1 = 0.0339252 loss) +I0616 16:03:54.791133 9857 solver.cpp:571] Iteration 84020, lr = 0.0001 +I0616 16:04:06.205816 9857 solver.cpp:242] Iteration 84040, loss = 0.792349 +I0616 16:04:06.205858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190338 (* 1 = 0.190338 loss) +I0616 16:04:06.205864 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279637 (* 1 = 0.279637 loss) +I0616 16:04:06.205869 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0405434 (* 1 = 0.0405434 loss) +I0616 16:04:06.205873 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0849686 (* 1 = 0.0849686 loss) +I0616 16:04:06.205876 9857 solver.cpp:571] Iteration 84040, lr = 0.0001 +I0616 16:04:17.729717 9857 solver.cpp:242] Iteration 84060, loss = 0.579991 +I0616 16:04:17.729758 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.362674 (* 1 = 0.362674 loss) +I0616 16:04:17.729764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.384816 (* 1 = 0.384816 loss) +I0616 16:04:17.729768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102766 (* 1 = 0.102766 loss) +I0616 16:04:17.729773 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0371518 (* 1 = 0.0371518 loss) +I0616 16:04:17.729775 9857 solver.cpp:571] Iteration 84060, lr = 0.0001 +I0616 16:04:29.180692 9857 solver.cpp:242] Iteration 84080, loss = 0.782538 +I0616 16:04:29.180734 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.234515 (* 1 = 0.234515 loss) +I0616 16:04:29.180739 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418989 (* 1 = 0.418989 loss) +I0616 16:04:29.180744 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.10762 (* 1 = 0.10762 loss) +I0616 16:04:29.180748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0817607 (* 1 = 0.0817607 loss) +I0616 16:04:29.180752 9857 solver.cpp:571] Iteration 84080, lr = 0.0001 +I0616 16:04:40.690032 9857 solver.cpp:242] Iteration 84100, loss = 0.415998 +I0616 16:04:40.690070 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.147747 (* 1 = 0.147747 loss) +I0616 16:04:40.690078 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0970566 (* 1 = 0.0970566 loss) +I0616 16:04:40.690081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136093 (* 1 = 0.136093 loss) +I0616 16:04:40.690084 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.267433 (* 1 = 0.267433 loss) +I0616 16:04:40.690088 9857 solver.cpp:571] Iteration 84100, lr = 0.0001 +I0616 16:04:52.355114 9857 solver.cpp:242] Iteration 84120, loss = 0.465828 +I0616 16:04:52.355156 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0944226 (* 1 = 0.0944226 loss) +I0616 16:04:52.355162 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0853527 (* 1 = 0.0853527 loss) +I0616 16:04:52.355166 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114811 (* 1 = 0.0114811 loss) +I0616 16:04:52.355170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0146788 (* 1 = 0.0146788 loss) +I0616 16:04:52.355173 9857 solver.cpp:571] Iteration 84120, lr = 0.0001 +I0616 16:05:03.810199 9857 solver.cpp:242] Iteration 84140, loss = 0.557908 +I0616 16:05:03.810242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.379071 (* 1 = 0.379071 loss) +I0616 16:05:03.810247 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49445 (* 1 = 0.49445 loss) +I0616 16:05:03.810252 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0682088 (* 1 = 0.0682088 loss) +I0616 16:05:03.810256 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0258872 (* 1 = 0.0258872 loss) +I0616 16:05:03.810259 9857 solver.cpp:571] Iteration 84140, lr = 0.0001 +I0616 16:05:15.490844 9857 solver.cpp:242] Iteration 84160, loss = 1.27473 +I0616 16:05:15.490886 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.326739 (* 1 = 0.326739 loss) +I0616 16:05:15.490891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.4218 (* 1 = 0.4218 loss) +I0616 16:05:15.490896 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.190684 (* 1 = 0.190684 loss) +I0616 16:05:15.490900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104345 (* 1 = 0.104345 loss) +I0616 16:05:15.490903 9857 solver.cpp:571] Iteration 84160, lr = 0.0001 +I0616 16:05:26.914970 9857 solver.cpp:242] Iteration 84180, loss = 0.621158 +I0616 16:05:26.915011 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16776 (* 1 = 0.16776 loss) +I0616 16:05:26.915017 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22951 (* 1 = 0.22951 loss) +I0616 16:05:26.915021 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0816525 (* 1 = 0.0816525 loss) +I0616 16:05:26.915025 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00391854 (* 1 = 0.00391854 loss) +I0616 16:05:26.915029 9857 solver.cpp:571] Iteration 84180, lr = 0.0001 +speed: 0.598s / iter +I0616 16:05:38.312185 9857 solver.cpp:242] Iteration 84200, loss = 0.525501 +I0616 16:05:38.312225 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.194575 (* 1 = 0.194575 loss) +I0616 16:05:38.312232 9857 solver.cpp:258] Train net output #1: loss_cls = 0.2071 (* 1 = 0.2071 loss) +I0616 16:05:38.312235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0710392 (* 1 = 0.0710392 loss) +I0616 16:05:38.312239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.191977 (* 1 = 0.191977 loss) +I0616 16:05:38.312243 9857 solver.cpp:571] Iteration 84200, lr = 0.0001 +I0616 16:05:49.787144 9857 solver.cpp:242] Iteration 84220, loss = 0.677913 +I0616 16:05:49.787184 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424574 (* 1 = 0.424574 loss) +I0616 16:05:49.787190 9857 solver.cpp:258] Train net output #1: loss_cls = 0.441039 (* 1 = 0.441039 loss) +I0616 16:05:49.787195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0871087 (* 1 = 0.0871087 loss) +I0616 16:05:49.787199 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0429165 (* 1 = 0.0429165 loss) +I0616 16:05:49.787202 9857 solver.cpp:571] Iteration 84220, lr = 0.0001 +I0616 16:06:01.289751 9857 solver.cpp:242] Iteration 84240, loss = 0.389231 +I0616 16:06:01.289791 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.132664 (* 1 = 0.132664 loss) +I0616 16:06:01.289798 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161342 (* 1 = 0.161342 loss) +I0616 16:06:01.289801 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0196065 (* 1 = 0.0196065 loss) +I0616 16:06:01.289805 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00223904 (* 1 = 0.00223904 loss) +I0616 16:06:01.289809 9857 solver.cpp:571] Iteration 84240, lr = 0.0001 +I0616 16:06:12.628002 9857 solver.cpp:242] Iteration 84260, loss = 0.439342 +I0616 16:06:12.628043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192023 (* 1 = 0.192023 loss) +I0616 16:06:12.628049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.159848 (* 1 = 0.159848 loss) +I0616 16:06:12.628053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0331659 (* 1 = 0.0331659 loss) +I0616 16:06:12.628057 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00967329 (* 1 = 0.00967329 loss) +I0616 16:06:12.628062 9857 solver.cpp:571] Iteration 84260, lr = 0.0001 +I0616 16:06:24.456449 9857 solver.cpp:242] Iteration 84280, loss = 0.339177 +I0616 16:06:24.456490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154441 (* 1 = 0.154441 loss) +I0616 16:06:24.456495 9857 solver.cpp:258] Train net output #1: loss_cls = 0.198747 (* 1 = 0.198747 loss) +I0616 16:06:24.456498 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.015877 (* 1 = 0.015877 loss) +I0616 16:06:24.456502 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0394248 (* 1 = 0.0394248 loss) +I0616 16:06:24.456506 9857 solver.cpp:571] Iteration 84280, lr = 0.0001 +I0616 16:06:36.033638 9857 solver.cpp:242] Iteration 84300, loss = 0.621979 +I0616 16:06:36.033675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.302167 (* 1 = 0.302167 loss) +I0616 16:06:36.033681 9857 solver.cpp:258] Train net output #1: loss_cls = 0.581908 (* 1 = 0.581908 loss) +I0616 16:06:36.033685 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0412752 (* 1 = 0.0412752 loss) +I0616 16:06:36.033689 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0513546 (* 1 = 0.0513546 loss) +I0616 16:06:36.033694 9857 solver.cpp:571] Iteration 84300, lr = 0.0001 +I0616 16:06:47.466051 9857 solver.cpp:242] Iteration 84320, loss = 0.868477 +I0616 16:06:47.466091 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365234 (* 1 = 0.365234 loss) +I0616 16:06:47.466096 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368031 (* 1 = 0.368031 loss) +I0616 16:06:47.466101 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.380044 (* 1 = 0.380044 loss) +I0616 16:06:47.466105 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107809 (* 1 = 0.107809 loss) +I0616 16:06:47.466109 9857 solver.cpp:571] Iteration 84320, lr = 0.0001 +I0616 16:06:58.668107 9857 solver.cpp:242] Iteration 84340, loss = 0.238078 +I0616 16:06:58.668148 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725708 (* 1 = 0.0725708 loss) +I0616 16:06:58.668154 9857 solver.cpp:258] Train net output #1: loss_cls = 0.066055 (* 1 = 0.066055 loss) +I0616 16:06:58.668157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00196593 (* 1 = 0.00196593 loss) +I0616 16:06:58.668161 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0320739 (* 1 = 0.0320739 loss) +I0616 16:06:58.668165 9857 solver.cpp:571] Iteration 84340, lr = 0.0001 +I0616 16:07:10.308089 9857 solver.cpp:242] Iteration 84360, loss = 0.839814 +I0616 16:07:10.308130 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.409998 (* 1 = 0.409998 loss) +I0616 16:07:10.308135 9857 solver.cpp:258] Train net output #1: loss_cls = 0.51048 (* 1 = 0.51048 loss) +I0616 16:07:10.308140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.192766 (* 1 = 0.192766 loss) +I0616 16:07:10.308143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0949536 (* 1 = 0.0949536 loss) +I0616 16:07:10.308147 9857 solver.cpp:571] Iteration 84360, lr = 0.0001 +I0616 16:07:21.750036 9857 solver.cpp:242] Iteration 84380, loss = 0.437438 +I0616 16:07:21.750078 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.202345 (* 1 = 0.202345 loss) +I0616 16:07:21.750084 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149067 (* 1 = 0.149067 loss) +I0616 16:07:21.750088 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.013224 (* 1 = 0.013224 loss) +I0616 16:07:21.750092 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0729686 (* 1 = 0.0729686 loss) +I0616 16:07:21.750097 9857 solver.cpp:571] Iteration 84380, lr = 0.0001 +speed: 0.598s / iter +I0616 16:07:32.883901 9857 solver.cpp:242] Iteration 84400, loss = 0.448958 +I0616 16:07:32.883946 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0854681 (* 1 = 0.0854681 loss) +I0616 16:07:32.883951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135177 (* 1 = 0.135177 loss) +I0616 16:07:32.883955 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.025622 (* 1 = 0.025622 loss) +I0616 16:07:32.883960 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404557 (* 1 = 0.0404557 loss) +I0616 16:07:32.883962 9857 solver.cpp:571] Iteration 84400, lr = 0.0001 +I0616 16:07:44.262886 9857 solver.cpp:242] Iteration 84420, loss = 0.448711 +I0616 16:07:44.262929 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.208641 (* 1 = 0.208641 loss) +I0616 16:07:44.262934 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21727 (* 1 = 0.21727 loss) +I0616 16:07:44.262939 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0892817 (* 1 = 0.0892817 loss) +I0616 16:07:44.262943 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0591793 (* 1 = 0.0591793 loss) +I0616 16:07:44.262946 9857 solver.cpp:571] Iteration 84420, lr = 0.0001 +I0616 16:07:55.630355 9857 solver.cpp:242] Iteration 84440, loss = 0.514119 +I0616 16:07:55.630398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196485 (* 1 = 0.196485 loss) +I0616 16:07:55.630403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.325068 (* 1 = 0.325068 loss) +I0616 16:07:55.630409 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0956511 (* 1 = 0.0956511 loss) +I0616 16:07:55.630412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0906622 (* 1 = 0.0906622 loss) +I0616 16:07:55.630415 9857 solver.cpp:571] Iteration 84440, lr = 0.0001 +I0616 16:08:07.150727 9857 solver.cpp:242] Iteration 84460, loss = 0.501093 +I0616 16:08:07.150789 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283167 (* 1 = 0.283167 loss) +I0616 16:08:07.150794 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229013 (* 1 = 0.229013 loss) +I0616 16:08:07.150813 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0529353 (* 1 = 0.0529353 loss) +I0616 16:08:07.150817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0465565 (* 1 = 0.0465565 loss) +I0616 16:08:07.150821 9857 solver.cpp:571] Iteration 84460, lr = 0.0001 +I0616 16:08:18.652623 9857 solver.cpp:242] Iteration 84480, loss = 0.471544 +I0616 16:08:18.652664 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0636022 (* 1 = 0.0636022 loss) +I0616 16:08:18.652670 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0963846 (* 1 = 0.0963846 loss) +I0616 16:08:18.652674 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0325844 (* 1 = 0.0325844 loss) +I0616 16:08:18.652678 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105311 (* 1 = 0.0105311 loss) +I0616 16:08:18.652681 9857 solver.cpp:571] Iteration 84480, lr = 0.0001 +I0616 16:08:30.269762 9857 solver.cpp:242] Iteration 84500, loss = 0.526433 +I0616 16:08:30.269804 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.175532 (* 1 = 0.175532 loss) +I0616 16:08:30.269810 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177913 (* 1 = 0.177913 loss) +I0616 16:08:30.269814 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.133945 (* 1 = 0.133945 loss) +I0616 16:08:30.269819 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0500864 (* 1 = 0.0500864 loss) +I0616 16:08:30.269822 9857 solver.cpp:571] Iteration 84500, lr = 0.0001 +I0616 16:08:41.649724 9857 solver.cpp:242] Iteration 84520, loss = 0.644244 +I0616 16:08:41.649765 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394361 (* 1 = 0.394361 loss) +I0616 16:08:41.649770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232499 (* 1 = 0.232499 loss) +I0616 16:08:41.649775 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0462481 (* 1 = 0.0462481 loss) +I0616 16:08:41.649778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0358243 (* 1 = 0.0358243 loss) +I0616 16:08:41.649782 9857 solver.cpp:571] Iteration 84520, lr = 0.0001 +I0616 16:08:52.975251 9857 solver.cpp:242] Iteration 84540, loss = 0.701805 +I0616 16:08:52.975289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.219124 (* 1 = 0.219124 loss) +I0616 16:08:52.975296 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153615 (* 1 = 0.153615 loss) +I0616 16:08:52.975301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0235985 (* 1 = 0.0235985 loss) +I0616 16:08:52.975304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0716341 (* 1 = 0.0716341 loss) +I0616 16:08:52.975307 9857 solver.cpp:571] Iteration 84540, lr = 0.0001 +I0616 16:09:04.063865 9857 solver.cpp:242] Iteration 84560, loss = 0.558268 +I0616 16:09:04.063908 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125604 (* 1 = 0.125604 loss) +I0616 16:09:04.063915 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0806211 (* 1 = 0.0806211 loss) +I0616 16:09:04.063918 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0285634 (* 1 = 0.0285634 loss) +I0616 16:09:04.063922 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0106963 (* 1 = 0.0106963 loss) +I0616 16:09:04.063926 9857 solver.cpp:571] Iteration 84560, lr = 0.0001 +I0616 16:09:15.478961 9857 solver.cpp:242] Iteration 84580, loss = 0.229095 +I0616 16:09:15.479003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0669595 (* 1 = 0.0669595 loss) +I0616 16:09:15.479008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110044 (* 1 = 0.110044 loss) +I0616 16:09:15.479012 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0819416 (* 1 = 0.0819416 loss) +I0616 16:09:15.479017 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00589069 (* 1 = 0.00589069 loss) +I0616 16:09:15.479020 9857 solver.cpp:571] Iteration 84580, lr = 0.0001 +speed: 0.598s / iter +I0616 16:09:27.060832 9857 solver.cpp:242] Iteration 84600, loss = 0.445589 +I0616 16:09:27.060873 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222131 (* 1 = 0.222131 loss) +I0616 16:09:27.060878 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152928 (* 1 = 0.152928 loss) +I0616 16:09:27.060883 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114294 (* 1 = 0.0114294 loss) +I0616 16:09:27.060885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0300973 (* 1 = 0.0300973 loss) +I0616 16:09:27.060889 9857 solver.cpp:571] Iteration 84600, lr = 0.0001 +I0616 16:09:38.787211 9857 solver.cpp:242] Iteration 84620, loss = 0.251196 +I0616 16:09:38.787252 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112893 (* 1 = 0.112893 loss) +I0616 16:09:38.787258 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146732 (* 1 = 0.146732 loss) +I0616 16:09:38.787262 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0308688 (* 1 = 0.0308688 loss) +I0616 16:09:38.787266 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00540199 (* 1 = 0.00540199 loss) +I0616 16:09:38.787269 9857 solver.cpp:571] Iteration 84620, lr = 0.0001 +I0616 16:09:50.314376 9857 solver.cpp:242] Iteration 84640, loss = 0.63352 +I0616 16:09:50.314419 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0938151 (* 1 = 0.0938151 loss) +I0616 16:09:50.314424 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185715 (* 1 = 0.185715 loss) +I0616 16:09:50.314429 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00923741 (* 1 = 0.00923741 loss) +I0616 16:09:50.314434 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0268335 (* 1 = 0.0268335 loss) +I0616 16:09:50.314436 9857 solver.cpp:571] Iteration 84640, lr = 0.0001 +I0616 16:10:01.935434 9857 solver.cpp:242] Iteration 84660, loss = 0.562012 +I0616 16:10:01.935478 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203539 (* 1 = 0.203539 loss) +I0616 16:10:01.935484 9857 solver.cpp:258] Train net output #1: loss_cls = 0.170515 (* 1 = 0.170515 loss) +I0616 16:10:01.935488 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0438339 (* 1 = 0.0438339 loss) +I0616 16:10:01.935492 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0474579 (* 1 = 0.0474579 loss) +I0616 16:10:01.935497 9857 solver.cpp:571] Iteration 84660, lr = 0.0001 +I0616 16:10:13.185896 9857 solver.cpp:242] Iteration 84680, loss = 0.431247 +I0616 16:10:13.185940 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120777 (* 1 = 0.120777 loss) +I0616 16:10:13.185945 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135557 (* 1 = 0.135557 loss) +I0616 16:10:13.185950 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0491899 (* 1 = 0.0491899 loss) +I0616 16:10:13.185953 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0297971 (* 1 = 0.0297971 loss) +I0616 16:10:13.185957 9857 solver.cpp:571] Iteration 84680, lr = 0.0001 +I0616 16:10:24.653295 9857 solver.cpp:242] Iteration 84700, loss = 0.406495 +I0616 16:10:24.653337 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0789892 (* 1 = 0.0789892 loss) +I0616 16:10:24.653343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147999 (* 1 = 0.147999 loss) +I0616 16:10:24.653347 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0261065 (* 1 = 0.0261065 loss) +I0616 16:10:24.653352 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00688452 (* 1 = 0.00688452 loss) +I0616 16:10:24.653355 9857 solver.cpp:571] Iteration 84700, lr = 0.0001 +I0616 16:10:36.123414 9857 solver.cpp:242] Iteration 84720, loss = 0.580063 +I0616 16:10:36.123453 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0425348 (* 1 = 0.0425348 loss) +I0616 16:10:36.123459 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0597369 (* 1 = 0.0597369 loss) +I0616 16:10:36.123463 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0169119 (* 1 = 0.0169119 loss) +I0616 16:10:36.123466 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132945 (* 1 = 0.0132945 loss) +I0616 16:10:36.123471 9857 solver.cpp:571] Iteration 84720, lr = 0.0001 +I0616 16:10:47.596309 9857 solver.cpp:242] Iteration 84740, loss = 0.884009 +I0616 16:10:47.596351 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338081 (* 1 = 0.338081 loss) +I0616 16:10:47.596357 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281153 (* 1 = 0.281153 loss) +I0616 16:10:47.596361 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.207921 (* 1 = 0.207921 loss) +I0616 16:10:47.596365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0656721 (* 1 = 0.0656721 loss) +I0616 16:10:47.596369 9857 solver.cpp:571] Iteration 84740, lr = 0.0001 +I0616 16:10:59.385071 9857 solver.cpp:242] Iteration 84760, loss = 0.440691 +I0616 16:10:59.385113 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222365 (* 1 = 0.222365 loss) +I0616 16:10:59.385118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.193511 (* 1 = 0.193511 loss) +I0616 16:10:59.385123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0357098 (* 1 = 0.0357098 loss) +I0616 16:10:59.385126 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.039603 (* 1 = 0.039603 loss) +I0616 16:10:59.385130 9857 solver.cpp:571] Iteration 84760, lr = 0.0001 +I0616 16:11:10.965121 9857 solver.cpp:242] Iteration 84780, loss = 0.553752 +I0616 16:11:10.965163 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.208528 (* 1 = 0.208528 loss) +I0616 16:11:10.965169 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222773 (* 1 = 0.222773 loss) +I0616 16:11:10.965173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.108589 (* 1 = 0.108589 loss) +I0616 16:11:10.965178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.268569 (* 1 = 0.268569 loss) +I0616 16:11:10.965181 9857 solver.cpp:571] Iteration 84780, lr = 0.0001 +speed: 0.598s / iter +I0616 16:11:22.676687 9857 solver.cpp:242] Iteration 84800, loss = 0.347252 +I0616 16:11:22.676730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0764338 (* 1 = 0.0764338 loss) +I0616 16:11:22.676736 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219575 (* 1 = 0.219575 loss) +I0616 16:11:22.676740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.01946 (* 1 = 0.01946 loss) +I0616 16:11:22.676744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0053537 (* 1 = 0.0053537 loss) +I0616 16:11:22.676748 9857 solver.cpp:571] Iteration 84800, lr = 0.0001 +I0616 16:11:34.286458 9857 solver.cpp:242] Iteration 84820, loss = 0.971717 +I0616 16:11:34.286500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.425966 (* 1 = 0.425966 loss) +I0616 16:11:34.286505 9857 solver.cpp:258] Train net output #1: loss_cls = 0.430753 (* 1 = 0.430753 loss) +I0616 16:11:34.286510 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0513654 (* 1 = 0.0513654 loss) +I0616 16:11:34.286514 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132998 (* 1 = 0.0132998 loss) +I0616 16:11:34.286517 9857 solver.cpp:571] Iteration 84820, lr = 0.0001 +I0616 16:11:45.847220 9857 solver.cpp:242] Iteration 84840, loss = 0.36588 +I0616 16:11:45.847264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.129445 (* 1 = 0.129445 loss) +I0616 16:11:45.847270 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204855 (* 1 = 0.204855 loss) +I0616 16:11:45.847275 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0170334 (* 1 = 0.0170334 loss) +I0616 16:11:45.847277 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.114905 (* 1 = 0.114905 loss) +I0616 16:11:45.847281 9857 solver.cpp:571] Iteration 84840, lr = 0.0001 +I0616 16:11:57.283992 9857 solver.cpp:242] Iteration 84860, loss = 0.750845 +I0616 16:11:57.284034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292611 (* 1 = 0.292611 loss) +I0616 16:11:57.284039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.437813 (* 1 = 0.437813 loss) +I0616 16:11:57.284042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.181857 (* 1 = 0.181857 loss) +I0616 16:11:57.284046 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.163793 (* 1 = 0.163793 loss) +I0616 16:11:57.284050 9857 solver.cpp:571] Iteration 84860, lr = 0.0001 +I0616 16:12:08.752236 9857 solver.cpp:242] Iteration 84880, loss = 0.719803 +I0616 16:12:08.752279 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173251 (* 1 = 0.173251 loss) +I0616 16:12:08.752285 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210699 (* 1 = 0.210699 loss) +I0616 16:12:08.752288 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0113251 (* 1 = 0.0113251 loss) +I0616 16:12:08.752292 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0102972 (* 1 = 0.0102972 loss) +I0616 16:12:08.752296 9857 solver.cpp:571] Iteration 84880, lr = 0.0001 +I0616 16:12:20.419808 9857 solver.cpp:242] Iteration 84900, loss = 0.386351 +I0616 16:12:20.419852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102332 (* 1 = 0.102332 loss) +I0616 16:12:20.419857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210048 (* 1 = 0.210048 loss) +I0616 16:12:20.419862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0628925 (* 1 = 0.0628925 loss) +I0616 16:12:20.419865 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.03411 (* 1 = 0.03411 loss) +I0616 16:12:20.419868 9857 solver.cpp:571] Iteration 84900, lr = 0.0001 +I0616 16:12:32.041080 9857 solver.cpp:242] Iteration 84920, loss = 0.395835 +I0616 16:12:32.041122 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118932 (* 1 = 0.118932 loss) +I0616 16:12:32.041128 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173636 (* 1 = 0.173636 loss) +I0616 16:12:32.041132 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00297829 (* 1 = 0.00297829 loss) +I0616 16:12:32.041136 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220301 (* 1 = 0.0220301 loss) +I0616 16:12:32.041139 9857 solver.cpp:571] Iteration 84920, lr = 0.0001 +I0616 16:12:43.728157 9857 solver.cpp:242] Iteration 84940, loss = 0.191514 +I0616 16:12:43.728200 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0523665 (* 1 = 0.0523665 loss) +I0616 16:12:43.728206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0804104 (* 1 = 0.0804104 loss) +I0616 16:12:43.728210 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114547 (* 1 = 0.0114547 loss) +I0616 16:12:43.728214 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00272811 (* 1 = 0.00272811 loss) +I0616 16:12:43.728219 9857 solver.cpp:571] Iteration 84940, lr = 0.0001 +I0616 16:12:55.042848 9857 solver.cpp:242] Iteration 84960, loss = 0.501331 +I0616 16:12:55.042917 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201947 (* 1 = 0.201947 loss) +I0616 16:12:55.042923 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216426 (* 1 = 0.216426 loss) +I0616 16:12:55.042928 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0580813 (* 1 = 0.0580813 loss) +I0616 16:12:55.042932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.243605 (* 1 = 0.243605 loss) +I0616 16:12:55.042935 9857 solver.cpp:571] Iteration 84960, lr = 0.0001 +I0616 16:13:06.365805 9857 solver.cpp:242] Iteration 84980, loss = 0.248403 +I0616 16:13:06.365847 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0873971 (* 1 = 0.0873971 loss) +I0616 16:13:06.365854 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116187 (* 1 = 0.116187 loss) +I0616 16:13:06.365857 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0168049 (* 1 = 0.0168049 loss) +I0616 16:13:06.365861 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00877606 (* 1 = 0.00877606 loss) +I0616 16:13:06.365865 9857 solver.cpp:571] Iteration 84980, lr = 0.0001 +speed: 0.598s / iter +I0616 16:13:17.795610 9857 solver.cpp:242] Iteration 85000, loss = 0.679587 +I0616 16:13:17.795652 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.156976 (* 1 = 0.156976 loss) +I0616 16:13:17.795657 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104663 (* 1 = 0.104663 loss) +I0616 16:13:17.795661 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194368 (* 1 = 0.194368 loss) +I0616 16:13:17.795665 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0626532 (* 1 = 0.0626532 loss) +I0616 16:13:17.795670 9857 solver.cpp:571] Iteration 85000, lr = 0.0001 +I0616 16:13:29.560734 9857 solver.cpp:242] Iteration 85020, loss = 0.596544 +I0616 16:13:29.560777 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.225782 (* 1 = 0.225782 loss) +I0616 16:13:29.560782 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180345 (* 1 = 0.180345 loss) +I0616 16:13:29.560786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.100327 (* 1 = 0.100327 loss) +I0616 16:13:29.560791 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.298251 (* 1 = 0.298251 loss) +I0616 16:13:29.560794 9857 solver.cpp:571] Iteration 85020, lr = 0.0001 +I0616 16:13:41.094599 9857 solver.cpp:242] Iteration 85040, loss = 0.486386 +I0616 16:13:41.094641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.37892 (* 1 = 0.37892 loss) +I0616 16:13:41.094647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298088 (* 1 = 0.298088 loss) +I0616 16:13:41.094651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0364283 (* 1 = 0.0364283 loss) +I0616 16:13:41.094655 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0842074 (* 1 = 0.0842074 loss) +I0616 16:13:41.094660 9857 solver.cpp:571] Iteration 85040, lr = 0.0001 +I0616 16:13:52.588170 9857 solver.cpp:242] Iteration 85060, loss = 0.668075 +I0616 16:13:52.588210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.359493 (* 1 = 0.359493 loss) +I0616 16:13:52.588215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328816 (* 1 = 0.328816 loss) +I0616 16:13:52.588219 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127715 (* 1 = 0.127715 loss) +I0616 16:13:52.588223 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.154343 (* 1 = 0.154343 loss) +I0616 16:13:52.588229 9857 solver.cpp:571] Iteration 85060, lr = 0.0001 +I0616 16:14:04.030061 9857 solver.cpp:242] Iteration 85080, loss = 0.452619 +I0616 16:14:04.030102 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177686 (* 1 = 0.177686 loss) +I0616 16:14:04.030107 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145169 (* 1 = 0.145169 loss) +I0616 16:14:04.030112 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00771486 (* 1 = 0.00771486 loss) +I0616 16:14:04.030115 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00711329 (* 1 = 0.00711329 loss) +I0616 16:14:04.030120 9857 solver.cpp:571] Iteration 85080, lr = 0.0001 +I0616 16:14:15.450268 9857 solver.cpp:242] Iteration 85100, loss = 0.506592 +I0616 16:14:15.450309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332402 (* 1 = 0.332402 loss) +I0616 16:14:15.450315 9857 solver.cpp:258] Train net output #1: loss_cls = 0.234396 (* 1 = 0.234396 loss) +I0616 16:14:15.450320 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.202337 (* 1 = 0.202337 loss) +I0616 16:14:15.450323 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404123 (* 1 = 0.0404123 loss) +I0616 16:14:15.450327 9857 solver.cpp:571] Iteration 85100, lr = 0.0001 +I0616 16:14:26.814884 9857 solver.cpp:242] Iteration 85120, loss = 0.358949 +I0616 16:14:26.814926 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.044386 (* 1 = 0.044386 loss) +I0616 16:14:26.814931 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0842228 (* 1 = 0.0842228 loss) +I0616 16:14:26.814935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00538885 (* 1 = 0.00538885 loss) +I0616 16:14:26.814939 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00243311 (* 1 = 0.00243311 loss) +I0616 16:14:26.814944 9857 solver.cpp:571] Iteration 85120, lr = 0.0001 +I0616 16:14:38.287470 9857 solver.cpp:242] Iteration 85140, loss = 0.349384 +I0616 16:14:38.287511 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192443 (* 1 = 0.192443 loss) +I0616 16:14:38.287518 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144243 (* 1 = 0.144243 loss) +I0616 16:14:38.287521 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0207433 (* 1 = 0.0207433 loss) +I0616 16:14:38.287525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0450163 (* 1 = 0.0450163 loss) +I0616 16:14:38.287529 9857 solver.cpp:571] Iteration 85140, lr = 0.0001 +I0616 16:14:49.743696 9857 solver.cpp:242] Iteration 85160, loss = 0.189606 +I0616 16:14:49.743739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0459781 (* 1 = 0.0459781 loss) +I0616 16:14:49.743744 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0854079 (* 1 = 0.0854079 loss) +I0616 16:14:49.743749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00534876 (* 1 = 0.00534876 loss) +I0616 16:14:49.743752 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00678375 (* 1 = 0.00678375 loss) +I0616 16:14:49.743757 9857 solver.cpp:571] Iteration 85160, lr = 0.0001 +I0616 16:15:01.323796 9857 solver.cpp:242] Iteration 85180, loss = 0.313621 +I0616 16:15:01.323835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141321 (* 1 = 0.141321 loss) +I0616 16:15:01.323842 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200435 (* 1 = 0.200435 loss) +I0616 16:15:01.323845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104269 (* 1 = 0.104269 loss) +I0616 16:15:01.323849 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113229 (* 1 = 0.0113229 loss) +I0616 16:15:01.323853 9857 solver.cpp:571] Iteration 85180, lr = 0.0001 +speed: 0.598s / iter +I0616 16:15:12.763576 9857 solver.cpp:242] Iteration 85200, loss = 0.608105 +I0616 16:15:12.763618 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187172 (* 1 = 0.187172 loss) +I0616 16:15:12.763624 9857 solver.cpp:258] Train net output #1: loss_cls = 0.455113 (* 1 = 0.455113 loss) +I0616 16:15:12.763628 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.038576 (* 1 = 0.038576 loss) +I0616 16:15:12.763633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0617293 (* 1 = 0.0617293 loss) +I0616 16:15:12.763636 9857 solver.cpp:571] Iteration 85200, lr = 0.0001 +I0616 16:15:24.325543 9857 solver.cpp:242] Iteration 85220, loss = 0.204875 +I0616 16:15:24.325584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0989439 (* 1 = 0.0989439 loss) +I0616 16:15:24.325590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.075783 (* 1 = 0.075783 loss) +I0616 16:15:24.325594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0479904 (* 1 = 0.0479904 loss) +I0616 16:15:24.325598 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0059369 (* 1 = 0.0059369 loss) +I0616 16:15:24.325601 9857 solver.cpp:571] Iteration 85220, lr = 0.0001 +I0616 16:15:35.821526 9857 solver.cpp:242] Iteration 85240, loss = 0.411966 +I0616 16:15:35.821568 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0558486 (* 1 = 0.0558486 loss) +I0616 16:15:35.821573 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113189 (* 1 = 0.113189 loss) +I0616 16:15:35.821578 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0981263 (* 1 = 0.0981263 loss) +I0616 16:15:35.821580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00774758 (* 1 = 0.00774758 loss) +I0616 16:15:35.821584 9857 solver.cpp:571] Iteration 85240, lr = 0.0001 +I0616 16:15:47.454957 9857 solver.cpp:242] Iteration 85260, loss = 0.728633 +I0616 16:15:47.454998 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293856 (* 1 = 0.293856 loss) +I0616 16:15:47.455003 9857 solver.cpp:258] Train net output #1: loss_cls = 0.342072 (* 1 = 0.342072 loss) +I0616 16:15:47.455008 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152276 (* 1 = 0.152276 loss) +I0616 16:15:47.455011 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.351117 (* 1 = 0.351117 loss) +I0616 16:15:47.455015 9857 solver.cpp:571] Iteration 85260, lr = 0.0001 +I0616 16:15:58.863095 9857 solver.cpp:242] Iteration 85280, loss = 0.424997 +I0616 16:15:58.863138 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0895953 (* 1 = 0.0895953 loss) +I0616 16:15:58.863143 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178665 (* 1 = 0.178665 loss) +I0616 16:15:58.863148 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0413523 (* 1 = 0.0413523 loss) +I0616 16:15:58.863152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00249535 (* 1 = 0.00249535 loss) +I0616 16:15:58.863155 9857 solver.cpp:571] Iteration 85280, lr = 0.0001 +I0616 16:16:10.424331 9857 solver.cpp:242] Iteration 85300, loss = 0.601635 +I0616 16:16:10.424371 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.382278 (* 1 = 0.382278 loss) +I0616 16:16:10.424377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.355383 (* 1 = 0.355383 loss) +I0616 16:16:10.424381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149046 (* 1 = 0.149046 loss) +I0616 16:16:10.424386 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.068003 (* 1 = 0.068003 loss) +I0616 16:16:10.424388 9857 solver.cpp:571] Iteration 85300, lr = 0.0001 +I0616 16:16:21.778198 9857 solver.cpp:242] Iteration 85320, loss = 0.603288 +I0616 16:16:21.778240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217564 (* 1 = 0.217564 loss) +I0616 16:16:21.778246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.258229 (* 1 = 0.258229 loss) +I0616 16:16:21.778251 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0903229 (* 1 = 0.0903229 loss) +I0616 16:16:21.778254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0294792 (* 1 = 0.0294792 loss) +I0616 16:16:21.778259 9857 solver.cpp:571] Iteration 85320, lr = 0.0001 +I0616 16:16:33.158306 9857 solver.cpp:242] Iteration 85340, loss = 0.506546 +I0616 16:16:33.158347 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259767 (* 1 = 0.259767 loss) +I0616 16:16:33.158352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.28082 (* 1 = 0.28082 loss) +I0616 16:16:33.158357 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167158 (* 1 = 0.167158 loss) +I0616 16:16:33.158361 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.016125 (* 1 = 0.016125 loss) +I0616 16:16:33.158365 9857 solver.cpp:571] Iteration 85340, lr = 0.0001 +I0616 16:16:44.804808 9857 solver.cpp:242] Iteration 85360, loss = 0.526694 +I0616 16:16:44.804850 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337376 (* 1 = 0.337376 loss) +I0616 16:16:44.804857 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309159 (* 1 = 0.309159 loss) +I0616 16:16:44.804862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.159165 (* 1 = 0.159165 loss) +I0616 16:16:44.804864 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0440818 (* 1 = 0.0440818 loss) +I0616 16:16:44.804868 9857 solver.cpp:571] Iteration 85360, lr = 0.0001 +I0616 16:16:56.448626 9857 solver.cpp:242] Iteration 85380, loss = 0.396421 +I0616 16:16:56.448668 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0741199 (* 1 = 0.0741199 loss) +I0616 16:16:56.448674 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0977098 (* 1 = 0.0977098 loss) +I0616 16:16:56.448679 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00386572 (* 1 = 0.00386572 loss) +I0616 16:16:56.448683 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00661295 (* 1 = 0.00661295 loss) +I0616 16:16:56.448686 9857 solver.cpp:571] Iteration 85380, lr = 0.0001 +speed: 0.598s / iter +I0616 16:17:07.790740 9857 solver.cpp:242] Iteration 85400, loss = 0.638492 +I0616 16:17:07.790786 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.255809 (* 1 = 0.255809 loss) +I0616 16:17:07.790791 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392568 (* 1 = 0.392568 loss) +I0616 16:17:07.790796 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.255518 (* 1 = 0.255518 loss) +I0616 16:17:07.790799 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104997 (* 1 = 0.104997 loss) +I0616 16:17:07.790803 9857 solver.cpp:571] Iteration 85400, lr = 0.0001 +I0616 16:17:19.336781 9857 solver.cpp:242] Iteration 85420, loss = 0.349132 +I0616 16:17:19.336822 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13707 (* 1 = 0.13707 loss) +I0616 16:17:19.336827 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158794 (* 1 = 0.158794 loss) +I0616 16:17:19.336833 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0569266 (* 1 = 0.0569266 loss) +I0616 16:17:19.336836 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0200323 (* 1 = 0.0200323 loss) +I0616 16:17:19.336840 9857 solver.cpp:571] Iteration 85420, lr = 0.0001 +I0616 16:17:30.929052 9857 solver.cpp:242] Iteration 85440, loss = 0.510729 +I0616 16:17:30.929095 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.277252 (* 1 = 0.277252 loss) +I0616 16:17:30.929100 9857 solver.cpp:258] Train net output #1: loss_cls = 0.480146 (* 1 = 0.480146 loss) +I0616 16:17:30.929105 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0567058 (* 1 = 0.0567058 loss) +I0616 16:17:30.929108 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0110149 (* 1 = 0.0110149 loss) +I0616 16:17:30.929112 9857 solver.cpp:571] Iteration 85440, lr = 0.0001 +I0616 16:17:42.590914 9857 solver.cpp:242] Iteration 85460, loss = 0.288324 +I0616 16:17:42.590941 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0485715 (* 1 = 0.0485715 loss) +I0616 16:17:42.590946 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139336 (* 1 = 0.139336 loss) +I0616 16:17:42.590950 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0449809 (* 1 = 0.0449809 loss) +I0616 16:17:42.590955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00147198 (* 1 = 0.00147198 loss) +I0616 16:17:42.590958 9857 solver.cpp:571] Iteration 85460, lr = 0.0001 +I0616 16:17:54.146746 9857 solver.cpp:242] Iteration 85480, loss = 0.835261 +I0616 16:17:54.146792 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231414 (* 1 = 0.231414 loss) +I0616 16:17:54.146797 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249391 (* 1 = 0.249391 loss) +I0616 16:17:54.146801 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.200861 (* 1 = 0.200861 loss) +I0616 16:17:54.146806 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0476096 (* 1 = 0.0476096 loss) +I0616 16:17:54.146809 9857 solver.cpp:571] Iteration 85480, lr = 0.0001 +I0616 16:18:05.441495 9857 solver.cpp:242] Iteration 85500, loss = 0.401294 +I0616 16:18:05.441537 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195944 (* 1 = 0.195944 loss) +I0616 16:18:05.441543 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246869 (* 1 = 0.246869 loss) +I0616 16:18:05.441547 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0874139 (* 1 = 0.0874139 loss) +I0616 16:18:05.441552 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0284135 (* 1 = 0.0284135 loss) +I0616 16:18:05.441555 9857 solver.cpp:571] Iteration 85500, lr = 0.0001 +I0616 16:18:17.035701 9857 solver.cpp:242] Iteration 85520, loss = 0.216285 +I0616 16:18:17.035742 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0548058 (* 1 = 0.0548058 loss) +I0616 16:18:17.035748 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0519173 (* 1 = 0.0519173 loss) +I0616 16:18:17.035753 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0360404 (* 1 = 0.0360404 loss) +I0616 16:18:17.035756 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0172878 (* 1 = 0.0172878 loss) +I0616 16:18:17.035759 9857 solver.cpp:571] Iteration 85520, lr = 0.0001 +I0616 16:18:28.707631 9857 solver.cpp:242] Iteration 85540, loss = 0.333714 +I0616 16:18:28.707674 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13787 (* 1 = 0.13787 loss) +I0616 16:18:28.707679 9857 solver.cpp:258] Train net output #1: loss_cls = 0.242812 (* 1 = 0.242812 loss) +I0616 16:18:28.707682 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0381301 (* 1 = 0.0381301 loss) +I0616 16:18:28.707686 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0494488 (* 1 = 0.0494488 loss) +I0616 16:18:28.707690 9857 solver.cpp:571] Iteration 85540, lr = 0.0001 +I0616 16:18:40.290057 9857 solver.cpp:242] Iteration 85560, loss = 0.525614 +I0616 16:18:40.290099 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267303 (* 1 = 0.267303 loss) +I0616 16:18:40.290105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359687 (* 1 = 0.359687 loss) +I0616 16:18:40.290109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.169678 (* 1 = 0.169678 loss) +I0616 16:18:40.290113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0397877 (* 1 = 0.0397877 loss) +I0616 16:18:40.290117 9857 solver.cpp:571] Iteration 85560, lr = 0.0001 +I0616 16:18:52.045294 9857 solver.cpp:242] Iteration 85580, loss = 0.273199 +I0616 16:18:52.045334 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0730536 (* 1 = 0.0730536 loss) +I0616 16:18:52.045341 9857 solver.cpp:258] Train net output #1: loss_cls = 0.087594 (* 1 = 0.087594 loss) +I0616 16:18:52.045344 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163183 (* 1 = 0.0163183 loss) +I0616 16:18:52.045348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123677 (* 1 = 0.0123677 loss) +I0616 16:18:52.045352 9857 solver.cpp:571] Iteration 85580, lr = 0.0001 +speed: 0.598s / iter +I0616 16:19:03.798099 9857 solver.cpp:242] Iteration 85600, loss = 0.328298 +I0616 16:19:03.798141 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0697157 (* 1 = 0.0697157 loss) +I0616 16:19:03.798147 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100272 (* 1 = 0.100272 loss) +I0616 16:19:03.798151 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0509589 (* 1 = 0.0509589 loss) +I0616 16:19:03.798156 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240105 (* 1 = 0.0240105 loss) +I0616 16:19:03.798158 9857 solver.cpp:571] Iteration 85600, lr = 0.0001 +I0616 16:19:14.985606 9857 solver.cpp:242] Iteration 85620, loss = 0.654679 +I0616 16:19:14.985647 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.113907 (* 1 = 0.113907 loss) +I0616 16:19:14.985653 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143769 (* 1 = 0.143769 loss) +I0616 16:19:14.985657 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0204552 (* 1 = 0.0204552 loss) +I0616 16:19:14.985661 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010084 (* 1 = 0.010084 loss) +I0616 16:19:14.985666 9857 solver.cpp:571] Iteration 85620, lr = 0.0001 +I0616 16:19:26.475872 9857 solver.cpp:242] Iteration 85640, loss = 0.486363 +I0616 16:19:26.475916 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195126 (* 1 = 0.195126 loss) +I0616 16:19:26.475922 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413277 (* 1 = 0.413277 loss) +I0616 16:19:26.475926 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142127 (* 1 = 0.142127 loss) +I0616 16:19:26.475929 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013989 (* 1 = 0.013989 loss) +I0616 16:19:26.475934 9857 solver.cpp:571] Iteration 85640, lr = 0.0001 +I0616 16:19:38.057479 9857 solver.cpp:242] Iteration 85660, loss = 0.317838 +I0616 16:19:38.057523 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114331 (* 1 = 0.114331 loss) +I0616 16:19:38.057528 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232657 (* 1 = 0.232657 loss) +I0616 16:19:38.057533 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.067547 (* 1 = 0.067547 loss) +I0616 16:19:38.057536 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113894 (* 1 = 0.0113894 loss) +I0616 16:19:38.057539 9857 solver.cpp:571] Iteration 85660, lr = 0.0001 +I0616 16:19:49.573948 9857 solver.cpp:242] Iteration 85680, loss = 0.332504 +I0616 16:19:49.573992 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10511 (* 1 = 0.10511 loss) +I0616 16:19:49.573997 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160239 (* 1 = 0.160239 loss) +I0616 16:19:49.574000 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.016546 (* 1 = 0.016546 loss) +I0616 16:19:49.574004 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013553 (* 1 = 0.013553 loss) +I0616 16:19:49.574023 9857 solver.cpp:571] Iteration 85680, lr = 0.0001 +I0616 16:20:01.146046 9857 solver.cpp:242] Iteration 85700, loss = 0.203001 +I0616 16:20:01.146088 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0777136 (* 1 = 0.0777136 loss) +I0616 16:20:01.146095 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132068 (* 1 = 0.132068 loss) +I0616 16:20:01.146098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00117068 (* 1 = 0.00117068 loss) +I0616 16:20:01.146102 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00702029 (* 1 = 0.00702029 loss) +I0616 16:20:01.146106 9857 solver.cpp:571] Iteration 85700, lr = 0.0001 +I0616 16:20:12.775640 9857 solver.cpp:242] Iteration 85720, loss = 0.48658 +I0616 16:20:12.775682 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10009 (* 1 = 0.10009 loss) +I0616 16:20:12.775688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211467 (* 1 = 0.211467 loss) +I0616 16:20:12.775692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0705071 (* 1 = 0.0705071 loss) +I0616 16:20:12.775696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0966717 (* 1 = 0.0966717 loss) +I0616 16:20:12.775701 9857 solver.cpp:571] Iteration 85720, lr = 0.0001 +I0616 16:20:24.036916 9857 solver.cpp:242] Iteration 85740, loss = 0.245151 +I0616 16:20:24.036957 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0685028 (* 1 = 0.0685028 loss) +I0616 16:20:24.036963 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11472 (* 1 = 0.11472 loss) +I0616 16:20:24.036967 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0178557 (* 1 = 0.0178557 loss) +I0616 16:20:24.036972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0080074 (* 1 = 0.0080074 loss) +I0616 16:20:24.036978 9857 solver.cpp:571] Iteration 85740, lr = 0.0001 +I0616 16:20:35.667444 9857 solver.cpp:242] Iteration 85760, loss = 0.57475 +I0616 16:20:35.667486 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233692 (* 1 = 0.233692 loss) +I0616 16:20:35.667492 9857 solver.cpp:258] Train net output #1: loss_cls = 0.27357 (* 1 = 0.27357 loss) +I0616 16:20:35.667497 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102679 (* 1 = 0.102679 loss) +I0616 16:20:35.667500 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0777964 (* 1 = 0.0777964 loss) +I0616 16:20:35.667505 9857 solver.cpp:571] Iteration 85760, lr = 0.0001 +I0616 16:20:47.006841 9857 solver.cpp:242] Iteration 85780, loss = 0.261765 +I0616 16:20:47.006881 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0952801 (* 1 = 0.0952801 loss) +I0616 16:20:47.006887 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17604 (* 1 = 0.17604 loss) +I0616 16:20:47.006891 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00173831 (* 1 = 0.00173831 loss) +I0616 16:20:47.006896 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00673393 (* 1 = 0.00673393 loss) +I0616 16:20:47.006898 9857 solver.cpp:571] Iteration 85780, lr = 0.0001 +speed: 0.598s / iter +I0616 16:20:58.429723 9857 solver.cpp:242] Iteration 85800, loss = 0.505174 +I0616 16:20:58.429764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.251958 (* 1 = 0.251958 loss) +I0616 16:20:58.429770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265179 (* 1 = 0.265179 loss) +I0616 16:20:58.429774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0673058 (* 1 = 0.0673058 loss) +I0616 16:20:58.429779 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0712704 (* 1 = 0.0712704 loss) +I0616 16:20:58.429782 9857 solver.cpp:571] Iteration 85800, lr = 0.0001 +I0616 16:21:10.015046 9857 solver.cpp:242] Iteration 85820, loss = 0.553473 +I0616 16:21:10.015087 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179903 (* 1 = 0.179903 loss) +I0616 16:21:10.015094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313425 (* 1 = 0.313425 loss) +I0616 16:21:10.015097 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0725341 (* 1 = 0.0725341 loss) +I0616 16:21:10.015101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0768872 (* 1 = 0.0768872 loss) +I0616 16:21:10.015105 9857 solver.cpp:571] Iteration 85820, lr = 0.0001 +I0616 16:21:21.406988 9857 solver.cpp:242] Iteration 85840, loss = 0.559121 +I0616 16:21:21.407030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112778 (* 1 = 0.112778 loss) +I0616 16:21:21.407037 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189439 (* 1 = 0.189439 loss) +I0616 16:21:21.407040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0684731 (* 1 = 0.0684731 loss) +I0616 16:21:21.407044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00887109 (* 1 = 0.00887109 loss) +I0616 16:21:21.407050 9857 solver.cpp:571] Iteration 85840, lr = 0.0001 +I0616 16:21:32.823071 9857 solver.cpp:242] Iteration 85860, loss = 0.232105 +I0616 16:21:32.823112 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136763 (* 1 = 0.136763 loss) +I0616 16:21:32.823118 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142042 (* 1 = 0.142042 loss) +I0616 16:21:32.823122 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0127143 (* 1 = 0.0127143 loss) +I0616 16:21:32.823127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00687122 (* 1 = 0.00687122 loss) +I0616 16:21:32.823130 9857 solver.cpp:571] Iteration 85860, lr = 0.0001 +I0616 16:21:44.475599 9857 solver.cpp:242] Iteration 85880, loss = 0.381893 +I0616 16:21:44.475641 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116415 (* 1 = 0.116415 loss) +I0616 16:21:44.475646 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250508 (* 1 = 0.250508 loss) +I0616 16:21:44.475651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112149 (* 1 = 0.112149 loss) +I0616 16:21:44.475653 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144398 (* 1 = 0.144398 loss) +I0616 16:21:44.475657 9857 solver.cpp:571] Iteration 85880, lr = 0.0001 +I0616 16:21:55.849818 9857 solver.cpp:242] Iteration 85900, loss = 0.324869 +I0616 16:21:55.849860 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117813 (* 1 = 0.117813 loss) +I0616 16:21:55.849866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.149718 (* 1 = 0.149718 loss) +I0616 16:21:55.849870 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00965494 (* 1 = 0.00965494 loss) +I0616 16:21:55.849874 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223905 (* 1 = 0.0223905 loss) +I0616 16:21:55.849877 9857 solver.cpp:571] Iteration 85900, lr = 0.0001 +I0616 16:22:07.381068 9857 solver.cpp:242] Iteration 85920, loss = 0.275636 +I0616 16:22:07.381110 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0669911 (* 1 = 0.0669911 loss) +I0616 16:22:07.381116 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0990578 (* 1 = 0.0990578 loss) +I0616 16:22:07.381120 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.000484638 (* 1 = 0.000484638 loss) +I0616 16:22:07.381124 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00376828 (* 1 = 0.00376828 loss) +I0616 16:22:07.381127 9857 solver.cpp:571] Iteration 85920, lr = 0.0001 +I0616 16:22:18.773573 9857 solver.cpp:242] Iteration 85940, loss = 0.521201 +I0616 16:22:18.773614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.290624 (* 1 = 0.290624 loss) +I0616 16:22:18.773619 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205232 (* 1 = 0.205232 loss) +I0616 16:22:18.773623 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00996289 (* 1 = 0.00996289 loss) +I0616 16:22:18.773627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.022742 (* 1 = 0.022742 loss) +I0616 16:22:18.773632 9857 solver.cpp:571] Iteration 85940, lr = 0.0001 +I0616 16:22:30.237587 9857 solver.cpp:242] Iteration 85960, loss = 1.05514 +I0616 16:22:30.237629 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.208217 (* 1 = 0.208217 loss) +I0616 16:22:30.237635 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185879 (* 1 = 0.185879 loss) +I0616 16:22:30.237639 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130158 (* 1 = 0.130158 loss) +I0616 16:22:30.237643 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0599735 (* 1 = 0.0599735 loss) +I0616 16:22:30.237648 9857 solver.cpp:571] Iteration 85960, lr = 0.0001 +I0616 16:22:41.404218 9857 solver.cpp:242] Iteration 85980, loss = 0.23551 +I0616 16:22:41.404260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.150865 (* 1 = 0.150865 loss) +I0616 16:22:41.404266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111345 (* 1 = 0.111345 loss) +I0616 16:22:41.404270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0383124 (* 1 = 0.0383124 loss) +I0616 16:22:41.404274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0500276 (* 1 = 0.0500276 loss) +I0616 16:22:41.404278 9857 solver.cpp:571] Iteration 85980, lr = 0.0001 +speed: 0.598s / iter +I0616 16:22:53.086164 9857 solver.cpp:242] Iteration 86000, loss = 0.414059 +I0616 16:22:53.086206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.198506 (* 1 = 0.198506 loss) +I0616 16:22:53.086212 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21829 (* 1 = 0.21829 loss) +I0616 16:22:53.086216 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0157161 (* 1 = 0.0157161 loss) +I0616 16:22:53.086220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0628032 (* 1 = 0.0628032 loss) +I0616 16:22:53.086223 9857 solver.cpp:571] Iteration 86000, lr = 0.0001 +I0616 16:23:04.792608 9857 solver.cpp:242] Iteration 86020, loss = 0.59212 +I0616 16:23:04.792649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.320143 (* 1 = 0.320143 loss) +I0616 16:23:04.792655 9857 solver.cpp:258] Train net output #1: loss_cls = 0.338211 (* 1 = 0.338211 loss) +I0616 16:23:04.792659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.238814 (* 1 = 0.238814 loss) +I0616 16:23:04.792664 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0753645 (* 1 = 0.0753645 loss) +I0616 16:23:04.792666 9857 solver.cpp:571] Iteration 86020, lr = 0.0001 +I0616 16:23:16.264756 9857 solver.cpp:242] Iteration 86040, loss = 0.461169 +I0616 16:23:16.264798 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.173847 (* 1 = 0.173847 loss) +I0616 16:23:16.264804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247079 (* 1 = 0.247079 loss) +I0616 16:23:16.264808 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0520933 (* 1 = 0.0520933 loss) +I0616 16:23:16.264812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0401923 (* 1 = 0.0401923 loss) +I0616 16:23:16.264816 9857 solver.cpp:571] Iteration 86040, lr = 0.0001 +I0616 16:23:28.067524 9857 solver.cpp:242] Iteration 86060, loss = 0.352792 +I0616 16:23:28.067566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183396 (* 1 = 0.183396 loss) +I0616 16:23:28.067572 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196879 (* 1 = 0.196879 loss) +I0616 16:23:28.067576 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0465166 (* 1 = 0.0465166 loss) +I0616 16:23:28.067580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0257014 (* 1 = 0.0257014 loss) +I0616 16:23:28.067584 9857 solver.cpp:571] Iteration 86060, lr = 0.0001 +I0616 16:23:39.520797 9857 solver.cpp:242] Iteration 86080, loss = 0.473522 +I0616 16:23:39.520838 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184587 (* 1 = 0.184587 loss) +I0616 16:23:39.520843 9857 solver.cpp:258] Train net output #1: loss_cls = 0.285791 (* 1 = 0.285791 loss) +I0616 16:23:39.520848 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0522492 (* 1 = 0.0522492 loss) +I0616 16:23:39.520851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00904595 (* 1 = 0.00904595 loss) +I0616 16:23:39.520854 9857 solver.cpp:571] Iteration 86080, lr = 0.0001 +I0616 16:23:51.076267 9857 solver.cpp:242] Iteration 86100, loss = 0.336573 +I0616 16:23:51.076308 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.120423 (* 1 = 0.120423 loss) +I0616 16:23:51.076314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183624 (* 1 = 0.183624 loss) +I0616 16:23:51.076318 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0307337 (* 1 = 0.0307337 loss) +I0616 16:23:51.076323 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168654 (* 1 = 0.0168654 loss) +I0616 16:23:51.076326 9857 solver.cpp:571] Iteration 86100, lr = 0.0001 +I0616 16:24:02.580680 9857 solver.cpp:242] Iteration 86120, loss = 0.602804 +I0616 16:24:02.580724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293174 (* 1 = 0.293174 loss) +I0616 16:24:02.580729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375709 (* 1 = 0.375709 loss) +I0616 16:24:02.580732 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0835301 (* 1 = 0.0835301 loss) +I0616 16:24:02.580736 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0204941 (* 1 = 0.0204941 loss) +I0616 16:24:02.580739 9857 solver.cpp:571] Iteration 86120, lr = 0.0001 +I0616 16:24:13.992099 9857 solver.cpp:242] Iteration 86140, loss = 0.252813 +I0616 16:24:13.992141 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0308823 (* 1 = 0.0308823 loss) +I0616 16:24:13.992147 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0738753 (* 1 = 0.0738753 loss) +I0616 16:24:13.992151 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0149265 (* 1 = 0.0149265 loss) +I0616 16:24:13.992154 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00481674 (* 1 = 0.00481674 loss) +I0616 16:24:13.992158 9857 solver.cpp:571] Iteration 86140, lr = 0.0001 +I0616 16:24:25.479478 9857 solver.cpp:242] Iteration 86160, loss = 0.5681 +I0616 16:24:25.479521 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.339489 (* 1 = 0.339489 loss) +I0616 16:24:25.479526 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29961 (* 1 = 0.29961 loss) +I0616 16:24:25.479531 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.230594 (* 1 = 0.230594 loss) +I0616 16:24:25.479534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0681038 (* 1 = 0.0681038 loss) +I0616 16:24:25.479538 9857 solver.cpp:571] Iteration 86160, lr = 0.0001 +I0616 16:24:37.116376 9857 solver.cpp:242] Iteration 86180, loss = 0.525124 +I0616 16:24:37.116418 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281123 (* 1 = 0.281123 loss) +I0616 16:24:37.116425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217317 (* 1 = 0.217317 loss) +I0616 16:24:37.116428 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125325 (* 1 = 0.125325 loss) +I0616 16:24:37.116433 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313581 (* 1 = 0.0313581 loss) +I0616 16:24:37.116436 9857 solver.cpp:571] Iteration 86180, lr = 0.0001 +speed: 0.598s / iter +I0616 16:24:48.596482 9857 solver.cpp:242] Iteration 86200, loss = 0.445579 +I0616 16:24:48.596523 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22016 (* 1 = 0.22016 loss) +I0616 16:24:48.596527 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134245 (* 1 = 0.134245 loss) +I0616 16:24:48.596532 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00333966 (* 1 = 0.00333966 loss) +I0616 16:24:48.596536 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138339 (* 1 = 0.0138339 loss) +I0616 16:24:48.596539 9857 solver.cpp:571] Iteration 86200, lr = 0.0001 +I0616 16:25:00.316797 9857 solver.cpp:242] Iteration 86220, loss = 0.510886 +I0616 16:25:00.316838 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228046 (* 1 = 0.228046 loss) +I0616 16:25:00.316843 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244247 (* 1 = 0.244247 loss) +I0616 16:25:00.316848 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.077669 (* 1 = 0.077669 loss) +I0616 16:25:00.316851 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0126379 (* 1 = 0.0126379 loss) +I0616 16:25:00.316854 9857 solver.cpp:571] Iteration 86220, lr = 0.0001 +I0616 16:25:12.052140 9857 solver.cpp:242] Iteration 86240, loss = 0.901259 +I0616 16:25:12.052183 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130982 (* 1 = 0.130982 loss) +I0616 16:25:12.052189 9857 solver.cpp:258] Train net output #1: loss_cls = 0.271199 (* 1 = 0.271199 loss) +I0616 16:25:12.052193 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0804381 (* 1 = 0.0804381 loss) +I0616 16:25:12.052197 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199118 (* 1 = 0.0199118 loss) +I0616 16:25:12.052201 9857 solver.cpp:571] Iteration 86240, lr = 0.0001 +I0616 16:25:23.299734 9857 solver.cpp:242] Iteration 86260, loss = 1.14658 +I0616 16:25:23.299778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.412769 (* 1 = 0.412769 loss) +I0616 16:25:23.299783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.588131 (* 1 = 0.588131 loss) +I0616 16:25:23.299787 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.241984 (* 1 = 0.241984 loss) +I0616 16:25:23.299792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144209 (* 1 = 0.144209 loss) +I0616 16:25:23.299795 9857 solver.cpp:571] Iteration 86260, lr = 0.0001 +I0616 16:25:34.583894 9857 solver.cpp:242] Iteration 86280, loss = 0.390855 +I0616 16:25:34.583935 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0924137 (* 1 = 0.0924137 loss) +I0616 16:25:34.583940 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140871 (* 1 = 0.140871 loss) +I0616 16:25:34.583945 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.033607 (* 1 = 0.033607 loss) +I0616 16:25:34.583948 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104605 (* 1 = 0.0104605 loss) +I0616 16:25:34.583952 9857 solver.cpp:571] Iteration 86280, lr = 0.0001 +I0616 16:25:46.019457 9857 solver.cpp:242] Iteration 86300, loss = 0.397403 +I0616 16:25:46.019500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292178 (* 1 = 0.292178 loss) +I0616 16:25:46.019505 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203712 (* 1 = 0.203712 loss) +I0616 16:25:46.019508 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0673049 (* 1 = 0.0673049 loss) +I0616 16:25:46.019512 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0423822 (* 1 = 0.0423822 loss) +I0616 16:25:46.019516 9857 solver.cpp:571] Iteration 86300, lr = 0.0001 +I0616 16:25:57.604064 9857 solver.cpp:242] Iteration 86320, loss = 0.244374 +I0616 16:25:57.604105 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123175 (* 1 = 0.123175 loss) +I0616 16:25:57.604110 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0919137 (* 1 = 0.0919137 loss) +I0616 16:25:57.604115 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.06019 (* 1 = 0.06019 loss) +I0616 16:25:57.604120 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0177924 (* 1 = 0.0177924 loss) +I0616 16:25:57.604122 9857 solver.cpp:571] Iteration 86320, lr = 0.0001 +I0616 16:26:09.084178 9857 solver.cpp:242] Iteration 86340, loss = 0.414903 +I0616 16:26:09.084206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315412 (* 1 = 0.315412 loss) +I0616 16:26:09.084213 9857 solver.cpp:258] Train net output #1: loss_cls = 0.201923 (* 1 = 0.201923 loss) +I0616 16:26:09.084216 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0495154 (* 1 = 0.0495154 loss) +I0616 16:26:09.084220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0454369 (* 1 = 0.0454369 loss) +I0616 16:26:09.084224 9857 solver.cpp:571] Iteration 86340, lr = 0.0001 +I0616 16:26:20.412263 9857 solver.cpp:242] Iteration 86360, loss = 0.634278 +I0616 16:26:20.412303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145571 (* 1 = 0.145571 loss) +I0616 16:26:20.412310 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111078 (* 1 = 0.111078 loss) +I0616 16:26:20.412314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212133 (* 1 = 0.0212133 loss) +I0616 16:26:20.412318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0279135 (* 1 = 0.0279135 loss) +I0616 16:26:20.412322 9857 solver.cpp:571] Iteration 86360, lr = 0.0001 +I0616 16:26:31.808681 9857 solver.cpp:242] Iteration 86380, loss = 0.539148 +I0616 16:26:31.808722 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242857 (* 1 = 0.242857 loss) +I0616 16:26:31.808727 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286208 (* 1 = 0.286208 loss) +I0616 16:26:31.808732 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.076705 (* 1 = 0.076705 loss) +I0616 16:26:31.808735 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.055362 (* 1 = 0.055362 loss) +I0616 16:26:31.808739 9857 solver.cpp:571] Iteration 86380, lr = 0.0001 +speed: 0.598s / iter +I0616 16:26:43.246882 9857 solver.cpp:242] Iteration 86400, loss = 0.199432 +I0616 16:26:43.246923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0674831 (* 1 = 0.0674831 loss) +I0616 16:26:43.246930 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0335648 (* 1 = 0.0335648 loss) +I0616 16:26:43.246934 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00844869 (* 1 = 0.00844869 loss) +I0616 16:26:43.246938 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00849808 (* 1 = 0.00849808 loss) +I0616 16:26:43.246943 9857 solver.cpp:571] Iteration 86400, lr = 0.0001 +I0616 16:26:54.672688 9857 solver.cpp:242] Iteration 86420, loss = 0.365066 +I0616 16:26:54.672731 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.20109 (* 1 = 0.20109 loss) +I0616 16:26:54.672736 9857 solver.cpp:258] Train net output #1: loss_cls = 0.214661 (* 1 = 0.214661 loss) +I0616 16:26:54.672740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0320634 (* 1 = 0.0320634 loss) +I0616 16:26:54.672744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0226307 (* 1 = 0.0226307 loss) +I0616 16:26:54.672747 9857 solver.cpp:571] Iteration 86420, lr = 0.0001 +I0616 16:27:06.112321 9857 solver.cpp:242] Iteration 86440, loss = 0.209877 +I0616 16:27:06.112362 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0979481 (* 1 = 0.0979481 loss) +I0616 16:27:06.112368 9857 solver.cpp:258] Train net output #1: loss_cls = 0.095918 (* 1 = 0.095918 loss) +I0616 16:27:06.112372 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0509945 (* 1 = 0.0509945 loss) +I0616 16:27:06.112376 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00240074 (* 1 = 0.00240074 loss) +I0616 16:27:06.112380 9857 solver.cpp:571] Iteration 86440, lr = 0.0001 +I0616 16:27:17.583581 9857 solver.cpp:242] Iteration 86460, loss = 0.709709 +I0616 16:27:17.583624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.430577 (* 1 = 0.430577 loss) +I0616 16:27:17.583631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.503049 (* 1 = 0.503049 loss) +I0616 16:27:17.583636 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.138049 (* 1 = 0.138049 loss) +I0616 16:27:17.583639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0890744 (* 1 = 0.0890744 loss) +I0616 16:27:17.583642 9857 solver.cpp:571] Iteration 86460, lr = 0.0001 +I0616 16:27:29.217030 9857 solver.cpp:242] Iteration 86480, loss = 1.19168 +I0616 16:27:29.217073 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0598419 (* 1 = 0.0598419 loss) +I0616 16:27:29.217079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154454 (* 1 = 0.154454 loss) +I0616 16:27:29.217083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0367938 (* 1 = 0.0367938 loss) +I0616 16:27:29.217087 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00248992 (* 1 = 0.00248992 loss) +I0616 16:27:29.217090 9857 solver.cpp:571] Iteration 86480, lr = 0.0001 +I0616 16:27:40.811758 9857 solver.cpp:242] Iteration 86500, loss = 0.53507 +I0616 16:27:40.811800 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.160001 (* 1 = 0.160001 loss) +I0616 16:27:40.811806 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184591 (* 1 = 0.184591 loss) +I0616 16:27:40.811810 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0208714 (* 1 = 0.0208714 loss) +I0616 16:27:40.811815 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00579644 (* 1 = 0.00579644 loss) +I0616 16:27:40.811818 9857 solver.cpp:571] Iteration 86500, lr = 0.0001 +I0616 16:27:52.244257 9857 solver.cpp:242] Iteration 86520, loss = 0.359093 +I0616 16:27:52.244300 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.198205 (* 1 = 0.198205 loss) +I0616 16:27:52.244305 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210644 (* 1 = 0.210644 loss) +I0616 16:27:52.244309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0344769 (* 1 = 0.0344769 loss) +I0616 16:27:52.244313 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0408147 (* 1 = 0.0408147 loss) +I0616 16:27:52.244318 9857 solver.cpp:571] Iteration 86520, lr = 0.0001 +I0616 16:28:03.806983 9857 solver.cpp:242] Iteration 86540, loss = 0.333335 +I0616 16:28:03.807021 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0708044 (* 1 = 0.0708044 loss) +I0616 16:28:03.807027 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12335 (* 1 = 0.12335 loss) +I0616 16:28:03.807031 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0166884 (* 1 = 0.0166884 loss) +I0616 16:28:03.807035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00443086 (* 1 = 0.00443086 loss) +I0616 16:28:03.807039 9857 solver.cpp:571] Iteration 86540, lr = 0.0001 +I0616 16:28:15.325986 9857 solver.cpp:242] Iteration 86560, loss = 0.947937 +I0616 16:28:15.326026 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297237 (* 1 = 0.297237 loss) +I0616 16:28:15.326032 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245707 (* 1 = 0.245707 loss) +I0616 16:28:15.326036 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.125578 (* 1 = 0.125578 loss) +I0616 16:28:15.326040 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0415922 (* 1 = 0.0415922 loss) +I0616 16:28:15.326045 9857 solver.cpp:571] Iteration 86560, lr = 0.0001 +I0616 16:28:26.915315 9857 solver.cpp:242] Iteration 86580, loss = 0.39587 +I0616 16:28:26.915356 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.14416 (* 1 = 0.14416 loss) +I0616 16:28:26.915362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.261702 (* 1 = 0.261702 loss) +I0616 16:28:26.915366 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104298 (* 1 = 0.104298 loss) +I0616 16:28:26.915370 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0488688 (* 1 = 0.0488688 loss) +I0616 16:28:26.915374 9857 solver.cpp:571] Iteration 86580, lr = 0.0001 +speed: 0.598s / iter +I0616 16:28:38.432538 9857 solver.cpp:242] Iteration 86600, loss = 0.818808 +I0616 16:28:38.432579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209384 (* 1 = 0.209384 loss) +I0616 16:28:38.432585 9857 solver.cpp:258] Train net output #1: loss_cls = 0.353726 (* 1 = 0.353726 loss) +I0616 16:28:38.432590 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.254888 (* 1 = 0.254888 loss) +I0616 16:28:38.432593 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0705782 (* 1 = 0.0705782 loss) +I0616 16:28:38.432597 9857 solver.cpp:571] Iteration 86600, lr = 0.0001 +I0616 16:28:49.705849 9857 solver.cpp:242] Iteration 86620, loss = 1.07398 +I0616 16:28:49.705891 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269131 (* 1 = 0.269131 loss) +I0616 16:28:49.705898 9857 solver.cpp:258] Train net output #1: loss_cls = 0.506683 (* 1 = 0.506683 loss) +I0616 16:28:49.705901 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.195733 (* 1 = 0.195733 loss) +I0616 16:28:49.705905 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.13271 (* 1 = 0.13271 loss) +I0616 16:28:49.705909 9857 solver.cpp:571] Iteration 86620, lr = 0.0001 +I0616 16:29:01.014205 9857 solver.cpp:242] Iteration 86640, loss = 0.2781 +I0616 16:29:01.014247 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126528 (* 1 = 0.126528 loss) +I0616 16:29:01.014253 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116149 (* 1 = 0.116149 loss) +I0616 16:29:01.014258 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139514 (* 1 = 0.0139514 loss) +I0616 16:29:01.014261 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011028 (* 1 = 0.011028 loss) +I0616 16:29:01.014266 9857 solver.cpp:571] Iteration 86640, lr = 0.0001 +I0616 16:29:12.409428 9857 solver.cpp:242] Iteration 86660, loss = 0.458092 +I0616 16:29:12.409471 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101997 (* 1 = 0.101997 loss) +I0616 16:29:12.409476 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186978 (* 1 = 0.186978 loss) +I0616 16:29:12.409481 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0627226 (* 1 = 0.0627226 loss) +I0616 16:29:12.409484 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103005 (* 1 = 0.0103005 loss) +I0616 16:29:12.409487 9857 solver.cpp:571] Iteration 86660, lr = 0.0001 +I0616 16:29:24.114682 9857 solver.cpp:242] Iteration 86680, loss = 0.273974 +I0616 16:29:24.114722 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0828366 (* 1 = 0.0828366 loss) +I0616 16:29:24.114728 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189604 (* 1 = 0.189604 loss) +I0616 16:29:24.114732 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0124287 (* 1 = 0.0124287 loss) +I0616 16:29:24.114737 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120926 (* 1 = 0.0120926 loss) +I0616 16:29:24.114739 9857 solver.cpp:571] Iteration 86680, lr = 0.0001 +I0616 16:29:35.636723 9857 solver.cpp:242] Iteration 86700, loss = 0.368364 +I0616 16:29:35.636765 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190195 (* 1 = 0.190195 loss) +I0616 16:29:35.636770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226977 (* 1 = 0.226977 loss) +I0616 16:29:35.636775 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.149388 (* 1 = 0.149388 loss) +I0616 16:29:35.636778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0539246 (* 1 = 0.0539246 loss) +I0616 16:29:35.636782 9857 solver.cpp:571] Iteration 86700, lr = 0.0001 +I0616 16:29:47.278201 9857 solver.cpp:242] Iteration 86720, loss = 0.414645 +I0616 16:29:47.278244 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136871 (* 1 = 0.136871 loss) +I0616 16:29:47.278250 9857 solver.cpp:258] Train net output #1: loss_cls = 0.42912 (* 1 = 0.42912 loss) +I0616 16:29:47.278254 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0293052 (* 1 = 0.0293052 loss) +I0616 16:29:47.278259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0338021 (* 1 = 0.0338021 loss) +I0616 16:29:47.278262 9857 solver.cpp:571] Iteration 86720, lr = 0.0001 +I0616 16:29:58.815976 9857 solver.cpp:242] Iteration 86740, loss = 0.34211 +I0616 16:29:58.816017 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0809701 (* 1 = 0.0809701 loss) +I0616 16:29:58.816023 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22522 (* 1 = 0.22522 loss) +I0616 16:29:58.816027 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.140696 (* 1 = 0.140696 loss) +I0616 16:29:58.816031 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0221902 (* 1 = 0.0221902 loss) +I0616 16:29:58.816035 9857 solver.cpp:571] Iteration 86740, lr = 0.0001 +I0616 16:30:10.277505 9857 solver.cpp:242] Iteration 86760, loss = 0.5028 +I0616 16:30:10.277546 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.079573 (* 1 = 0.079573 loss) +I0616 16:30:10.277552 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0905016 (* 1 = 0.0905016 loss) +I0616 16:30:10.277556 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0152212 (* 1 = 0.0152212 loss) +I0616 16:30:10.277560 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142189 (* 1 = 0.0142189 loss) +I0616 16:30:10.277564 9857 solver.cpp:571] Iteration 86760, lr = 0.0001 +I0616 16:30:21.864114 9857 solver.cpp:242] Iteration 86780, loss = 0.397593 +I0616 16:30:21.864156 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0709984 (* 1 = 0.0709984 loss) +I0616 16:30:21.864162 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0973631 (* 1 = 0.0973631 loss) +I0616 16:30:21.864166 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0202543 (* 1 = 0.0202543 loss) +I0616 16:30:21.864171 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0011667 (* 1 = 0.0011667 loss) +I0616 16:30:21.864173 9857 solver.cpp:571] Iteration 86780, lr = 0.0001 +speed: 0.598s / iter +I0616 16:30:33.339638 9857 solver.cpp:242] Iteration 86800, loss = 0.333693 +I0616 16:30:33.339681 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151688 (* 1 = 0.151688 loss) +I0616 16:30:33.339687 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148446 (* 1 = 0.148446 loss) +I0616 16:30:33.339691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0425376 (* 1 = 0.0425376 loss) +I0616 16:30:33.339695 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00961689 (* 1 = 0.00961689 loss) +I0616 16:30:33.339699 9857 solver.cpp:571] Iteration 86800, lr = 0.0001 +I0616 16:30:44.776273 9857 solver.cpp:242] Iteration 86820, loss = 0.531637 +I0616 16:30:44.776319 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24923 (* 1 = 0.24923 loss) +I0616 16:30:44.776324 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296878 (* 1 = 0.296878 loss) +I0616 16:30:44.776329 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163751 (* 1 = 0.163751 loss) +I0616 16:30:44.776332 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.136269 (* 1 = 0.136269 loss) +I0616 16:30:44.776335 9857 solver.cpp:571] Iteration 86820, lr = 0.0001 +I0616 16:30:56.368551 9857 solver.cpp:242] Iteration 86840, loss = 0.432863 +I0616 16:30:56.368592 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204534 (* 1 = 0.204534 loss) +I0616 16:30:56.368598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.310792 (* 1 = 0.310792 loss) +I0616 16:30:56.368602 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.063939 (* 1 = 0.063939 loss) +I0616 16:30:56.368607 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0170652 (* 1 = 0.0170652 loss) +I0616 16:30:56.368610 9857 solver.cpp:571] Iteration 86840, lr = 0.0001 +I0616 16:31:07.663213 9857 solver.cpp:242] Iteration 86860, loss = 0.251797 +I0616 16:31:07.663255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0763346 (* 1 = 0.0763346 loss) +I0616 16:31:07.663261 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132072 (* 1 = 0.132072 loss) +I0616 16:31:07.663266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0103075 (* 1 = 0.0103075 loss) +I0616 16:31:07.663269 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00815656 (* 1 = 0.00815656 loss) +I0616 16:31:07.663274 9857 solver.cpp:571] Iteration 86860, lr = 0.0001 +I0616 16:31:18.996994 9857 solver.cpp:242] Iteration 86880, loss = 0.653146 +I0616 16:31:18.997036 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1908 (* 1 = 0.1908 loss) +I0616 16:31:18.997041 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178548 (* 1 = 0.178548 loss) +I0616 16:31:18.997046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114214 (* 1 = 0.0114214 loss) +I0616 16:31:18.997051 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00642457 (* 1 = 0.00642457 loss) +I0616 16:31:18.997053 9857 solver.cpp:571] Iteration 86880, lr = 0.0001 +I0616 16:31:30.499274 9857 solver.cpp:242] Iteration 86900, loss = 0.845752 +I0616 16:31:30.499315 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.098212 (* 1 = 0.098212 loss) +I0616 16:31:30.499321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150484 (* 1 = 0.150484 loss) +I0616 16:31:30.499325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0300351 (* 1 = 0.0300351 loss) +I0616 16:31:30.499330 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.102436 (* 1 = 0.102436 loss) +I0616 16:31:30.499336 9857 solver.cpp:571] Iteration 86900, lr = 0.0001 +I0616 16:31:42.192242 9857 solver.cpp:242] Iteration 86920, loss = 0.365506 +I0616 16:31:42.192283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168722 (* 1 = 0.168722 loss) +I0616 16:31:42.192289 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180432 (* 1 = 0.180432 loss) +I0616 16:31:42.192293 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11127 (* 1 = 0.11127 loss) +I0616 16:31:42.192297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0348027 (* 1 = 0.0348027 loss) +I0616 16:31:42.192301 9857 solver.cpp:571] Iteration 86920, lr = 0.0001 +I0616 16:31:53.803951 9857 solver.cpp:242] Iteration 86940, loss = 0.460005 +I0616 16:31:53.803992 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170256 (* 1 = 0.170256 loss) +I0616 16:31:53.803998 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205499 (* 1 = 0.205499 loss) +I0616 16:31:53.804003 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119961 (* 1 = 0.0119961 loss) +I0616 16:31:53.804006 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0233295 (* 1 = 0.0233295 loss) +I0616 16:31:53.804010 9857 solver.cpp:571] Iteration 86940, lr = 0.0001 +I0616 16:32:05.483253 9857 solver.cpp:242] Iteration 86960, loss = 0.533573 +I0616 16:32:05.483294 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0445592 (* 1 = 0.0445592 loss) +I0616 16:32:05.483300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112959 (* 1 = 0.112959 loss) +I0616 16:32:05.483304 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00294642 (* 1 = 0.00294642 loss) +I0616 16:32:05.483309 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00454199 (* 1 = 0.00454199 loss) +I0616 16:32:05.483311 9857 solver.cpp:571] Iteration 86960, lr = 0.0001 +I0616 16:32:16.771647 9857 solver.cpp:242] Iteration 86980, loss = 0.872106 +I0616 16:32:16.771688 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.340145 (* 1 = 0.340145 loss) +I0616 16:32:16.771694 9857 solver.cpp:258] Train net output #1: loss_cls = 0.696924 (* 1 = 0.696924 loss) +I0616 16:32:16.771698 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.311124 (* 1 = 0.311124 loss) +I0616 16:32:16.771703 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0654105 (* 1 = 0.0654105 loss) +I0616 16:32:16.771706 9857 solver.cpp:571] Iteration 86980, lr = 0.0001 +speed: 0.597s / iter +I0616 16:32:28.475071 9857 solver.cpp:242] Iteration 87000, loss = 0.404895 +I0616 16:32:28.475114 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231088 (* 1 = 0.231088 loss) +I0616 16:32:28.475119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21917 (* 1 = 0.21917 loss) +I0616 16:32:28.475123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0459382 (* 1 = 0.0459382 loss) +I0616 16:32:28.475127 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0286159 (* 1 = 0.0286159 loss) +I0616 16:32:28.475131 9857 solver.cpp:571] Iteration 87000, lr = 0.0001 +I0616 16:32:40.153354 9857 solver.cpp:242] Iteration 87020, loss = 0.609643 +I0616 16:32:40.153381 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.421987 (* 1 = 0.421987 loss) +I0616 16:32:40.153400 9857 solver.cpp:258] Train net output #1: loss_cls = 0.314144 (* 1 = 0.314144 loss) +I0616 16:32:40.153404 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0819523 (* 1 = 0.0819523 loss) +I0616 16:32:40.153409 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0538469 (* 1 = 0.0538469 loss) +I0616 16:32:40.153412 9857 solver.cpp:571] Iteration 87020, lr = 0.0001 +I0616 16:32:51.501498 9857 solver.cpp:242] Iteration 87040, loss = 0.961546 +I0616 16:32:51.501540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.422695 (* 1 = 0.422695 loss) +I0616 16:32:51.501546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.653478 (* 1 = 0.653478 loss) +I0616 16:32:51.501550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219492 (* 1 = 0.219492 loss) +I0616 16:32:51.501554 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.117795 (* 1 = 0.117795 loss) +I0616 16:32:51.501559 9857 solver.cpp:571] Iteration 87040, lr = 0.0001 +I0616 16:33:03.306964 9857 solver.cpp:242] Iteration 87060, loss = 1.02686 +I0616 16:33:03.307006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.346535 (* 1 = 0.346535 loss) +I0616 16:33:03.307011 9857 solver.cpp:258] Train net output #1: loss_cls = 0.376978 (* 1 = 0.376978 loss) +I0616 16:33:03.307015 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.131476 (* 1 = 0.131476 loss) +I0616 16:33:03.307019 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.128998 (* 1 = 0.128998 loss) +I0616 16:33:03.307024 9857 solver.cpp:571] Iteration 87060, lr = 0.0001 +I0616 16:33:14.941938 9857 solver.cpp:242] Iteration 87080, loss = 0.680891 +I0616 16:33:14.941980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241387 (* 1 = 0.241387 loss) +I0616 16:33:14.941987 9857 solver.cpp:258] Train net output #1: loss_cls = 0.313305 (* 1 = 0.313305 loss) +I0616 16:33:14.941990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.106436 (* 1 = 0.106436 loss) +I0616 16:33:14.941994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.208447 (* 1 = 0.208447 loss) +I0616 16:33:14.941998 9857 solver.cpp:571] Iteration 87080, lr = 0.0001 +I0616 16:33:26.739007 9857 solver.cpp:242] Iteration 87100, loss = 0.500015 +I0616 16:33:26.739048 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227677 (* 1 = 0.227677 loss) +I0616 16:33:26.739054 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145907 (* 1 = 0.145907 loss) +I0616 16:33:26.739058 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0661226 (* 1 = 0.0661226 loss) +I0616 16:33:26.739063 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0254738 (* 1 = 0.0254738 loss) +I0616 16:33:26.739065 9857 solver.cpp:571] Iteration 87100, lr = 0.0001 +I0616 16:33:37.990171 9857 solver.cpp:242] Iteration 87120, loss = 0.361078 +I0616 16:33:37.990213 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.00534502 (* 1 = 0.00534502 loss) +I0616 16:33:37.990219 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0522855 (* 1 = 0.0522855 loss) +I0616 16:33:37.990224 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0505786 (* 1 = 0.0505786 loss) +I0616 16:33:37.990227 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.173055 (* 1 = 0.173055 loss) +I0616 16:33:37.990231 9857 solver.cpp:571] Iteration 87120, lr = 0.0001 +I0616 16:33:49.547077 9857 solver.cpp:242] Iteration 87140, loss = 0.235498 +I0616 16:33:49.547119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0607801 (* 1 = 0.0607801 loss) +I0616 16:33:49.547125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0694619 (* 1 = 0.0694619 loss) +I0616 16:33:49.547130 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00571733 (* 1 = 0.00571733 loss) +I0616 16:33:49.547133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0268952 (* 1 = 0.0268952 loss) +I0616 16:33:49.547137 9857 solver.cpp:571] Iteration 87140, lr = 0.0001 +I0616 16:34:01.260200 9857 solver.cpp:242] Iteration 87160, loss = 0.925173 +I0616 16:34:01.260242 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264883 (* 1 = 0.264883 loss) +I0616 16:34:01.260248 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392624 (* 1 = 0.392624 loss) +I0616 16:34:01.260252 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152895 (* 1 = 0.152895 loss) +I0616 16:34:01.260257 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0821209 (* 1 = 0.0821209 loss) +I0616 16:34:01.260260 9857 solver.cpp:571] Iteration 87160, lr = 0.0001 +I0616 16:34:12.941830 9857 solver.cpp:242] Iteration 87180, loss = 0.149285 +I0616 16:34:12.941871 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0381857 (* 1 = 0.0381857 loss) +I0616 16:34:12.941876 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0604547 (* 1 = 0.0604547 loss) +I0616 16:34:12.941881 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0091943 (* 1 = 0.0091943 loss) +I0616 16:34:12.941885 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00683092 (* 1 = 0.00683092 loss) +I0616 16:34:12.941890 9857 solver.cpp:571] Iteration 87180, lr = 0.0001 +speed: 0.597s / iter +I0616 16:34:24.572973 9857 solver.cpp:242] Iteration 87200, loss = 0.499211 +I0616 16:34:24.573014 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.319914 (* 1 = 0.319914 loss) +I0616 16:34:24.573020 9857 solver.cpp:258] Train net output #1: loss_cls = 0.260846 (* 1 = 0.260846 loss) +I0616 16:34:24.573024 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127193 (* 1 = 0.127193 loss) +I0616 16:34:24.573029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278509 (* 1 = 0.0278509 loss) +I0616 16:34:24.573031 9857 solver.cpp:571] Iteration 87200, lr = 0.0001 +I0616 16:34:35.868093 9857 solver.cpp:242] Iteration 87220, loss = 0.329032 +I0616 16:34:35.868134 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224708 (* 1 = 0.224708 loss) +I0616 16:34:35.868139 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267139 (* 1 = 0.267139 loss) +I0616 16:34:35.868144 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0130904 (* 1 = 0.0130904 loss) +I0616 16:34:35.868149 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00755409 (* 1 = 0.00755409 loss) +I0616 16:34:35.868152 9857 solver.cpp:571] Iteration 87220, lr = 0.0001 +I0616 16:34:47.476259 9857 solver.cpp:242] Iteration 87240, loss = 0.406723 +I0616 16:34:47.476300 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172129 (* 1 = 0.172129 loss) +I0616 16:34:47.476306 9857 solver.cpp:258] Train net output #1: loss_cls = 0.241925 (* 1 = 0.241925 loss) +I0616 16:34:47.476310 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0273866 (* 1 = 0.0273866 loss) +I0616 16:34:47.476313 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0213574 (* 1 = 0.0213574 loss) +I0616 16:34:47.476317 9857 solver.cpp:571] Iteration 87240, lr = 0.0001 +I0616 16:34:58.997989 9857 solver.cpp:242] Iteration 87260, loss = 0.622121 +I0616 16:34:58.998031 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134539 (* 1 = 0.134539 loss) +I0616 16:34:58.998036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.276237 (* 1 = 0.276237 loss) +I0616 16:34:58.998040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0309386 (* 1 = 0.0309386 loss) +I0616 16:34:58.998044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.123449 (* 1 = 0.123449 loss) +I0616 16:34:58.998049 9857 solver.cpp:571] Iteration 87260, lr = 0.0001 +I0616 16:35:10.408773 9857 solver.cpp:242] Iteration 87280, loss = 0.289435 +I0616 16:35:10.408814 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0886223 (* 1 = 0.0886223 loss) +I0616 16:35:10.408820 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123223 (* 1 = 0.123223 loss) +I0616 16:35:10.408824 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0777986 (* 1 = 0.0777986 loss) +I0616 16:35:10.408828 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240656 (* 1 = 0.0240656 loss) +I0616 16:35:10.408833 9857 solver.cpp:571] Iteration 87280, lr = 0.0001 +I0616 16:35:21.813287 9857 solver.cpp:242] Iteration 87300, loss = 0.389344 +I0616 16:35:21.813328 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.332691 (* 1 = 0.332691 loss) +I0616 16:35:21.813333 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163018 (* 1 = 0.163018 loss) +I0616 16:35:21.813338 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0938318 (* 1 = 0.0938318 loss) +I0616 16:35:21.813343 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0282639 (* 1 = 0.0282639 loss) +I0616 16:35:21.813345 9857 solver.cpp:571] Iteration 87300, lr = 0.0001 +I0616 16:35:33.438884 9857 solver.cpp:242] Iteration 87320, loss = 0.429974 +I0616 16:35:33.438925 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0373342 (* 1 = 0.0373342 loss) +I0616 16:35:33.438931 9857 solver.cpp:258] Train net output #1: loss_cls = 0.18873 (* 1 = 0.18873 loss) +I0616 16:35:33.438935 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00266062 (* 1 = 0.00266062 loss) +I0616 16:35:33.438940 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00204148 (* 1 = 0.00204148 loss) +I0616 16:35:33.438944 9857 solver.cpp:571] Iteration 87320, lr = 0.0001 +I0616 16:35:44.810376 9857 solver.cpp:242] Iteration 87340, loss = 0.505488 +I0616 16:35:44.810420 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0477657 (* 1 = 0.0477657 loss) +I0616 16:35:44.810425 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144187 (* 1 = 0.144187 loss) +I0616 16:35:44.810430 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119529 (* 1 = 0.0119529 loss) +I0616 16:35:44.810433 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140249 (* 1 = 0.0140249 loss) +I0616 16:35:44.810436 9857 solver.cpp:571] Iteration 87340, lr = 0.0001 +I0616 16:35:56.538569 9857 solver.cpp:242] Iteration 87360, loss = 0.539963 +I0616 16:35:56.538611 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23311 (* 1 = 0.23311 loss) +I0616 16:35:56.538617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128112 (* 1 = 0.128112 loss) +I0616 16:35:56.538621 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0321131 (* 1 = 0.0321131 loss) +I0616 16:35:56.538625 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00512475 (* 1 = 0.00512475 loss) +I0616 16:35:56.538630 9857 solver.cpp:571] Iteration 87360, lr = 0.0001 +I0616 16:36:07.706537 9857 solver.cpp:242] Iteration 87380, loss = 0.182165 +I0616 16:36:07.706579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0863438 (* 1 = 0.0863438 loss) +I0616 16:36:07.706585 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116137 (* 1 = 0.116137 loss) +I0616 16:36:07.706589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0117543 (* 1 = 0.0117543 loss) +I0616 16:36:07.706593 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0327383 (* 1 = 0.0327383 loss) +I0616 16:36:07.706596 9857 solver.cpp:571] Iteration 87380, lr = 0.0001 +speed: 0.597s / iter +I0616 16:36:19.539991 9857 solver.cpp:242] Iteration 87400, loss = 0.277329 +I0616 16:36:19.540033 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114615 (* 1 = 0.114615 loss) +I0616 16:36:19.540040 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160324 (* 1 = 0.160324 loss) +I0616 16:36:19.540043 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0142243 (* 1 = 0.0142243 loss) +I0616 16:36:19.540047 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.019473 (* 1 = 0.019473 loss) +I0616 16:36:19.540050 9857 solver.cpp:571] Iteration 87400, lr = 0.0001 +I0616 16:36:31.146342 9857 solver.cpp:242] Iteration 87420, loss = 0.598567 +I0616 16:36:31.146384 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118959 (* 1 = 0.118959 loss) +I0616 16:36:31.146389 9857 solver.cpp:258] Train net output #1: loss_cls = 0.20568 (* 1 = 0.20568 loss) +I0616 16:36:31.146394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.241037 (* 1 = 0.241037 loss) +I0616 16:36:31.146397 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00867869 (* 1 = 0.00867869 loss) +I0616 16:36:31.146401 9857 solver.cpp:571] Iteration 87420, lr = 0.0001 +I0616 16:36:42.864806 9857 solver.cpp:242] Iteration 87440, loss = 0.203967 +I0616 16:36:42.864848 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0384924 (* 1 = 0.0384924 loss) +I0616 16:36:42.864855 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118366 (* 1 = 0.118366 loss) +I0616 16:36:42.864858 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0202766 (* 1 = 0.0202766 loss) +I0616 16:36:42.864862 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00190992 (* 1 = 0.00190992 loss) +I0616 16:36:42.864866 9857 solver.cpp:571] Iteration 87440, lr = 0.0001 +I0616 16:36:54.428937 9857 solver.cpp:242] Iteration 87460, loss = 0.569195 +I0616 16:36:54.428980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123474 (* 1 = 0.123474 loss) +I0616 16:36:54.428987 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107229 (* 1 = 0.107229 loss) +I0616 16:36:54.428990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0172652 (* 1 = 0.0172652 loss) +I0616 16:36:54.428994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00161867 (* 1 = 0.00161867 loss) +I0616 16:36:54.428998 9857 solver.cpp:571] Iteration 87460, lr = 0.0001 +I0616 16:37:05.780982 9857 solver.cpp:242] Iteration 87480, loss = 0.422748 +I0616 16:37:05.781024 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.240456 (* 1 = 0.240456 loss) +I0616 16:37:05.781030 9857 solver.cpp:258] Train net output #1: loss_cls = 0.250869 (* 1 = 0.250869 loss) +I0616 16:37:05.781034 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0150762 (* 1 = 0.0150762 loss) +I0616 16:37:05.781038 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0329973 (* 1 = 0.0329973 loss) +I0616 16:37:05.781041 9857 solver.cpp:571] Iteration 87480, lr = 0.0001 +I0616 16:37:17.163662 9857 solver.cpp:242] Iteration 87500, loss = 0.376618 +I0616 16:37:17.163705 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0981625 (* 1 = 0.0981625 loss) +I0616 16:37:17.163712 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192459 (* 1 = 0.192459 loss) +I0616 16:37:17.163717 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00695109 (* 1 = 0.00695109 loss) +I0616 16:37:17.163720 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0485841 (* 1 = 0.0485841 loss) +I0616 16:37:17.163724 9857 solver.cpp:571] Iteration 87500, lr = 0.0001 +I0616 16:37:28.738158 9857 solver.cpp:242] Iteration 87520, loss = 0.360495 +I0616 16:37:28.738200 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0643227 (* 1 = 0.0643227 loss) +I0616 16:37:28.738206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11939 (* 1 = 0.11939 loss) +I0616 16:37:28.738210 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0134665 (* 1 = 0.0134665 loss) +I0616 16:37:28.738214 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00844723 (* 1 = 0.00844723 loss) +I0616 16:37:28.738217 9857 solver.cpp:571] Iteration 87520, lr = 0.0001 +I0616 16:37:40.151973 9857 solver.cpp:242] Iteration 87540, loss = 0.903266 +I0616 16:37:40.152016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267297 (* 1 = 0.267297 loss) +I0616 16:37:40.152021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254306 (* 1 = 0.254306 loss) +I0616 16:37:40.152026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0813364 (* 1 = 0.0813364 loss) +I0616 16:37:40.152029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0782742 (* 1 = 0.0782742 loss) +I0616 16:37:40.152034 9857 solver.cpp:571] Iteration 87540, lr = 0.0001 +I0616 16:37:51.492135 9857 solver.cpp:242] Iteration 87560, loss = 0.366192 +I0616 16:37:51.492175 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197658 (* 1 = 0.197658 loss) +I0616 16:37:51.492182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.246564 (* 1 = 0.246564 loss) +I0616 16:37:51.492185 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.025173 (* 1 = 0.025173 loss) +I0616 16:37:51.492189 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0232512 (* 1 = 0.0232512 loss) +I0616 16:37:51.492208 9857 solver.cpp:571] Iteration 87560, lr = 0.0001 +I0616 16:38:03.135162 9857 solver.cpp:242] Iteration 87580, loss = 0.784987 +I0616 16:38:03.135205 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.27257 (* 1 = 0.27257 loss) +I0616 16:38:03.135210 9857 solver.cpp:258] Train net output #1: loss_cls = 0.463678 (* 1 = 0.463678 loss) +I0616 16:38:03.135215 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0488092 (* 1 = 0.0488092 loss) +I0616 16:38:03.135220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.070672 (* 1 = 0.070672 loss) +I0616 16:38:03.135222 9857 solver.cpp:571] Iteration 87580, lr = 0.0001 +speed: 0.597s / iter +I0616 16:38:14.856148 9857 solver.cpp:242] Iteration 87600, loss = 0.505282 +I0616 16:38:14.856189 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130694 (* 1 = 0.130694 loss) +I0616 16:38:14.856195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0675514 (* 1 = 0.0675514 loss) +I0616 16:38:14.856199 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0298243 (* 1 = 0.0298243 loss) +I0616 16:38:14.856202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0430607 (* 1 = 0.0430607 loss) +I0616 16:38:14.856206 9857 solver.cpp:571] Iteration 87600, lr = 0.0001 +I0616 16:38:26.473109 9857 solver.cpp:242] Iteration 87620, loss = 0.422286 +I0616 16:38:26.473152 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201166 (* 1 = 0.201166 loss) +I0616 16:38:26.473157 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136631 (* 1 = 0.136631 loss) +I0616 16:38:26.473162 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.030627 (* 1 = 0.030627 loss) +I0616 16:38:26.473165 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0207117 (* 1 = 0.0207117 loss) +I0616 16:38:26.473170 9857 solver.cpp:571] Iteration 87620, lr = 0.0001 +I0616 16:38:37.929304 9857 solver.cpp:242] Iteration 87640, loss = 0.341103 +I0616 16:38:37.929347 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17812 (* 1 = 0.17812 loss) +I0616 16:38:37.929352 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191118 (* 1 = 0.191118 loss) +I0616 16:38:37.929357 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0882604 (* 1 = 0.0882604 loss) +I0616 16:38:37.929360 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00135765 (* 1 = 0.00135765 loss) +I0616 16:38:37.929363 9857 solver.cpp:571] Iteration 87640, lr = 0.0001 +I0616 16:38:49.481017 9857 solver.cpp:242] Iteration 87660, loss = 0.415127 +I0616 16:38:49.481060 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0451756 (* 1 = 0.0451756 loss) +I0616 16:38:49.481066 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0453864 (* 1 = 0.0453864 loss) +I0616 16:38:49.481070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0105287 (* 1 = 0.0105287 loss) +I0616 16:38:49.481075 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0040102 (* 1 = 0.0040102 loss) +I0616 16:38:49.481077 9857 solver.cpp:571] Iteration 87660, lr = 0.0001 +I0616 16:39:00.749238 9857 solver.cpp:242] Iteration 87680, loss = 0.245895 +I0616 16:39:00.749281 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.116715 (* 1 = 0.116715 loss) +I0616 16:39:00.749287 9857 solver.cpp:258] Train net output #1: loss_cls = 0.098511 (* 1 = 0.098511 loss) +I0616 16:39:00.749291 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.025 (* 1 = 0.025 loss) +I0616 16:39:00.749295 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0351686 (* 1 = 0.0351686 loss) +I0616 16:39:00.749299 9857 solver.cpp:571] Iteration 87680, lr = 0.0001 +I0616 16:39:12.427978 9857 solver.cpp:242] Iteration 87700, loss = 0.612156 +I0616 16:39:12.428020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.334238 (* 1 = 0.334238 loss) +I0616 16:39:12.428025 9857 solver.cpp:258] Train net output #1: loss_cls = 0.38638 (* 1 = 0.38638 loss) +I0616 16:39:12.428030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.136384 (* 1 = 0.136384 loss) +I0616 16:39:12.428035 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0933467 (* 1 = 0.0933467 loss) +I0616 16:39:12.428037 9857 solver.cpp:571] Iteration 87700, lr = 0.0001 +I0616 16:39:23.951627 9857 solver.cpp:242] Iteration 87720, loss = 0.41076 +I0616 16:39:23.951666 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127086 (* 1 = 0.127086 loss) +I0616 16:39:23.951673 9857 solver.cpp:258] Train net output #1: loss_cls = 0.320592 (* 1 = 0.320592 loss) +I0616 16:39:23.951676 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0888645 (* 1 = 0.0888645 loss) +I0616 16:39:23.951681 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00803758 (* 1 = 0.00803758 loss) +I0616 16:39:23.951688 9857 solver.cpp:571] Iteration 87720, lr = 0.0001 +I0616 16:39:35.493921 9857 solver.cpp:242] Iteration 87740, loss = 0.3979 +I0616 16:39:35.493964 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0627572 (* 1 = 0.0627572 loss) +I0616 16:39:35.493969 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104139 (* 1 = 0.104139 loss) +I0616 16:39:35.493974 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00458977 (* 1 = 0.00458977 loss) +I0616 16:39:35.493978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111701 (* 1 = 0.0111701 loss) +I0616 16:39:35.493981 9857 solver.cpp:571] Iteration 87740, lr = 0.0001 +I0616 16:39:47.232185 9857 solver.cpp:242] Iteration 87760, loss = 0.215829 +I0616 16:39:47.232228 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0452665 (* 1 = 0.0452665 loss) +I0616 16:39:47.232234 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130924 (* 1 = 0.130924 loss) +I0616 16:39:47.232237 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0109802 (* 1 = 0.0109802 loss) +I0616 16:39:47.232241 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00364108 (* 1 = 0.00364108 loss) +I0616 16:39:47.232245 9857 solver.cpp:571] Iteration 87760, lr = 0.0001 +I0616 16:39:58.669622 9857 solver.cpp:242] Iteration 87780, loss = 0.450479 +I0616 16:39:58.669663 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170678 (* 1 = 0.170678 loss) +I0616 16:39:58.669668 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167855 (* 1 = 0.167855 loss) +I0616 16:39:58.669672 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0497118 (* 1 = 0.0497118 loss) +I0616 16:39:58.669677 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0738891 (* 1 = 0.0738891 loss) +I0616 16:39:58.669680 9857 solver.cpp:571] Iteration 87780, lr = 0.0001 +speed: 0.597s / iter +I0616 16:40:10.268923 9857 solver.cpp:242] Iteration 87800, loss = 0.233697 +I0616 16:40:10.268965 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0983286 (* 1 = 0.0983286 loss) +I0616 16:40:10.268970 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127307 (* 1 = 0.127307 loss) +I0616 16:40:10.268975 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209005 (* 1 = 0.0209005 loss) +I0616 16:40:10.268978 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00700264 (* 1 = 0.00700264 loss) +I0616 16:40:10.268981 9857 solver.cpp:571] Iteration 87800, lr = 0.0001 +I0616 16:40:21.952780 9857 solver.cpp:242] Iteration 87820, loss = 0.39232 +I0616 16:40:21.952818 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.030685 (* 1 = 0.030685 loss) +I0616 16:40:21.952824 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0636112 (* 1 = 0.0636112 loss) +I0616 16:40:21.952828 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0184927 (* 1 = 0.0184927 loss) +I0616 16:40:21.952832 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.105606 (* 1 = 0.105606 loss) +I0616 16:40:21.952836 9857 solver.cpp:571] Iteration 87820, lr = 0.0001 +I0616 16:40:33.492437 9857 solver.cpp:242] Iteration 87840, loss = 0.205313 +I0616 16:40:33.492480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.044556 (* 1 = 0.044556 loss) +I0616 16:40:33.492486 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0864041 (* 1 = 0.0864041 loss) +I0616 16:40:33.492489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0874374 (* 1 = 0.0874374 loss) +I0616 16:40:33.492493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.029099 (* 1 = 0.029099 loss) +I0616 16:40:33.492496 9857 solver.cpp:571] Iteration 87840, lr = 0.0001 +I0616 16:40:44.709100 9857 solver.cpp:242] Iteration 87860, loss = 0.417605 +I0616 16:40:44.709142 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199319 (* 1 = 0.199319 loss) +I0616 16:40:44.709148 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263014 (* 1 = 0.263014 loss) +I0616 16:40:44.709152 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0697586 (* 1 = 0.0697586 loss) +I0616 16:40:44.709156 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0155294 (* 1 = 0.0155294 loss) +I0616 16:40:44.709159 9857 solver.cpp:571] Iteration 87860, lr = 0.0001 +I0616 16:40:56.190330 9857 solver.cpp:242] Iteration 87880, loss = 0.403314 +I0616 16:40:56.190371 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.273876 (* 1 = 0.273876 loss) +I0616 16:40:56.190376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296468 (* 1 = 0.296468 loss) +I0616 16:40:56.190381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0385442 (* 1 = 0.0385442 loss) +I0616 16:40:56.190384 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0232635 (* 1 = 0.0232635 loss) +I0616 16:40:56.190387 9857 solver.cpp:571] Iteration 87880, lr = 0.0001 +I0616 16:41:07.824533 9857 solver.cpp:242] Iteration 87900, loss = 0.587839 +I0616 16:41:07.824575 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.287424 (* 1 = 0.287424 loss) +I0616 16:41:07.824580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.35273 (* 1 = 0.35273 loss) +I0616 16:41:07.824585 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0390495 (* 1 = 0.0390495 loss) +I0616 16:41:07.824589 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.189203 (* 1 = 0.189203 loss) +I0616 16:41:07.824592 9857 solver.cpp:571] Iteration 87900, lr = 0.0001 +I0616 16:41:19.447165 9857 solver.cpp:242] Iteration 87920, loss = 0.20242 +I0616 16:41:19.447207 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0424669 (* 1 = 0.0424669 loss) +I0616 16:41:19.447213 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0688556 (* 1 = 0.0688556 loss) +I0616 16:41:19.447217 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0563957 (* 1 = 0.0563957 loss) +I0616 16:41:19.447221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142296 (* 1 = 0.0142296 loss) +I0616 16:41:19.447227 9857 solver.cpp:571] Iteration 87920, lr = 0.0001 +I0616 16:41:30.992959 9857 solver.cpp:242] Iteration 87940, loss = 0.591079 +I0616 16:41:30.993005 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242376 (* 1 = 0.242376 loss) +I0616 16:41:30.993010 9857 solver.cpp:258] Train net output #1: loss_cls = 0.286118 (* 1 = 0.286118 loss) +I0616 16:41:30.993015 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0888191 (* 1 = 0.0888191 loss) +I0616 16:41:30.993018 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0228123 (* 1 = 0.0228123 loss) +I0616 16:41:30.993022 9857 solver.cpp:571] Iteration 87940, lr = 0.0001 +I0616 16:41:42.545373 9857 solver.cpp:242] Iteration 87960, loss = 0.396924 +I0616 16:41:42.545415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16797 (* 1 = 0.16797 loss) +I0616 16:41:42.545421 9857 solver.cpp:258] Train net output #1: loss_cls = 0.273936 (* 1 = 0.273936 loss) +I0616 16:41:42.545425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0152789 (* 1 = 0.0152789 loss) +I0616 16:41:42.545429 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122432 (* 1 = 0.0122432 loss) +I0616 16:41:42.545433 9857 solver.cpp:571] Iteration 87960, lr = 0.0001 +I0616 16:41:54.260468 9857 solver.cpp:242] Iteration 87980, loss = 0.225564 +I0616 16:41:54.260509 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0637898 (* 1 = 0.0637898 loss) +I0616 16:41:54.260514 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0823281 (* 1 = 0.0823281 loss) +I0616 16:41:54.260519 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0102388 (* 1 = 0.0102388 loss) +I0616 16:41:54.260522 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0185244 (* 1 = 0.0185244 loss) +I0616 16:41:54.260540 9857 solver.cpp:571] Iteration 87980, lr = 0.0001 +speed: 0.597s / iter +I0616 16:42:05.862262 9857 solver.cpp:242] Iteration 88000, loss = 0.29802 +I0616 16:42:05.862304 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0627673 (* 1 = 0.0627673 loss) +I0616 16:42:05.862309 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109578 (* 1 = 0.109578 loss) +I0616 16:42:05.862314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00793773 (* 1 = 0.00793773 loss) +I0616 16:42:05.862318 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00482405 (* 1 = 0.00482405 loss) +I0616 16:42:05.862321 9857 solver.cpp:571] Iteration 88000, lr = 0.0001 +I0616 16:42:17.576405 9857 solver.cpp:242] Iteration 88020, loss = 0.272889 +I0616 16:42:17.576444 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200673 (* 1 = 0.200673 loss) +I0616 16:42:17.576450 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145568 (* 1 = 0.145568 loss) +I0616 16:42:17.576454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0230237 (* 1 = 0.0230237 loss) +I0616 16:42:17.576458 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152819 (* 1 = 0.0152819 loss) +I0616 16:42:17.576462 9857 solver.cpp:571] Iteration 88020, lr = 0.0001 +I0616 16:42:28.905973 9857 solver.cpp:242] Iteration 88040, loss = 0.378943 +I0616 16:42:28.906015 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0918596 (* 1 = 0.0918596 loss) +I0616 16:42:28.906021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156313 (* 1 = 0.156313 loss) +I0616 16:42:28.906026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0540775 (* 1 = 0.0540775 loss) +I0616 16:42:28.906030 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.099614 (* 1 = 0.099614 loss) +I0616 16:42:28.906036 9857 solver.cpp:571] Iteration 88040, lr = 0.0001 +I0616 16:42:40.467237 9857 solver.cpp:242] Iteration 88060, loss = 0.640186 +I0616 16:42:40.467277 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0713478 (* 1 = 0.0713478 loss) +I0616 16:42:40.467281 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11648 (* 1 = 0.11648 loss) +I0616 16:42:40.467286 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101711 (* 1 = 0.101711 loss) +I0616 16:42:40.467290 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0784685 (* 1 = 0.0784685 loss) +I0616 16:42:40.467293 9857 solver.cpp:571] Iteration 88060, lr = 0.0001 +I0616 16:42:52.168929 9857 solver.cpp:242] Iteration 88080, loss = 0.212258 +I0616 16:42:52.168972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0867712 (* 1 = 0.0867712 loss) +I0616 16:42:52.168977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101112 (* 1 = 0.101112 loss) +I0616 16:42:52.168982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0410034 (* 1 = 0.0410034 loss) +I0616 16:42:52.168985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0165221 (* 1 = 0.0165221 loss) +I0616 16:42:52.168988 9857 solver.cpp:571] Iteration 88080, lr = 0.0001 +I0616 16:43:03.555403 9857 solver.cpp:242] Iteration 88100, loss = 0.63262 +I0616 16:43:03.555445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.286733 (* 1 = 0.286733 loss) +I0616 16:43:03.555451 9857 solver.cpp:258] Train net output #1: loss_cls = 0.456792 (* 1 = 0.456792 loss) +I0616 16:43:03.555455 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0401808 (* 1 = 0.0401808 loss) +I0616 16:43:03.555459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0493131 (* 1 = 0.0493131 loss) +I0616 16:43:03.555464 9857 solver.cpp:571] Iteration 88100, lr = 0.0001 +I0616 16:43:15.059569 9857 solver.cpp:242] Iteration 88120, loss = 0.326334 +I0616 16:43:15.059612 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0595949 (* 1 = 0.0595949 loss) +I0616 16:43:15.059617 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0886891 (* 1 = 0.0886891 loss) +I0616 16:43:15.059622 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0440303 (* 1 = 0.0440303 loss) +I0616 16:43:15.059624 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00246441 (* 1 = 0.00246441 loss) +I0616 16:43:15.059628 9857 solver.cpp:571] Iteration 88120, lr = 0.0001 +I0616 16:43:26.505625 9857 solver.cpp:242] Iteration 88140, loss = 0.375688 +I0616 16:43:26.505668 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111139 (* 1 = 0.111139 loss) +I0616 16:43:26.505673 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131785 (* 1 = 0.131785 loss) +I0616 16:43:26.505677 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.167823 (* 1 = 0.167823 loss) +I0616 16:43:26.505681 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0195336 (* 1 = 0.0195336 loss) +I0616 16:43:26.505686 9857 solver.cpp:571] Iteration 88140, lr = 0.0001 +I0616 16:43:37.912789 9857 solver.cpp:242] Iteration 88160, loss = 0.765971 +I0616 16:43:37.912830 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.269388 (* 1 = 0.269388 loss) +I0616 16:43:37.912835 9857 solver.cpp:258] Train net output #1: loss_cls = 0.264296 (* 1 = 0.264296 loss) +I0616 16:43:37.912839 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.192037 (* 1 = 0.192037 loss) +I0616 16:43:37.912843 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167139 (* 1 = 0.0167139 loss) +I0616 16:43:37.912847 9857 solver.cpp:571] Iteration 88160, lr = 0.0001 +I0616 16:43:49.624102 9857 solver.cpp:242] Iteration 88180, loss = 0.218558 +I0616 16:43:49.624146 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104915 (* 1 = 0.104915 loss) +I0616 16:43:49.624151 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138105 (* 1 = 0.138105 loss) +I0616 16:43:49.624156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00158711 (* 1 = 0.00158711 loss) +I0616 16:43:49.624161 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00180438 (* 1 = 0.00180438 loss) +I0616 16:43:49.624163 9857 solver.cpp:571] Iteration 88180, lr = 0.0001 +speed: 0.597s / iter +I0616 16:44:01.096530 9857 solver.cpp:242] Iteration 88200, loss = 0.590303 +I0616 16:44:01.096573 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.210968 (* 1 = 0.210968 loss) +I0616 16:44:01.096578 9857 solver.cpp:258] Train net output #1: loss_cls = 0.265654 (* 1 = 0.265654 loss) +I0616 16:44:01.096582 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0424623 (* 1 = 0.0424623 loss) +I0616 16:44:01.096586 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00865248 (* 1 = 0.00865248 loss) +I0616 16:44:01.096590 9857 solver.cpp:571] Iteration 88200, lr = 0.0001 +I0616 16:44:12.763556 9857 solver.cpp:242] Iteration 88220, loss = 0.72651 +I0616 16:44:12.763597 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.371543 (* 1 = 0.371543 loss) +I0616 16:44:12.763602 9857 solver.cpp:258] Train net output #1: loss_cls = 0.534563 (* 1 = 0.534563 loss) +I0616 16:44:12.763607 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.17322 (* 1 = 0.17322 loss) +I0616 16:44:12.763612 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0328498 (* 1 = 0.0328498 loss) +I0616 16:44:12.763614 9857 solver.cpp:571] Iteration 88220, lr = 0.0001 +I0616 16:44:24.186592 9857 solver.cpp:242] Iteration 88240, loss = 0.369688 +I0616 16:44:24.186635 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.179829 (* 1 = 0.179829 loss) +I0616 16:44:24.186640 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208847 (* 1 = 0.208847 loss) +I0616 16:44:24.186645 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114953 (* 1 = 0.114953 loss) +I0616 16:44:24.186647 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0255667 (* 1 = 0.0255667 loss) +I0616 16:44:24.186651 9857 solver.cpp:571] Iteration 88240, lr = 0.0001 +I0616 16:44:35.597214 9857 solver.cpp:242] Iteration 88260, loss = 0.709124 +I0616 16:44:35.597256 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122481 (* 1 = 0.122481 loss) +I0616 16:44:35.597261 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189414 (* 1 = 0.189414 loss) +I0616 16:44:35.597266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0747035 (* 1 = 0.0747035 loss) +I0616 16:44:35.597270 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101947 (* 1 = 0.0101947 loss) +I0616 16:44:35.597273 9857 solver.cpp:571] Iteration 88260, lr = 0.0001 +I0616 16:44:47.088964 9857 solver.cpp:242] Iteration 88280, loss = 0.392801 +I0616 16:44:47.089006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19073 (* 1 = 0.19073 loss) +I0616 16:44:47.089012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134551 (* 1 = 0.134551 loss) +I0616 16:44:47.089016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0784396 (* 1 = 0.0784396 loss) +I0616 16:44:47.089020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0713785 (* 1 = 0.0713785 loss) +I0616 16:44:47.089023 9857 solver.cpp:571] Iteration 88280, lr = 0.0001 +I0616 16:44:58.383536 9857 solver.cpp:242] Iteration 88300, loss = 0.175305 +I0616 16:44:58.383579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.047095 (* 1 = 0.047095 loss) +I0616 16:44:58.383584 9857 solver.cpp:258] Train net output #1: loss_cls = 0.078193 (* 1 = 0.078193 loss) +I0616 16:44:58.383589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00140084 (* 1 = 0.00140084 loss) +I0616 16:44:58.383592 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00737366 (* 1 = 0.00737366 loss) +I0616 16:44:58.383596 9857 solver.cpp:571] Iteration 88300, lr = 0.0001 +I0616 16:45:09.810268 9857 solver.cpp:242] Iteration 88320, loss = 0.41508 +I0616 16:45:09.810312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188954 (* 1 = 0.188954 loss) +I0616 16:45:09.810317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181292 (* 1 = 0.181292 loss) +I0616 16:45:09.810320 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0455515 (* 1 = 0.0455515 loss) +I0616 16:45:09.810324 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033258 (* 1 = 0.033258 loss) +I0616 16:45:09.810328 9857 solver.cpp:571] Iteration 88320, lr = 0.0001 +I0616 16:45:21.256469 9857 solver.cpp:242] Iteration 88340, loss = 0.575021 +I0616 16:45:21.256510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0963902 (* 1 = 0.0963902 loss) +I0616 16:45:21.256516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139725 (* 1 = 0.139725 loss) +I0616 16:45:21.256520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104433 (* 1 = 0.104433 loss) +I0616 16:45:21.256525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01144 (* 1 = 0.01144 loss) +I0616 16:45:21.256527 9857 solver.cpp:571] Iteration 88340, lr = 0.0001 +I0616 16:45:32.715170 9857 solver.cpp:242] Iteration 88360, loss = 0.745323 +I0616 16:45:32.715211 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.301693 (* 1 = 0.301693 loss) +I0616 16:45:32.715231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.298477 (* 1 = 0.298477 loss) +I0616 16:45:32.715235 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.139873 (* 1 = 0.139873 loss) +I0616 16:45:32.715240 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319951 (* 1 = 0.0319951 loss) +I0616 16:45:32.715243 9857 solver.cpp:571] Iteration 88360, lr = 0.0001 +I0616 16:45:44.270927 9857 solver.cpp:242] Iteration 88380, loss = 0.301541 +I0616 16:45:44.270970 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0972443 (* 1 = 0.0972443 loss) +I0616 16:45:44.270977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0871852 (* 1 = 0.0871852 loss) +I0616 16:45:44.270980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0495795 (* 1 = 0.0495795 loss) +I0616 16:45:44.270983 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00839351 (* 1 = 0.00839351 loss) +I0616 16:45:44.270987 9857 solver.cpp:571] Iteration 88380, lr = 0.0001 +speed: 0.597s / iter +I0616 16:45:55.959085 9857 solver.cpp:242] Iteration 88400, loss = 0.40025 +I0616 16:45:55.959127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0994843 (* 1 = 0.0994843 loss) +I0616 16:45:55.959133 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0675631 (* 1 = 0.0675631 loss) +I0616 16:45:55.959137 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0129959 (* 1 = 0.0129959 loss) +I0616 16:45:55.959141 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00372624 (* 1 = 0.00372624 loss) +I0616 16:45:55.959144 9857 solver.cpp:571] Iteration 88400, lr = 0.0001 +I0616 16:46:07.237260 9857 solver.cpp:242] Iteration 88420, loss = 0.379632 +I0616 16:46:07.237303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195935 (* 1 = 0.195935 loss) +I0616 16:46:07.237308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16709 (* 1 = 0.16709 loss) +I0616 16:46:07.237313 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0752411 (* 1 = 0.0752411 loss) +I0616 16:46:07.237316 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00917486 (* 1 = 0.00917486 loss) +I0616 16:46:07.237320 9857 solver.cpp:571] Iteration 88420, lr = 0.0001 +I0616 16:46:18.711483 9857 solver.cpp:242] Iteration 88440, loss = 0.260467 +I0616 16:46:18.711526 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10369 (* 1 = 0.10369 loss) +I0616 16:46:18.711532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125684 (* 1 = 0.125684 loss) +I0616 16:46:18.711536 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0560052 (* 1 = 0.0560052 loss) +I0616 16:46:18.711540 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00646424 (* 1 = 0.00646424 loss) +I0616 16:46:18.711544 9857 solver.cpp:571] Iteration 88440, lr = 0.0001 +I0616 16:46:30.409965 9857 solver.cpp:242] Iteration 88460, loss = 0.533446 +I0616 16:46:30.410006 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0324841 (* 1 = 0.0324841 loss) +I0616 16:46:30.410012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0794268 (* 1 = 0.0794268 loss) +I0616 16:46:30.410017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0067363 (* 1 = 0.0067363 loss) +I0616 16:46:30.410020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00468438 (* 1 = 0.00468438 loss) +I0616 16:46:30.410023 9857 solver.cpp:571] Iteration 88460, lr = 0.0001 +I0616 16:46:41.886610 9857 solver.cpp:242] Iteration 88480, loss = 0.551642 +I0616 16:46:41.886649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24024 (* 1 = 0.24024 loss) +I0616 16:46:41.886656 9857 solver.cpp:258] Train net output #1: loss_cls = 0.328246 (* 1 = 0.328246 loss) +I0616 16:46:41.886659 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0667905 (* 1 = 0.0667905 loss) +I0616 16:46:41.886663 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.146454 (* 1 = 0.146454 loss) +I0616 16:46:41.886667 9857 solver.cpp:571] Iteration 88480, lr = 0.0001 +I0616 16:46:53.461216 9857 solver.cpp:242] Iteration 88500, loss = 0.595128 +I0616 16:46:53.461258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.176781 (* 1 = 0.176781 loss) +I0616 16:46:53.461263 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283089 (* 1 = 0.283089 loss) +I0616 16:46:53.461267 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.161221 (* 1 = 0.161221 loss) +I0616 16:46:53.461272 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0695933 (* 1 = 0.0695933 loss) +I0616 16:46:53.461274 9857 solver.cpp:571] Iteration 88500, lr = 0.0001 +I0616 16:47:05.098763 9857 solver.cpp:242] Iteration 88520, loss = 0.545593 +I0616 16:47:05.098805 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0444454 (* 1 = 0.0444454 loss) +I0616 16:47:05.098810 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113626 (* 1 = 0.113626 loss) +I0616 16:47:05.098815 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00820693 (* 1 = 0.00820693 loss) +I0616 16:47:05.098819 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00504827 (* 1 = 0.00504827 loss) +I0616 16:47:05.098822 9857 solver.cpp:571] Iteration 88520, lr = 0.0001 +I0616 16:47:16.186524 9857 solver.cpp:242] Iteration 88540, loss = 0.439047 +I0616 16:47:16.186566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228537 (* 1 = 0.228537 loss) +I0616 16:47:16.186573 9857 solver.cpp:258] Train net output #1: loss_cls = 0.311186 (* 1 = 0.311186 loss) +I0616 16:47:16.186576 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0534797 (* 1 = 0.0534797 loss) +I0616 16:47:16.186580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0296499 (* 1 = 0.0296499 loss) +I0616 16:47:16.186583 9857 solver.cpp:571] Iteration 88540, lr = 0.0001 +I0616 16:47:27.950134 9857 solver.cpp:242] Iteration 88560, loss = 0.361635 +I0616 16:47:27.950177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0683092 (* 1 = 0.0683092 loss) +I0616 16:47:27.950183 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109479 (* 1 = 0.109479 loss) +I0616 16:47:27.950187 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00802091 (* 1 = 0.00802091 loss) +I0616 16:47:27.950191 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00395004 (* 1 = 0.00395004 loss) +I0616 16:47:27.950196 9857 solver.cpp:571] Iteration 88560, lr = 0.0001 +I0616 16:47:39.556154 9857 solver.cpp:242] Iteration 88580, loss = 0.618079 +I0616 16:47:39.556195 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.291839 (* 1 = 0.291839 loss) +I0616 16:47:39.556200 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289355 (* 1 = 0.289355 loss) +I0616 16:47:39.556203 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104895 (* 1 = 0.104895 loss) +I0616 16:47:39.556207 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0536137 (* 1 = 0.0536137 loss) +I0616 16:47:39.556211 9857 solver.cpp:571] Iteration 88580, lr = 0.0001 +speed: 0.597s / iter +I0616 16:47:51.036008 9857 solver.cpp:242] Iteration 88600, loss = 0.484391 +I0616 16:47:51.036049 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272531 (* 1 = 0.272531 loss) +I0616 16:47:51.036054 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226098 (* 1 = 0.226098 loss) +I0616 16:47:51.036058 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0339378 (* 1 = 0.0339378 loss) +I0616 16:47:51.036062 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0915699 (* 1 = 0.0915699 loss) +I0616 16:47:51.036067 9857 solver.cpp:571] Iteration 88600, lr = 0.0001 +I0616 16:48:02.984845 9857 solver.cpp:242] Iteration 88620, loss = 0.467354 +I0616 16:48:02.984887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0570371 (* 1 = 0.0570371 loss) +I0616 16:48:02.984894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.094973 (* 1 = 0.094973 loss) +I0616 16:48:02.984897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.027646 (* 1 = 0.027646 loss) +I0616 16:48:02.984901 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00255048 (* 1 = 0.00255048 loss) +I0616 16:48:02.984905 9857 solver.cpp:571] Iteration 88620, lr = 0.0001 +I0616 16:48:14.561203 9857 solver.cpp:242] Iteration 88640, loss = 0.276361 +I0616 16:48:14.561245 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0645761 (* 1 = 0.0645761 loss) +I0616 16:48:14.561250 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136898 (* 1 = 0.136898 loss) +I0616 16:48:14.561255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00265388 (* 1 = 0.00265388 loss) +I0616 16:48:14.561259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000574713 (* 1 = 0.000574713 loss) +I0616 16:48:14.561262 9857 solver.cpp:571] Iteration 88640, lr = 0.0001 +I0616 16:48:26.251606 9857 solver.cpp:242] Iteration 88660, loss = 0.825312 +I0616 16:48:26.251648 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.352565 (* 1 = 0.352565 loss) +I0616 16:48:26.251654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.332929 (* 1 = 0.332929 loss) +I0616 16:48:26.251658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.232689 (* 1 = 0.232689 loss) +I0616 16:48:26.251662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0844836 (* 1 = 0.0844836 loss) +I0616 16:48:26.251667 9857 solver.cpp:571] Iteration 88660, lr = 0.0001 +I0616 16:48:37.926220 9857 solver.cpp:242] Iteration 88680, loss = 0.609296 +I0616 16:48:37.926261 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0488213 (* 1 = 0.0488213 loss) +I0616 16:48:37.926267 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0938847 (* 1 = 0.0938847 loss) +I0616 16:48:37.926271 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0150489 (* 1 = 0.0150489 loss) +I0616 16:48:37.926275 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562418 (* 1 = 0.00562418 loss) +I0616 16:48:37.926278 9857 solver.cpp:571] Iteration 88680, lr = 0.0001 +I0616 16:48:49.620781 9857 solver.cpp:242] Iteration 88700, loss = 0.45572 +I0616 16:48:49.620823 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0706897 (* 1 = 0.0706897 loss) +I0616 16:48:49.620829 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132438 (* 1 = 0.132438 loss) +I0616 16:48:49.620833 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0284988 (* 1 = 0.0284988 loss) +I0616 16:48:49.620837 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0210979 (* 1 = 0.0210979 loss) +I0616 16:48:49.620841 9857 solver.cpp:571] Iteration 88700, lr = 0.0001 +I0616 16:49:00.738919 9857 solver.cpp:242] Iteration 88720, loss = 0.709849 +I0616 16:49:00.738960 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0944204 (* 1 = 0.0944204 loss) +I0616 16:49:00.738965 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109408 (* 1 = 0.109408 loss) +I0616 16:49:00.738970 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0949857 (* 1 = 0.0949857 loss) +I0616 16:49:00.738972 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.032671 (* 1 = 0.032671 loss) +I0616 16:49:00.738976 9857 solver.cpp:571] Iteration 88720, lr = 0.0001 +I0616 16:49:12.361544 9857 solver.cpp:242] Iteration 88740, loss = 0.629781 +I0616 16:49:12.361585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172029 (* 1 = 0.172029 loss) +I0616 16:49:12.361591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.261615 (* 1 = 0.261615 loss) +I0616 16:49:12.361595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0657704 (* 1 = 0.0657704 loss) +I0616 16:49:12.361599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00879005 (* 1 = 0.00879005 loss) +I0616 16:49:12.361603 9857 solver.cpp:571] Iteration 88740, lr = 0.0001 +I0616 16:49:23.831743 9857 solver.cpp:242] Iteration 88760, loss = 0.356369 +I0616 16:49:23.831784 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166305 (* 1 = 0.166305 loss) +I0616 16:49:23.831789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186021 (* 1 = 0.186021 loss) +I0616 16:49:23.831794 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.057492 (* 1 = 0.057492 loss) +I0616 16:49:23.831797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0602965 (* 1 = 0.0602965 loss) +I0616 16:49:23.831801 9857 solver.cpp:571] Iteration 88760, lr = 0.0001 +I0616 16:49:35.404469 9857 solver.cpp:242] Iteration 88780, loss = 0.516757 +I0616 16:49:35.404511 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.258259 (* 1 = 0.258259 loss) +I0616 16:49:35.404516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.364831 (* 1 = 0.364831 loss) +I0616 16:49:35.404521 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0619125 (* 1 = 0.0619125 loss) +I0616 16:49:35.404525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.151059 (* 1 = 0.151059 loss) +I0616 16:49:35.404528 9857 solver.cpp:571] Iteration 88780, lr = 0.0001 +speed: 0.597s / iter +I0616 16:49:46.889259 9857 solver.cpp:242] Iteration 88800, loss = 0.172935 +I0616 16:49:46.889302 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0538977 (* 1 = 0.0538977 loss) +I0616 16:49:46.889307 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0859847 (* 1 = 0.0859847 loss) +I0616 16:49:46.889312 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00946978 (* 1 = 0.00946978 loss) +I0616 16:49:46.889315 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00880487 (* 1 = 0.00880487 loss) +I0616 16:49:46.889319 9857 solver.cpp:571] Iteration 88800, lr = 0.0001 +I0616 16:49:58.246707 9857 solver.cpp:242] Iteration 88820, loss = 0.742626 +I0616 16:49:58.246749 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.223465 (* 1 = 0.223465 loss) +I0616 16:49:58.246755 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249374 (* 1 = 0.249374 loss) +I0616 16:49:58.246767 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.087088 (* 1 = 0.087088 loss) +I0616 16:49:58.246773 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0540941 (* 1 = 0.0540941 loss) +I0616 16:49:58.246779 9857 solver.cpp:571] Iteration 88820, lr = 0.0001 +I0616 16:50:09.817669 9857 solver.cpp:242] Iteration 88840, loss = 0.244667 +I0616 16:50:09.817711 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0765687 (* 1 = 0.0765687 loss) +I0616 16:50:09.817716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100329 (* 1 = 0.100329 loss) +I0616 16:50:09.817720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0304561 (* 1 = 0.0304561 loss) +I0616 16:50:09.817724 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00648767 (* 1 = 0.00648767 loss) +I0616 16:50:09.817728 9857 solver.cpp:571] Iteration 88840, lr = 0.0001 +I0616 16:50:21.382834 9857 solver.cpp:242] Iteration 88860, loss = 0.477178 +I0616 16:50:21.382875 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360972 (* 1 = 0.360972 loss) +I0616 16:50:21.382881 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255402 (* 1 = 0.255402 loss) +I0616 16:50:21.382885 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0506918 (* 1 = 0.0506918 loss) +I0616 16:50:21.382889 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0253322 (* 1 = 0.0253322 loss) +I0616 16:50:21.382892 9857 solver.cpp:571] Iteration 88860, lr = 0.0001 +I0616 16:50:32.769691 9857 solver.cpp:242] Iteration 88880, loss = 0.455009 +I0616 16:50:32.769734 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187999 (* 1 = 0.187999 loss) +I0616 16:50:32.769740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26228 (* 1 = 0.26228 loss) +I0616 16:50:32.769745 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0863772 (* 1 = 0.0863772 loss) +I0616 16:50:32.769748 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0982935 (* 1 = 0.0982935 loss) +I0616 16:50:32.769752 9857 solver.cpp:571] Iteration 88880, lr = 0.0001 +I0616 16:50:44.139209 9857 solver.cpp:242] Iteration 88900, loss = 0.296652 +I0616 16:50:44.139250 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184423 (* 1 = 0.184423 loss) +I0616 16:50:44.139255 9857 solver.cpp:258] Train net output #1: loss_cls = 0.106538 (* 1 = 0.106538 loss) +I0616 16:50:44.139258 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00296378 (* 1 = 0.00296378 loss) +I0616 16:50:44.139262 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00427127 (* 1 = 0.00427127 loss) +I0616 16:50:44.139266 9857 solver.cpp:571] Iteration 88900, lr = 0.0001 +I0616 16:50:55.785795 9857 solver.cpp:242] Iteration 88920, loss = 0.575934 +I0616 16:50:55.785835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201834 (* 1 = 0.201834 loss) +I0616 16:50:55.785840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169399 (* 1 = 0.169399 loss) +I0616 16:50:55.785845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0167618 (* 1 = 0.0167618 loss) +I0616 16:50:55.785848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0456533 (* 1 = 0.0456533 loss) +I0616 16:50:55.785852 9857 solver.cpp:571] Iteration 88920, lr = 0.0001 +I0616 16:51:07.197177 9857 solver.cpp:242] Iteration 88940, loss = 1.0342 +I0616 16:51:07.197219 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336809 (* 1 = 0.336809 loss) +I0616 16:51:07.197224 9857 solver.cpp:258] Train net output #1: loss_cls = 0.255468 (* 1 = 0.255468 loss) +I0616 16:51:07.197228 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22471 (* 1 = 0.22471 loss) +I0616 16:51:07.197232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0902754 (* 1 = 0.0902754 loss) +I0616 16:51:07.197237 9857 solver.cpp:571] Iteration 88940, lr = 0.0001 +I0616 16:51:18.550966 9857 solver.cpp:242] Iteration 88960, loss = 0.330498 +I0616 16:51:18.551007 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168266 (* 1 = 0.168266 loss) +I0616 16:51:18.551012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191803 (* 1 = 0.191803 loss) +I0616 16:51:18.551017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0162938 (* 1 = 0.0162938 loss) +I0616 16:51:18.551020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00265467 (* 1 = 0.00265467 loss) +I0616 16:51:18.551024 9857 solver.cpp:571] Iteration 88960, lr = 0.0001 +I0616 16:51:30.262644 9857 solver.cpp:242] Iteration 88980, loss = 0.549838 +I0616 16:51:30.262684 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141383 (* 1 = 0.141383 loss) +I0616 16:51:30.262689 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19785 (* 1 = 0.19785 loss) +I0616 16:51:30.262694 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0808077 (* 1 = 0.0808077 loss) +I0616 16:51:30.262697 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0369154 (* 1 = 0.0369154 loss) +I0616 16:51:30.262701 9857 solver.cpp:571] Iteration 88980, lr = 0.0001 +speed: 0.597s / iter +I0616 16:51:41.583629 9857 solver.cpp:242] Iteration 89000, loss = 0.524856 +I0616 16:51:41.583672 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.236841 (* 1 = 0.236841 loss) +I0616 16:51:41.583676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.434798 (* 1 = 0.434798 loss) +I0616 16:51:41.583681 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0659662 (* 1 = 0.0659662 loss) +I0616 16:51:41.583684 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0198795 (* 1 = 0.0198795 loss) +I0616 16:51:41.583688 9857 solver.cpp:571] Iteration 89000, lr = 0.0001 +I0616 16:51:53.111306 9857 solver.cpp:242] Iteration 89020, loss = 0.159506 +I0616 16:51:53.111348 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0621785 (* 1 = 0.0621785 loss) +I0616 16:51:53.111354 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0955175 (* 1 = 0.0955175 loss) +I0616 16:51:53.111358 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0237105 (* 1 = 0.0237105 loss) +I0616 16:51:53.111362 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00499851 (* 1 = 0.00499851 loss) +I0616 16:51:53.111366 9857 solver.cpp:571] Iteration 89020, lr = 0.0001 +I0616 16:52:04.726819 9857 solver.cpp:242] Iteration 89040, loss = 0.475519 +I0616 16:52:04.726861 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115337 (* 1 = 0.115337 loss) +I0616 16:52:04.726866 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247923 (* 1 = 0.247923 loss) +I0616 16:52:04.726871 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00726908 (* 1 = 0.00726908 loss) +I0616 16:52:04.726874 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0722128 (* 1 = 0.0722128 loss) +I0616 16:52:04.726878 9857 solver.cpp:571] Iteration 89040, lr = 0.0001 +I0616 16:52:16.386229 9857 solver.cpp:242] Iteration 89060, loss = 0.668858 +I0616 16:52:16.386270 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.35433 (* 1 = 0.35433 loss) +I0616 16:52:16.386276 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350214 (* 1 = 0.350214 loss) +I0616 16:52:16.386281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.115766 (* 1 = 0.115766 loss) +I0616 16:52:16.386284 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0520983 (* 1 = 0.0520983 loss) +I0616 16:52:16.386288 9857 solver.cpp:571] Iteration 89060, lr = 0.0001 +I0616 16:52:27.895761 9857 solver.cpp:242] Iteration 89080, loss = 0.677147 +I0616 16:52:27.895803 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191061 (* 1 = 0.191061 loss) +I0616 16:52:27.895808 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177878 (* 1 = 0.177878 loss) +I0616 16:52:27.895813 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.101239 (* 1 = 0.101239 loss) +I0616 16:52:27.895817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0852362 (* 1 = 0.0852362 loss) +I0616 16:52:27.895820 9857 solver.cpp:571] Iteration 89080, lr = 0.0001 +I0616 16:52:39.714840 9857 solver.cpp:242] Iteration 89100, loss = 0.566627 +I0616 16:52:39.714884 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139519 (* 1 = 0.139519 loss) +I0616 16:52:39.714890 9857 solver.cpp:258] Train net output #1: loss_cls = 0.563278 (* 1 = 0.563278 loss) +I0616 16:52:39.714893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151398 (* 1 = 0.151398 loss) +I0616 16:52:39.714897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01747 (* 1 = 0.01747 loss) +I0616 16:52:39.714900 9857 solver.cpp:571] Iteration 89100, lr = 0.0001 +I0616 16:52:51.420861 9857 solver.cpp:242] Iteration 89120, loss = 0.38437 +I0616 16:52:51.420902 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212404 (* 1 = 0.212404 loss) +I0616 16:52:51.420908 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218594 (* 1 = 0.218594 loss) +I0616 16:52:51.420912 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0947024 (* 1 = 0.0947024 loss) +I0616 16:52:51.420917 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0171017 (* 1 = 0.0171017 loss) +I0616 16:52:51.420919 9857 solver.cpp:571] Iteration 89120, lr = 0.0001 +I0616 16:53:02.922194 9857 solver.cpp:242] Iteration 89140, loss = 0.475211 +I0616 16:53:02.922235 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0878094 (* 1 = 0.0878094 loss) +I0616 16:53:02.922241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135502 (* 1 = 0.135502 loss) +I0616 16:53:02.922246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00924075 (* 1 = 0.00924075 loss) +I0616 16:53:02.922250 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00855312 (* 1 = 0.00855312 loss) +I0616 16:53:02.922253 9857 solver.cpp:571] Iteration 89140, lr = 0.0001 +I0616 16:53:14.173739 9857 solver.cpp:242] Iteration 89160, loss = 0.475169 +I0616 16:53:14.173782 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.358334 (* 1 = 0.358334 loss) +I0616 16:53:14.173789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275314 (* 1 = 0.275314 loss) +I0616 16:53:14.173792 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0458929 (* 1 = 0.0458929 loss) +I0616 16:53:14.173796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0622516 (* 1 = 0.0622516 loss) +I0616 16:53:14.173799 9857 solver.cpp:571] Iteration 89160, lr = 0.0001 +I0616 16:53:25.771247 9857 solver.cpp:242] Iteration 89180, loss = 0.554707 +I0616 16:53:25.771289 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.224238 (* 1 = 0.224238 loss) +I0616 16:53:25.771296 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413505 (* 1 = 0.413505 loss) +I0616 16:53:25.771299 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.105878 (* 1 = 0.105878 loss) +I0616 16:53:25.771303 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.037724 (* 1 = 0.037724 loss) +I0616 16:53:25.771306 9857 solver.cpp:571] Iteration 89180, lr = 0.0001 +speed: 0.597s / iter +I0616 16:53:37.532182 9857 solver.cpp:242] Iteration 89200, loss = 0.674507 +I0616 16:53:37.532223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10333 (* 1 = 0.10333 loss) +I0616 16:53:37.532229 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113167 (* 1 = 0.113167 loss) +I0616 16:53:37.532233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0635224 (* 1 = 0.0635224 loss) +I0616 16:53:37.532236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0921241 (* 1 = 0.0921241 loss) +I0616 16:53:37.532240 9857 solver.cpp:571] Iteration 89200, lr = 0.0001 +I0616 16:53:49.029463 9857 solver.cpp:242] Iteration 89220, loss = 0.252692 +I0616 16:53:49.029505 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11089 (* 1 = 0.11089 loss) +I0616 16:53:49.029510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13588 (* 1 = 0.13588 loss) +I0616 16:53:49.029515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0148579 (* 1 = 0.0148579 loss) +I0616 16:53:49.029518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.021696 (* 1 = 0.021696 loss) +I0616 16:53:49.029521 9857 solver.cpp:571] Iteration 89220, lr = 0.0001 +I0616 16:54:00.406690 9857 solver.cpp:242] Iteration 89240, loss = 0.498952 +I0616 16:54:00.406728 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191048 (* 1 = 0.191048 loss) +I0616 16:54:00.406734 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251817 (* 1 = 0.251817 loss) +I0616 16:54:00.406738 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0206157 (* 1 = 0.0206157 loss) +I0616 16:54:00.406743 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0570509 (* 1 = 0.0570509 loss) +I0616 16:54:00.406746 9857 solver.cpp:571] Iteration 89240, lr = 0.0001 +I0616 16:54:12.046650 9857 solver.cpp:242] Iteration 89260, loss = 0.299239 +I0616 16:54:12.046691 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114665 (* 1 = 0.114665 loss) +I0616 16:54:12.046697 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17147 (* 1 = 0.17147 loss) +I0616 16:54:12.046702 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0459812 (* 1 = 0.0459812 loss) +I0616 16:54:12.046705 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0313832 (* 1 = 0.0313832 loss) +I0616 16:54:12.046710 9857 solver.cpp:571] Iteration 89260, lr = 0.0001 +I0616 16:54:23.681939 9857 solver.cpp:242] Iteration 89280, loss = 0.685584 +I0616 16:54:23.681982 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0606629 (* 1 = 0.0606629 loss) +I0616 16:54:23.681987 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14936 (* 1 = 0.14936 loss) +I0616 16:54:23.681990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0161037 (* 1 = 0.0161037 loss) +I0616 16:54:23.681994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140883 (* 1 = 0.0140883 loss) +I0616 16:54:23.681998 9857 solver.cpp:571] Iteration 89280, lr = 0.0001 +I0616 16:54:35.462792 9857 solver.cpp:242] Iteration 89300, loss = 0.192605 +I0616 16:54:35.462834 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0700583 (* 1 = 0.0700583 loss) +I0616 16:54:35.462841 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101853 (* 1 = 0.101853 loss) +I0616 16:54:35.462844 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00659003 (* 1 = 0.00659003 loss) +I0616 16:54:35.462848 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00878565 (* 1 = 0.00878565 loss) +I0616 16:54:35.462852 9857 solver.cpp:571] Iteration 89300, lr = 0.0001 +I0616 16:54:46.930932 9857 solver.cpp:242] Iteration 89320, loss = 0.341257 +I0616 16:54:46.930972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0922981 (* 1 = 0.0922981 loss) +I0616 16:54:46.930979 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116987 (* 1 = 0.116987 loss) +I0616 16:54:46.930982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0200835 (* 1 = 0.0200835 loss) +I0616 16:54:46.930986 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0879129 (* 1 = 0.0879129 loss) +I0616 16:54:46.930990 9857 solver.cpp:571] Iteration 89320, lr = 0.0001 +I0616 16:54:58.427081 9857 solver.cpp:242] Iteration 89340, loss = 0.784233 +I0616 16:54:58.427122 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229557 (* 1 = 0.229557 loss) +I0616 16:54:58.427127 9857 solver.cpp:258] Train net output #1: loss_cls = 0.358572 (* 1 = 0.358572 loss) +I0616 16:54:58.427131 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.174967 (* 1 = 0.174967 loss) +I0616 16:54:58.427135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.166043 (* 1 = 0.166043 loss) +I0616 16:54:58.427139 9857 solver.cpp:571] Iteration 89340, lr = 0.0001 +I0616 16:55:10.022702 9857 solver.cpp:242] Iteration 89360, loss = 0.310025 +I0616 16:55:10.022742 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0836952 (* 1 = 0.0836952 loss) +I0616 16:55:10.022747 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0905846 (* 1 = 0.0905846 loss) +I0616 16:55:10.022752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0164807 (* 1 = 0.0164807 loss) +I0616 16:55:10.022755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0027905 (* 1 = 0.0027905 loss) +I0616 16:55:10.022761 9857 solver.cpp:571] Iteration 89360, lr = 0.0001 +I0616 16:55:21.453841 9857 solver.cpp:242] Iteration 89380, loss = 0.524199 +I0616 16:55:21.453884 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114726 (* 1 = 0.114726 loss) +I0616 16:55:21.453891 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186573 (* 1 = 0.186573 loss) +I0616 16:55:21.453894 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395025 (* 1 = 0.0395025 loss) +I0616 16:55:21.453897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.030361 (* 1 = 0.030361 loss) +I0616 16:55:21.453902 9857 solver.cpp:571] Iteration 89380, lr = 0.0001 +speed: 0.597s / iter +I0616 16:55:32.942023 9857 solver.cpp:242] Iteration 89400, loss = 0.595864 +I0616 16:55:32.942065 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267655 (* 1 = 0.267655 loss) +I0616 16:55:32.942070 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388448 (* 1 = 0.388448 loss) +I0616 16:55:32.942075 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0749237 (* 1 = 0.0749237 loss) +I0616 16:55:32.942077 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0460618 (* 1 = 0.0460618 loss) +I0616 16:55:32.942083 9857 solver.cpp:571] Iteration 89400, lr = 0.0001 +I0616 16:55:44.575490 9857 solver.cpp:242] Iteration 89420, loss = 0.350662 +I0616 16:55:44.575531 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0993706 (* 1 = 0.0993706 loss) +I0616 16:55:44.575537 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19626 (* 1 = 0.19626 loss) +I0616 16:55:44.575541 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0613212 (* 1 = 0.0613212 loss) +I0616 16:55:44.575546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00719903 (* 1 = 0.00719903 loss) +I0616 16:55:44.575549 9857 solver.cpp:571] Iteration 89420, lr = 0.0001 +I0616 16:55:56.076270 9857 solver.cpp:242] Iteration 89440, loss = 0.396639 +I0616 16:55:56.076313 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0755275 (* 1 = 0.0755275 loss) +I0616 16:55:56.076319 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142958 (* 1 = 0.142958 loss) +I0616 16:55:56.076323 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389455 (* 1 = 0.0389455 loss) +I0616 16:55:56.076326 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00379539 (* 1 = 0.00379539 loss) +I0616 16:55:56.076330 9857 solver.cpp:571] Iteration 89440, lr = 0.0001 +I0616 16:56:07.816762 9857 solver.cpp:242] Iteration 89460, loss = 0.586139 +I0616 16:56:07.816803 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.246406 (* 1 = 0.246406 loss) +I0616 16:56:07.816808 9857 solver.cpp:258] Train net output #1: loss_cls = 0.443107 (* 1 = 0.443107 loss) +I0616 16:56:07.816812 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141914 (* 1 = 0.141914 loss) +I0616 16:56:07.816817 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.100945 (* 1 = 0.100945 loss) +I0616 16:56:07.816820 9857 solver.cpp:571] Iteration 89460, lr = 0.0001 +I0616 16:56:19.223031 9857 solver.cpp:242] Iteration 89480, loss = 0.526105 +I0616 16:56:19.223073 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.373602 (* 1 = 0.373602 loss) +I0616 16:56:19.223079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303364 (* 1 = 0.303364 loss) +I0616 16:56:19.223083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.094186 (* 1 = 0.094186 loss) +I0616 16:56:19.223088 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0469075 (* 1 = 0.0469075 loss) +I0616 16:56:19.223090 9857 solver.cpp:571] Iteration 89480, lr = 0.0001 +I0616 16:56:30.569504 9857 solver.cpp:242] Iteration 89500, loss = 0.28145 +I0616 16:56:30.569540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0578653 (* 1 = 0.0578653 loss) +I0616 16:56:30.569561 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109225 (* 1 = 0.109225 loss) +I0616 16:56:30.569566 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0102905 (* 1 = 0.0102905 loss) +I0616 16:56:30.569569 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00864174 (* 1 = 0.00864174 loss) +I0616 16:56:30.569572 9857 solver.cpp:571] Iteration 89500, lr = 0.0001 +I0616 16:56:42.201208 9857 solver.cpp:242] Iteration 89520, loss = 0.793388 +I0616 16:56:42.201249 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0925717 (* 1 = 0.0925717 loss) +I0616 16:56:42.201256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127225 (* 1 = 0.127225 loss) +I0616 16:56:42.201259 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0247298 (* 1 = 0.0247298 loss) +I0616 16:56:42.201263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.018969 (* 1 = 0.018969 loss) +I0616 16:56:42.201267 9857 solver.cpp:571] Iteration 89520, lr = 0.0001 +I0616 16:56:53.376085 9857 solver.cpp:242] Iteration 89540, loss = 0.605859 +I0616 16:56:53.376127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249762 (* 1 = 0.249762 loss) +I0616 16:56:53.376132 9857 solver.cpp:258] Train net output #1: loss_cls = 0.393412 (* 1 = 0.393412 loss) +I0616 16:56:53.376137 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.171687 (* 1 = 0.171687 loss) +I0616 16:56:53.376139 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0136652 (* 1 = 0.0136652 loss) +I0616 16:56:53.376143 9857 solver.cpp:571] Iteration 89540, lr = 0.0001 +I0616 16:57:04.886453 9857 solver.cpp:242] Iteration 89560, loss = 0.44461 +I0616 16:57:04.886494 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.319103 (* 1 = 0.319103 loss) +I0616 16:57:04.886499 9857 solver.cpp:258] Train net output #1: loss_cls = 0.225692 (* 1 = 0.225692 loss) +I0616 16:57:04.886504 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118522 (* 1 = 0.118522 loss) +I0616 16:57:04.886507 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325618 (* 1 = 0.0325618 loss) +I0616 16:57:04.886512 9857 solver.cpp:571] Iteration 89560, lr = 0.0001 +I0616 16:57:16.336463 9857 solver.cpp:242] Iteration 89580, loss = 0.26942 +I0616 16:57:16.336500 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0606167 (* 1 = 0.0606167 loss) +I0616 16:57:16.336506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143232 (* 1 = 0.143232 loss) +I0616 16:57:16.336510 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00668812 (* 1 = 0.00668812 loss) +I0616 16:57:16.336514 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0195225 (* 1 = 0.0195225 loss) +I0616 16:57:16.336518 9857 solver.cpp:571] Iteration 89580, lr = 0.0001 +speed: 0.597s / iter +I0616 16:57:27.788364 9857 solver.cpp:242] Iteration 89600, loss = 0.368607 +I0616 16:57:27.788408 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0455043 (* 1 = 0.0455043 loss) +I0616 16:57:27.788414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0409211 (* 1 = 0.0409211 loss) +I0616 16:57:27.788417 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00659787 (* 1 = 0.00659787 loss) +I0616 16:57:27.788420 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0047045 (* 1 = 0.0047045 loss) +I0616 16:57:27.788424 9857 solver.cpp:571] Iteration 89600, lr = 0.0001 +I0616 16:57:39.509641 9857 solver.cpp:242] Iteration 89620, loss = 0.487198 +I0616 16:57:39.509685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0936041 (* 1 = 0.0936041 loss) +I0616 16:57:39.509691 9857 solver.cpp:258] Train net output #1: loss_cls = 0.106899 (* 1 = 0.106899 loss) +I0616 16:57:39.509696 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0050412 (* 1 = 0.0050412 loss) +I0616 16:57:39.509699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0123821 (* 1 = 0.0123821 loss) +I0616 16:57:39.509702 9857 solver.cpp:571] Iteration 89620, lr = 0.0001 +I0616 16:57:51.256412 9857 solver.cpp:242] Iteration 89640, loss = 0.234155 +I0616 16:57:51.256456 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124736 (* 1 = 0.124736 loss) +I0616 16:57:51.256461 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127602 (* 1 = 0.127602 loss) +I0616 16:57:51.256465 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00612683 (* 1 = 0.00612683 loss) +I0616 16:57:51.256469 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00788878 (* 1 = 0.00788878 loss) +I0616 16:57:51.256474 9857 solver.cpp:571] Iteration 89640, lr = 0.0001 +I0616 16:58:02.707849 9857 solver.cpp:242] Iteration 89660, loss = 0.503702 +I0616 16:58:02.707892 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122402 (* 1 = 0.122402 loss) +I0616 16:58:02.707897 9857 solver.cpp:258] Train net output #1: loss_cls = 0.212866 (* 1 = 0.212866 loss) +I0616 16:58:02.707901 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0665687 (* 1 = 0.0665687 loss) +I0616 16:58:02.707906 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0634044 (* 1 = 0.0634044 loss) +I0616 16:58:02.707909 9857 solver.cpp:571] Iteration 89660, lr = 0.0001 +I0616 16:58:14.019469 9857 solver.cpp:242] Iteration 89680, loss = 0.305481 +I0616 16:58:14.019510 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0812971 (* 1 = 0.0812971 loss) +I0616 16:58:14.019516 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0751696 (* 1 = 0.0751696 loss) +I0616 16:58:14.019520 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00432137 (* 1 = 0.00432137 loss) +I0616 16:58:14.019525 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00228839 (* 1 = 0.00228839 loss) +I0616 16:58:14.019527 9857 solver.cpp:571] Iteration 89680, lr = 0.0001 +I0616 16:58:25.583842 9857 solver.cpp:242] Iteration 89700, loss = 0.361439 +I0616 16:58:25.583881 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141072 (* 1 = 0.141072 loss) +I0616 16:58:25.583887 9857 solver.cpp:258] Train net output #1: loss_cls = 0.231776 (* 1 = 0.231776 loss) +I0616 16:58:25.583891 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0682379 (* 1 = 0.0682379 loss) +I0616 16:58:25.583895 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0436539 (* 1 = 0.0436539 loss) +I0616 16:58:25.583899 9857 solver.cpp:571] Iteration 89700, lr = 0.0001 +I0616 16:58:36.739275 9857 solver.cpp:242] Iteration 89720, loss = 0.46908 +I0616 16:58:36.739317 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.19704 (* 1 = 0.19704 loss) +I0616 16:58:36.739322 9857 solver.cpp:258] Train net output #1: loss_cls = 0.388088 (* 1 = 0.388088 loss) +I0616 16:58:36.739326 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0577368 (* 1 = 0.0577368 loss) +I0616 16:58:36.739331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0624171 (* 1 = 0.0624171 loss) +I0616 16:58:36.739334 9857 solver.cpp:571] Iteration 89720, lr = 0.0001 +I0616 16:58:48.316072 9857 solver.cpp:242] Iteration 89740, loss = 0.310626 +I0616 16:58:48.316115 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0967829 (* 1 = 0.0967829 loss) +I0616 16:58:48.316121 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216872 (* 1 = 0.216872 loss) +I0616 16:58:48.316125 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0453384 (* 1 = 0.0453384 loss) +I0616 16:58:48.316129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0410554 (* 1 = 0.0410554 loss) +I0616 16:58:48.316133 9857 solver.cpp:571] Iteration 89740, lr = 0.0001 +I0616 16:59:00.132144 9857 solver.cpp:242] Iteration 89760, loss = 0.628935 +I0616 16:59:00.132184 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193293 (* 1 = 0.193293 loss) +I0616 16:59:00.132189 9857 solver.cpp:258] Train net output #1: loss_cls = 0.296151 (* 1 = 0.296151 loss) +I0616 16:59:00.132194 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.11697 (* 1 = 0.11697 loss) +I0616 16:59:00.132197 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0329131 (* 1 = 0.0329131 loss) +I0616 16:59:00.132202 9857 solver.cpp:571] Iteration 89760, lr = 0.0001 +I0616 16:59:11.968139 9857 solver.cpp:242] Iteration 89780, loss = 0.380617 +I0616 16:59:11.968180 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217513 (* 1 = 0.217513 loss) +I0616 16:59:11.968186 9857 solver.cpp:258] Train net output #1: loss_cls = 0.226117 (* 1 = 0.226117 loss) +I0616 16:59:11.968190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151821 (* 1 = 0.151821 loss) +I0616 16:59:11.968194 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00492811 (* 1 = 0.00492811 loss) +I0616 16:59:11.968197 9857 solver.cpp:571] Iteration 89780, lr = 0.0001 +speed: 0.597s / iter +I0616 16:59:23.494752 9857 solver.cpp:242] Iteration 89800, loss = 0.641354 +I0616 16:59:23.494797 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12966 (* 1 = 0.12966 loss) +I0616 16:59:23.494803 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0933968 (* 1 = 0.0933968 loss) +I0616 16:59:23.494807 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0319574 (* 1 = 0.0319574 loss) +I0616 16:59:23.494812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0161103 (* 1 = 0.0161103 loss) +I0616 16:59:23.494814 9857 solver.cpp:571] Iteration 89800, lr = 0.0001 +I0616 16:59:35.107043 9857 solver.cpp:242] Iteration 89820, loss = 0.22542 +I0616 16:59:35.107084 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0748254 (* 1 = 0.0748254 loss) +I0616 16:59:35.107089 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0631466 (* 1 = 0.0631466 loss) +I0616 16:59:35.107094 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0306843 (* 1 = 0.0306843 loss) +I0616 16:59:35.107097 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0330509 (* 1 = 0.0330509 loss) +I0616 16:59:35.107101 9857 solver.cpp:571] Iteration 89820, lr = 0.0001 +I0616 16:59:46.605427 9857 solver.cpp:242] Iteration 89840, loss = 0.707999 +I0616 16:59:46.605470 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111714 (* 1 = 0.111714 loss) +I0616 16:59:46.605475 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184976 (* 1 = 0.184976 loss) +I0616 16:59:46.605479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121322 (* 1 = 0.121322 loss) +I0616 16:59:46.605484 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275766 (* 1 = 0.0275766 loss) +I0616 16:59:46.605487 9857 solver.cpp:571] Iteration 89840, lr = 0.0001 +I0616 16:59:58.376543 9857 solver.cpp:242] Iteration 89860, loss = 0.551877 +I0616 16:59:58.376585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0753992 (* 1 = 0.0753992 loss) +I0616 16:59:58.376590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114216 (* 1 = 0.114216 loss) +I0616 16:59:58.376595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00231502 (* 1 = 0.00231502 loss) +I0616 16:59:58.376597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000568391 (* 1 = 0.000568391 loss) +I0616 16:59:58.376601 9857 solver.cpp:571] Iteration 89860, lr = 0.0001 +I0616 17:00:09.880195 9857 solver.cpp:242] Iteration 89880, loss = 0.49421 +I0616 17:00:09.880236 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131204 (* 1 = 0.131204 loss) +I0616 17:00:09.880241 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22585 (* 1 = 0.22585 loss) +I0616 17:00:09.880246 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0321187 (* 1 = 0.0321187 loss) +I0616 17:00:09.880249 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011272 (* 1 = 0.011272 loss) +I0616 17:00:09.880254 9857 solver.cpp:571] Iteration 89880, lr = 0.0001 +I0616 17:00:21.509449 9857 solver.cpp:242] Iteration 89900, loss = 0.427001 +I0616 17:00:21.509490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0506522 (* 1 = 0.0506522 loss) +I0616 17:00:21.509495 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144371 (* 1 = 0.144371 loss) +I0616 17:00:21.509500 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00467952 (* 1 = 0.00467952 loss) +I0616 17:00:21.509503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0127967 (* 1 = 0.0127967 loss) +I0616 17:00:21.509507 9857 solver.cpp:571] Iteration 89900, lr = 0.0001 +I0616 17:00:33.121356 9857 solver.cpp:242] Iteration 89920, loss = 0.532308 +I0616 17:00:33.121399 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.143467 (* 1 = 0.143467 loss) +I0616 17:00:33.121404 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130305 (* 1 = 0.130305 loss) +I0616 17:00:33.121408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0227276 (* 1 = 0.0227276 loss) +I0616 17:00:33.121412 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0325592 (* 1 = 0.0325592 loss) +I0616 17:00:33.121417 9857 solver.cpp:571] Iteration 89920, lr = 0.0001 +I0616 17:00:44.867429 9857 solver.cpp:242] Iteration 89940, loss = 0.466588 +I0616 17:00:44.867470 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.267627 (* 1 = 0.267627 loss) +I0616 17:00:44.867475 9857 solver.cpp:258] Train net output #1: loss_cls = 0.377446 (* 1 = 0.377446 loss) +I0616 17:00:44.867480 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.086433 (* 1 = 0.086433 loss) +I0616 17:00:44.867482 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.099534 (* 1 = 0.099534 loss) +I0616 17:00:44.867486 9857 solver.cpp:571] Iteration 89940, lr = 0.0001 +I0616 17:00:56.211994 9857 solver.cpp:242] Iteration 89960, loss = 0.361424 +I0616 17:00:56.212035 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134817 (* 1 = 0.134817 loss) +I0616 17:00:56.212041 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168408 (* 1 = 0.168408 loss) +I0616 17:00:56.212045 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0231658 (* 1 = 0.0231658 loss) +I0616 17:00:56.212049 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0379249 (* 1 = 0.0379249 loss) +I0616 17:00:56.212054 9857 solver.cpp:571] Iteration 89960, lr = 0.0001 +I0616 17:01:07.737141 9857 solver.cpp:242] Iteration 89980, loss = 0.682241 +I0616 17:01:07.737180 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0535271 (* 1 = 0.0535271 loss) +I0616 17:01:07.737185 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109412 (* 1 = 0.109412 loss) +I0616 17:01:07.737190 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0135249 (* 1 = 0.0135249 loss) +I0616 17:01:07.737193 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00485812 (* 1 = 0.00485812 loss) +I0616 17:01:07.737197 9857 solver.cpp:571] Iteration 89980, lr = 0.0001 +speed: 0.597s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_90000.caffemodel +I0616 17:01:20.879814 9857 solver.cpp:242] Iteration 90000, loss = 0.330845 +I0616 17:01:20.879856 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16386 (* 1 = 0.16386 loss) +I0616 17:01:20.879861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222473 (* 1 = 0.222473 loss) +I0616 17:01:20.879865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0172145 (* 1 = 0.0172145 loss) +I0616 17:01:20.879869 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0753543 (* 1 = 0.0753543 loss) +I0616 17:01:20.879873 9857 solver.cpp:571] Iteration 90000, lr = 0.0001 +I0616 17:01:32.416146 9857 solver.cpp:242] Iteration 90020, loss = 0.430374 +I0616 17:01:32.416187 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0609576 (* 1 = 0.0609576 loss) +I0616 17:01:32.416193 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0811114 (* 1 = 0.0811114 loss) +I0616 17:01:32.416196 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00920109 (* 1 = 0.00920109 loss) +I0616 17:01:32.416200 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00902319 (* 1 = 0.00902319 loss) +I0616 17:01:32.416203 9857 solver.cpp:571] Iteration 90020, lr = 0.0001 +I0616 17:01:44.171262 9857 solver.cpp:242] Iteration 90040, loss = 0.555487 +I0616 17:01:44.171303 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.22149 (* 1 = 0.22149 loss) +I0616 17:01:44.171308 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209898 (* 1 = 0.209898 loss) +I0616 17:01:44.171314 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0237081 (* 1 = 0.0237081 loss) +I0616 17:01:44.171317 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128754 (* 1 = 0.0128754 loss) +I0616 17:01:44.171320 9857 solver.cpp:571] Iteration 90040, lr = 0.0001 +I0616 17:01:55.716012 9857 solver.cpp:242] Iteration 90060, loss = 0.442923 +I0616 17:01:55.716051 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139626 (* 1 = 0.139626 loss) +I0616 17:01:55.716056 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139763 (* 1 = 0.139763 loss) +I0616 17:01:55.716060 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0655685 (* 1 = 0.0655685 loss) +I0616 17:01:55.716064 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0679876 (* 1 = 0.0679876 loss) +I0616 17:01:55.716068 9857 solver.cpp:571] Iteration 90060, lr = 0.0001 +I0616 17:02:07.291268 9857 solver.cpp:242] Iteration 90080, loss = 0.753891 +I0616 17:02:07.291309 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21134 (* 1 = 0.21134 loss) +I0616 17:02:07.291314 9857 solver.cpp:258] Train net output #1: loss_cls = 0.266524 (* 1 = 0.266524 loss) +I0616 17:02:07.291319 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0267683 (* 1 = 0.0267683 loss) +I0616 17:02:07.291322 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132282 (* 1 = 0.0132282 loss) +I0616 17:02:07.291326 9857 solver.cpp:571] Iteration 90080, lr = 0.0001 +I0616 17:02:18.701405 9857 solver.cpp:242] Iteration 90100, loss = 0.649343 +I0616 17:02:18.701445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266891 (* 1 = 0.266891 loss) +I0616 17:02:18.701452 9857 solver.cpp:258] Train net output #1: loss_cls = 0.421143 (* 1 = 0.421143 loss) +I0616 17:02:18.701455 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.275518 (* 1 = 0.275518 loss) +I0616 17:02:18.701458 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.151633 (* 1 = 0.151633 loss) +I0616 17:02:18.701462 9857 solver.cpp:571] Iteration 90100, lr = 0.0001 +I0616 17:02:30.217660 9857 solver.cpp:242] Iteration 90120, loss = 0.384689 +I0616 17:02:30.217702 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.068171 (* 1 = 0.068171 loss) +I0616 17:02:30.217708 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0772469 (* 1 = 0.0772469 loss) +I0616 17:02:30.217712 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163639 (* 1 = 0.0163639 loss) +I0616 17:02:30.217715 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00446385 (* 1 = 0.00446385 loss) +I0616 17:02:30.217720 9857 solver.cpp:571] Iteration 90120, lr = 0.0001 +I0616 17:02:41.749143 9857 solver.cpp:242] Iteration 90140, loss = 0.340681 +I0616 17:02:41.749186 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.138663 (* 1 = 0.138663 loss) +I0616 17:02:41.749191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134678 (* 1 = 0.134678 loss) +I0616 17:02:41.749194 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0244512 (* 1 = 0.0244512 loss) +I0616 17:02:41.749198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.073134 (* 1 = 0.073134 loss) +I0616 17:02:41.749202 9857 solver.cpp:571] Iteration 90140, lr = 0.0001 +I0616 17:02:53.301087 9857 solver.cpp:242] Iteration 90160, loss = 0.231143 +I0616 17:02:53.301131 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0564417 (* 1 = 0.0564417 loss) +I0616 17:02:53.301136 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0819714 (* 1 = 0.0819714 loss) +I0616 17:02:53.301139 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.000833669 (* 1 = 0.000833669 loss) +I0616 17:02:53.301143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125104 (* 1 = 0.0125104 loss) +I0616 17:02:53.301147 9857 solver.cpp:571] Iteration 90160, lr = 0.0001 +I0616 17:03:04.866132 9857 solver.cpp:242] Iteration 90180, loss = 1.03763 +I0616 17:03:04.866178 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259673 (* 1 = 0.259673 loss) +I0616 17:03:04.866184 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219992 (* 1 = 0.219992 loss) +I0616 17:03:04.866189 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0122654 (* 1 = 0.0122654 loss) +I0616 17:03:04.866192 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184582 (* 1 = 0.0184582 loss) +I0616 17:03:04.866196 9857 solver.cpp:571] Iteration 90180, lr = 0.0001 +speed: 0.597s / iter +I0616 17:03:16.521814 9857 solver.cpp:242] Iteration 90200, loss = 0.612216 +I0616 17:03:16.521854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333562 (* 1 = 0.333562 loss) +I0616 17:03:16.521859 9857 solver.cpp:258] Train net output #1: loss_cls = 0.322045 (* 1 = 0.322045 loss) +I0616 17:03:16.521864 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.114186 (* 1 = 0.114186 loss) +I0616 17:03:16.521867 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0673652 (* 1 = 0.0673652 loss) +I0616 17:03:16.521872 9857 solver.cpp:571] Iteration 90200, lr = 0.0001 +I0616 17:03:28.290518 9857 solver.cpp:242] Iteration 90220, loss = 0.628116 +I0616 17:03:28.290556 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271391 (* 1 = 0.271391 loss) +I0616 17:03:28.290561 9857 solver.cpp:258] Train net output #1: loss_cls = 0.377555 (* 1 = 0.377555 loss) +I0616 17:03:28.290565 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.239734 (* 1 = 0.239734 loss) +I0616 17:03:28.290570 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110503 (* 1 = 0.110503 loss) +I0616 17:03:28.290573 9857 solver.cpp:571] Iteration 90220, lr = 0.0001 +I0616 17:03:39.742733 9857 solver.cpp:242] Iteration 90240, loss = 0.288194 +I0616 17:03:39.742780 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12632 (* 1 = 0.12632 loss) +I0616 17:03:39.742786 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216018 (* 1 = 0.216018 loss) +I0616 17:03:39.742790 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0738585 (* 1 = 0.0738585 loss) +I0616 17:03:39.742794 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000997592 (* 1 = 0.000997592 loss) +I0616 17:03:39.742797 9857 solver.cpp:571] Iteration 90240, lr = 0.0001 +I0616 17:03:51.279767 9857 solver.cpp:242] Iteration 90260, loss = 0.490953 +I0616 17:03:51.279810 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.15864 (* 1 = 0.15864 loss) +I0616 17:03:51.279816 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210001 (* 1 = 0.210001 loss) +I0616 17:03:51.279820 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0804527 (* 1 = 0.0804527 loss) +I0616 17:03:51.279824 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.254576 (* 1 = 0.254576 loss) +I0616 17:03:51.279827 9857 solver.cpp:571] Iteration 90260, lr = 0.0001 +I0616 17:04:02.824374 9857 solver.cpp:242] Iteration 90280, loss = 0.935899 +I0616 17:04:02.824416 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401119 (* 1 = 0.401119 loss) +I0616 17:04:02.824421 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292501 (* 1 = 0.292501 loss) +I0616 17:04:02.824425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0771522 (* 1 = 0.0771522 loss) +I0616 17:04:02.824429 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182718 (* 1 = 0.0182718 loss) +I0616 17:04:02.824434 9857 solver.cpp:571] Iteration 90280, lr = 0.0001 +I0616 17:04:14.191588 9857 solver.cpp:242] Iteration 90300, loss = 0.21328 +I0616 17:04:14.191630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0964721 (* 1 = 0.0964721 loss) +I0616 17:04:14.191637 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131332 (* 1 = 0.131332 loss) +I0616 17:04:14.191640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104802 (* 1 = 0.0104802 loss) +I0616 17:04:14.191644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00493786 (* 1 = 0.00493786 loss) +I0616 17:04:14.191648 9857 solver.cpp:571] Iteration 90300, lr = 0.0001 +I0616 17:04:25.742512 9857 solver.cpp:242] Iteration 90320, loss = 0.417268 +I0616 17:04:25.742557 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0550028 (* 1 = 0.0550028 loss) +I0616 17:04:25.742561 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114128 (* 1 = 0.114128 loss) +I0616 17:04:25.742565 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0464563 (* 1 = 0.0464563 loss) +I0616 17:04:25.742569 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0130324 (* 1 = 0.0130324 loss) +I0616 17:04:25.742573 9857 solver.cpp:571] Iteration 90320, lr = 0.0001 +I0616 17:04:37.444599 9857 solver.cpp:242] Iteration 90340, loss = 0.170587 +I0616 17:04:37.444643 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0578066 (* 1 = 0.0578066 loss) +I0616 17:04:37.444648 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0408749 (* 1 = 0.0408749 loss) +I0616 17:04:37.444651 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0150991 (* 1 = 0.0150991 loss) +I0616 17:04:37.444655 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00553588 (* 1 = 0.00553588 loss) +I0616 17:04:37.444659 9857 solver.cpp:571] Iteration 90340, lr = 0.0001 +I0616 17:04:48.954077 9857 solver.cpp:242] Iteration 90360, loss = 0.225693 +I0616 17:04:48.954119 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0463986 (* 1 = 0.0463986 loss) +I0616 17:04:48.954125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144583 (* 1 = 0.144583 loss) +I0616 17:04:48.954129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.000701776 (* 1 = 0.000701776 loss) +I0616 17:04:48.954133 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00312615 (* 1 = 0.00312615 loss) +I0616 17:04:48.954136 9857 solver.cpp:571] Iteration 90360, lr = 0.0001 +I0616 17:05:00.530733 9857 solver.cpp:242] Iteration 90380, loss = 0.383947 +I0616 17:05:00.530774 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0694241 (* 1 = 0.0694241 loss) +I0616 17:05:00.530781 9857 solver.cpp:258] Train net output #1: loss_cls = 0.148051 (* 1 = 0.148051 loss) +I0616 17:05:00.530786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0111772 (* 1 = 0.0111772 loss) +I0616 17:05:00.530788 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00292561 (* 1 = 0.00292561 loss) +I0616 17:05:00.530792 9857 solver.cpp:571] Iteration 90380, lr = 0.0001 +speed: 0.597s / iter +I0616 17:05:12.083945 9857 solver.cpp:242] Iteration 90400, loss = 0.762535 +I0616 17:05:12.083986 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268409 (* 1 = 0.268409 loss) +I0616 17:05:12.083992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.364551 (* 1 = 0.364551 loss) +I0616 17:05:12.083995 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116942 (* 1 = 0.116942 loss) +I0616 17:05:12.083999 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0305949 (* 1 = 0.0305949 loss) +I0616 17:05:12.084002 9857 solver.cpp:571] Iteration 90400, lr = 0.0001 +I0616 17:05:23.627624 9857 solver.cpp:242] Iteration 90420, loss = 0.302825 +I0616 17:05:23.627665 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145076 (* 1 = 0.145076 loss) +I0616 17:05:23.627671 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143183 (* 1 = 0.143183 loss) +I0616 17:05:23.627676 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00979801 (* 1 = 0.00979801 loss) +I0616 17:05:23.627679 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132444 (* 1 = 0.0132444 loss) +I0616 17:05:23.627682 9857 solver.cpp:571] Iteration 90420, lr = 0.0001 +I0616 17:05:34.973286 9857 solver.cpp:242] Iteration 90440, loss = 0.517233 +I0616 17:05:34.973327 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260182 (* 1 = 0.260182 loss) +I0616 17:05:34.973333 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190976 (* 1 = 0.190976 loss) +I0616 17:05:34.973337 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0933937 (* 1 = 0.0933937 loss) +I0616 17:05:34.973341 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0158227 (* 1 = 0.0158227 loss) +I0616 17:05:34.973345 9857 solver.cpp:571] Iteration 90440, lr = 0.0001 +I0616 17:05:46.667459 9857 solver.cpp:242] Iteration 90460, loss = 0.454906 +I0616 17:05:46.667501 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122587 (* 1 = 0.122587 loss) +I0616 17:05:46.667506 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15218 (* 1 = 0.15218 loss) +I0616 17:05:46.667511 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0609647 (* 1 = 0.0609647 loss) +I0616 17:05:46.667515 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144058 (* 1 = 0.144058 loss) +I0616 17:05:46.667518 9857 solver.cpp:571] Iteration 90460, lr = 0.0001 +I0616 17:05:58.016281 9857 solver.cpp:242] Iteration 90480, loss = 0.439922 +I0616 17:05:58.016336 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.227844 (* 1 = 0.227844 loss) +I0616 17:05:58.016341 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303418 (* 1 = 0.303418 loss) +I0616 17:05:58.016346 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010663 (* 1 = 0.010663 loss) +I0616 17:05:58.016350 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0363071 (* 1 = 0.0363071 loss) +I0616 17:05:58.016353 9857 solver.cpp:571] Iteration 90480, lr = 0.0001 +I0616 17:06:09.498061 9857 solver.cpp:242] Iteration 90500, loss = 0.360878 +I0616 17:06:09.498105 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190778 (* 1 = 0.190778 loss) +I0616 17:06:09.498109 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167817 (* 1 = 0.167817 loss) +I0616 17:06:09.498113 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0259789 (* 1 = 0.0259789 loss) +I0616 17:06:09.498117 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122951 (* 1 = 0.0122951 loss) +I0616 17:06:09.498121 9857 solver.cpp:571] Iteration 90500, lr = 0.0001 +I0616 17:06:21.013798 9857 solver.cpp:242] Iteration 90520, loss = 0.495751 +I0616 17:06:21.013839 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.135349 (* 1 = 0.135349 loss) +I0616 17:06:21.013845 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10811 (* 1 = 0.10811 loss) +I0616 17:06:21.013849 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0311291 (* 1 = 0.0311291 loss) +I0616 17:06:21.013854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028129 (* 1 = 0.028129 loss) +I0616 17:06:21.013856 9857 solver.cpp:571] Iteration 90520, lr = 0.0001 +I0616 17:06:32.804702 9857 solver.cpp:242] Iteration 90540, loss = 0.568648 +I0616 17:06:32.804743 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.272913 (* 1 = 0.272913 loss) +I0616 17:06:32.804749 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176311 (* 1 = 0.176311 loss) +I0616 17:06:32.804752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0208091 (* 1 = 0.0208091 loss) +I0616 17:06:32.804755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0138989 (* 1 = 0.0138989 loss) +I0616 17:06:32.804759 9857 solver.cpp:571] Iteration 90540, lr = 0.0001 +I0616 17:06:44.224551 9857 solver.cpp:242] Iteration 90560, loss = 0.571919 +I0616 17:06:44.224592 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.259614 (* 1 = 0.259614 loss) +I0616 17:06:44.224598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15602 (* 1 = 0.15602 loss) +I0616 17:06:44.224602 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0283835 (* 1 = 0.0283835 loss) +I0616 17:06:44.224606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.072374 (* 1 = 0.072374 loss) +I0616 17:06:44.224609 9857 solver.cpp:571] Iteration 90560, lr = 0.0001 +I0616 17:06:55.495978 9857 solver.cpp:242] Iteration 90580, loss = 0.730297 +I0616 17:06:55.496016 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177103 (* 1 = 0.177103 loss) +I0616 17:06:55.496021 9857 solver.cpp:258] Train net output #1: loss_cls = 0.295161 (* 1 = 0.295161 loss) +I0616 17:06:55.496026 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.121082 (* 1 = 0.121082 loss) +I0616 17:06:55.496029 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0639334 (* 1 = 0.0639334 loss) +I0616 17:06:55.496033 9857 solver.cpp:571] Iteration 90580, lr = 0.0001 +speed: 0.597s / iter +I0616 17:07:06.620095 9857 solver.cpp:242] Iteration 90600, loss = 0.294349 +I0616 17:07:06.620136 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0988685 (* 1 = 0.0988685 loss) +I0616 17:07:06.620141 9857 solver.cpp:258] Train net output #1: loss_cls = 0.173675 (* 1 = 0.173675 loss) +I0616 17:07:06.620146 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0399462 (* 1 = 0.0399462 loss) +I0616 17:07:06.620148 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0314857 (* 1 = 0.0314857 loss) +I0616 17:07:06.620152 9857 solver.cpp:571] Iteration 90600, lr = 0.0001 +I0616 17:07:18.163048 9857 solver.cpp:242] Iteration 90620, loss = 0.226035 +I0616 17:07:18.163090 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0719552 (* 1 = 0.0719552 loss) +I0616 17:07:18.163095 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15191 (* 1 = 0.15191 loss) +I0616 17:07:18.163100 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0154935 (* 1 = 0.0154935 loss) +I0616 17:07:18.163103 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00493784 (* 1 = 0.00493784 loss) +I0616 17:07:18.163107 9857 solver.cpp:571] Iteration 90620, lr = 0.0001 +I0616 17:07:29.630215 9857 solver.cpp:242] Iteration 90640, loss = 0.877812 +I0616 17:07:29.630259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.261251 (* 1 = 0.261251 loss) +I0616 17:07:29.630264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245202 (* 1 = 0.245202 loss) +I0616 17:07:29.630267 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0812865 (* 1 = 0.0812865 loss) +I0616 17:07:29.630271 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.018302 (* 1 = 0.018302 loss) +I0616 17:07:29.630275 9857 solver.cpp:571] Iteration 90640, lr = 0.0001 +I0616 17:07:41.313482 9857 solver.cpp:242] Iteration 90660, loss = 0.450557 +I0616 17:07:41.313524 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182722 (* 1 = 0.182722 loss) +I0616 17:07:41.313530 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145111 (* 1 = 0.145111 loss) +I0616 17:07:41.313534 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0326595 (* 1 = 0.0326595 loss) +I0616 17:07:41.313537 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167158 (* 1 = 0.0167158 loss) +I0616 17:07:41.313541 9857 solver.cpp:571] Iteration 90660, lr = 0.0001 +I0616 17:07:52.674911 9857 solver.cpp:242] Iteration 90680, loss = 0.497515 +I0616 17:07:52.674953 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271305 (* 1 = 0.271305 loss) +I0616 17:07:52.674958 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371982 (* 1 = 0.371982 loss) +I0616 17:07:52.674962 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0717762 (* 1 = 0.0717762 loss) +I0616 17:07:52.674965 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0975844 (* 1 = 0.0975844 loss) +I0616 17:07:52.674969 9857 solver.cpp:571] Iteration 90680, lr = 0.0001 +I0616 17:08:04.161908 9857 solver.cpp:242] Iteration 90700, loss = 0.807515 +I0616 17:08:04.161950 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0752264 (* 1 = 0.0752264 loss) +I0616 17:08:04.161955 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132177 (* 1 = 0.132177 loss) +I0616 17:08:04.161959 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0430542 (* 1 = 0.0430542 loss) +I0616 17:08:04.161963 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0095516 (* 1 = 0.0095516 loss) +I0616 17:08:04.161967 9857 solver.cpp:571] Iteration 90700, lr = 0.0001 +I0616 17:08:15.833655 9857 solver.cpp:242] Iteration 90720, loss = 0.44539 +I0616 17:08:15.833698 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.185235 (* 1 = 0.185235 loss) +I0616 17:08:15.833703 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134398 (* 1 = 0.134398 loss) +I0616 17:08:15.833708 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0528616 (* 1 = 0.0528616 loss) +I0616 17:08:15.833711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.16554 (* 1 = 0.16554 loss) +I0616 17:08:15.833714 9857 solver.cpp:571] Iteration 90720, lr = 0.0001 +I0616 17:08:27.301182 9857 solver.cpp:242] Iteration 90740, loss = 0.472513 +I0616 17:08:27.301221 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145453 (* 1 = 0.145453 loss) +I0616 17:08:27.301228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161017 (* 1 = 0.161017 loss) +I0616 17:08:27.301231 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141822 (* 1 = 0.141822 loss) +I0616 17:08:27.301234 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.038432 (* 1 = 0.038432 loss) +I0616 17:08:27.301239 9857 solver.cpp:571] Iteration 90740, lr = 0.0001 +I0616 17:08:38.481866 9857 solver.cpp:242] Iteration 90760, loss = 0.215415 +I0616 17:08:38.481909 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0894831 (* 1 = 0.0894831 loss) +I0616 17:08:38.481914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0888667 (* 1 = 0.0888667 loss) +I0616 17:08:38.481919 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00519931 (* 1 = 0.00519931 loss) +I0616 17:08:38.481921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00478646 (* 1 = 0.00478646 loss) +I0616 17:08:38.481925 9857 solver.cpp:571] Iteration 90760, lr = 0.0001 +I0616 17:08:50.174815 9857 solver.cpp:242] Iteration 90780, loss = 0.626355 +I0616 17:08:50.174855 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.271045 (* 1 = 0.271045 loss) +I0616 17:08:50.174861 9857 solver.cpp:258] Train net output #1: loss_cls = 0.383586 (* 1 = 0.383586 loss) +I0616 17:08:50.174865 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0647457 (* 1 = 0.0647457 loss) +I0616 17:08:50.174870 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0692113 (* 1 = 0.0692113 loss) +I0616 17:08:50.174873 9857 solver.cpp:571] Iteration 90780, lr = 0.0001 +speed: 0.597s / iter +I0616 17:09:01.481559 9857 solver.cpp:242] Iteration 90800, loss = 0.670317 +I0616 17:09:01.481600 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.119937 (* 1 = 0.119937 loss) +I0616 17:09:01.481606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.114373 (* 1 = 0.114373 loss) +I0616 17:09:01.481609 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0565471 (* 1 = 0.0565471 loss) +I0616 17:09:01.481613 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00512947 (* 1 = 0.00512947 loss) +I0616 17:09:01.481617 9857 solver.cpp:571] Iteration 90800, lr = 0.0001 +I0616 17:09:13.187456 9857 solver.cpp:242] Iteration 90820, loss = 0.637457 +I0616 17:09:13.187499 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299979 (* 1 = 0.299979 loss) +I0616 17:09:13.187505 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418689 (* 1 = 0.418689 loss) +I0616 17:09:13.187508 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.210952 (* 1 = 0.210952 loss) +I0616 17:09:13.187511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0828393 (* 1 = 0.0828393 loss) +I0616 17:09:13.187515 9857 solver.cpp:571] Iteration 90820, lr = 0.0001 +I0616 17:09:24.778540 9857 solver.cpp:242] Iteration 90840, loss = 0.425694 +I0616 17:09:24.778583 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264623 (* 1 = 0.264623 loss) +I0616 17:09:24.778589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1905 (* 1 = 0.1905 loss) +I0616 17:09:24.778594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0406114 (* 1 = 0.0406114 loss) +I0616 17:09:24.778596 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0566908 (* 1 = 0.0566908 loss) +I0616 17:09:24.778600 9857 solver.cpp:571] Iteration 90840, lr = 0.0001 +I0616 17:09:36.385773 9857 solver.cpp:242] Iteration 90860, loss = 0.155748 +I0616 17:09:36.385814 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.095551 (* 1 = 0.095551 loss) +I0616 17:09:36.385820 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0693516 (* 1 = 0.0693516 loss) +I0616 17:09:36.385824 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00931069 (* 1 = 0.00931069 loss) +I0616 17:09:36.385828 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104656 (* 1 = 0.0104656 loss) +I0616 17:09:36.385831 9857 solver.cpp:571] Iteration 90860, lr = 0.0001 +I0616 17:09:47.637454 9857 solver.cpp:242] Iteration 90880, loss = 0.78412 +I0616 17:09:47.637496 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308823 (* 1 = 0.308823 loss) +I0616 17:09:47.637501 9857 solver.cpp:258] Train net output #1: loss_cls = 0.521422 (* 1 = 0.521422 loss) +I0616 17:09:47.637506 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.195188 (* 1 = 0.195188 loss) +I0616 17:09:47.637509 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.121533 (* 1 = 0.121533 loss) +I0616 17:09:47.637514 9857 solver.cpp:571] Iteration 90880, lr = 0.0001 +I0616 17:09:59.412231 9857 solver.cpp:242] Iteration 90900, loss = 0.603073 +I0616 17:09:59.412274 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.336882 (* 1 = 0.336882 loss) +I0616 17:09:59.412279 9857 solver.cpp:258] Train net output #1: loss_cls = 0.41817 (* 1 = 0.41817 loss) +I0616 17:09:59.412283 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.145739 (* 1 = 0.145739 loss) +I0616 17:09:59.412287 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125144 (* 1 = 0.0125144 loss) +I0616 17:09:59.412291 9857 solver.cpp:571] Iteration 90900, lr = 0.0001 +I0616 17:10:10.913707 9857 solver.cpp:242] Iteration 90920, loss = 0.687008 +I0616 17:10:10.913750 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196239 (* 1 = 0.196239 loss) +I0616 17:10:10.913755 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143057 (* 1 = 0.143057 loss) +I0616 17:10:10.913759 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.04977 (* 1 = 0.04977 loss) +I0616 17:10:10.913763 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0766473 (* 1 = 0.0766473 loss) +I0616 17:10:10.913766 9857 solver.cpp:571] Iteration 90920, lr = 0.0001 +I0616 17:10:22.211501 9857 solver.cpp:242] Iteration 90940, loss = 0.150821 +I0616 17:10:22.211542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.04369 (* 1 = 0.04369 loss) +I0616 17:10:22.211549 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0786301 (* 1 = 0.0786301 loss) +I0616 17:10:22.211552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0217055 (* 1 = 0.0217055 loss) +I0616 17:10:22.211556 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0035484 (* 1 = 0.0035484 loss) +I0616 17:10:22.211560 9857 solver.cpp:571] Iteration 90940, lr = 0.0001 +I0616 17:10:33.694142 9857 solver.cpp:242] Iteration 90960, loss = 0.477135 +I0616 17:10:33.694183 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127072 (* 1 = 0.127072 loss) +I0616 17:10:33.694190 9857 solver.cpp:258] Train net output #1: loss_cls = 0.35325 (* 1 = 0.35325 loss) +I0616 17:10:33.694193 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0282123 (* 1 = 0.0282123 loss) +I0616 17:10:33.694196 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00929692 (* 1 = 0.00929692 loss) +I0616 17:10:33.694200 9857 solver.cpp:571] Iteration 90960, lr = 0.0001 +I0616 17:10:45.142210 9857 solver.cpp:242] Iteration 90980, loss = 0.998599 +I0616 17:10:45.142251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.181443 (* 1 = 0.181443 loss) +I0616 17:10:45.142256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.531483 (* 1 = 0.531483 loss) +I0616 17:10:45.142261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.130382 (* 1 = 0.130382 loss) +I0616 17:10:45.142264 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160958 (* 1 = 0.0160958 loss) +I0616 17:10:45.142267 9857 solver.cpp:571] Iteration 90980, lr = 0.0001 +speed: 0.597s / iter +I0616 17:10:56.725958 9857 solver.cpp:242] Iteration 91000, loss = 0.355082 +I0616 17:10:56.726001 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.102297 (* 1 = 0.102297 loss) +I0616 17:10:56.726006 9857 solver.cpp:258] Train net output #1: loss_cls = 0.124723 (* 1 = 0.124723 loss) +I0616 17:10:56.726011 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.012962 (* 1 = 0.012962 loss) +I0616 17:10:56.726013 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000885212 (* 1 = 0.000885212 loss) +I0616 17:10:56.726018 9857 solver.cpp:571] Iteration 91000, lr = 0.0001 +I0616 17:11:08.362457 9857 solver.cpp:242] Iteration 91020, loss = 0.514174 +I0616 17:11:08.362498 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124527 (* 1 = 0.124527 loss) +I0616 17:11:08.362504 9857 solver.cpp:258] Train net output #1: loss_cls = 0.251028 (* 1 = 0.251028 loss) +I0616 17:11:08.362507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.155018 (* 1 = 0.155018 loss) +I0616 17:11:08.362510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.144338 (* 1 = 0.144338 loss) +I0616 17:11:08.362514 9857 solver.cpp:571] Iteration 91020, lr = 0.0001 +I0616 17:11:19.701551 9857 solver.cpp:242] Iteration 91040, loss = 0.179913 +I0616 17:11:19.701592 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0594339 (* 1 = 0.0594339 loss) +I0616 17:11:19.701598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138341 (* 1 = 0.138341 loss) +I0616 17:11:19.701602 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00210858 (* 1 = 0.00210858 loss) +I0616 17:11:19.701606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.011343 (* 1 = 0.011343 loss) +I0616 17:11:19.701609 9857 solver.cpp:571] Iteration 91040, lr = 0.0001 +I0616 17:11:31.240501 9857 solver.cpp:242] Iteration 91060, loss = 0.499869 +I0616 17:11:31.240545 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327887 (* 1 = 0.327887 loss) +I0616 17:11:31.240550 9857 solver.cpp:258] Train net output #1: loss_cls = 0.263234 (* 1 = 0.263234 loss) +I0616 17:11:31.240555 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0766901 (* 1 = 0.0766901 loss) +I0616 17:11:31.240558 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0503541 (* 1 = 0.0503541 loss) +I0616 17:11:31.240562 9857 solver.cpp:571] Iteration 91060, lr = 0.0001 +I0616 17:11:43.024607 9857 solver.cpp:242] Iteration 91080, loss = 0.959489 +I0616 17:11:43.024649 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0728561 (* 1 = 0.0728561 loss) +I0616 17:11:43.024654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192158 (* 1 = 0.192158 loss) +I0616 17:11:43.024658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00761106 (* 1 = 0.00761106 loss) +I0616 17:11:43.024662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00540667 (* 1 = 0.00540667 loss) +I0616 17:11:43.024667 9857 solver.cpp:571] Iteration 91080, lr = 0.0001 +I0616 17:11:54.700266 9857 solver.cpp:242] Iteration 91100, loss = 0.478286 +I0616 17:11:54.700304 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228894 (* 1 = 0.228894 loss) +I0616 17:11:54.700309 9857 solver.cpp:258] Train net output #1: loss_cls = 0.323575 (* 1 = 0.323575 loss) +I0616 17:11:54.700327 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.175372 (* 1 = 0.175372 loss) +I0616 17:11:54.700331 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0780566 (* 1 = 0.0780566 loss) +I0616 17:11:54.700335 9857 solver.cpp:571] Iteration 91100, lr = 0.0001 +I0616 17:12:06.071928 9857 solver.cpp:242] Iteration 91120, loss = 0.455729 +I0616 17:12:06.071971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126285 (* 1 = 0.126285 loss) +I0616 17:12:06.071976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155777 (* 1 = 0.155777 loss) +I0616 17:12:06.071980 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0046924 (* 1 = 0.0046924 loss) +I0616 17:12:06.071985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00973368 (* 1 = 0.00973368 loss) +I0616 17:12:06.071988 9857 solver.cpp:571] Iteration 91120, lr = 0.0001 +I0616 17:12:17.838855 9857 solver.cpp:242] Iteration 91140, loss = 0.552167 +I0616 17:12:17.838896 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0883429 (* 1 = 0.0883429 loss) +I0616 17:12:17.838901 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204041 (* 1 = 0.204041 loss) +I0616 17:12:17.838906 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00889561 (* 1 = 0.00889561 loss) +I0616 17:12:17.838909 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00705813 (* 1 = 0.00705813 loss) +I0616 17:12:17.838913 9857 solver.cpp:571] Iteration 91140, lr = 0.0001 +I0616 17:12:29.393708 9857 solver.cpp:242] Iteration 91160, loss = 0.630273 +I0616 17:12:29.393749 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.327281 (* 1 = 0.327281 loss) +I0616 17:12:29.393755 9857 solver.cpp:258] Train net output #1: loss_cls = 0.36777 (* 1 = 0.36777 loss) +I0616 17:12:29.393759 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.132922 (* 1 = 0.132922 loss) +I0616 17:12:29.393764 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0389898 (* 1 = 0.0389898 loss) +I0616 17:12:29.393766 9857 solver.cpp:571] Iteration 91160, lr = 0.0001 +I0616 17:12:40.774068 9857 solver.cpp:242] Iteration 91180, loss = 0.616567 +I0616 17:12:40.774111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278904 (* 1 = 0.278904 loss) +I0616 17:12:40.774117 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371522 (* 1 = 0.371522 loss) +I0616 17:12:40.774121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.218024 (* 1 = 0.218024 loss) +I0616 17:12:40.774126 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156764 (* 1 = 0.156764 loss) +I0616 17:12:40.774130 9857 solver.cpp:571] Iteration 91180, lr = 0.0001 +speed: 0.597s / iter +I0616 17:12:52.457543 9857 solver.cpp:242] Iteration 91200, loss = 0.316611 +I0616 17:12:52.457584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166014 (* 1 = 0.166014 loss) +I0616 17:12:52.457590 9857 solver.cpp:258] Train net output #1: loss_cls = 0.241311 (* 1 = 0.241311 loss) +I0616 17:12:52.457594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0265105 (* 1 = 0.0265105 loss) +I0616 17:12:52.457597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292156 (* 1 = 0.0292156 loss) +I0616 17:12:52.457602 9857 solver.cpp:571] Iteration 91200, lr = 0.0001 +I0616 17:13:03.797941 9857 solver.cpp:242] Iteration 91220, loss = 0.41255 +I0616 17:13:03.797982 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.119955 (* 1 = 0.119955 loss) +I0616 17:13:03.797988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153455 (* 1 = 0.153455 loss) +I0616 17:13:03.797992 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0602141 (* 1 = 0.0602141 loss) +I0616 17:13:03.797996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0242258 (* 1 = 0.0242258 loss) +I0616 17:13:03.798001 9857 solver.cpp:571] Iteration 91220, lr = 0.0001 +I0616 17:13:15.431601 9857 solver.cpp:242] Iteration 91240, loss = 0.659949 +I0616 17:13:15.431643 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.388782 (* 1 = 0.388782 loss) +I0616 17:13:15.431648 9857 solver.cpp:258] Train net output #1: loss_cls = 0.365996 (* 1 = 0.365996 loss) +I0616 17:13:15.431653 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14511 (* 1 = 0.14511 loss) +I0616 17:13:15.431656 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.209454 (* 1 = 0.209454 loss) +I0616 17:13:15.431660 9857 solver.cpp:571] Iteration 91240, lr = 0.0001 +I0616 17:13:27.019230 9857 solver.cpp:242] Iteration 91260, loss = 0.696223 +I0616 17:13:27.019271 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279481 (* 1 = 0.279481 loss) +I0616 17:13:27.019278 9857 solver.cpp:258] Train net output #1: loss_cls = 0.381744 (* 1 = 0.381744 loss) +I0616 17:13:27.019281 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0296631 (* 1 = 0.0296631 loss) +I0616 17:13:27.019285 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.010915 (* 1 = 0.010915 loss) +I0616 17:13:27.019289 9857 solver.cpp:571] Iteration 91260, lr = 0.0001 +I0616 17:13:38.677613 9857 solver.cpp:242] Iteration 91280, loss = 0.343181 +I0616 17:13:38.677654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0814929 (* 1 = 0.0814929 loss) +I0616 17:13:38.677660 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131027 (* 1 = 0.131027 loss) +I0616 17:13:38.677664 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00469041 (* 1 = 0.00469041 loss) +I0616 17:13:38.677667 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00735223 (* 1 = 0.00735223 loss) +I0616 17:13:38.677671 9857 solver.cpp:571] Iteration 91280, lr = 0.0001 +I0616 17:13:50.065088 9857 solver.cpp:242] Iteration 91300, loss = 0.375051 +I0616 17:13:50.065130 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214787 (* 1 = 0.214787 loss) +I0616 17:13:50.065135 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221492 (* 1 = 0.221492 loss) +I0616 17:13:50.065140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0265693 (* 1 = 0.0265693 loss) +I0616 17:13:50.065143 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160994 (* 1 = 0.0160994 loss) +I0616 17:13:50.065147 9857 solver.cpp:571] Iteration 91300, lr = 0.0001 +I0616 17:14:01.342337 9857 solver.cpp:242] Iteration 91320, loss = 0.495825 +I0616 17:14:01.342378 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233364 (* 1 = 0.233364 loss) +I0616 17:14:01.342384 9857 solver.cpp:258] Train net output #1: loss_cls = 0.349266 (* 1 = 0.349266 loss) +I0616 17:14:01.342388 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0703434 (* 1 = 0.0703434 loss) +I0616 17:14:01.342391 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.049241 (* 1 = 0.049241 loss) +I0616 17:14:01.342396 9857 solver.cpp:571] Iteration 91320, lr = 0.0001 +I0616 17:14:12.794258 9857 solver.cpp:242] Iteration 91340, loss = 1.15191 +I0616 17:14:12.794301 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.313839 (* 1 = 0.313839 loss) +I0616 17:14:12.794306 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269975 (* 1 = 0.269975 loss) +I0616 17:14:12.794312 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.104754 (* 1 = 0.104754 loss) +I0616 17:14:12.794314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714672 (* 1 = 0.0714672 loss) +I0616 17:14:12.794318 9857 solver.cpp:571] Iteration 91340, lr = 0.0001 +I0616 17:14:24.371290 9857 solver.cpp:242] Iteration 91360, loss = 0.358363 +I0616 17:14:24.371332 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.144479 (* 1 = 0.144479 loss) +I0616 17:14:24.371338 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222846 (* 1 = 0.222846 loss) +I0616 17:14:24.371342 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.146492 (* 1 = 0.146492 loss) +I0616 17:14:24.371346 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0222797 (* 1 = 0.0222797 loss) +I0616 17:14:24.371351 9857 solver.cpp:571] Iteration 91360, lr = 0.0001 +I0616 17:14:35.959329 9857 solver.cpp:242] Iteration 91380, loss = 0.195964 +I0616 17:14:35.959372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0557668 (* 1 = 0.0557668 loss) +I0616 17:14:35.959378 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13854 (* 1 = 0.13854 loss) +I0616 17:14:35.959381 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0185505 (* 1 = 0.0185505 loss) +I0616 17:14:35.959385 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0257321 (* 1 = 0.0257321 loss) +I0616 17:14:35.959388 9857 solver.cpp:571] Iteration 91380, lr = 0.0001 +speed: 0.596s / iter +I0616 17:14:47.222651 9857 solver.cpp:242] Iteration 91400, loss = 0.176947 +I0616 17:14:47.222692 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0502517 (* 1 = 0.0502517 loss) +I0616 17:14:47.222697 9857 solver.cpp:258] Train net output #1: loss_cls = 0.081002 (* 1 = 0.081002 loss) +I0616 17:14:47.222700 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00983401 (* 1 = 0.00983401 loss) +I0616 17:14:47.222704 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00714744 (* 1 = 0.00714744 loss) +I0616 17:14:47.222708 9857 solver.cpp:571] Iteration 91400, lr = 0.0001 +I0616 17:14:58.811871 9857 solver.cpp:242] Iteration 91420, loss = 0.43004 +I0616 17:14:58.811913 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.285578 (* 1 = 0.285578 loss) +I0616 17:14:58.811918 9857 solver.cpp:258] Train net output #1: loss_cls = 0.235553 (* 1 = 0.235553 loss) +I0616 17:14:58.811923 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0141236 (* 1 = 0.0141236 loss) +I0616 17:14:58.811926 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0157772 (* 1 = 0.0157772 loss) +I0616 17:14:58.811929 9857 solver.cpp:571] Iteration 91420, lr = 0.0001 +I0616 17:15:10.196511 9857 solver.cpp:242] Iteration 91440, loss = 0.377401 +I0616 17:15:10.196553 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0693483 (* 1 = 0.0693483 loss) +I0616 17:15:10.196558 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120198 (* 1 = 0.120198 loss) +I0616 17:15:10.196563 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0841874 (* 1 = 0.0841874 loss) +I0616 17:15:10.196566 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0390138 (* 1 = 0.0390138 loss) +I0616 17:15:10.196570 9857 solver.cpp:571] Iteration 91440, lr = 0.0001 +I0616 17:15:21.727603 9857 solver.cpp:242] Iteration 91460, loss = 0.831568 +I0616 17:15:21.727645 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.32205 (* 1 = 0.32205 loss) +I0616 17:15:21.727650 9857 solver.cpp:258] Train net output #1: loss_cls = 0.448233 (* 1 = 0.448233 loss) +I0616 17:15:21.727654 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14335 (* 1 = 0.14335 loss) +I0616 17:15:21.727658 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.482071 (* 1 = 0.482071 loss) +I0616 17:15:21.727663 9857 solver.cpp:571] Iteration 91460, lr = 0.0001 +I0616 17:15:33.269491 9857 solver.cpp:242] Iteration 91480, loss = 0.67063 +I0616 17:15:33.269533 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.315397 (* 1 = 0.315397 loss) +I0616 17:15:33.269538 9857 solver.cpp:258] Train net output #1: loss_cls = 0.35993 (* 1 = 0.35993 loss) +I0616 17:15:33.269542 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.20286 (* 1 = 0.20286 loss) +I0616 17:15:33.269546 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.179546 (* 1 = 0.179546 loss) +I0616 17:15:33.269549 9857 solver.cpp:571] Iteration 91480, lr = 0.0001 +I0616 17:15:44.797456 9857 solver.cpp:242] Iteration 91500, loss = 0.425167 +I0616 17:15:44.797480 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124954 (* 1 = 0.124954 loss) +I0616 17:15:44.797485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232672 (* 1 = 0.232672 loss) +I0616 17:15:44.797489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.045643 (* 1 = 0.045643 loss) +I0616 17:15:44.797493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00417306 (* 1 = 0.00417306 loss) +I0616 17:15:44.797500 9857 solver.cpp:571] Iteration 91500, lr = 0.0001 +I0616 17:15:56.347681 9857 solver.cpp:242] Iteration 91520, loss = 0.729563 +I0616 17:15:56.347724 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.43843 (* 1 = 0.43843 loss) +I0616 17:15:56.347729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232788 (* 1 = 0.232788 loss) +I0616 17:15:56.347734 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0192187 (* 1 = 0.0192187 loss) +I0616 17:15:56.347738 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0329907 (* 1 = 0.0329907 loss) +I0616 17:15:56.347741 9857 solver.cpp:571] Iteration 91520, lr = 0.0001 +I0616 17:16:07.577039 9857 solver.cpp:242] Iteration 91540, loss = 1.11866 +I0616 17:16:07.577082 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307082 (* 1 = 0.307082 loss) +I0616 17:16:07.577087 9857 solver.cpp:258] Train net output #1: loss_cls = 0.438887 (* 1 = 0.438887 loss) +I0616 17:16:07.577092 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0866237 (* 1 = 0.0866237 loss) +I0616 17:16:07.577095 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033532 (* 1 = 0.033532 loss) +I0616 17:16:07.577100 9857 solver.cpp:571] Iteration 91540, lr = 0.0001 +I0616 17:16:19.244590 9857 solver.cpp:242] Iteration 91560, loss = 0.444452 +I0616 17:16:19.244631 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.153548 (* 1 = 0.153548 loss) +I0616 17:16:19.244637 9857 solver.cpp:258] Train net output #1: loss_cls = 0.200444 (* 1 = 0.200444 loss) +I0616 17:16:19.244640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00833476 (* 1 = 0.00833476 loss) +I0616 17:16:19.244644 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0120473 (* 1 = 0.0120473 loss) +I0616 17:16:19.244647 9857 solver.cpp:571] Iteration 91560, lr = 0.0001 +I0616 17:16:30.692569 9857 solver.cpp:242] Iteration 91580, loss = 0.903306 +I0616 17:16:30.692610 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.29853 (* 1 = 0.29853 loss) +I0616 17:16:30.692615 9857 solver.cpp:258] Train net output #1: loss_cls = 0.458876 (* 1 = 0.458876 loss) +I0616 17:16:30.692620 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.127659 (* 1 = 0.127659 loss) +I0616 17:16:30.692623 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.150341 (* 1 = 0.150341 loss) +I0616 17:16:30.692626 9857 solver.cpp:571] Iteration 91580, lr = 0.0001 +speed: 0.596s / iter +I0616 17:16:42.310389 9857 solver.cpp:242] Iteration 91600, loss = 0.45764 +I0616 17:16:42.310431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0799226 (* 1 = 0.0799226 loss) +I0616 17:16:42.310436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0874836 (* 1 = 0.0874836 loss) +I0616 17:16:42.310441 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00976788 (* 1 = 0.00976788 loss) +I0616 17:16:42.310443 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00543658 (* 1 = 0.00543658 loss) +I0616 17:16:42.310447 9857 solver.cpp:571] Iteration 91600, lr = 0.0001 +I0616 17:16:54.041683 9857 solver.cpp:242] Iteration 91620, loss = 0.47527 +I0616 17:16:54.041725 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.294474 (* 1 = 0.294474 loss) +I0616 17:16:54.041731 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21047 (* 1 = 0.21047 loss) +I0616 17:16:54.041735 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151602 (* 1 = 0.151602 loss) +I0616 17:16:54.041739 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0675505 (* 1 = 0.0675505 loss) +I0616 17:16:54.041743 9857 solver.cpp:571] Iteration 91620, lr = 0.0001 +I0616 17:17:05.619213 9857 solver.cpp:242] Iteration 91640, loss = 0.743555 +I0616 17:17:05.619256 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.492512 (* 1 = 0.492512 loss) +I0616 17:17:05.619261 9857 solver.cpp:258] Train net output #1: loss_cls = 0.395344 (* 1 = 0.395344 loss) +I0616 17:17:05.619266 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144728 (* 1 = 0.144728 loss) +I0616 17:17:05.619269 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0924173 (* 1 = 0.0924173 loss) +I0616 17:17:05.619273 9857 solver.cpp:571] Iteration 91640, lr = 0.0001 +I0616 17:17:17.113816 9857 solver.cpp:242] Iteration 91660, loss = 0.389134 +I0616 17:17:17.113860 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0821536 (* 1 = 0.0821536 loss) +I0616 17:17:17.113867 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112496 (* 1 = 0.112496 loss) +I0616 17:17:17.113873 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0132358 (* 1 = 0.0132358 loss) +I0616 17:17:17.113878 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0010491 (* 1 = 0.0010491 loss) +I0616 17:17:17.113883 9857 solver.cpp:571] Iteration 91660, lr = 0.0001 +I0616 17:17:28.524049 9857 solver.cpp:242] Iteration 91680, loss = 0.531077 +I0616 17:17:28.524090 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.325375 (* 1 = 0.325375 loss) +I0616 17:17:28.524094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139199 (* 1 = 0.139199 loss) +I0616 17:17:28.524098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0115862 (* 1 = 0.0115862 loss) +I0616 17:17:28.524102 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0190458 (* 1 = 0.0190458 loss) +I0616 17:17:28.524106 9857 solver.cpp:571] Iteration 91680, lr = 0.0001 +I0616 17:17:40.148176 9857 solver.cpp:242] Iteration 91700, loss = 0.271761 +I0616 17:17:40.148218 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0501917 (* 1 = 0.0501917 loss) +I0616 17:17:40.148236 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0701785 (* 1 = 0.0701785 loss) +I0616 17:17:40.148241 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0154087 (* 1 = 0.0154087 loss) +I0616 17:17:40.148258 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0056357 (* 1 = 0.0056357 loss) +I0616 17:17:40.148262 9857 solver.cpp:571] Iteration 91700, lr = 0.0001 +I0616 17:17:51.387608 9857 solver.cpp:242] Iteration 91720, loss = 0.375303 +I0616 17:17:51.387650 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0779179 (* 1 = 0.0779179 loss) +I0616 17:17:51.387655 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13435 (* 1 = 0.13435 loss) +I0616 17:17:51.387660 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0348631 (* 1 = 0.0348631 loss) +I0616 17:17:51.387662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235489 (* 1 = 0.0235489 loss) +I0616 17:17:51.387666 9857 solver.cpp:571] Iteration 91720, lr = 0.0001 +I0616 17:18:02.889395 9857 solver.cpp:242] Iteration 91740, loss = 0.712894 +I0616 17:18:02.889437 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.333725 (* 1 = 0.333725 loss) +I0616 17:18:02.889443 9857 solver.cpp:258] Train net output #1: loss_cls = 0.252628 (* 1 = 0.252628 loss) +I0616 17:18:02.889447 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0828412 (* 1 = 0.0828412 loss) +I0616 17:18:02.889451 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0385412 (* 1 = 0.0385412 loss) +I0616 17:18:02.889454 9857 solver.cpp:571] Iteration 91740, lr = 0.0001 +I0616 17:18:14.235096 9857 solver.cpp:242] Iteration 91760, loss = 0.882017 +I0616 17:18:14.235138 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3648 (* 1 = 0.3648 loss) +I0616 17:18:14.235144 9857 solver.cpp:258] Train net output #1: loss_cls = 0.553001 (* 1 = 0.553001 loss) +I0616 17:18:14.235148 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158511 (* 1 = 0.158511 loss) +I0616 17:18:14.235152 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.522234 (* 1 = 0.522234 loss) +I0616 17:18:14.235155 9857 solver.cpp:571] Iteration 91760, lr = 0.0001 +I0616 17:18:25.976068 9857 solver.cpp:242] Iteration 91780, loss = 0.285513 +I0616 17:18:25.976111 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0906031 (* 1 = 0.0906031 loss) +I0616 17:18:25.976116 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0902704 (* 1 = 0.0902704 loss) +I0616 17:18:25.976121 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0562554 (* 1 = 0.0562554 loss) +I0616 17:18:25.976125 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0240978 (* 1 = 0.0240978 loss) +I0616 17:18:25.976128 9857 solver.cpp:571] Iteration 91780, lr = 0.0001 +speed: 0.596s / iter +I0616 17:18:37.502826 9857 solver.cpp:242] Iteration 91800, loss = 0.515287 +I0616 17:18:37.502869 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0967951 (* 1 = 0.0967951 loss) +I0616 17:18:37.502874 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229738 (* 1 = 0.229738 loss) +I0616 17:18:37.502878 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0336521 (* 1 = 0.0336521 loss) +I0616 17:18:37.502882 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.120646 (* 1 = 0.120646 loss) +I0616 17:18:37.502885 9857 solver.cpp:571] Iteration 91800, lr = 0.0001 +I0616 17:18:49.097847 9857 solver.cpp:242] Iteration 91820, loss = 0.525576 +I0616 17:18:49.097890 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0468461 (* 1 = 0.0468461 loss) +I0616 17:18:49.097895 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0597147 (* 1 = 0.0597147 loss) +I0616 17:18:49.097900 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00372272 (* 1 = 0.00372272 loss) +I0616 17:18:49.097904 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00165002 (* 1 = 0.00165002 loss) +I0616 17:18:49.097908 9857 solver.cpp:571] Iteration 91820, lr = 0.0001 +I0616 17:19:00.367547 9857 solver.cpp:242] Iteration 91840, loss = 0.630853 +I0616 17:19:00.367588 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.057837 (* 1 = 0.057837 loss) +I0616 17:19:00.367594 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104598 (* 1 = 0.104598 loss) +I0616 17:19:00.367597 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0458985 (* 1 = 0.0458985 loss) +I0616 17:19:00.367601 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0194088 (* 1 = 0.0194088 loss) +I0616 17:19:00.367604 9857 solver.cpp:571] Iteration 91840, lr = 0.0001 +I0616 17:19:12.004487 9857 solver.cpp:242] Iteration 91860, loss = 0.556751 +I0616 17:19:12.004530 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111044 (* 1 = 0.111044 loss) +I0616 17:19:12.004535 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125768 (* 1 = 0.125768 loss) +I0616 17:19:12.004540 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0535967 (* 1 = 0.0535967 loss) +I0616 17:19:12.004544 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0188244 (* 1 = 0.0188244 loss) +I0616 17:19:12.004547 9857 solver.cpp:571] Iteration 91860, lr = 0.0001 +I0616 17:19:23.493078 9857 solver.cpp:242] Iteration 91880, loss = 0.232657 +I0616 17:19:23.493120 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0875513 (* 1 = 0.0875513 loss) +I0616 17:19:23.493125 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107673 (* 1 = 0.107673 loss) +I0616 17:19:23.493129 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0852358 (* 1 = 0.0852358 loss) +I0616 17:19:23.493134 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000983589 (* 1 = 0.000983589 loss) +I0616 17:19:23.493137 9857 solver.cpp:571] Iteration 91880, lr = 0.0001 +I0616 17:19:35.178423 9857 solver.cpp:242] Iteration 91900, loss = 0.365838 +I0616 17:19:35.178463 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0593443 (* 1 = 0.0593443 loss) +I0616 17:19:35.178469 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111517 (* 1 = 0.111517 loss) +I0616 17:19:35.178473 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00258996 (* 1 = 0.00258996 loss) +I0616 17:19:35.178477 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134947 (* 1 = 0.0134947 loss) +I0616 17:19:35.178480 9857 solver.cpp:571] Iteration 91900, lr = 0.0001 +I0616 17:19:46.895005 9857 solver.cpp:242] Iteration 91920, loss = 0.794722 +I0616 17:19:46.895045 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.148857 (* 1 = 0.148857 loss) +I0616 17:19:46.895052 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17263 (* 1 = 0.17263 loss) +I0616 17:19:46.895056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0579065 (* 1 = 0.0579065 loss) +I0616 17:19:46.895061 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00468303 (* 1 = 0.00468303 loss) +I0616 17:19:46.895063 9857 solver.cpp:571] Iteration 91920, lr = 0.0001 +I0616 17:19:58.563087 9857 solver.cpp:242] Iteration 91940, loss = 0.460728 +I0616 17:19:58.563128 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.05117 (* 1 = 0.05117 loss) +I0616 17:19:58.563133 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0699179 (* 1 = 0.0699179 loss) +I0616 17:19:58.563138 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0345655 (* 1 = 0.0345655 loss) +I0616 17:19:58.563141 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00459127 (* 1 = 0.00459127 loss) +I0616 17:19:58.563144 9857 solver.cpp:571] Iteration 91940, lr = 0.0001 +I0616 17:20:10.055686 9857 solver.cpp:242] Iteration 91960, loss = 0.840616 +I0616 17:20:10.055730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283315 (* 1 = 0.283315 loss) +I0616 17:20:10.055737 9857 solver.cpp:258] Train net output #1: loss_cls = 0.429004 (* 1 = 0.429004 loss) +I0616 17:20:10.055740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.281563 (* 1 = 0.281563 loss) +I0616 17:20:10.055743 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.411035 (* 1 = 0.411035 loss) +I0616 17:20:10.055747 9857 solver.cpp:571] Iteration 91960, lr = 0.0001 +I0616 17:20:21.778367 9857 solver.cpp:242] Iteration 91980, loss = 0.64593 +I0616 17:20:21.778409 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.300248 (* 1 = 0.300248 loss) +I0616 17:20:21.778414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.592116 (* 1 = 0.592116 loss) +I0616 17:20:21.778419 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0610135 (* 1 = 0.0610135 loss) +I0616 17:20:21.778422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0422338 (* 1 = 0.0422338 loss) +I0616 17:20:21.778426 9857 solver.cpp:571] Iteration 91980, lr = 0.0001 +speed: 0.596s / iter +I0616 17:20:33.494454 9857 solver.cpp:242] Iteration 92000, loss = 0.458959 +I0616 17:20:33.494498 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.211619 (* 1 = 0.211619 loss) +I0616 17:20:33.494503 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257827 (* 1 = 0.257827 loss) +I0616 17:20:33.494506 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0550383 (* 1 = 0.0550383 loss) +I0616 17:20:33.494510 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0172362 (* 1 = 0.0172362 loss) +I0616 17:20:33.494514 9857 solver.cpp:571] Iteration 92000, lr = 0.0001 +I0616 17:20:44.921563 9857 solver.cpp:242] Iteration 92020, loss = 1.08952 +I0616 17:20:44.921605 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.306198 (* 1 = 0.306198 loss) +I0616 17:20:44.921612 9857 solver.cpp:258] Train net output #1: loss_cls = 0.257578 (* 1 = 0.257578 loss) +I0616 17:20:44.921615 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.163689 (* 1 = 0.163689 loss) +I0616 17:20:44.921618 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.215921 (* 1 = 0.215921 loss) +I0616 17:20:44.921622 9857 solver.cpp:571] Iteration 92020, lr = 0.0001 +I0616 17:20:56.408759 9857 solver.cpp:242] Iteration 92040, loss = 0.439564 +I0616 17:20:56.408802 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.264675 (* 1 = 0.264675 loss) +I0616 17:20:56.408807 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22772 (* 1 = 0.22772 loss) +I0616 17:20:56.408812 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120471 (* 1 = 0.120471 loss) +I0616 17:20:56.408815 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0162649 (* 1 = 0.0162649 loss) +I0616 17:20:56.408818 9857 solver.cpp:571] Iteration 92040, lr = 0.0001 +I0616 17:21:08.144110 9857 solver.cpp:242] Iteration 92060, loss = 0.596062 +I0616 17:21:08.144153 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0638213 (* 1 = 0.0638213 loss) +I0616 17:21:08.144160 9857 solver.cpp:258] Train net output #1: loss_cls = 0.109622 (* 1 = 0.109622 loss) +I0616 17:21:08.144163 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00350732 (* 1 = 0.00350732 loss) +I0616 17:21:08.144167 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142597 (* 1 = 0.0142597 loss) +I0616 17:21:08.144170 9857 solver.cpp:571] Iteration 92060, lr = 0.0001 +I0616 17:21:20.060178 9857 solver.cpp:242] Iteration 92080, loss = 0.510193 +I0616 17:21:20.060215 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0798203 (* 1 = 0.0798203 loss) +I0616 17:21:20.060221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.183495 (* 1 = 0.183495 loss) +I0616 17:21:20.060225 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0536191 (* 1 = 0.0536191 loss) +I0616 17:21:20.060228 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.033404 (* 1 = 0.033404 loss) +I0616 17:21:20.060232 9857 solver.cpp:571] Iteration 92080, lr = 0.0001 +I0616 17:21:31.734638 9857 solver.cpp:242] Iteration 92100, loss = 0.331082 +I0616 17:21:31.734681 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.18169 (* 1 = 0.18169 loss) +I0616 17:21:31.734688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163805 (* 1 = 0.163805 loss) +I0616 17:21:31.734691 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0371999 (* 1 = 0.0371999 loss) +I0616 17:21:31.734695 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00437924 (* 1 = 0.00437924 loss) +I0616 17:21:31.734699 9857 solver.cpp:571] Iteration 92100, lr = 0.0001 +I0616 17:21:43.166931 9857 solver.cpp:242] Iteration 92120, loss = 0.531674 +I0616 17:21:43.166971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.209492 (* 1 = 0.209492 loss) +I0616 17:21:43.166977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.309906 (* 1 = 0.309906 loss) +I0616 17:21:43.166982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0526362 (* 1 = 0.0526362 loss) +I0616 17:21:43.166985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0209231 (* 1 = 0.0209231 loss) +I0616 17:21:43.166990 9857 solver.cpp:571] Iteration 92120, lr = 0.0001 +I0616 17:21:54.694993 9857 solver.cpp:242] Iteration 92140, loss = 0.366197 +I0616 17:21:54.695034 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222022 (* 1 = 0.222022 loss) +I0616 17:21:54.695039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154309 (* 1 = 0.154309 loss) +I0616 17:21:54.695042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.016099 (* 1 = 0.016099 loss) +I0616 17:21:54.695046 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0929406 (* 1 = 0.0929406 loss) +I0616 17:21:54.695050 9857 solver.cpp:571] Iteration 92140, lr = 0.0001 +I0616 17:22:06.026281 9857 solver.cpp:242] Iteration 92160, loss = 0.406418 +I0616 17:22:06.026324 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0693681 (* 1 = 0.0693681 loss) +I0616 17:22:06.026329 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134555 (* 1 = 0.134555 loss) +I0616 17:22:06.026334 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.020928 (* 1 = 0.020928 loss) +I0616 17:22:06.026337 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0233008 (* 1 = 0.0233008 loss) +I0616 17:22:06.026341 9857 solver.cpp:571] Iteration 92160, lr = 0.0001 +I0616 17:22:17.718686 9857 solver.cpp:242] Iteration 92180, loss = 0.289636 +I0616 17:22:17.718729 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106107 (* 1 = 0.106107 loss) +I0616 17:22:17.718734 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108621 (* 1 = 0.108621 loss) +I0616 17:22:17.718739 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00385943 (* 1 = 0.00385943 loss) +I0616 17:22:17.718741 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112538 (* 1 = 0.0112538 loss) +I0616 17:22:17.718745 9857 solver.cpp:571] Iteration 92180, lr = 0.0001 +speed: 0.596s / iter +I0616 17:22:29.210610 9857 solver.cpp:242] Iteration 92200, loss = 0.510311 +I0616 17:22:29.210654 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0901236 (* 1 = 0.0901236 loss) +I0616 17:22:29.210659 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136334 (* 1 = 0.136334 loss) +I0616 17:22:29.210662 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0149916 (* 1 = 0.0149916 loss) +I0616 17:22:29.210666 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0135575 (* 1 = 0.0135575 loss) +I0616 17:22:29.210670 9857 solver.cpp:571] Iteration 92200, lr = 0.0001 +I0616 17:22:40.978680 9857 solver.cpp:242] Iteration 92220, loss = 0.548914 +I0616 17:22:40.978721 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117106 (* 1 = 0.117106 loss) +I0616 17:22:40.978727 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0941144 (* 1 = 0.0941144 loss) +I0616 17:22:40.978731 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0747226 (* 1 = 0.0747226 loss) +I0616 17:22:40.978735 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.038523 (* 1 = 0.038523 loss) +I0616 17:22:40.978739 9857 solver.cpp:571] Iteration 92220, lr = 0.0001 +I0616 17:22:52.411253 9857 solver.cpp:242] Iteration 92240, loss = 0.707563 +I0616 17:22:52.411294 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.39269 (* 1 = 0.39269 loss) +I0616 17:22:52.411300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.501513 (* 1 = 0.501513 loss) +I0616 17:22:52.411304 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.120341 (* 1 = 0.120341 loss) +I0616 17:22:52.411308 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14171 (* 1 = 0.14171 loss) +I0616 17:22:52.411311 9857 solver.cpp:571] Iteration 92240, lr = 0.0001 +I0616 17:23:04.218798 9857 solver.cpp:242] Iteration 92260, loss = 0.603246 +I0616 17:23:04.218840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.338142 (* 1 = 0.338142 loss) +I0616 17:23:04.218847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.392333 (* 1 = 0.392333 loss) +I0616 17:23:04.218850 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.14444 (* 1 = 0.14444 loss) +I0616 17:23:04.218854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0727988 (* 1 = 0.0727988 loss) +I0616 17:23:04.218858 9857 solver.cpp:571] Iteration 92260, lr = 0.0001 +I0616 17:23:15.847816 9857 solver.cpp:242] Iteration 92280, loss = 0.526897 +I0616 17:23:15.847858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231495 (* 1 = 0.231495 loss) +I0616 17:23:15.847864 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279332 (* 1 = 0.279332 loss) +I0616 17:23:15.847868 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0391508 (* 1 = 0.0391508 loss) +I0616 17:23:15.847872 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134354 (* 1 = 0.0134354 loss) +I0616 17:23:15.847875 9857 solver.cpp:571] Iteration 92280, lr = 0.0001 +I0616 17:23:27.438707 9857 solver.cpp:242] Iteration 92300, loss = 0.682543 +I0616 17:23:27.438748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.074755 (* 1 = 0.074755 loss) +I0616 17:23:27.438753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161557 (* 1 = 0.161557 loss) +I0616 17:23:27.438761 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00648703 (* 1 = 0.00648703 loss) +I0616 17:23:27.438765 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00825814 (* 1 = 0.00825814 loss) +I0616 17:23:27.438768 9857 solver.cpp:571] Iteration 92300, lr = 0.0001 +I0616 17:23:39.188024 9857 solver.cpp:242] Iteration 92320, loss = 0.542815 +I0616 17:23:39.188065 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.195402 (* 1 = 0.195402 loss) +I0616 17:23:39.188071 9857 solver.cpp:258] Train net output #1: loss_cls = 0.163039 (* 1 = 0.163039 loss) +I0616 17:23:39.188074 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00982366 (* 1 = 0.00982366 loss) +I0616 17:23:39.188078 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143029 (* 1 = 0.0143029 loss) +I0616 17:23:39.188081 9857 solver.cpp:571] Iteration 92320, lr = 0.0001 +I0616 17:23:50.795095 9857 solver.cpp:242] Iteration 92340, loss = 0.227617 +I0616 17:23:50.795137 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0503049 (* 1 = 0.0503049 loss) +I0616 17:23:50.795143 9857 solver.cpp:258] Train net output #1: loss_cls = 0.152546 (* 1 = 0.152546 loss) +I0616 17:23:50.795147 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0168446 (* 1 = 0.0168446 loss) +I0616 17:23:50.795151 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00741835 (* 1 = 0.00741835 loss) +I0616 17:23:50.795155 9857 solver.cpp:571] Iteration 92340, lr = 0.0001 +I0616 17:24:02.214539 9857 solver.cpp:242] Iteration 92360, loss = 0.358052 +I0616 17:24:02.214577 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0974025 (* 1 = 0.0974025 loss) +I0616 17:24:02.214583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.141703 (* 1 = 0.141703 loss) +I0616 17:24:02.214587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0206461 (* 1 = 0.0206461 loss) +I0616 17:24:02.214591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00236993 (* 1 = 0.00236993 loss) +I0616 17:24:02.214594 9857 solver.cpp:571] Iteration 92360, lr = 0.0001 +I0616 17:24:13.763536 9857 solver.cpp:242] Iteration 92380, loss = 0.829767 +I0616 17:24:13.763577 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0796584 (* 1 = 0.0796584 loss) +I0616 17:24:13.763583 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175728 (* 1 = 0.175728 loss) +I0616 17:24:13.763587 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109471 (* 1 = 0.109471 loss) +I0616 17:24:13.763591 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00883985 (* 1 = 0.00883985 loss) +I0616 17:24:13.763594 9857 solver.cpp:571] Iteration 92380, lr = 0.0001 +speed: 0.596s / iter +I0616 17:24:25.418491 9857 solver.cpp:242] Iteration 92400, loss = 0.397237 +I0616 17:24:25.418534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0521349 (* 1 = 0.0521349 loss) +I0616 17:24:25.418540 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123872 (* 1 = 0.123872 loss) +I0616 17:24:25.418545 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.020064 (* 1 = 0.020064 loss) +I0616 17:24:25.418548 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0235408 (* 1 = 0.0235408 loss) +I0616 17:24:25.418551 9857 solver.cpp:571] Iteration 92400, lr = 0.0001 +I0616 17:24:36.652209 9857 solver.cpp:242] Iteration 92420, loss = 0.390573 +I0616 17:24:36.652251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0772775 (* 1 = 0.0772775 loss) +I0616 17:24:36.652256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0833132 (* 1 = 0.0833132 loss) +I0616 17:24:36.652261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00534994 (* 1 = 0.00534994 loss) +I0616 17:24:36.652264 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00419175 (* 1 = 0.00419175 loss) +I0616 17:24:36.652267 9857 solver.cpp:571] Iteration 92420, lr = 0.0001 +I0616 17:24:48.075196 9857 solver.cpp:242] Iteration 92440, loss = 0.354131 +I0616 17:24:48.075238 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0707366 (* 1 = 0.0707366 loss) +I0616 17:24:48.075243 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0843112 (* 1 = 0.0843112 loss) +I0616 17:24:48.075248 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0202744 (* 1 = 0.0202744 loss) +I0616 17:24:48.075251 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0192671 (* 1 = 0.0192671 loss) +I0616 17:24:48.075258 9857 solver.cpp:571] Iteration 92440, lr = 0.0001 +I0616 17:24:59.570451 9857 solver.cpp:242] Iteration 92460, loss = 0.369572 +I0616 17:24:59.570493 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0562501 (* 1 = 0.0562501 loss) +I0616 17:24:59.570499 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0848974 (* 1 = 0.0848974 loss) +I0616 17:24:59.570503 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00411841 (* 1 = 0.00411841 loss) +I0616 17:24:59.570508 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0026505 (* 1 = 0.0026505 loss) +I0616 17:24:59.570510 9857 solver.cpp:571] Iteration 92460, lr = 0.0001 +I0616 17:25:11.301584 9857 solver.cpp:242] Iteration 92480, loss = 0.886232 +I0616 17:25:11.301625 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.401383 (* 1 = 0.401383 loss) +I0616 17:25:11.301631 9857 solver.cpp:258] Train net output #1: loss_cls = 0.526999 (* 1 = 0.526999 loss) +I0616 17:25:11.301635 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.219 (* 1 = 0.219 loss) +I0616 17:25:11.301640 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.125076 (* 1 = 0.125076 loss) +I0616 17:25:11.301643 9857 solver.cpp:571] Iteration 92480, lr = 0.0001 +I0616 17:25:22.829531 9857 solver.cpp:242] Iteration 92500, loss = 0.64262 +I0616 17:25:22.829574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237609 (* 1 = 0.237609 loss) +I0616 17:25:22.829581 9857 solver.cpp:258] Train net output #1: loss_cls = 0.281811 (* 1 = 0.281811 loss) +I0616 17:25:22.829584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389397 (* 1 = 0.0389397 loss) +I0616 17:25:22.829587 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122611 (* 1 = 0.0122611 loss) +I0616 17:25:22.829591 9857 solver.cpp:571] Iteration 92500, lr = 0.0001 +I0616 17:25:34.336554 9857 solver.cpp:242] Iteration 92520, loss = 0.361434 +I0616 17:25:34.336592 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.101921 (* 1 = 0.101921 loss) +I0616 17:25:34.336598 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0688076 (* 1 = 0.0688076 loss) +I0616 17:25:34.336602 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00912377 (* 1 = 0.00912377 loss) +I0616 17:25:34.336606 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0214054 (* 1 = 0.0214054 loss) +I0616 17:25:34.336609 9857 solver.cpp:571] Iteration 92520, lr = 0.0001 +I0616 17:25:45.683567 9857 solver.cpp:242] Iteration 92540, loss = 0.391881 +I0616 17:25:45.683609 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168023 (* 1 = 0.168023 loss) +I0616 17:25:45.683615 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181623 (* 1 = 0.181623 loss) +I0616 17:25:45.683619 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0084459 (* 1 = 0.0084459 loss) +I0616 17:25:45.683622 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0730437 (* 1 = 0.0730437 loss) +I0616 17:25:45.683626 9857 solver.cpp:571] Iteration 92540, lr = 0.0001 +I0616 17:25:57.200721 9857 solver.cpp:242] Iteration 92560, loss = 0.686995 +I0616 17:25:57.200764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.37706 (* 1 = 0.37706 loss) +I0616 17:25:57.200769 9857 solver.cpp:258] Train net output #1: loss_cls = 0.341516 (* 1 = 0.341516 loss) +I0616 17:25:57.200773 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18545 (* 1 = 0.18545 loss) +I0616 17:25:57.200778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.203846 (* 1 = 0.203846 loss) +I0616 17:25:57.200781 9857 solver.cpp:571] Iteration 92560, lr = 0.0001 +I0616 17:26:08.636299 9857 solver.cpp:242] Iteration 92580, loss = 1.09089 +I0616 17:26:08.636343 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0602855 (* 1 = 0.0602855 loss) +I0616 17:26:08.636348 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0969926 (* 1 = 0.0969926 loss) +I0616 17:26:08.636351 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119301 (* 1 = 0.0119301 loss) +I0616 17:26:08.636355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00245162 (* 1 = 0.00245162 loss) +I0616 17:26:08.636359 9857 solver.cpp:571] Iteration 92580, lr = 0.0001 +speed: 0.596s / iter +I0616 17:26:20.349495 9857 solver.cpp:242] Iteration 92600, loss = 0.390025 +I0616 17:26:20.349536 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229012 (* 1 = 0.229012 loss) +I0616 17:26:20.349542 9857 solver.cpp:258] Train net output #1: loss_cls = 0.23891 (* 1 = 0.23891 loss) +I0616 17:26:20.349546 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0293028 (* 1 = 0.0293028 loss) +I0616 17:26:20.349550 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0342307 (* 1 = 0.0342307 loss) +I0616 17:26:20.349553 9857 solver.cpp:571] Iteration 92600, lr = 0.0001 +I0616 17:26:31.859525 9857 solver.cpp:242] Iteration 92620, loss = 0.681828 +I0616 17:26:31.859566 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.391838 (* 1 = 0.391838 loss) +I0616 17:26:31.859572 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289297 (* 1 = 0.289297 loss) +I0616 17:26:31.859575 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.144032 (* 1 = 0.144032 loss) +I0616 17:26:31.859580 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0550938 (* 1 = 0.0550938 loss) +I0616 17:26:31.859583 9857 solver.cpp:571] Iteration 92620, lr = 0.0001 +I0616 17:26:43.265488 9857 solver.cpp:242] Iteration 92640, loss = 0.412775 +I0616 17:26:43.265527 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103159 (* 1 = 0.103159 loss) +I0616 17:26:43.265532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.204463 (* 1 = 0.204463 loss) +I0616 17:26:43.265537 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.096092 (* 1 = 0.096092 loss) +I0616 17:26:43.265539 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00215794 (* 1 = 0.00215794 loss) +I0616 17:26:43.265543 9857 solver.cpp:571] Iteration 92640, lr = 0.0001 +I0616 17:26:54.960448 9857 solver.cpp:242] Iteration 92660, loss = 0.765817 +I0616 17:26:54.960490 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.323611 (* 1 = 0.323611 loss) +I0616 17:26:54.960496 9857 solver.cpp:258] Train net output #1: loss_cls = 0.354914 (* 1 = 0.354914 loss) +I0616 17:26:54.960500 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.23314 (* 1 = 0.23314 loss) +I0616 17:26:54.960503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.415748 (* 1 = 0.415748 loss) +I0616 17:26:54.960507 9857 solver.cpp:571] Iteration 92660, lr = 0.0001 +I0616 17:27:06.262516 9857 solver.cpp:242] Iteration 92680, loss = 0.318627 +I0616 17:27:06.262558 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197317 (* 1 = 0.197317 loss) +I0616 17:27:06.262564 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232925 (* 1 = 0.232925 loss) +I0616 17:27:06.262568 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0154109 (* 1 = 0.0154109 loss) +I0616 17:27:06.262573 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0199865 (* 1 = 0.0199865 loss) +I0616 17:27:06.262575 9857 solver.cpp:571] Iteration 92680, lr = 0.0001 +I0616 17:27:18.079138 9857 solver.cpp:242] Iteration 92700, loss = 0.550665 +I0616 17:27:18.079181 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.243238 (* 1 = 0.243238 loss) +I0616 17:27:18.079187 9857 solver.cpp:258] Train net output #1: loss_cls = 0.274912 (* 1 = 0.274912 loss) +I0616 17:27:18.079191 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0943613 (* 1 = 0.0943613 loss) +I0616 17:27:18.079195 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283211 (* 1 = 0.0283211 loss) +I0616 17:27:18.079200 9857 solver.cpp:571] Iteration 92700, lr = 0.0001 +I0616 17:27:29.619241 9857 solver.cpp:242] Iteration 92720, loss = 0.573274 +I0616 17:27:29.619283 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149473 (* 1 = 0.149473 loss) +I0616 17:27:29.619288 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217787 (* 1 = 0.217787 loss) +I0616 17:27:29.619293 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.153631 (* 1 = 0.153631 loss) +I0616 17:27:29.619297 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.459304 (* 1 = 0.459304 loss) +I0616 17:27:29.619300 9857 solver.cpp:571] Iteration 92720, lr = 0.0001 +I0616 17:27:41.238046 9857 solver.cpp:242] Iteration 92740, loss = 0.971655 +I0616 17:27:41.238090 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118439 (* 1 = 0.118439 loss) +I0616 17:27:41.238095 9857 solver.cpp:258] Train net output #1: loss_cls = 0.210989 (* 1 = 0.210989 loss) +I0616 17:27:41.238098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0434137 (* 1 = 0.0434137 loss) +I0616 17:27:41.238102 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160805 (* 1 = 0.0160805 loss) +I0616 17:27:41.238106 9857 solver.cpp:571] Iteration 92740, lr = 0.0001 +I0616 17:27:52.729672 9857 solver.cpp:242] Iteration 92760, loss = 0.297907 +I0616 17:27:52.729714 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112429 (* 1 = 0.112429 loss) +I0616 17:27:52.729719 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136081 (* 1 = 0.136081 loss) +I0616 17:27:52.729724 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0608738 (* 1 = 0.0608738 loss) +I0616 17:27:52.729727 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0270942 (* 1 = 0.0270942 loss) +I0616 17:27:52.729732 9857 solver.cpp:571] Iteration 92760, lr = 0.0001 +I0616 17:28:04.469084 9857 solver.cpp:242] Iteration 92780, loss = 0.825197 +I0616 17:28:04.469125 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.277866 (* 1 = 0.277866 loss) +I0616 17:28:04.469131 9857 solver.cpp:258] Train net output #1: loss_cls = 0.390261 (* 1 = 0.390261 loss) +I0616 17:28:04.469135 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19374 (* 1 = 0.19374 loss) +I0616 17:28:04.469140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.344757 (* 1 = 0.344757 loss) +I0616 17:28:04.469142 9857 solver.cpp:571] Iteration 92780, lr = 0.0001 +speed: 0.596s / iter +I0616 17:28:15.705150 9857 solver.cpp:242] Iteration 92800, loss = 0.392649 +I0616 17:28:15.705194 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10941 (* 1 = 0.10941 loss) +I0616 17:28:15.705200 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279471 (* 1 = 0.279471 loss) +I0616 17:28:15.705204 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0716084 (* 1 = 0.0716084 loss) +I0616 17:28:15.705207 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0153478 (* 1 = 0.0153478 loss) +I0616 17:28:15.705211 9857 solver.cpp:571] Iteration 92800, lr = 0.0001 +I0616 17:28:27.081547 9857 solver.cpp:242] Iteration 92820, loss = 0.284581 +I0616 17:28:27.081589 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0955057 (* 1 = 0.0955057 loss) +I0616 17:28:27.081595 9857 solver.cpp:258] Train net output #1: loss_cls = 0.207316 (* 1 = 0.207316 loss) +I0616 17:28:27.081600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0201246 (* 1 = 0.0201246 loss) +I0616 17:28:27.081604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0083873 (* 1 = 0.0083873 loss) +I0616 17:28:27.081607 9857 solver.cpp:571] Iteration 92820, lr = 0.0001 +I0616 17:28:38.731765 9857 solver.cpp:242] Iteration 92840, loss = 0.813638 +I0616 17:28:38.731806 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.424883 (* 1 = 0.424883 loss) +I0616 17:28:38.731811 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432518 (* 1 = 0.432518 loss) +I0616 17:28:38.731815 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.26332 (* 1 = 0.26332 loss) +I0616 17:28:38.731818 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.2237 (* 1 = 0.2237 loss) +I0616 17:28:38.731822 9857 solver.cpp:571] Iteration 92840, lr = 0.0001 +I0616 17:28:50.464020 9857 solver.cpp:242] Iteration 92860, loss = 0.422866 +I0616 17:28:50.464062 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0902463 (* 1 = 0.0902463 loss) +I0616 17:28:50.464067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147205 (* 1 = 0.147205 loss) +I0616 17:28:50.464071 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058047 (* 1 = 0.058047 loss) +I0616 17:28:50.464076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00837746 (* 1 = 0.00837746 loss) +I0616 17:28:50.464079 9857 solver.cpp:571] Iteration 92860, lr = 0.0001 +I0616 17:29:01.963405 9857 solver.cpp:242] Iteration 92880, loss = 0.425754 +I0616 17:29:01.963433 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.246556 (* 1 = 0.246556 loss) +I0616 17:29:01.963438 9857 solver.cpp:258] Train net output #1: loss_cls = 0.220041 (* 1 = 0.220041 loss) +I0616 17:29:01.963443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0849397 (* 1 = 0.0849397 loss) +I0616 17:29:01.963446 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0848752 (* 1 = 0.0848752 loss) +I0616 17:29:01.963450 9857 solver.cpp:571] Iteration 92880, lr = 0.0001 +I0616 17:29:13.540279 9857 solver.cpp:242] Iteration 92900, loss = 0.750071 +I0616 17:29:13.540323 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.365389 (* 1 = 0.365389 loss) +I0616 17:29:13.540328 9857 solver.cpp:258] Train net output #1: loss_cls = 0.539585 (* 1 = 0.539585 loss) +I0616 17:29:13.540331 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.22245 (* 1 = 0.22245 loss) +I0616 17:29:13.540335 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0815048 (* 1 = 0.0815048 loss) +I0616 17:29:13.540339 9857 solver.cpp:571] Iteration 92900, lr = 0.0001 +I0616 17:29:25.114152 9857 solver.cpp:242] Iteration 92920, loss = 0.362469 +I0616 17:29:25.114192 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163499 (* 1 = 0.163499 loss) +I0616 17:29:25.114198 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130995 (* 1 = 0.130995 loss) +I0616 17:29:25.114202 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.073087 (* 1 = 0.073087 loss) +I0616 17:29:25.114207 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0310182 (* 1 = 0.0310182 loss) +I0616 17:29:25.114210 9857 solver.cpp:571] Iteration 92920, lr = 0.0001 +I0616 17:29:36.698931 9857 solver.cpp:242] Iteration 92940, loss = 0.562514 +I0616 17:29:36.698972 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.324686 (* 1 = 0.324686 loss) +I0616 17:29:36.698977 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154723 (* 1 = 0.154723 loss) +I0616 17:29:36.698982 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0390608 (* 1 = 0.0390608 loss) +I0616 17:29:36.698985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173433 (* 1 = 0.0173433 loss) +I0616 17:29:36.698988 9857 solver.cpp:571] Iteration 92940, lr = 0.0001 +I0616 17:29:48.247388 9857 solver.cpp:242] Iteration 92960, loss = 0.80308 +I0616 17:29:48.247429 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0781509 (* 1 = 0.0781509 loss) +I0616 17:29:48.247436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0958181 (* 1 = 0.0958181 loss) +I0616 17:29:48.247439 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.053474 (* 1 = 0.053474 loss) +I0616 17:29:48.247443 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0154765 (* 1 = 0.0154765 loss) +I0616 17:29:48.247447 9857 solver.cpp:571] Iteration 92960, lr = 0.0001 +I0616 17:29:59.872030 9857 solver.cpp:242] Iteration 92980, loss = 0.370636 +I0616 17:29:59.872071 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0514447 (* 1 = 0.0514447 loss) +I0616 17:29:59.872076 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142734 (* 1 = 0.142734 loss) +I0616 17:29:59.872081 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0416354 (* 1 = 0.0416354 loss) +I0616 17:29:59.872084 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0191846 (* 1 = 0.0191846 loss) +I0616 17:29:59.872087 9857 solver.cpp:571] Iteration 92980, lr = 0.0001 +speed: 0.596s / iter +I0616 17:30:11.083371 9857 solver.cpp:242] Iteration 93000, loss = 0.267721 +I0616 17:30:11.083415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0637684 (* 1 = 0.0637684 loss) +I0616 17:30:11.083420 9857 solver.cpp:258] Train net output #1: loss_cls = 0.116388 (* 1 = 0.116388 loss) +I0616 17:30:11.083425 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0497089 (* 1 = 0.0497089 loss) +I0616 17:30:11.083428 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0118289 (* 1 = 0.0118289 loss) +I0616 17:30:11.083432 9857 solver.cpp:571] Iteration 93000, lr = 0.0001 +I0616 17:30:22.700999 9857 solver.cpp:242] Iteration 93020, loss = 0.749704 +I0616 17:30:22.701041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0963035 (* 1 = 0.0963035 loss) +I0616 17:30:22.701046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112477 (* 1 = 0.112477 loss) +I0616 17:30:22.701050 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00749692 (* 1 = 0.00749692 loss) +I0616 17:30:22.701055 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00501554 (* 1 = 0.00501554 loss) +I0616 17:30:22.701057 9857 solver.cpp:571] Iteration 93020, lr = 0.0001 +I0616 17:30:34.286054 9857 solver.cpp:242] Iteration 93040, loss = 0.883914 +I0616 17:30:34.286097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196619 (* 1 = 0.196619 loss) +I0616 17:30:34.286101 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359048 (* 1 = 0.359048 loss) +I0616 17:30:34.286106 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0747701 (* 1 = 0.0747701 loss) +I0616 17:30:34.286109 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.375945 (* 1 = 0.375945 loss) +I0616 17:30:34.286113 9857 solver.cpp:571] Iteration 93040, lr = 0.0001 +I0616 17:30:45.803359 9857 solver.cpp:242] Iteration 93060, loss = 0.145938 +I0616 17:30:45.803402 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0632859 (* 1 = 0.0632859 loss) +I0616 17:30:45.803408 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0570848 (* 1 = 0.0570848 loss) +I0616 17:30:45.803412 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0152999 (* 1 = 0.0152999 loss) +I0616 17:30:45.803416 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0217296 (* 1 = 0.0217296 loss) +I0616 17:30:45.803421 9857 solver.cpp:571] Iteration 93060, lr = 0.0001 +I0616 17:30:57.165695 9857 solver.cpp:242] Iteration 93080, loss = 0.554744 +I0616 17:30:57.165737 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0893105 (* 1 = 0.0893105 loss) +I0616 17:30:57.165743 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111122 (* 1 = 0.111122 loss) +I0616 17:30:57.165747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0235756 (* 1 = 0.0235756 loss) +I0616 17:30:57.165750 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0264585 (* 1 = 0.0264585 loss) +I0616 17:30:57.165755 9857 solver.cpp:571] Iteration 93080, lr = 0.0001 +I0616 17:31:08.460157 9857 solver.cpp:242] Iteration 93100, loss = 0.506688 +I0616 17:31:08.460199 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0617983 (* 1 = 0.0617983 loss) +I0616 17:31:08.460206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136891 (* 1 = 0.136891 loss) +I0616 17:31:08.460208 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0487674 (* 1 = 0.0487674 loss) +I0616 17:31:08.460212 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0528668 (* 1 = 0.0528668 loss) +I0616 17:31:08.460216 9857 solver.cpp:571] Iteration 93100, lr = 0.0001 +I0616 17:31:19.937142 9857 solver.cpp:242] Iteration 93120, loss = 0.286037 +I0616 17:31:19.937183 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0592368 (* 1 = 0.0592368 loss) +I0616 17:31:19.937189 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105586 (* 1 = 0.105586 loss) +I0616 17:31:19.937193 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.064413 (* 1 = 0.064413 loss) +I0616 17:31:19.937197 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0837717 (* 1 = 0.0837717 loss) +I0616 17:31:19.937201 9857 solver.cpp:571] Iteration 93120, lr = 0.0001 +I0616 17:31:31.530346 9857 solver.cpp:242] Iteration 93140, loss = 0.580743 +I0616 17:31:31.530387 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203281 (* 1 = 0.203281 loss) +I0616 17:31:31.530392 9857 solver.cpp:258] Train net output #1: loss_cls = 0.375997 (* 1 = 0.375997 loss) +I0616 17:31:31.530397 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0966331 (* 1 = 0.0966331 loss) +I0616 17:31:31.530400 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0658697 (* 1 = 0.0658697 loss) +I0616 17:31:31.530405 9857 solver.cpp:571] Iteration 93140, lr = 0.0001 +I0616 17:31:42.907166 9857 solver.cpp:242] Iteration 93160, loss = 0.52971 +I0616 17:31:42.907207 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.203918 (* 1 = 0.203918 loss) +I0616 17:31:42.907212 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232902 (* 1 = 0.232902 loss) +I0616 17:31:42.907217 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0212583 (* 1 = 0.0212583 loss) +I0616 17:31:42.907220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0142347 (* 1 = 0.0142347 loss) +I0616 17:31:42.907223 9857 solver.cpp:571] Iteration 93160, lr = 0.0001 +I0616 17:31:54.293692 9857 solver.cpp:242] Iteration 93180, loss = 0.370994 +I0616 17:31:54.293733 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0436431 (* 1 = 0.0436431 loss) +I0616 17:31:54.293740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0669493 (* 1 = 0.0669493 loss) +I0616 17:31:54.293743 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00725208 (* 1 = 0.00725208 loss) +I0616 17:31:54.293746 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00332088 (* 1 = 0.00332088 loss) +I0616 17:31:54.293751 9857 solver.cpp:571] Iteration 93180, lr = 0.0001 +speed: 0.596s / iter +I0616 17:32:05.637224 9857 solver.cpp:242] Iteration 93200, loss = 0.221314 +I0616 17:32:05.637266 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0711153 (* 1 = 0.0711153 loss) +I0616 17:32:05.637271 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118073 (* 1 = 0.118073 loss) +I0616 17:32:05.637276 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0432539 (* 1 = 0.0432539 loss) +I0616 17:32:05.637279 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122681 (* 1 = 0.0122681 loss) +I0616 17:32:05.637284 9857 solver.cpp:571] Iteration 93200, lr = 0.0001 +I0616 17:32:17.104918 9857 solver.cpp:242] Iteration 93220, loss = 0.577816 +I0616 17:32:17.104961 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217552 (* 1 = 0.217552 loss) +I0616 17:32:17.104966 9857 solver.cpp:258] Train net output #1: loss_cls = 0.223401 (* 1 = 0.223401 loss) +I0616 17:32:17.104970 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19598 (* 1 = 0.19598 loss) +I0616 17:32:17.104974 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0531418 (* 1 = 0.0531418 loss) +I0616 17:32:17.104979 9857 solver.cpp:571] Iteration 93220, lr = 0.0001 +I0616 17:32:28.355391 9857 solver.cpp:242] Iteration 93240, loss = 0.431966 +I0616 17:32:28.355434 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12543 (* 1 = 0.12543 loss) +I0616 17:32:28.355439 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151087 (* 1 = 0.151087 loss) +I0616 17:32:28.355443 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0235329 (* 1 = 0.0235329 loss) +I0616 17:32:28.355448 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0184658 (* 1 = 0.0184658 loss) +I0616 17:32:28.355450 9857 solver.cpp:571] Iteration 93240, lr = 0.0001 +I0616 17:32:39.941494 9857 solver.cpp:242] Iteration 93260, loss = 0.518026 +I0616 17:32:39.941535 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.162069 (* 1 = 0.162069 loss) +I0616 17:32:39.941541 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111883 (* 1 = 0.111883 loss) +I0616 17:32:39.941545 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0462987 (* 1 = 0.0462987 loss) +I0616 17:32:39.941550 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.019693 (* 1 = 0.019693 loss) +I0616 17:32:39.941552 9857 solver.cpp:571] Iteration 93260, lr = 0.0001 +I0616 17:32:51.527293 9857 solver.cpp:242] Iteration 93280, loss = 0.573829 +I0616 17:32:51.527334 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0614844 (* 1 = 0.0614844 loss) +I0616 17:32:51.527340 9857 solver.cpp:258] Train net output #1: loss_cls = 0.111012 (* 1 = 0.111012 loss) +I0616 17:32:51.527344 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0197194 (* 1 = 0.0197194 loss) +I0616 17:32:51.527348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0273689 (* 1 = 0.0273689 loss) +I0616 17:32:51.527351 9857 solver.cpp:571] Iteration 93280, lr = 0.0001 +I0616 17:33:03.039563 9857 solver.cpp:242] Iteration 93300, loss = 0.664299 +I0616 17:33:03.039605 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.281482 (* 1 = 0.281482 loss) +I0616 17:33:03.039611 9857 solver.cpp:258] Train net output #1: loss_cls = 0.450556 (* 1 = 0.450556 loss) +I0616 17:33:03.039615 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0998189 (* 1 = 0.0998189 loss) +I0616 17:33:03.039619 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0203 (* 1 = 0.0203 loss) +I0616 17:33:03.039623 9857 solver.cpp:571] Iteration 93300, lr = 0.0001 +I0616 17:33:14.446394 9857 solver.cpp:242] Iteration 93320, loss = 0.260723 +I0616 17:33:14.446436 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0511212 (* 1 = 0.0511212 loss) +I0616 17:33:14.446442 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131385 (* 1 = 0.131385 loss) +I0616 17:33:14.446446 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00289721 (* 1 = 0.00289721 loss) +I0616 17:33:14.446450 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0092438 (* 1 = 0.0092438 loss) +I0616 17:33:14.446454 9857 solver.cpp:571] Iteration 93320, lr = 0.0001 +I0616 17:33:26.271003 9857 solver.cpp:242] Iteration 93340, loss = 0.691895 +I0616 17:33:26.271044 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.299233 (* 1 = 0.299233 loss) +I0616 17:33:26.271050 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343629 (* 1 = 0.343629 loss) +I0616 17:33:26.271055 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103708 (* 1 = 0.103708 loss) +I0616 17:33:26.271059 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0873491 (* 1 = 0.0873491 loss) +I0616 17:33:26.271062 9857 solver.cpp:571] Iteration 93340, lr = 0.0001 +I0616 17:33:37.637239 9857 solver.cpp:242] Iteration 93360, loss = 0.579831 +I0616 17:33:37.637295 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.253324 (* 1 = 0.253324 loss) +I0616 17:33:37.637300 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191072 (* 1 = 0.191072 loss) +I0616 17:33:37.637305 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0678345 (* 1 = 0.0678345 loss) +I0616 17:33:37.637308 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.163921 (* 1 = 0.163921 loss) +I0616 17:33:37.637311 9857 solver.cpp:571] Iteration 93360, lr = 0.0001 +I0616 17:33:49.164698 9857 solver.cpp:242] Iteration 93380, loss = 0.35608 +I0616 17:33:49.164741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.126877 (* 1 = 0.126877 loss) +I0616 17:33:49.164746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.190211 (* 1 = 0.190211 loss) +I0616 17:33:49.164752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0889336 (* 1 = 0.0889336 loss) +I0616 17:33:49.164754 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107394 (* 1 = 0.0107394 loss) +I0616 17:33:49.164758 9857 solver.cpp:571] Iteration 93380, lr = 0.0001 +speed: 0.596s / iter +I0616 17:34:00.748133 9857 solver.cpp:242] Iteration 93400, loss = 0.342631 +I0616 17:34:00.748177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0572049 (* 1 = 0.0572049 loss) +I0616 17:34:00.748183 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104031 (* 1 = 0.104031 loss) +I0616 17:34:00.748186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0616349 (* 1 = 0.0616349 loss) +I0616 17:34:00.748190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0129362 (* 1 = 0.0129362 loss) +I0616 17:34:00.748194 9857 solver.cpp:571] Iteration 93400, lr = 0.0001 +I0616 17:34:12.273963 9857 solver.cpp:242] Iteration 93420, loss = 0.343927 +I0616 17:34:12.274003 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0730572 (* 1 = 0.0730572 loss) +I0616 17:34:12.274008 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118203 (* 1 = 0.118203 loss) +I0616 17:34:12.274013 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0216142 (* 1 = 0.0216142 loss) +I0616 17:34:12.274016 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180746 (* 1 = 0.0180746 loss) +I0616 17:34:12.274020 9857 solver.cpp:571] Iteration 93420, lr = 0.0001 +I0616 17:34:23.929538 9857 solver.cpp:242] Iteration 93440, loss = 0.586615 +I0616 17:34:23.929579 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.182115 (* 1 = 0.182115 loss) +I0616 17:34:23.929584 9857 solver.cpp:258] Train net output #1: loss_cls = 0.228573 (* 1 = 0.228573 loss) +I0616 17:34:23.929589 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.103873 (* 1 = 0.103873 loss) +I0616 17:34:23.929592 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0754298 (* 1 = 0.0754298 loss) +I0616 17:34:23.929596 9857 solver.cpp:571] Iteration 93440, lr = 0.0001 +I0616 17:34:35.651325 9857 solver.cpp:242] Iteration 93460, loss = 0.509829 +I0616 17:34:35.651367 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0898136 (* 1 = 0.0898136 loss) +I0616 17:34:35.651373 9857 solver.cpp:258] Train net output #1: loss_cls = 0.167758 (* 1 = 0.167758 loss) +I0616 17:34:35.651377 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0240936 (* 1 = 0.0240936 loss) +I0616 17:34:35.651381 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0282246 (* 1 = 0.0282246 loss) +I0616 17:34:35.651384 9857 solver.cpp:571] Iteration 93460, lr = 0.0001 +I0616 17:34:47.228701 9857 solver.cpp:242] Iteration 93480, loss = 0.635046 +I0616 17:34:47.228744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.292835 (* 1 = 0.292835 loss) +I0616 17:34:47.228749 9857 solver.cpp:258] Train net output #1: loss_cls = 0.308684 (* 1 = 0.308684 loss) +I0616 17:34:47.228752 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0969399 (* 1 = 0.0969399 loss) +I0616 17:34:47.228756 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.085423 (* 1 = 0.085423 loss) +I0616 17:34:47.228760 9857 solver.cpp:571] Iteration 93480, lr = 0.0001 +I0616 17:34:59.043673 9857 solver.cpp:242] Iteration 93500, loss = 0.510562 +I0616 17:34:59.043715 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183874 (* 1 = 0.183874 loss) +I0616 17:34:59.043720 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205652 (* 1 = 0.205652 loss) +I0616 17:34:59.043725 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0718187 (* 1 = 0.0718187 loss) +I0616 17:34:59.043728 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0306416 (* 1 = 0.0306416 loss) +I0616 17:34:59.043732 9857 solver.cpp:571] Iteration 93500, lr = 0.0001 +I0616 17:35:10.691797 9857 solver.cpp:242] Iteration 93520, loss = 0.420226 +I0616 17:35:10.691840 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0545934 (* 1 = 0.0545934 loss) +I0616 17:35:10.691846 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0956594 (* 1 = 0.0956594 loss) +I0616 17:35:10.691850 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00188225 (* 1 = 0.00188225 loss) +I0616 17:35:10.691854 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00218426 (* 1 = 0.00218426 loss) +I0616 17:35:10.691857 9857 solver.cpp:571] Iteration 93520, lr = 0.0001 +I0616 17:35:22.375370 9857 solver.cpp:242] Iteration 93540, loss = 0.838663 +I0616 17:35:22.375408 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.328357 (* 1 = 0.328357 loss) +I0616 17:35:22.375414 9857 solver.cpp:258] Train net output #1: loss_cls = 0.435748 (* 1 = 0.435748 loss) +I0616 17:35:22.375418 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0691638 (* 1 = 0.0691638 loss) +I0616 17:35:22.375422 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0734539 (* 1 = 0.0734539 loss) +I0616 17:35:22.375427 9857 solver.cpp:571] Iteration 93540, lr = 0.0001 +I0616 17:35:34.147143 9857 solver.cpp:242] Iteration 93560, loss = 0.794251 +I0616 17:35:34.147183 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.310156 (* 1 = 0.310156 loss) +I0616 17:35:34.147189 9857 solver.cpp:258] Train net output #1: loss_cls = 0.485501 (* 1 = 0.485501 loss) +I0616 17:35:34.147193 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.102066 (* 1 = 0.102066 loss) +I0616 17:35:34.147197 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0747491 (* 1 = 0.0747491 loss) +I0616 17:35:34.147200 9857 solver.cpp:571] Iteration 93560, lr = 0.0001 +I0616 17:35:45.304872 9857 solver.cpp:242] Iteration 93580, loss = 0.738946 +I0616 17:35:45.304913 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0598554 (* 1 = 0.0598554 loss) +I0616 17:35:45.304919 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0993081 (* 1 = 0.0993081 loss) +I0616 17:35:45.304924 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0135972 (* 1 = 0.0135972 loss) +I0616 17:35:45.304927 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00890728 (* 1 = 0.00890728 loss) +I0616 17:35:45.304931 9857 solver.cpp:571] Iteration 93580, lr = 0.0001 +speed: 0.596s / iter +I0616 17:35:56.589041 9857 solver.cpp:242] Iteration 93600, loss = 0.343707 +I0616 17:35:56.589084 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0591419 (* 1 = 0.0591419 loss) +I0616 17:35:56.589089 9857 solver.cpp:258] Train net output #1: loss_cls = 0.142373 (* 1 = 0.142373 loss) +I0616 17:35:56.589094 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0194161 (* 1 = 0.0194161 loss) +I0616 17:35:56.589097 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0132723 (* 1 = 0.0132723 loss) +I0616 17:35:56.589102 9857 solver.cpp:571] Iteration 93600, lr = 0.0001 +I0616 17:36:08.224309 9857 solver.cpp:242] Iteration 93620, loss = 0.607057 +I0616 17:36:08.224352 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.149556 (* 1 = 0.149556 loss) +I0616 17:36:08.224359 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132146 (* 1 = 0.132146 loss) +I0616 17:36:08.224362 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0196782 (* 1 = 0.0196782 loss) +I0616 17:36:08.224365 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168728 (* 1 = 0.0168728 loss) +I0616 17:36:08.224370 9857 solver.cpp:571] Iteration 93620, lr = 0.0001 +I0616 17:36:19.814620 9857 solver.cpp:242] Iteration 93640, loss = 0.478676 +I0616 17:36:19.814662 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.341442 (* 1 = 0.341442 loss) +I0616 17:36:19.814667 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272935 (* 1 = 0.272935 loss) +I0616 17:36:19.814671 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.068286 (* 1 = 0.068286 loss) +I0616 17:36:19.814676 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0779573 (* 1 = 0.0779573 loss) +I0616 17:36:19.814678 9857 solver.cpp:571] Iteration 93640, lr = 0.0001 +I0616 17:36:31.356528 9857 solver.cpp:242] Iteration 93660, loss = 0.440986 +I0616 17:36:31.356570 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.200806 (* 1 = 0.200806 loss) +I0616 17:36:31.356575 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19568 (* 1 = 0.19568 loss) +I0616 17:36:31.356580 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0323949 (* 1 = 0.0323949 loss) +I0616 17:36:31.356583 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231321 (* 1 = 0.0231321 loss) +I0616 17:36:31.356586 9857 solver.cpp:571] Iteration 93660, lr = 0.0001 +I0616 17:36:42.610924 9857 solver.cpp:242] Iteration 93680, loss = 0.281605 +I0616 17:36:42.610965 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0629259 (* 1 = 0.0629259 loss) +I0616 17:36:42.610971 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132693 (* 1 = 0.132693 loss) +I0616 17:36:42.610975 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00788981 (* 1 = 0.00788981 loss) +I0616 17:36:42.610980 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0011864 (* 1 = 0.0011864 loss) +I0616 17:36:42.610983 9857 solver.cpp:571] Iteration 93680, lr = 0.0001 +I0616 17:36:54.040132 9857 solver.cpp:242] Iteration 93700, loss = 0.409436 +I0616 17:36:54.040174 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0989913 (* 1 = 0.0989913 loss) +I0616 17:36:54.040179 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14703 (* 1 = 0.14703 loss) +I0616 17:36:54.040184 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0329087 (* 1 = 0.0329087 loss) +I0616 17:36:54.040187 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145431 (* 1 = 0.0145431 loss) +I0616 17:36:54.040191 9857 solver.cpp:571] Iteration 93700, lr = 0.0001 +I0616 17:37:05.531689 9857 solver.cpp:242] Iteration 93720, loss = 0.557433 +I0616 17:37:05.531730 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.133731 (* 1 = 0.133731 loss) +I0616 17:37:05.531736 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184067 (* 1 = 0.184067 loss) +I0616 17:37:05.531740 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.012789 (* 1 = 0.012789 loss) +I0616 17:37:05.531744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012865 (* 1 = 0.012865 loss) +I0616 17:37:05.531747 9857 solver.cpp:571] Iteration 93720, lr = 0.0001 +I0616 17:37:17.135476 9857 solver.cpp:242] Iteration 93740, loss = 0.759357 +I0616 17:37:17.135519 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.159354 (* 1 = 0.159354 loss) +I0616 17:37:17.135524 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17777 (* 1 = 0.17777 loss) +I0616 17:37:17.135529 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0242859 (* 1 = 0.0242859 loss) +I0616 17:37:17.135531 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0694783 (* 1 = 0.0694783 loss) +I0616 17:37:17.135535 9857 solver.cpp:571] Iteration 93740, lr = 0.0001 +I0616 17:37:28.684705 9857 solver.cpp:242] Iteration 93760, loss = 0.843531 +I0616 17:37:28.684748 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0967812 (* 1 = 0.0967812 loss) +I0616 17:37:28.684753 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158813 (* 1 = 0.158813 loss) +I0616 17:37:28.684757 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168869 (* 1 = 0.168869 loss) +I0616 17:37:28.684762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.202208 (* 1 = 0.202208 loss) +I0616 17:37:28.684764 9857 solver.cpp:571] Iteration 93760, lr = 0.0001 +I0616 17:37:40.105012 9857 solver.cpp:242] Iteration 93780, loss = 0.229088 +I0616 17:37:40.105056 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0687986 (* 1 = 0.0687986 loss) +I0616 17:37:40.105062 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131333 (* 1 = 0.131333 loss) +I0616 17:37:40.105065 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0638482 (* 1 = 0.0638482 loss) +I0616 17:37:40.105068 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0152313 (* 1 = 0.0152313 loss) +I0616 17:37:40.105072 9857 solver.cpp:571] Iteration 93780, lr = 0.0001 +speed: 0.596s / iter +I0616 17:37:51.545177 9857 solver.cpp:242] Iteration 93800, loss = 0.25995 +I0616 17:37:51.545220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130361 (* 1 = 0.130361 loss) +I0616 17:37:51.545225 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182037 (* 1 = 0.182037 loss) +I0616 17:37:51.545229 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0296637 (* 1 = 0.0296637 loss) +I0616 17:37:51.545233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0168713 (* 1 = 0.0168713 loss) +I0616 17:37:51.545238 9857 solver.cpp:571] Iteration 93800, lr = 0.0001 +I0616 17:38:03.147388 9857 solver.cpp:242] Iteration 93820, loss = 0.830075 +I0616 17:38:03.147429 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180553 (* 1 = 0.180553 loss) +I0616 17:38:03.147435 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180188 (* 1 = 0.180188 loss) +I0616 17:38:03.147440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0338599 (* 1 = 0.0338599 loss) +I0616 17:38:03.147444 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0491247 (* 1 = 0.0491247 loss) +I0616 17:38:03.147447 9857 solver.cpp:571] Iteration 93820, lr = 0.0001 +I0616 17:38:14.485693 9857 solver.cpp:242] Iteration 93840, loss = 0.737178 +I0616 17:38:14.485735 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0606141 (* 1 = 0.0606141 loss) +I0616 17:38:14.485740 9857 solver.cpp:258] Train net output #1: loss_cls = 0.176425 (* 1 = 0.176425 loss) +I0616 17:38:14.485745 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0395299 (* 1 = 0.0395299 loss) +I0616 17:38:14.485749 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015393 (* 1 = 0.015393 loss) +I0616 17:38:14.485752 9857 solver.cpp:571] Iteration 93840, lr = 0.0001 +I0616 17:38:25.927058 9857 solver.cpp:242] Iteration 93860, loss = 0.913341 +I0616 17:38:25.927098 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.214879 (* 1 = 0.214879 loss) +I0616 17:38:25.927104 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162837 (* 1 = 0.162837 loss) +I0616 17:38:25.927109 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0758229 (* 1 = 0.0758229 loss) +I0616 17:38:25.927112 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00637371 (* 1 = 0.00637371 loss) +I0616 17:38:25.927116 9857 solver.cpp:571] Iteration 93860, lr = 0.0001 +I0616 17:38:37.392390 9857 solver.cpp:242] Iteration 93880, loss = 0.415129 +I0616 17:38:37.392431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0678038 (* 1 = 0.0678038 loss) +I0616 17:38:37.392436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0629746 (* 1 = 0.0629746 loss) +I0616 17:38:37.392441 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00762646 (* 1 = 0.00762646 loss) +I0616 17:38:37.392444 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278463 (* 1 = 0.0278463 loss) +I0616 17:38:37.392447 9857 solver.cpp:571] Iteration 93880, lr = 0.0001 +I0616 17:38:48.800856 9857 solver.cpp:242] Iteration 93900, loss = 0.483278 +I0616 17:38:48.800899 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.357715 (* 1 = 0.357715 loss) +I0616 17:38:48.800904 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294201 (* 1 = 0.294201 loss) +I0616 17:38:48.800907 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.02857 (* 1 = 0.02857 loss) +I0616 17:38:48.800911 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0318805 (* 1 = 0.0318805 loss) +I0616 17:38:48.800915 9857 solver.cpp:571] Iteration 93900, lr = 0.0001 +I0616 17:39:00.473116 9857 solver.cpp:242] Iteration 93920, loss = 0.714096 +I0616 17:39:00.473158 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242858 (* 1 = 0.242858 loss) +I0616 17:39:00.473165 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350003 (* 1 = 0.350003 loss) +I0616 17:39:00.473168 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151722 (* 1 = 0.151722 loss) +I0616 17:39:00.473172 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0220094 (* 1 = 0.0220094 loss) +I0616 17:39:00.473177 9857 solver.cpp:571] Iteration 93920, lr = 0.0001 +I0616 17:39:12.333438 9857 solver.cpp:242] Iteration 93940, loss = 1.02501 +I0616 17:39:12.333479 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.396133 (* 1 = 0.396133 loss) +I0616 17:39:12.333485 9857 solver.cpp:258] Train net output #1: loss_cls = 0.632717 (* 1 = 0.632717 loss) +I0616 17:39:12.333489 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.185919 (* 1 = 0.185919 loss) +I0616 17:39:12.333493 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.217879 (* 1 = 0.217879 loss) +I0616 17:39:12.333497 9857 solver.cpp:571] Iteration 93940, lr = 0.0001 +I0616 17:39:23.785295 9857 solver.cpp:242] Iteration 93960, loss = 0.229297 +I0616 17:39:23.785336 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0891699 (* 1 = 0.0891699 loss) +I0616 17:39:23.785341 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16566 (* 1 = 0.16566 loss) +I0616 17:39:23.785346 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0500609 (* 1 = 0.0500609 loss) +I0616 17:39:23.785348 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0277175 (* 1 = 0.0277175 loss) +I0616 17:39:23.785352 9857 solver.cpp:571] Iteration 93960, lr = 0.0001 +I0616 17:39:35.179801 9857 solver.cpp:242] Iteration 93980, loss = 0.342064 +I0616 17:39:35.179842 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.112095 (* 1 = 0.112095 loss) +I0616 17:39:35.179848 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146849 (* 1 = 0.146849 loss) +I0616 17:39:35.179852 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0639741 (* 1 = 0.0639741 loss) +I0616 17:39:35.179857 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112258 (* 1 = 0.0112258 loss) +I0616 17:39:35.179862 9857 solver.cpp:571] Iteration 93980, lr = 0.0001 +speed: 0.596s / iter +I0616 17:39:47.002789 9857 solver.cpp:242] Iteration 94000, loss = 0.221832 +I0616 17:39:47.002831 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0503357 (* 1 = 0.0503357 loss) +I0616 17:39:47.002837 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151883 (* 1 = 0.151883 loss) +I0616 17:39:47.002841 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0314142 (* 1 = 0.0314142 loss) +I0616 17:39:47.002846 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0143827 (* 1 = 0.0143827 loss) +I0616 17:39:47.002849 9857 solver.cpp:571] Iteration 94000, lr = 0.0001 +I0616 17:39:58.476202 9857 solver.cpp:242] Iteration 94020, loss = 0.558882 +I0616 17:39:58.476244 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.10986 (* 1 = 0.10986 loss) +I0616 17:39:58.476249 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248324 (* 1 = 0.248324 loss) +I0616 17:39:58.476254 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0962199 (* 1 = 0.0962199 loss) +I0616 17:39:58.476258 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.124593 (* 1 = 0.124593 loss) +I0616 17:39:58.476261 9857 solver.cpp:571] Iteration 94020, lr = 0.0001 +I0616 17:40:10.053166 9857 solver.cpp:242] Iteration 94040, loss = 0.392743 +I0616 17:40:10.053208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117552 (* 1 = 0.117552 loss) +I0616 17:40:10.053215 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166613 (* 1 = 0.166613 loss) +I0616 17:40:10.053218 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0809889 (* 1 = 0.0809889 loss) +I0616 17:40:10.053221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.015779 (* 1 = 0.015779 loss) +I0616 17:40:10.053225 9857 solver.cpp:571] Iteration 94040, lr = 0.0001 +I0616 17:40:21.487740 9857 solver.cpp:242] Iteration 94060, loss = 0.38031 +I0616 17:40:21.487782 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186574 (* 1 = 0.186574 loss) +I0616 17:40:21.487788 9857 solver.cpp:258] Train net output #1: loss_cls = 0.359751 (* 1 = 0.359751 loss) +I0616 17:40:21.487792 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0863995 (* 1 = 0.0863995 loss) +I0616 17:40:21.487797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0182232 (* 1 = 0.0182232 loss) +I0616 17:40:21.487802 9857 solver.cpp:571] Iteration 94060, lr = 0.0001 +I0616 17:40:32.840595 9857 solver.cpp:242] Iteration 94080, loss = 0.420796 +I0616 17:40:32.840638 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204584 (* 1 = 0.204584 loss) +I0616 17:40:32.840643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232494 (* 1 = 0.232494 loss) +I0616 17:40:32.840647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0262121 (* 1 = 0.0262121 loss) +I0616 17:40:32.840651 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00974531 (* 1 = 0.00974531 loss) +I0616 17:40:32.840656 9857 solver.cpp:571] Iteration 94080, lr = 0.0001 +I0616 17:40:44.475759 9857 solver.cpp:242] Iteration 94100, loss = 0.622045 +I0616 17:40:44.475800 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.205907 (* 1 = 0.205907 loss) +I0616 17:40:44.475806 9857 solver.cpp:258] Train net output #1: loss_cls = 0.174036 (* 1 = 0.174036 loss) +I0616 17:40:44.475811 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0149864 (* 1 = 0.0149864 loss) +I0616 17:40:44.475814 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00814415 (* 1 = 0.00814415 loss) +I0616 17:40:44.475817 9857 solver.cpp:571] Iteration 94100, lr = 0.0001 +I0616 17:40:55.991027 9857 solver.cpp:242] Iteration 94120, loss = 0.440149 +I0616 17:40:55.991066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0600172 (* 1 = 0.0600172 loss) +I0616 17:40:55.991071 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0751145 (* 1 = 0.0751145 loss) +I0616 17:40:55.991075 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.029682 (* 1 = 0.029682 loss) +I0616 17:40:55.991080 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00339045 (* 1 = 0.00339045 loss) +I0616 17:40:55.991083 9857 solver.cpp:571] Iteration 94120, lr = 0.0001 +I0616 17:41:07.496016 9857 solver.cpp:242] Iteration 94140, loss = 0.391267 +I0616 17:41:07.496058 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.23748 (* 1 = 0.23748 loss) +I0616 17:41:07.496063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232461 (* 1 = 0.232461 loss) +I0616 17:41:07.496068 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0575344 (* 1 = 0.0575344 loss) +I0616 17:41:07.496071 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0621314 (* 1 = 0.0621314 loss) +I0616 17:41:07.496075 9857 solver.cpp:571] Iteration 94140, lr = 0.0001 +I0616 17:41:18.915684 9857 solver.cpp:242] Iteration 94160, loss = 0.235955 +I0616 17:41:18.915727 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0877711 (* 1 = 0.0877711 loss) +I0616 17:41:18.915732 9857 solver.cpp:258] Train net output #1: loss_cls = 0.156214 (* 1 = 0.156214 loss) +I0616 17:41:18.915736 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00270433 (* 1 = 0.00270433 loss) +I0616 17:41:18.915740 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246896 (* 1 = 0.0246896 loss) +I0616 17:41:18.915746 9857 solver.cpp:571] Iteration 94160, lr = 0.0001 +I0616 17:41:30.404271 9857 solver.cpp:242] Iteration 94180, loss = 0.473281 +I0616 17:41:30.404314 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.279385 (* 1 = 0.279385 loss) +I0616 17:41:30.404320 9857 solver.cpp:258] Train net output #1: loss_cls = 0.399855 (* 1 = 0.399855 loss) +I0616 17:41:30.404325 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0215917 (* 1 = 0.0215917 loss) +I0616 17:41:30.404328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00773936 (* 1 = 0.00773936 loss) +I0616 17:41:30.404332 9857 solver.cpp:571] Iteration 94180, lr = 0.0001 +speed: 0.596s / iter +I0616 17:41:41.752651 9857 solver.cpp:242] Iteration 94200, loss = 0.437857 +I0616 17:41:41.752692 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.046765 (* 1 = 0.046765 loss) +I0616 17:41:41.752698 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202932 (* 1 = 0.202932 loss) +I0616 17:41:41.752702 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0020303 (* 1 = 0.0020303 loss) +I0616 17:41:41.752707 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0227181 (* 1 = 0.0227181 loss) +I0616 17:41:41.752709 9857 solver.cpp:571] Iteration 94200, lr = 0.0001 +I0616 17:41:53.331346 9857 solver.cpp:242] Iteration 94220, loss = 0.652722 +I0616 17:41:53.331387 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158219 (* 1 = 0.158219 loss) +I0616 17:41:53.331393 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134115 (* 1 = 0.134115 loss) +I0616 17:41:53.331398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0303189 (* 1 = 0.0303189 loss) +I0616 17:41:53.331401 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0269541 (* 1 = 0.0269541 loss) +I0616 17:41:53.331405 9857 solver.cpp:571] Iteration 94220, lr = 0.0001 +I0616 17:42:05.035918 9857 solver.cpp:242] Iteration 94240, loss = 0.179636 +I0616 17:42:05.035959 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0691812 (* 1 = 0.0691812 loss) +I0616 17:42:05.035964 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0814069 (* 1 = 0.0814069 loss) +I0616 17:42:05.035967 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0323117 (* 1 = 0.0323117 loss) +I0616 17:42:05.035971 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00558658 (* 1 = 0.00558658 loss) +I0616 17:42:05.035975 9857 solver.cpp:571] Iteration 94240, lr = 0.0001 +I0616 17:42:16.768355 9857 solver.cpp:242] Iteration 94260, loss = 0.430047 +I0616 17:42:16.768398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0693806 (* 1 = 0.0693806 loss) +I0616 17:42:16.768404 9857 solver.cpp:258] Train net output #1: loss_cls = 0.101533 (* 1 = 0.101533 loss) +I0616 17:42:16.768407 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00377746 (* 1 = 0.00377746 loss) +I0616 17:42:16.768411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0134931 (* 1 = 0.0134931 loss) +I0616 17:42:16.768415 9857 solver.cpp:571] Iteration 94260, lr = 0.0001 +I0616 17:42:28.356034 9857 solver.cpp:242] Iteration 94280, loss = 0.768326 +I0616 17:42:28.356075 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293113 (* 1 = 0.293113 loss) +I0616 17:42:28.356081 9857 solver.cpp:258] Train net output #1: loss_cls = 0.29258 (* 1 = 0.29258 loss) +I0616 17:42:28.356084 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.068643 (* 1 = 0.068643 loss) +I0616 17:42:28.356088 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.116172 (* 1 = 0.116172 loss) +I0616 17:42:28.356092 9857 solver.cpp:571] Iteration 94280, lr = 0.0001 +I0616 17:42:39.887802 9857 solver.cpp:242] Iteration 94300, loss = 0.260917 +I0616 17:42:39.887845 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115821 (* 1 = 0.115821 loss) +I0616 17:42:39.887851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.118605 (* 1 = 0.118605 loss) +I0616 17:42:39.887856 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0298309 (* 1 = 0.0298309 loss) +I0616 17:42:39.887859 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00593355 (* 1 = 0.00593355 loss) +I0616 17:42:39.887863 9857 solver.cpp:571] Iteration 94300, lr = 0.0001 +I0616 17:42:51.336086 9857 solver.cpp:242] Iteration 94320, loss = 0.823581 +I0616 17:42:51.336127 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241501 (* 1 = 0.241501 loss) +I0616 17:42:51.336133 9857 solver.cpp:258] Train net output #1: loss_cls = 0.371969 (* 1 = 0.371969 loss) +I0616 17:42:51.336138 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0478429 (* 1 = 0.0478429 loss) +I0616 17:42:51.336141 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0246858 (* 1 = 0.0246858 loss) +I0616 17:42:51.336144 9857 solver.cpp:571] Iteration 94320, lr = 0.0001 +I0616 17:43:02.977264 9857 solver.cpp:242] Iteration 94340, loss = 0.214962 +I0616 17:43:02.977306 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0799434 (* 1 = 0.0799434 loss) +I0616 17:43:02.977313 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123065 (* 1 = 0.123065 loss) +I0616 17:43:02.977316 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00184711 (* 1 = 0.00184711 loss) +I0616 17:43:02.977320 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00669857 (* 1 = 0.00669857 loss) +I0616 17:43:02.977324 9857 solver.cpp:571] Iteration 94340, lr = 0.0001 +I0616 17:43:14.508801 9857 solver.cpp:242] Iteration 94360, loss = 0.235773 +I0616 17:43:14.508844 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0881806 (* 1 = 0.0881806 loss) +I0616 17:43:14.508851 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132104 (* 1 = 0.132104 loss) +I0616 17:43:14.508854 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0182048 (* 1 = 0.0182048 loss) +I0616 17:43:14.508858 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0145273 (* 1 = 0.0145273 loss) +I0616 17:43:14.508862 9857 solver.cpp:571] Iteration 94360, lr = 0.0001 +I0616 17:43:26.196351 9857 solver.cpp:242] Iteration 94380, loss = 0.341919 +I0616 17:43:26.196393 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0666729 (* 1 = 0.0666729 loss) +I0616 17:43:26.196398 9857 solver.cpp:258] Train net output #1: loss_cls = 0.122345 (* 1 = 0.122345 loss) +I0616 17:43:26.196403 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0117381 (* 1 = 0.0117381 loss) +I0616 17:43:26.196406 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0291026 (* 1 = 0.0291026 loss) +I0616 17:43:26.196409 9857 solver.cpp:571] Iteration 94380, lr = 0.0001 +speed: 0.596s / iter +I0616 17:43:37.856478 9857 solver.cpp:242] Iteration 94400, loss = 0.444899 +I0616 17:43:37.856520 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.329595 (* 1 = 0.329595 loss) +I0616 17:43:37.856526 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222609 (* 1 = 0.222609 loss) +I0616 17:43:37.856530 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.041013 (* 1 = 0.041013 loss) +I0616 17:43:37.856534 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.014292 (* 1 = 0.014292 loss) +I0616 17:43:37.856537 9857 solver.cpp:571] Iteration 94400, lr = 0.0001 +I0616 17:43:49.345163 9857 solver.cpp:242] Iteration 94420, loss = 0.497944 +I0616 17:43:49.345206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0478204 (* 1 = 0.0478204 loss) +I0616 17:43:49.345211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0371602 (* 1 = 0.0371602 loss) +I0616 17:43:49.345216 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00282266 (* 1 = 0.00282266 loss) +I0616 17:43:49.345219 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000826302 (* 1 = 0.000826302 loss) +I0616 17:43:49.345223 9857 solver.cpp:571] Iteration 94420, lr = 0.0001 +I0616 17:44:00.697656 9857 solver.cpp:242] Iteration 94440, loss = 0.384172 +I0616 17:44:00.697697 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140994 (* 1 = 0.140994 loss) +I0616 17:44:00.697703 9857 solver.cpp:258] Train net output #1: loss_cls = 0.131971 (* 1 = 0.131971 loss) +I0616 17:44:00.697707 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.019546 (* 1 = 0.019546 loss) +I0616 17:44:00.697711 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0612999 (* 1 = 0.0612999 loss) +I0616 17:44:00.697715 9857 solver.cpp:571] Iteration 94440, lr = 0.0001 +I0616 17:44:12.064251 9857 solver.cpp:242] Iteration 94460, loss = 0.42947 +I0616 17:44:12.064291 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0680093 (* 1 = 0.0680093 loss) +I0616 17:44:12.064297 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192232 (* 1 = 0.192232 loss) +I0616 17:44:12.064301 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0421837 (* 1 = 0.0421837 loss) +I0616 17:44:12.064304 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0279113 (* 1 = 0.0279113 loss) +I0616 17:44:12.064307 9857 solver.cpp:571] Iteration 94460, lr = 0.0001 +I0616 17:44:23.589174 9857 solver.cpp:242] Iteration 94480, loss = 0.417603 +I0616 17:44:23.589215 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139591 (* 1 = 0.139591 loss) +I0616 17:44:23.589221 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128378 (* 1 = 0.128378 loss) +I0616 17:44:23.589226 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0056469 (* 1 = 0.0056469 loss) +I0616 17:44:23.589229 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00249491 (* 1 = 0.00249491 loss) +I0616 17:44:23.589232 9857 solver.cpp:571] Iteration 94480, lr = 0.0001 +I0616 17:44:35.135087 9857 solver.cpp:242] Iteration 94500, loss = 0.348179 +I0616 17:44:35.135126 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260826 (* 1 = 0.260826 loss) +I0616 17:44:35.135133 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0852893 (* 1 = 0.0852893 loss) +I0616 17:44:35.135136 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0185112 (* 1 = 0.0185112 loss) +I0616 17:44:35.135140 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00480605 (* 1 = 0.00480605 loss) +I0616 17:44:35.135143 9857 solver.cpp:571] Iteration 94500, lr = 0.0001 +I0616 17:44:46.831017 9857 solver.cpp:242] Iteration 94520, loss = 0.351714 +I0616 17:44:46.831058 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0830515 (* 1 = 0.0830515 loss) +I0616 17:44:46.831063 9857 solver.cpp:258] Train net output #1: loss_cls = 0.138509 (* 1 = 0.138509 loss) +I0616 17:44:46.831068 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.058817 (* 1 = 0.058817 loss) +I0616 17:44:46.831070 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0073271 (* 1 = 0.0073271 loss) +I0616 17:44:46.831074 9857 solver.cpp:571] Iteration 94520, lr = 0.0001 +I0616 17:44:58.573245 9857 solver.cpp:242] Iteration 94540, loss = 0.381451 +I0616 17:44:58.573285 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.244425 (* 1 = 0.244425 loss) +I0616 17:44:58.573290 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184057 (* 1 = 0.184057 loss) +I0616 17:44:58.573294 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0590885 (* 1 = 0.0590885 loss) +I0616 17:44:58.573298 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0835452 (* 1 = 0.0835452 loss) +I0616 17:44:58.573303 9857 solver.cpp:571] Iteration 94540, lr = 0.0001 +I0616 17:45:10.107666 9857 solver.cpp:242] Iteration 94560, loss = 0.615603 +I0616 17:45:10.107707 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.303737 (* 1 = 0.303737 loss) +I0616 17:45:10.107713 9857 solver.cpp:258] Train net output #1: loss_cls = 0.495008 (* 1 = 0.495008 loss) +I0616 17:45:10.107717 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141348 (* 1 = 0.141348 loss) +I0616 17:45:10.107720 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0256512 (* 1 = 0.0256512 loss) +I0616 17:45:10.107724 9857 solver.cpp:571] Iteration 94560, lr = 0.0001 +I0616 17:45:21.731081 9857 solver.cpp:242] Iteration 94580, loss = 1.01042 +I0616 17:45:21.731122 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.439781 (* 1 = 0.439781 loss) +I0616 17:45:21.731127 9857 solver.cpp:258] Train net output #1: loss_cls = 0.49954 (* 1 = 0.49954 loss) +I0616 17:45:21.731132 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143534 (* 1 = 0.143534 loss) +I0616 17:45:21.731135 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.221353 (* 1 = 0.221353 loss) +I0616 17:45:21.731138 9857 solver.cpp:571] Iteration 94580, lr = 0.0001 +speed: 0.596s / iter +I0616 17:45:33.257769 9857 solver.cpp:242] Iteration 94600, loss = 0.389244 +I0616 17:45:33.257812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167845 (* 1 = 0.167845 loss) +I0616 17:45:33.257817 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146835 (* 1 = 0.146835 loss) +I0616 17:45:33.257820 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0672558 (* 1 = 0.0672558 loss) +I0616 17:45:33.257824 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0991319 (* 1 = 0.0991319 loss) +I0616 17:45:33.257828 9857 solver.cpp:571] Iteration 94600, lr = 0.0001 +I0616 17:45:44.549031 9857 solver.cpp:242] Iteration 94620, loss = 0.560429 +I0616 17:45:44.549073 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0386923 (* 1 = 0.0386923 loss) +I0616 17:45:44.549079 9857 solver.cpp:258] Train net output #1: loss_cls = 0.196931 (* 1 = 0.196931 loss) +I0616 17:45:44.549083 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0935826 (* 1 = 0.0935826 loss) +I0616 17:45:44.549088 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0290458 (* 1 = 0.0290458 loss) +I0616 17:45:44.549090 9857 solver.cpp:571] Iteration 94620, lr = 0.0001 +I0616 17:45:56.205705 9857 solver.cpp:242] Iteration 94640, loss = 0.40148 +I0616 17:45:56.205744 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215308 (* 1 = 0.215308 loss) +I0616 17:45:56.205749 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168249 (* 1 = 0.168249 loss) +I0616 17:45:56.205754 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0449814 (* 1 = 0.0449814 loss) +I0616 17:45:56.205757 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0527746 (* 1 = 0.0527746 loss) +I0616 17:45:56.205761 9857 solver.cpp:571] Iteration 94640, lr = 0.0001 +I0616 17:46:07.712265 9857 solver.cpp:242] Iteration 94660, loss = 0.28961 +I0616 17:46:07.712306 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0645274 (* 1 = 0.0645274 loss) +I0616 17:46:07.712312 9857 solver.cpp:258] Train net output #1: loss_cls = 0.132833 (* 1 = 0.132833 loss) +I0616 17:46:07.712316 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0136963 (* 1 = 0.0136963 loss) +I0616 17:46:07.712319 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0355608 (* 1 = 0.0355608 loss) +I0616 17:46:07.712323 9857 solver.cpp:571] Iteration 94660, lr = 0.0001 +I0616 17:46:19.096727 9857 solver.cpp:242] Iteration 94680, loss = 0.252683 +I0616 17:46:19.096765 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122864 (* 1 = 0.122864 loss) +I0616 17:46:19.096771 9857 solver.cpp:258] Train net output #1: loss_cls = 0.147615 (* 1 = 0.147615 loss) +I0616 17:46:19.096776 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010845 (* 1 = 0.010845 loss) +I0616 17:46:19.096778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0125396 (* 1 = 0.0125396 loss) +I0616 17:46:19.096782 9857 solver.cpp:571] Iteration 94680, lr = 0.0001 +I0616 17:46:30.759413 9857 solver.cpp:242] Iteration 94700, loss = 0.615055 +I0616 17:46:30.759455 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.105213 (* 1 = 0.105213 loss) +I0616 17:46:30.759460 9857 solver.cpp:258] Train net output #1: loss_cls = 0.290865 (* 1 = 0.290865 loss) +I0616 17:46:30.759464 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0287219 (* 1 = 0.0287219 loss) +I0616 17:46:30.759469 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0514093 (* 1 = 0.0514093 loss) +I0616 17:46:30.759471 9857 solver.cpp:571] Iteration 94700, lr = 0.0001 +I0616 17:46:42.263183 9857 solver.cpp:242] Iteration 94720, loss = 0.618657 +I0616 17:46:42.263226 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.391731 (* 1 = 0.391731 loss) +I0616 17:46:42.263231 9857 solver.cpp:258] Train net output #1: loss_cls = 0.456893 (* 1 = 0.456893 loss) +I0616 17:46:42.263236 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0842887 (* 1 = 0.0842887 loss) +I0616 17:46:42.263239 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.106007 (* 1 = 0.106007 loss) +I0616 17:46:42.263242 9857 solver.cpp:571] Iteration 94720, lr = 0.0001 +I0616 17:46:53.579001 9857 solver.cpp:242] Iteration 94740, loss = 0.684606 +I0616 17:46:53.579043 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0773374 (* 1 = 0.0773374 loss) +I0616 17:46:53.579049 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0980811 (* 1 = 0.0980811 loss) +I0616 17:46:53.579053 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.205218 (* 1 = 0.205218 loss) +I0616 17:46:53.579057 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00455286 (* 1 = 0.00455286 loss) +I0616 17:46:53.579061 9857 solver.cpp:571] Iteration 94740, lr = 0.0001 +I0616 17:47:04.973470 9857 solver.cpp:242] Iteration 94760, loss = 0.498243 +I0616 17:47:04.973512 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.228319 (* 1 = 0.228319 loss) +I0616 17:47:04.973518 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279607 (* 1 = 0.279607 loss) +I0616 17:47:04.973522 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.18646 (* 1 = 0.18646 loss) +I0616 17:47:04.973526 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0943285 (* 1 = 0.0943285 loss) +I0616 17:47:04.973529 9857 solver.cpp:571] Iteration 94760, lr = 0.0001 +I0616 17:47:16.483543 9857 solver.cpp:242] Iteration 94780, loss = 0.376919 +I0616 17:47:16.483584 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204534 (* 1 = 0.204534 loss) +I0616 17:47:16.483589 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14715 (* 1 = 0.14715 loss) +I0616 17:47:16.483594 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0210074 (* 1 = 0.0210074 loss) +I0616 17:47:16.483597 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0140252 (* 1 = 0.0140252 loss) +I0616 17:47:16.483602 9857 solver.cpp:571] Iteration 94780, lr = 0.0001 +speed: 0.596s / iter +I0616 17:47:27.964812 9857 solver.cpp:242] Iteration 94800, loss = 0.509614 +I0616 17:47:27.964854 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0591663 (* 1 = 0.0591663 loss) +I0616 17:47:27.964859 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0940745 (* 1 = 0.0940745 loss) +I0616 17:47:27.964862 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0111538 (* 1 = 0.0111538 loss) +I0616 17:47:27.964866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0340496 (* 1 = 0.0340496 loss) +I0616 17:47:27.964870 9857 solver.cpp:571] Iteration 94800, lr = 0.0001 +I0616 17:47:39.190978 9857 solver.cpp:242] Iteration 94820, loss = 0.469421 +I0616 17:47:39.191020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197031 (* 1 = 0.197031 loss) +I0616 17:47:39.191025 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145544 (* 1 = 0.145544 loss) +I0616 17:47:39.191030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0374389 (* 1 = 0.0374389 loss) +I0616 17:47:39.191033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0588067 (* 1 = 0.0588067 loss) +I0616 17:47:39.191037 9857 solver.cpp:571] Iteration 94820, lr = 0.0001 +I0616 17:47:50.730455 9857 solver.cpp:242] Iteration 94840, loss = 0.450319 +I0616 17:47:50.730495 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266135 (* 1 = 0.266135 loss) +I0616 17:47:50.730501 9857 solver.cpp:258] Train net output #1: loss_cls = 0.253362 (* 1 = 0.253362 loss) +I0616 17:47:50.730505 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0939513 (* 1 = 0.0939513 loss) +I0616 17:47:50.730509 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216477 (* 1 = 0.0216477 loss) +I0616 17:47:50.730512 9857 solver.cpp:571] Iteration 94840, lr = 0.0001 +I0616 17:48:02.339339 9857 solver.cpp:242] Iteration 94860, loss = 0.250144 +I0616 17:48:02.339381 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0783126 (* 1 = 0.0783126 loss) +I0616 17:48:02.339387 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0791043 (* 1 = 0.0791043 loss) +I0616 17:48:02.339391 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00926812 (* 1 = 0.00926812 loss) +I0616 17:48:02.339395 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00887822 (* 1 = 0.00887822 loss) +I0616 17:48:02.339399 9857 solver.cpp:571] Iteration 94860, lr = 0.0001 +I0616 17:48:13.822098 9857 solver.cpp:242] Iteration 94880, loss = 0.325202 +I0616 17:48:13.822140 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.106404 (* 1 = 0.106404 loss) +I0616 17:48:13.822145 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15565 (* 1 = 0.15565 loss) +I0616 17:48:13.822150 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0634467 (* 1 = 0.0634467 loss) +I0616 17:48:13.822154 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0237233 (* 1 = 0.0237233 loss) +I0616 17:48:13.822157 9857 solver.cpp:571] Iteration 94880, lr = 0.0001 +I0616 17:48:25.190810 9857 solver.cpp:242] Iteration 94900, loss = 0.82133 +I0616 17:48:25.190852 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.156818 (* 1 = 0.156818 loss) +I0616 17:48:25.190858 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146598 (* 1 = 0.146598 loss) +I0616 17:48:25.190863 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0561124 (* 1 = 0.0561124 loss) +I0616 17:48:25.190866 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.028113 (* 1 = 0.028113 loss) +I0616 17:48:25.190871 9857 solver.cpp:571] Iteration 94900, lr = 0.0001 +I0616 17:48:36.765846 9857 solver.cpp:242] Iteration 94920, loss = 0.822745 +I0616 17:48:36.765887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0749081 (* 1 = 0.0749081 loss) +I0616 17:48:36.765892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.085943 (* 1 = 0.085943 loss) +I0616 17:48:36.765897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0248561 (* 1 = 0.0248561 loss) +I0616 17:48:36.765900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0427596 (* 1 = 0.0427596 loss) +I0616 17:48:36.765904 9857 solver.cpp:571] Iteration 94920, lr = 0.0001 +I0616 17:48:48.506300 9857 solver.cpp:242] Iteration 94940, loss = 0.67194 +I0616 17:48:48.506341 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.266202 (* 1 = 0.266202 loss) +I0616 17:48:48.506346 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218736 (* 1 = 0.218736 loss) +I0616 17:48:48.506350 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.050892 (* 1 = 0.050892 loss) +I0616 17:48:48.506355 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.165567 (* 1 = 0.165567 loss) +I0616 17:48:48.506357 9857 solver.cpp:571] Iteration 94940, lr = 0.0001 +I0616 17:49:00.038573 9857 solver.cpp:242] Iteration 94960, loss = 0.434039 +I0616 17:49:00.038614 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.21022 (* 1 = 0.21022 loss) +I0616 17:49:00.038619 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306391 (* 1 = 0.306391 loss) +I0616 17:49:00.038625 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0742612 (* 1 = 0.0742612 loss) +I0616 17:49:00.038627 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0531885 (* 1 = 0.0531885 loss) +I0616 17:49:00.038631 9857 solver.cpp:571] Iteration 94960, lr = 0.0001 +I0616 17:49:11.453841 9857 solver.cpp:242] Iteration 94980, loss = 0.23265 +I0616 17:49:11.453883 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.067077 (* 1 = 0.067077 loss) +I0616 17:49:11.453889 9857 solver.cpp:258] Train net output #1: loss_cls = 0.10016 (* 1 = 0.10016 loss) +I0616 17:49:11.453893 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010413 (* 1 = 0.010413 loss) +I0616 17:49:11.453897 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0658207 (* 1 = 0.0658207 loss) +I0616 17:49:11.453902 9857 solver.cpp:571] Iteration 94980, lr = 0.0001 +speed: 0.596s / iter +I0616 17:49:23.000519 9857 solver.cpp:242] Iteration 95000, loss = 0.79453 +I0616 17:49:23.000561 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.389056 (* 1 = 0.389056 loss) +I0616 17:49:23.000566 9857 solver.cpp:258] Train net output #1: loss_cls = 0.47344 (* 1 = 0.47344 loss) +I0616 17:49:23.000571 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.327824 (* 1 = 0.327824 loss) +I0616 17:49:23.000574 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.209093 (* 1 = 0.209093 loss) +I0616 17:49:23.000578 9857 solver.cpp:571] Iteration 95000, lr = 0.0001 +I0616 17:49:34.495327 9857 solver.cpp:242] Iteration 95020, loss = 0.239068 +I0616 17:49:34.495370 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0683695 (* 1 = 0.0683695 loss) +I0616 17:49:34.495376 9857 solver.cpp:258] Train net output #1: loss_cls = 0.092338 (* 1 = 0.092338 loss) +I0616 17:49:34.495380 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0103889 (* 1 = 0.0103889 loss) +I0616 17:49:34.495384 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00129201 (* 1 = 0.00129201 loss) +I0616 17:49:34.495388 9857 solver.cpp:571] Iteration 95020, lr = 0.0001 +I0616 17:49:45.982168 9857 solver.cpp:242] Iteration 95040, loss = 0.679011 +I0616 17:49:45.982210 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309449 (* 1 = 0.309449 loss) +I0616 17:49:45.982216 9857 solver.cpp:258] Train net output #1: loss_cls = 0.285539 (* 1 = 0.285539 loss) +I0616 17:49:45.982220 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0976746 (* 1 = 0.0976746 loss) +I0616 17:49:45.982224 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.105069 (* 1 = 0.105069 loss) +I0616 17:49:45.982228 9857 solver.cpp:571] Iteration 95040, lr = 0.0001 +I0616 17:49:57.311399 9857 solver.cpp:242] Iteration 95060, loss = 0.353809 +I0616 17:49:57.311440 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.190082 (* 1 = 0.190082 loss) +I0616 17:49:57.311445 9857 solver.cpp:258] Train net output #1: loss_cls = 0.14418 (* 1 = 0.14418 loss) +I0616 17:49:57.311450 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00863997 (* 1 = 0.00863997 loss) +I0616 17:49:57.311453 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00847764 (* 1 = 0.00847764 loss) +I0616 17:49:57.311457 9857 solver.cpp:571] Iteration 95060, lr = 0.0001 +I0616 17:50:08.681025 9857 solver.cpp:242] Iteration 95080, loss = 0.455354 +I0616 17:50:08.681066 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115047 (* 1 = 0.115047 loss) +I0616 17:50:08.681071 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160998 (* 1 = 0.160998 loss) +I0616 17:50:08.681076 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00140481 (* 1 = 0.00140481 loss) +I0616 17:50:08.681079 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0250286 (* 1 = 0.0250286 loss) +I0616 17:50:08.681082 9857 solver.cpp:571] Iteration 95080, lr = 0.0001 +I0616 17:50:20.390403 9857 solver.cpp:242] Iteration 95100, loss = 0.491142 +I0616 17:50:20.390444 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298299 (* 1 = 0.298299 loss) +I0616 17:50:20.390449 9857 solver.cpp:258] Train net output #1: loss_cls = 0.401123 (* 1 = 0.401123 loss) +I0616 17:50:20.390452 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0894265 (* 1 = 0.0894265 loss) +I0616 17:50:20.390456 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0334016 (* 1 = 0.0334016 loss) +I0616 17:50:20.390460 9857 solver.cpp:571] Iteration 95100, lr = 0.0001 +I0616 17:50:32.272203 9857 solver.cpp:242] Iteration 95120, loss = 0.207643 +I0616 17:50:32.272240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0846793 (* 1 = 0.0846793 loss) +I0616 17:50:32.272246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150167 (* 1 = 0.150167 loss) +I0616 17:50:32.272251 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010327 (* 1 = 0.010327 loss) +I0616 17:50:32.272254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0100802 (* 1 = 0.0100802 loss) +I0616 17:50:32.272258 9857 solver.cpp:571] Iteration 95120, lr = 0.0001 +I0616 17:50:43.763341 9857 solver.cpp:242] Iteration 95140, loss = 0.4348 +I0616 17:50:43.763384 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.307933 (* 1 = 0.307933 loss) +I0616 17:50:43.763391 9857 solver.cpp:258] Train net output #1: loss_cls = 0.288852 (* 1 = 0.288852 loss) +I0616 17:50:43.763394 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0645597 (* 1 = 0.0645597 loss) +I0616 17:50:43.763397 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308202 (* 1 = 0.0308202 loss) +I0616 17:50:43.763401 9857 solver.cpp:571] Iteration 95140, lr = 0.0001 +I0616 17:50:55.055411 9857 solver.cpp:242] Iteration 95160, loss = 0.408607 +I0616 17:50:55.055454 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0653764 (* 1 = 0.0653764 loss) +I0616 17:50:55.055459 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12399 (* 1 = 0.12399 loss) +I0616 17:50:55.055464 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0805861 (* 1 = 0.0805861 loss) +I0616 17:50:55.055467 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0278236 (* 1 = 0.0278236 loss) +I0616 17:50:55.055470 9857 solver.cpp:571] Iteration 95160, lr = 0.0001 +I0616 17:51:06.541281 9857 solver.cpp:242] Iteration 95180, loss = 0.379046 +I0616 17:51:06.541323 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164594 (* 1 = 0.164594 loss) +I0616 17:51:06.541328 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179508 (* 1 = 0.179508 loss) +I0616 17:51:06.541333 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00735912 (* 1 = 0.00735912 loss) +I0616 17:51:06.541337 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0159335 (* 1 = 0.0159335 loss) +I0616 17:51:06.541340 9857 solver.cpp:571] Iteration 95180, lr = 0.0001 +speed: 0.596s / iter +I0616 17:51:18.213768 9857 solver.cpp:242] Iteration 95200, loss = 0.225119 +I0616 17:51:18.213809 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0548941 (* 1 = 0.0548941 loss) +I0616 17:51:18.213814 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0476791 (* 1 = 0.0476791 loss) +I0616 17:51:18.213817 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00803263 (* 1 = 0.00803263 loss) +I0616 17:51:18.213821 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00280585 (* 1 = 0.00280585 loss) +I0616 17:51:18.213825 9857 solver.cpp:571] Iteration 95200, lr = 0.0001 +I0616 17:51:29.824723 9857 solver.cpp:242] Iteration 95220, loss = 0.176968 +I0616 17:51:29.824764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0944984 (* 1 = 0.0944984 loss) +I0616 17:51:29.824770 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0721762 (* 1 = 0.0721762 loss) +I0616 17:51:29.824774 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0145283 (* 1 = 0.0145283 loss) +I0616 17:51:29.824777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00294144 (* 1 = 0.00294144 loss) +I0616 17:51:29.824781 9857 solver.cpp:571] Iteration 95220, lr = 0.0001 +I0616 17:51:41.241065 9857 solver.cpp:242] Iteration 95240, loss = 0.395407 +I0616 17:51:41.241108 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115221 (* 1 = 0.115221 loss) +I0616 17:51:41.241114 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0811102 (* 1 = 0.0811102 loss) +I0616 17:51:41.241118 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0114001 (* 1 = 0.0114001 loss) +I0616 17:51:41.241122 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00817623 (* 1 = 0.00817623 loss) +I0616 17:51:41.241140 9857 solver.cpp:571] Iteration 95240, lr = 0.0001 +I0616 17:51:52.799939 9857 solver.cpp:242] Iteration 95260, loss = 0.771577 +I0616 17:51:52.799980 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.293612 (* 1 = 0.293612 loss) +I0616 17:51:52.799986 9857 solver.cpp:258] Train net output #1: loss_cls = 0.393197 (* 1 = 0.393197 loss) +I0616 17:51:52.799990 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.123086 (* 1 = 0.123086 loss) +I0616 17:51:52.799994 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.114616 (* 1 = 0.114616 loss) +I0616 17:51:52.799998 9857 solver.cpp:571] Iteration 95260, lr = 0.0001 +I0616 17:52:03.881985 9857 solver.cpp:242] Iteration 95280, loss = 0.414341 +I0616 17:52:03.882027 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212708 (* 1 = 0.212708 loss) +I0616 17:52:03.882033 9857 solver.cpp:258] Train net output #1: loss_cls = 0.32882 (* 1 = 0.32882 loss) +I0616 17:52:03.882037 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0664126 (* 1 = 0.0664126 loss) +I0616 17:52:03.882040 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319911 (* 1 = 0.0319911 loss) +I0616 17:52:03.882045 9857 solver.cpp:571] Iteration 95280, lr = 0.0001 +I0616 17:52:15.514605 9857 solver.cpp:242] Iteration 95300, loss = 0.261264 +I0616 17:52:15.514648 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0733749 (* 1 = 0.0733749 loss) +I0616 17:52:15.514653 9857 solver.cpp:258] Train net output #1: loss_cls = 0.160901 (* 1 = 0.160901 loss) +I0616 17:52:15.514658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0102441 (* 1 = 0.0102441 loss) +I0616 17:52:15.514662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0243412 (* 1 = 0.0243412 loss) +I0616 17:52:15.514667 9857 solver.cpp:571] Iteration 95300, lr = 0.0001 +I0616 17:52:27.005928 9857 solver.cpp:242] Iteration 95320, loss = 0.666926 +I0616 17:52:27.005971 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0499224 (* 1 = 0.0499224 loss) +I0616 17:52:27.005976 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115934 (* 1 = 0.115934 loss) +I0616 17:52:27.005981 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0433486 (* 1 = 0.0433486 loss) +I0616 17:52:27.005985 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00243547 (* 1 = 0.00243547 loss) +I0616 17:52:27.005988 9857 solver.cpp:571] Iteration 95320, lr = 0.0001 +I0616 17:52:38.254987 9857 solver.cpp:242] Iteration 95340, loss = 0.750808 +I0616 17:52:38.255029 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237327 (* 1 = 0.237327 loss) +I0616 17:52:38.255035 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26838 (* 1 = 0.26838 loss) +I0616 17:52:38.255039 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.19563 (* 1 = 0.19563 loss) +I0616 17:52:38.255043 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.470646 (* 1 = 0.470646 loss) +I0616 17:52:38.255048 9857 solver.cpp:571] Iteration 95340, lr = 0.0001 +I0616 17:52:49.818845 9857 solver.cpp:242] Iteration 95360, loss = 0.217492 +I0616 17:52:49.818886 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0777839 (* 1 = 0.0777839 loss) +I0616 17:52:49.818892 9857 solver.cpp:258] Train net output #1: loss_cls = 0.121091 (* 1 = 0.121091 loss) +I0616 17:52:49.818895 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0260975 (* 1 = 0.0260975 loss) +I0616 17:52:49.818899 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0183207 (* 1 = 0.0183207 loss) +I0616 17:52:49.818902 9857 solver.cpp:571] Iteration 95360, lr = 0.0001 +I0616 17:53:01.545089 9857 solver.cpp:242] Iteration 95380, loss = 0.380218 +I0616 17:53:01.545131 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.064664 (* 1 = 0.064664 loss) +I0616 17:53:01.545140 9857 solver.cpp:258] Train net output #1: loss_cls = 0.301802 (* 1 = 0.301802 loss) +I0616 17:53:01.545145 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0905134 (* 1 = 0.0905134 loss) +I0616 17:53:01.545150 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00990709 (* 1 = 0.00990709 loss) +I0616 17:53:01.545156 9857 solver.cpp:571] Iteration 95380, lr = 0.0001 +speed: 0.596s / iter +I0616 17:53:13.141270 9857 solver.cpp:242] Iteration 95400, loss = 0.399949 +I0616 17:53:13.141312 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139154 (* 1 = 0.139154 loss) +I0616 17:53:13.141317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.249179 (* 1 = 0.249179 loss) +I0616 17:53:13.141322 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0375733 (* 1 = 0.0375733 loss) +I0616 17:53:13.141325 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0186013 (* 1 = 0.0186013 loss) +I0616 17:53:13.141330 9857 solver.cpp:571] Iteration 95400, lr = 0.0001 +I0616 17:53:24.814220 9857 solver.cpp:242] Iteration 95420, loss = 0.39147 +I0616 17:53:24.814259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.277949 (* 1 = 0.277949 loss) +I0616 17:53:24.814265 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175863 (* 1 = 0.175863 loss) +I0616 17:53:24.814268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0327657 (* 1 = 0.0327657 loss) +I0616 17:53:24.814271 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103088 (* 1 = 0.0103088 loss) +I0616 17:53:24.814275 9857 solver.cpp:571] Iteration 95420, lr = 0.0001 +I0616 17:53:36.318027 9857 solver.cpp:242] Iteration 95440, loss = 0.310519 +I0616 17:53:36.318069 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0660238 (* 1 = 0.0660238 loss) +I0616 17:53:36.318074 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112841 (* 1 = 0.112841 loss) +I0616 17:53:36.318078 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.018521 (* 1 = 0.018521 loss) +I0616 17:53:36.318083 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00198167 (* 1 = 0.00198167 loss) +I0616 17:53:36.318086 9857 solver.cpp:571] Iteration 95440, lr = 0.0001 +I0616 17:53:47.898804 9857 solver.cpp:242] Iteration 95460, loss = 0.648539 +I0616 17:53:47.898844 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.260947 (* 1 = 0.260947 loss) +I0616 17:53:47.898849 9857 solver.cpp:258] Train net output #1: loss_cls = 0.343841 (* 1 = 0.343841 loss) +I0616 17:53:47.898854 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.242551 (* 1 = 0.242551 loss) +I0616 17:53:47.898856 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.130743 (* 1 = 0.130743 loss) +I0616 17:53:47.898860 9857 solver.cpp:571] Iteration 95460, lr = 0.0001 +I0616 17:53:59.481071 9857 solver.cpp:242] Iteration 95480, loss = 0.348281 +I0616 17:53:59.481114 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0853238 (* 1 = 0.0853238 loss) +I0616 17:53:59.481119 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0667946 (* 1 = 0.0667946 loss) +I0616 17:53:59.481123 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0345663 (* 1 = 0.0345663 loss) +I0616 17:53:59.481128 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00684755 (* 1 = 0.00684755 loss) +I0616 17:53:59.481133 9857 solver.cpp:571] Iteration 95480, lr = 0.0001 +I0616 17:54:11.050853 9857 solver.cpp:242] Iteration 95500, loss = 0.570867 +I0616 17:54:11.050894 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0567965 (* 1 = 0.0567965 loss) +I0616 17:54:11.050899 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0639314 (* 1 = 0.0639314 loss) +I0616 17:54:11.050904 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0085674 (* 1 = 0.0085674 loss) +I0616 17:54:11.050907 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0010169 (* 1 = 0.0010169 loss) +I0616 17:54:11.050911 9857 solver.cpp:571] Iteration 95500, lr = 0.0001 +I0616 17:54:22.723881 9857 solver.cpp:242] Iteration 95520, loss = 0.405104 +I0616 17:54:22.723922 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0537732 (* 1 = 0.0537732 loss) +I0616 17:54:22.723928 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0611795 (* 1 = 0.0611795 loss) +I0616 17:54:22.723933 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0342006 (* 1 = 0.0342006 loss) +I0616 17:54:22.723937 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00654735 (* 1 = 0.00654735 loss) +I0616 17:54:22.723940 9857 solver.cpp:571] Iteration 95520, lr = 0.0001 +I0616 17:54:34.128140 9857 solver.cpp:242] Iteration 95540, loss = 0.401619 +I0616 17:54:34.128182 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.199654 (* 1 = 0.199654 loss) +I0616 17:54:34.128188 9857 solver.cpp:258] Train net output #1: loss_cls = 0.256822 (* 1 = 0.256822 loss) +I0616 17:54:34.128192 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0449378 (* 1 = 0.0449378 loss) +I0616 17:54:34.128196 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0101731 (* 1 = 0.0101731 loss) +I0616 17:54:34.128201 9857 solver.cpp:571] Iteration 95540, lr = 0.0001 +I0616 17:54:45.689272 9857 solver.cpp:242] Iteration 95560, loss = 0.362515 +I0616 17:54:45.689314 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0985121 (* 1 = 0.0985121 loss) +I0616 17:54:45.689321 9857 solver.cpp:258] Train net output #1: loss_cls = 0.095831 (* 1 = 0.095831 loss) +I0616 17:54:45.689324 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00622417 (* 1 = 0.00622417 loss) +I0616 17:54:45.689328 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00313383 (* 1 = 0.00313383 loss) +I0616 17:54:45.689332 9857 solver.cpp:571] Iteration 95560, lr = 0.0001 +I0616 17:54:57.245165 9857 solver.cpp:242] Iteration 95580, loss = 0.602204 +I0616 17:54:57.245206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.232964 (* 1 = 0.232964 loss) +I0616 17:54:57.245211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.329201 (* 1 = 0.329201 loss) +I0616 17:54:57.245215 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12358 (* 1 = 0.12358 loss) +I0616 17:54:57.245219 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.156109 (* 1 = 0.156109 loss) +I0616 17:54:57.245223 9857 solver.cpp:571] Iteration 95580, lr = 0.0001 +speed: 0.596s / iter +I0616 17:55:08.524091 9857 solver.cpp:242] Iteration 95600, loss = 0.338984 +I0616 17:55:08.524132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0891304 (* 1 = 0.0891304 loss) +I0616 17:55:08.524152 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126732 (* 1 = 0.126732 loss) +I0616 17:55:08.524157 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0271497 (* 1 = 0.0271497 loss) +I0616 17:55:08.524160 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0341547 (* 1 = 0.0341547 loss) +I0616 17:55:08.524164 9857 solver.cpp:571] Iteration 95600, lr = 0.0001 +I0616 17:55:19.879107 9857 solver.cpp:242] Iteration 95620, loss = 0.414652 +I0616 17:55:19.879149 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134689 (* 1 = 0.134689 loss) +I0616 17:55:19.879155 9857 solver.cpp:258] Train net output #1: loss_cls = 0.327911 (* 1 = 0.327911 loss) +I0616 17:55:19.879173 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.029436 (* 1 = 0.029436 loss) +I0616 17:55:19.879178 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0378887 (* 1 = 0.0378887 loss) +I0616 17:55:19.879181 9857 solver.cpp:571] Iteration 95620, lr = 0.0001 +I0616 17:55:31.262198 9857 solver.cpp:242] Iteration 95640, loss = 0.435817 +I0616 17:55:31.262240 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.24005 (* 1 = 0.24005 loss) +I0616 17:55:31.262246 9857 solver.cpp:258] Train net output #1: loss_cls = 0.203822 (* 1 = 0.203822 loss) +I0616 17:55:31.262250 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.116113 (* 1 = 0.116113 loss) +I0616 17:55:31.262254 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0292219 (* 1 = 0.0292219 loss) +I0616 17:55:31.262258 9857 solver.cpp:571] Iteration 95640, lr = 0.0001 +I0616 17:55:43.094182 9857 solver.cpp:242] Iteration 95660, loss = 0.589981 +I0616 17:55:43.094223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.347747 (* 1 = 0.347747 loss) +I0616 17:55:43.094228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.404412 (* 1 = 0.404412 loss) +I0616 17:55:43.094233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209834 (* 1 = 0.0209834 loss) +I0616 17:55:43.094236 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0128922 (* 1 = 0.0128922 loss) +I0616 17:55:43.094240 9857 solver.cpp:571] Iteration 95660, lr = 0.0001 +I0616 17:55:54.597496 9857 solver.cpp:242] Iteration 95680, loss = 0.29891 +I0616 17:55:54.597538 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146932 (* 1 = 0.146932 loss) +I0616 17:55:54.597544 9857 solver.cpp:258] Train net output #1: loss_cls = 0.213003 (* 1 = 0.213003 loss) +I0616 17:55:54.597548 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0360334 (* 1 = 0.0360334 loss) +I0616 17:55:54.597553 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0233814 (* 1 = 0.0233814 loss) +I0616 17:55:54.597555 9857 solver.cpp:571] Iteration 95680, lr = 0.0001 +I0616 17:56:05.903506 9857 solver.cpp:242] Iteration 95700, loss = 0.404521 +I0616 17:56:05.903548 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0395403 (* 1 = 0.0395403 loss) +I0616 17:56:05.903554 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15545 (* 1 = 0.15545 loss) +I0616 17:56:05.903558 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0524728 (* 1 = 0.0524728 loss) +I0616 17:56:05.903561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0233406 (* 1 = 0.0233406 loss) +I0616 17:56:05.903565 9857 solver.cpp:571] Iteration 95700, lr = 0.0001 +I0616 17:56:17.545734 9857 solver.cpp:242] Iteration 95720, loss = 0.564281 +I0616 17:56:17.545778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192656 (* 1 = 0.192656 loss) +I0616 17:56:17.545783 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104272 (* 1 = 0.104272 loss) +I0616 17:56:17.545786 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0782575 (* 1 = 0.0782575 loss) +I0616 17:56:17.545790 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252135 (* 1 = 0.0252135 loss) +I0616 17:56:17.545794 9857 solver.cpp:571] Iteration 95720, lr = 0.0001 +I0616 17:56:28.938994 9857 solver.cpp:242] Iteration 95740, loss = 0.474642 +I0616 17:56:28.939038 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229674 (* 1 = 0.229674 loss) +I0616 17:56:28.939043 9857 solver.cpp:258] Train net output #1: loss_cls = 0.230663 (* 1 = 0.230663 loss) +I0616 17:56:28.939046 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.013225 (* 1 = 0.013225 loss) +I0616 17:56:28.939050 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00289204 (* 1 = 0.00289204 loss) +I0616 17:56:28.939054 9857 solver.cpp:571] Iteration 95740, lr = 0.0001 +I0616 17:56:40.577941 9857 solver.cpp:242] Iteration 95760, loss = 1.14929 +I0616 17:56:40.577983 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.342607 (* 1 = 0.342607 loss) +I0616 17:56:40.577988 9857 solver.cpp:258] Train net output #1: loss_cls = 0.469763 (* 1 = 0.469763 loss) +I0616 17:56:40.577992 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.321618 (* 1 = 0.321618 loss) +I0616 17:56:40.577996 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.499403 (* 1 = 0.499403 loss) +I0616 17:56:40.577999 9857 solver.cpp:571] Iteration 95760, lr = 0.0001 +I0616 17:56:52.201676 9857 solver.cpp:242] Iteration 95780, loss = 0.319624 +I0616 17:56:52.201717 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0845259 (* 1 = 0.0845259 loss) +I0616 17:56:52.201724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0878763 (* 1 = 0.0878763 loss) +I0616 17:56:52.201727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0105132 (* 1 = 0.0105132 loss) +I0616 17:56:52.201731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00177392 (* 1 = 0.00177392 loss) +I0616 17:56:52.201735 9857 solver.cpp:571] Iteration 95780, lr = 0.0001 +speed: 0.596s / iter +I0616 17:57:03.942055 9857 solver.cpp:242] Iteration 95800, loss = 0.539114 +I0616 17:57:03.942097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.393275 (* 1 = 0.393275 loss) +I0616 17:57:03.942103 9857 solver.cpp:258] Train net output #1: loss_cls = 0.243925 (* 1 = 0.243925 loss) +I0616 17:57:03.942107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0910816 (* 1 = 0.0910816 loss) +I0616 17:57:03.942111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0632013 (* 1 = 0.0632013 loss) +I0616 17:57:03.942114 9857 solver.cpp:571] Iteration 95800, lr = 0.0001 +I0616 17:57:15.556668 9857 solver.cpp:242] Iteration 95820, loss = 0.692511 +I0616 17:57:15.556710 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.188358 (* 1 = 0.188358 loss) +I0616 17:57:15.556716 9857 solver.cpp:258] Train net output #1: loss_cls = 0.244855 (* 1 = 0.244855 loss) +I0616 17:57:15.556720 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.038251 (* 1 = 0.038251 loss) +I0616 17:57:15.556725 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.065144 (* 1 = 0.065144 loss) +I0616 17:57:15.556730 9857 solver.cpp:571] Iteration 95820, lr = 0.0001 +I0616 17:57:27.105736 9857 solver.cpp:242] Iteration 95840, loss = 0.191925 +I0616 17:57:27.105778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0248417 (* 1 = 0.0248417 loss) +I0616 17:57:27.105784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0655593 (* 1 = 0.0655593 loss) +I0616 17:57:27.105788 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0079465 (* 1 = 0.0079465 loss) +I0616 17:57:27.105792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0176862 (* 1 = 0.0176862 loss) +I0616 17:57:27.105795 9857 solver.cpp:571] Iteration 95840, lr = 0.0001 +I0616 17:57:38.862246 9857 solver.cpp:242] Iteration 95860, loss = 0.592165 +I0616 17:57:38.862273 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.311924 (* 1 = 0.311924 loss) +I0616 17:57:38.862293 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22699 (* 1 = 0.22699 loss) +I0616 17:57:38.862298 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0107472 (* 1 = 0.0107472 loss) +I0616 17:57:38.862301 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0181714 (* 1 = 0.0181714 loss) +I0616 17:57:38.862305 9857 solver.cpp:571] Iteration 95860, lr = 0.0001 +I0616 17:57:50.454504 9857 solver.cpp:242] Iteration 95880, loss = 0.428313 +I0616 17:57:50.454547 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0505298 (* 1 = 0.0505298 loss) +I0616 17:57:50.454553 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0601001 (* 1 = 0.0601001 loss) +I0616 17:57:50.454557 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.011427 (* 1 = 0.011427 loss) +I0616 17:57:50.454561 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.013025 (* 1 = 0.013025 loss) +I0616 17:57:50.454565 9857 solver.cpp:571] Iteration 95880, lr = 0.0001 +I0616 17:58:02.032644 9857 solver.cpp:242] Iteration 95900, loss = 0.344394 +I0616 17:58:02.032685 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.212107 (* 1 = 0.212107 loss) +I0616 17:58:02.032691 9857 solver.cpp:258] Train net output #1: loss_cls = 0.202212 (* 1 = 0.202212 loss) +I0616 17:58:02.032696 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0368549 (* 1 = 0.0368549 loss) +I0616 17:58:02.032699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114926 (* 1 = 0.0114926 loss) +I0616 17:58:02.032702 9857 solver.cpp:571] Iteration 95900, lr = 0.0001 +I0616 17:58:13.246315 9857 solver.cpp:242] Iteration 95920, loss = 0.298468 +I0616 17:58:13.246356 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0458433 (* 1 = 0.0458433 loss) +I0616 17:58:13.246361 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0915605 (* 1 = 0.0915605 loss) +I0616 17:58:13.246366 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0122879 (* 1 = 0.0122879 loss) +I0616 17:58:13.246368 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0262724 (* 1 = 0.0262724 loss) +I0616 17:58:13.246372 9857 solver.cpp:571] Iteration 95920, lr = 0.0001 +I0616 17:58:24.730176 9857 solver.cpp:242] Iteration 95940, loss = 0.505947 +I0616 17:58:24.730217 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.318921 (* 1 = 0.318921 loss) +I0616 17:58:24.730223 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172212 (* 1 = 0.172212 loss) +I0616 17:58:24.730227 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0377352 (* 1 = 0.0377352 loss) +I0616 17:58:24.730232 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0216102 (* 1 = 0.0216102 loss) +I0616 17:58:24.730235 9857 solver.cpp:571] Iteration 95940, lr = 0.0001 +I0616 17:58:35.926589 9857 solver.cpp:242] Iteration 95960, loss = 0.327996 +I0616 17:58:35.926630 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.163114 (* 1 = 0.163114 loss) +I0616 17:58:35.926636 9857 solver.cpp:258] Train net output #1: loss_cls = 0.166977 (* 1 = 0.166977 loss) +I0616 17:58:35.926640 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.066477 (* 1 = 0.066477 loss) +I0616 17:58:35.926645 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0174981 (* 1 = 0.0174981 loss) +I0616 17:58:35.926647 9857 solver.cpp:571] Iteration 95960, lr = 0.0001 +I0616 17:58:47.442754 9857 solver.cpp:242] Iteration 95980, loss = 0.762873 +I0616 17:58:47.442798 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140981 (* 1 = 0.140981 loss) +I0616 17:58:47.442803 9857 solver.cpp:258] Train net output #1: loss_cls = 0.387606 (* 1 = 0.387606 loss) +I0616 17:58:47.442808 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0962789 (* 1 = 0.0962789 loss) +I0616 17:58:47.442811 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0413147 (* 1 = 0.0413147 loss) +I0616 17:58:47.442816 9857 solver.cpp:571] Iteration 95980, lr = 0.0001 +speed: 0.596s / iter +I0616 17:58:58.979437 9857 solver.cpp:242] Iteration 96000, loss = 1.25632 +I0616 17:58:58.979477 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.317886 (* 1 = 0.317886 loss) +I0616 17:58:58.979483 9857 solver.cpp:258] Train net output #1: loss_cls = 0.308378 (* 1 = 0.308378 loss) +I0616 17:58:58.979486 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.221361 (* 1 = 0.221361 loss) +I0616 17:58:58.979490 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110065 (* 1 = 0.110065 loss) +I0616 17:58:58.979495 9857 solver.cpp:571] Iteration 96000, lr = 0.0001 +I0616 17:59:10.594219 9857 solver.cpp:242] Iteration 96020, loss = 0.325881 +I0616 17:59:10.594260 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137419 (* 1 = 0.137419 loss) +I0616 17:59:10.594266 9857 solver.cpp:258] Train net output #1: loss_cls = 0.13298 (* 1 = 0.13298 loss) +I0616 17:59:10.594270 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0416807 (* 1 = 0.0416807 loss) +I0616 17:59:10.594274 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0151163 (* 1 = 0.0151163 loss) +I0616 17:59:10.594277 9857 solver.cpp:571] Iteration 96020, lr = 0.0001 +I0616 17:59:21.921844 9857 solver.cpp:242] Iteration 96040, loss = 0.402757 +I0616 17:59:21.921887 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180384 (* 1 = 0.180384 loss) +I0616 17:59:21.921893 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108283 (* 1 = 0.108283 loss) +I0616 17:59:21.921897 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0508973 (* 1 = 0.0508973 loss) +I0616 17:59:21.921900 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.15633 (* 1 = 0.15633 loss) +I0616 17:59:21.921905 9857 solver.cpp:571] Iteration 96040, lr = 0.0001 +I0616 17:59:33.260457 9857 solver.cpp:242] Iteration 96060, loss = 0.300119 +I0616 17:59:33.260499 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0656259 (* 1 = 0.0656259 loss) +I0616 17:59:33.260504 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100755 (* 1 = 0.100755 loss) +I0616 17:59:33.260507 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0426108 (* 1 = 0.0426108 loss) +I0616 17:59:33.260511 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00359295 (* 1 = 0.00359295 loss) +I0616 17:59:33.260514 9857 solver.cpp:571] Iteration 96060, lr = 0.0001 +I0616 17:59:44.782676 9857 solver.cpp:242] Iteration 96080, loss = 0.81162 +I0616 17:59:44.782719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145424 (* 1 = 0.145424 loss) +I0616 17:59:44.782724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.205556 (* 1 = 0.205556 loss) +I0616 17:59:44.782728 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00511533 (* 1 = 0.00511533 loss) +I0616 17:59:44.782732 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0720718 (* 1 = 0.0720718 loss) +I0616 17:59:44.782735 9857 solver.cpp:571] Iteration 96080, lr = 0.0001 +I0616 17:59:56.358340 9857 solver.cpp:242] Iteration 96100, loss = 0.305037 +I0616 17:59:56.358381 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114416 (* 1 = 0.114416 loss) +I0616 17:59:56.358386 9857 solver.cpp:258] Train net output #1: loss_cls = 0.189655 (* 1 = 0.189655 loss) +I0616 17:59:56.358392 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0178661 (* 1 = 0.0178661 loss) +I0616 17:59:56.358394 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0283452 (* 1 = 0.0283452 loss) +I0616 17:59:56.358398 9857 solver.cpp:571] Iteration 96100, lr = 0.0001 +I0616 18:00:07.941587 9857 solver.cpp:242] Iteration 96120, loss = 0.75056 +I0616 18:00:07.941629 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.322958 (* 1 = 0.322958 loss) +I0616 18:00:07.941634 9857 solver.cpp:258] Train net output #1: loss_cls = 0.443319 (* 1 = 0.443319 loss) +I0616 18:00:07.941639 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.194968 (* 1 = 0.194968 loss) +I0616 18:00:07.941642 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.334858 (* 1 = 0.334858 loss) +I0616 18:00:07.941647 9857 solver.cpp:571] Iteration 96120, lr = 0.0001 +I0616 18:00:19.434931 9857 solver.cpp:242] Iteration 96140, loss = 0.460105 +I0616 18:00:19.434973 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.137196 (* 1 = 0.137196 loss) +I0616 18:00:19.434978 9857 solver.cpp:258] Train net output #1: loss_cls = 0.177261 (* 1 = 0.177261 loss) +I0616 18:00:19.434983 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0471652 (* 1 = 0.0471652 loss) +I0616 18:00:19.434986 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0289799 (* 1 = 0.0289799 loss) +I0616 18:00:19.434989 9857 solver.cpp:571] Iteration 96140, lr = 0.0001 +I0616 18:00:31.010943 9857 solver.cpp:242] Iteration 96160, loss = 0.246613 +I0616 18:00:31.010985 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0870126 (* 1 = 0.0870126 loss) +I0616 18:00:31.010992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112364 (* 1 = 0.112364 loss) +I0616 18:00:31.010995 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0463174 (* 1 = 0.0463174 loss) +I0616 18:00:31.010999 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00914538 (* 1 = 0.00914538 loss) +I0616 18:00:31.011003 9857 solver.cpp:571] Iteration 96160, lr = 0.0001 +I0616 18:00:42.348799 9857 solver.cpp:242] Iteration 96180, loss = 0.286261 +I0616 18:00:42.348841 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140667 (* 1 = 0.140667 loss) +I0616 18:00:42.348847 9857 solver.cpp:258] Train net output #1: loss_cls = 0.105336 (* 1 = 0.105336 loss) +I0616 18:00:42.348851 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00446564 (* 1 = 0.00446564 loss) +I0616 18:00:42.348855 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00432298 (* 1 = 0.00432298 loss) +I0616 18:00:42.348860 9857 solver.cpp:571] Iteration 96180, lr = 0.0001 +speed: 0.595s / iter +I0616 18:00:53.716655 9857 solver.cpp:242] Iteration 96200, loss = 0.339347 +I0616 18:00:53.716696 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.136335 (* 1 = 0.136335 loss) +I0616 18:00:53.716702 9857 solver.cpp:258] Train net output #1: loss_cls = 0.140732 (* 1 = 0.140732 loss) +I0616 18:00:53.716706 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0252757 (* 1 = 0.0252757 loss) +I0616 18:00:53.716709 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.195293 (* 1 = 0.195293 loss) +I0616 18:00:53.716713 9857 solver.cpp:571] Iteration 96200, lr = 0.0001 +I0616 18:01:05.343652 9857 solver.cpp:242] Iteration 96220, loss = 0.83628 +I0616 18:01:05.343693 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.174937 (* 1 = 0.174937 loss) +I0616 18:01:05.343698 9857 solver.cpp:258] Train net output #1: loss_cls = 0.216686 (* 1 = 0.216686 loss) +I0616 18:01:05.343703 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0799173 (* 1 = 0.0799173 loss) +I0616 18:01:05.343706 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0189123 (* 1 = 0.0189123 loss) +I0616 18:01:05.343709 9857 solver.cpp:571] Iteration 96220, lr = 0.0001 +I0616 18:01:16.903162 9857 solver.cpp:242] Iteration 96240, loss = 0.990097 +I0616 18:01:16.903203 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.402902 (* 1 = 0.402902 loss) +I0616 18:01:16.903209 9857 solver.cpp:258] Train net output #1: loss_cls = 0.530426 (* 1 = 0.530426 loss) +I0616 18:01:16.903213 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.46976 (* 1 = 0.46976 loss) +I0616 18:01:16.903218 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0439679 (* 1 = 0.0439679 loss) +I0616 18:01:16.903220 9857 solver.cpp:571] Iteration 96240, lr = 0.0001 +I0616 18:01:28.620182 9857 solver.cpp:242] Iteration 96260, loss = 0.468752 +I0616 18:01:28.620223 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.103381 (* 1 = 0.103381 loss) +I0616 18:01:28.620229 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120759 (* 1 = 0.120759 loss) +I0616 18:01:28.620234 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0199912 (* 1 = 0.0199912 loss) +I0616 18:01:28.620237 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00562192 (* 1 = 0.00562192 loss) +I0616 18:01:28.620240 9857 solver.cpp:571] Iteration 96260, lr = 0.0001 +I0616 18:01:40.209328 9857 solver.cpp:242] Iteration 96280, loss = 0.238869 +I0616 18:01:40.209372 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.104155 (* 1 = 0.104155 loss) +I0616 18:01:40.209377 9857 solver.cpp:258] Train net output #1: loss_cls = 0.100799 (* 1 = 0.100799 loss) +I0616 18:01:40.209380 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0104243 (* 1 = 0.0104243 loss) +I0616 18:01:40.209384 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00769059 (* 1 = 0.00769059 loss) +I0616 18:01:40.209388 9857 solver.cpp:571] Iteration 96280, lr = 0.0001 +I0616 18:01:51.629567 9857 solver.cpp:242] Iteration 96300, loss = 0.338787 +I0616 18:01:51.629608 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117895 (* 1 = 0.117895 loss) +I0616 18:01:51.629614 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125973 (* 1 = 0.125973 loss) +I0616 18:01:51.629618 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00434913 (* 1 = 0.00434913 loss) +I0616 18:01:51.629622 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00575062 (* 1 = 0.00575062 loss) +I0616 18:01:51.629626 9857 solver.cpp:571] Iteration 96300, lr = 0.0001 +I0616 18:02:03.357944 9857 solver.cpp:242] Iteration 96320, loss = 0.252735 +I0616 18:02:03.357986 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0580175 (* 1 = 0.0580175 loss) +I0616 18:02:03.357992 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0731464 (* 1 = 0.0731464 loss) +I0616 18:02:03.357996 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00303071 (* 1 = 0.00303071 loss) +I0616 18:02:03.358000 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00711376 (* 1 = 0.00711376 loss) +I0616 18:02:03.358003 9857 solver.cpp:571] Iteration 96320, lr = 0.0001 +I0616 18:02:14.879222 9857 solver.cpp:242] Iteration 96340, loss = 0.380975 +I0616 18:02:14.879264 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.124967 (* 1 = 0.124967 loss) +I0616 18:02:14.879271 9857 solver.cpp:258] Train net output #1: loss_cls = 0.186703 (* 1 = 0.186703 loss) +I0616 18:02:14.879274 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0415752 (* 1 = 0.0415752 loss) +I0616 18:02:14.879278 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.019979 (* 1 = 0.019979 loss) +I0616 18:02:14.879281 9857 solver.cpp:571] Iteration 96340, lr = 0.0001 +I0616 18:02:26.399296 9857 solver.cpp:242] Iteration 96360, loss = 0.503635 +I0616 18:02:26.399338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0980313 (* 1 = 0.0980313 loss) +I0616 18:02:26.399343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.113973 (* 1 = 0.113973 loss) +I0616 18:02:26.399348 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0266214 (* 1 = 0.0266214 loss) +I0616 18:02:26.399350 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0018714 (* 1 = 0.0018714 loss) +I0616 18:02:26.399354 9857 solver.cpp:571] Iteration 96360, lr = 0.0001 +I0616 18:02:37.892482 9857 solver.cpp:242] Iteration 96380, loss = 0.423033 +I0616 18:02:37.892525 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.130937 (* 1 = 0.130937 loss) +I0616 18:02:37.892531 9857 solver.cpp:258] Train net output #1: loss_cls = 0.119329 (* 1 = 0.119329 loss) +I0616 18:02:37.892535 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0133881 (* 1 = 0.0133881 loss) +I0616 18:02:37.892539 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0163398 (* 1 = 0.0163398 loss) +I0616 18:02:37.892544 9857 solver.cpp:571] Iteration 96380, lr = 0.0001 +speed: 0.595s / iter +I0616 18:02:49.505014 9857 solver.cpp:242] Iteration 96400, loss = 0.539901 +I0616 18:02:49.505054 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.298247 (* 1 = 0.298247 loss) +I0616 18:02:49.505060 9857 solver.cpp:258] Train net output #1: loss_cls = 0.368485 (* 1 = 0.368485 loss) +I0616 18:02:49.505064 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162651 (* 1 = 0.162651 loss) +I0616 18:02:49.505067 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0340293 (* 1 = 0.0340293 loss) +I0616 18:02:49.505071 9857 solver.cpp:571] Iteration 96400, lr = 0.0001 +I0616 18:03:01.035017 9857 solver.cpp:242] Iteration 96420, loss = 0.264773 +I0616 18:03:01.035060 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.059875 (* 1 = 0.059875 loss) +I0616 18:03:01.035065 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0767755 (* 1 = 0.0767755 loss) +I0616 18:03:01.035070 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.03061 (* 1 = 0.03061 loss) +I0616 18:03:01.035073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0395567 (* 1 = 0.0395567 loss) +I0616 18:03:01.035078 9857 solver.cpp:571] Iteration 96420, lr = 0.0001 +I0616 18:03:12.366179 9857 solver.cpp:242] Iteration 96440, loss = 0.338395 +I0616 18:03:12.366220 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158622 (* 1 = 0.158622 loss) +I0616 18:03:12.366225 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172134 (* 1 = 0.172134 loss) +I0616 18:03:12.366230 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.036995 (* 1 = 0.036995 loss) +I0616 18:03:12.366233 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0297941 (* 1 = 0.0297941 loss) +I0616 18:03:12.366237 9857 solver.cpp:571] Iteration 96440, lr = 0.0001 +I0616 18:03:24.009280 9857 solver.cpp:242] Iteration 96460, loss = 0.17414 +I0616 18:03:24.009325 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0672202 (* 1 = 0.0672202 loss) +I0616 18:03:24.009330 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0902153 (* 1 = 0.0902153 loss) +I0616 18:03:24.009333 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.038639 (* 1 = 0.038639 loss) +I0616 18:03:24.009337 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.012387 (* 1 = 0.012387 loss) +I0616 18:03:24.009341 9857 solver.cpp:571] Iteration 96460, lr = 0.0001 +I0616 18:03:35.660738 9857 solver.cpp:242] Iteration 96480, loss = 0.208012 +I0616 18:03:35.660780 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0471858 (* 1 = 0.0471858 loss) +I0616 18:03:35.660786 9857 solver.cpp:258] Train net output #1: loss_cls = 0.155402 (* 1 = 0.155402 loss) +I0616 18:03:35.660790 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.031254 (* 1 = 0.031254 loss) +I0616 18:03:35.660794 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197675 (* 1 = 0.0197675 loss) +I0616 18:03:35.660797 9857 solver.cpp:571] Iteration 96480, lr = 0.0001 +I0616 18:03:47.118582 9857 solver.cpp:242] Iteration 96500, loss = 0.62606 +I0616 18:03:47.118624 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237885 (* 1 = 0.237885 loss) +I0616 18:03:47.118629 9857 solver.cpp:258] Train net output #1: loss_cls = 0.30566 (* 1 = 0.30566 loss) +I0616 18:03:47.118634 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.118546 (* 1 = 0.118546 loss) +I0616 18:03:47.118638 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0534944 (* 1 = 0.0534944 loss) +I0616 18:03:47.118641 9857 solver.cpp:571] Iteration 96500, lr = 0.0001 +I0616 18:03:58.615731 9857 solver.cpp:242] Iteration 96520, loss = 0.80318 +I0616 18:03:58.615772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.1998 (* 1 = 0.1998 loss) +I0616 18:03:58.615778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.175767 (* 1 = 0.175767 loss) +I0616 18:03:58.615782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.135679 (* 1 = 0.135679 loss) +I0616 18:03:58.615787 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.815412 (* 1 = 0.815412 loss) +I0616 18:03:58.615789 9857 solver.cpp:571] Iteration 96520, lr = 0.0001 +I0616 18:04:09.983096 9857 solver.cpp:242] Iteration 96540, loss = 0.39391 +I0616 18:04:09.983139 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111764 (* 1 = 0.111764 loss) +I0616 18:04:09.983144 9857 solver.cpp:258] Train net output #1: loss_cls = 0.206959 (* 1 = 0.206959 loss) +I0616 18:04:09.983147 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0242441 (* 1 = 0.0242441 loss) +I0616 18:04:09.983151 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00702671 (* 1 = 0.00702671 loss) +I0616 18:04:09.983155 9857 solver.cpp:571] Iteration 96540, lr = 0.0001 +I0616 18:04:21.539635 9857 solver.cpp:242] Iteration 96560, loss = 0.460921 +I0616 18:04:21.539676 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242419 (* 1 = 0.242419 loss) +I0616 18:04:21.539682 9857 solver.cpp:258] Train net output #1: loss_cls = 0.34838 (* 1 = 0.34838 loss) +I0616 18:04:21.539686 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.021075 (* 1 = 0.021075 loss) +I0616 18:04:21.539690 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0310751 (* 1 = 0.0310751 loss) +I0616 18:04:21.539693 9857 solver.cpp:571] Iteration 96560, lr = 0.0001 +I0616 18:04:32.957967 9857 solver.cpp:242] Iteration 96580, loss = 0.590468 +I0616 18:04:32.958008 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.360223 (* 1 = 0.360223 loss) +I0616 18:04:32.958012 9857 solver.cpp:258] Train net output #1: loss_cls = 0.340646 (* 1 = 0.340646 loss) +I0616 18:04:32.958017 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.112259 (* 1 = 0.112259 loss) +I0616 18:04:32.958021 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0508471 (* 1 = 0.0508471 loss) +I0616 18:04:32.958024 9857 solver.cpp:571] Iteration 96580, lr = 0.0001 +speed: 0.595s / iter +I0616 18:04:44.559650 9857 solver.cpp:242] Iteration 96600, loss = 0.288031 +I0616 18:04:44.559692 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0283376 (* 1 = 0.0283376 loss) +I0616 18:04:44.559698 9857 solver.cpp:258] Train net output #1: loss_cls = 0.104085 (* 1 = 0.104085 loss) +I0616 18:04:44.559702 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.014596 (* 1 = 0.014596 loss) +I0616 18:04:44.559706 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122722 (* 1 = 0.0122722 loss) +I0616 18:04:44.559710 9857 solver.cpp:571] Iteration 96600, lr = 0.0001 +I0616 18:04:56.039880 9857 solver.cpp:242] Iteration 96620, loss = 0.382609 +I0616 18:04:56.039919 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0798222 (* 1 = 0.0798222 loss) +I0616 18:04:56.039924 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0824804 (* 1 = 0.0824804 loss) +I0616 18:04:56.039928 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00761227 (* 1 = 0.00761227 loss) +I0616 18:04:56.039932 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00256416 (* 1 = 0.00256416 loss) +I0616 18:04:56.039937 9857 solver.cpp:571] Iteration 96620, lr = 0.0001 +I0616 18:05:07.460006 9857 solver.cpp:242] Iteration 96640, loss = 1.1798 +I0616 18:05:07.460049 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0577496 (* 1 = 0.0577496 loss) +I0616 18:05:07.460054 9857 solver.cpp:258] Train net output #1: loss_cls = 0.150086 (* 1 = 0.150086 loss) +I0616 18:05:07.460058 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.036215 (* 1 = 0.036215 loss) +I0616 18:05:07.460062 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00362806 (* 1 = 0.00362806 loss) +I0616 18:05:07.460065 9857 solver.cpp:571] Iteration 96640, lr = 0.0001 +I0616 18:05:19.239054 9857 solver.cpp:242] Iteration 96660, loss = 0.608656 +I0616 18:05:19.239097 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0592877 (* 1 = 0.0592877 loss) +I0616 18:05:19.239104 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0861606 (* 1 = 0.0861606 loss) +I0616 18:05:19.239107 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0172678 (* 1 = 0.0172678 loss) +I0616 18:05:19.239111 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00943917 (* 1 = 0.00943917 loss) +I0616 18:05:19.239114 9857 solver.cpp:571] Iteration 96660, lr = 0.0001 +I0616 18:05:30.863340 9857 solver.cpp:242] Iteration 96680, loss = 0.321287 +I0616 18:05:30.863382 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725905 (* 1 = 0.0725905 loss) +I0616 18:05:30.863389 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0870817 (* 1 = 0.0870817 loss) +I0616 18:05:30.863392 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0741822 (* 1 = 0.0741822 loss) +I0616 18:05:30.863396 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0147676 (* 1 = 0.0147676 loss) +I0616 18:05:30.863399 9857 solver.cpp:571] Iteration 96680, lr = 0.0001 +I0616 18:05:42.520740 9857 solver.cpp:242] Iteration 96700, loss = 0.570847 +I0616 18:05:42.520783 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.170428 (* 1 = 0.170428 loss) +I0616 18:05:42.520789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22948 (* 1 = 0.22948 loss) +I0616 18:05:42.520793 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0503533 (* 1 = 0.0503533 loss) +I0616 18:05:42.520797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0317859 (* 1 = 0.0317859 loss) +I0616 18:05:42.520800 9857 solver.cpp:571] Iteration 96700, lr = 0.0001 +I0616 18:05:53.946326 9857 solver.cpp:242] Iteration 96720, loss = 1.00587 +I0616 18:05:53.946369 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.309688 (* 1 = 0.309688 loss) +I0616 18:05:53.946374 9857 solver.cpp:258] Train net output #1: loss_cls = 0.413716 (* 1 = 0.413716 loss) +I0616 18:05:53.946378 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.246805 (* 1 = 0.246805 loss) +I0616 18:05:53.946383 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.604861 (* 1 = 0.604861 loss) +I0616 18:05:53.946388 9857 solver.cpp:571] Iteration 96720, lr = 0.0001 +I0616 18:06:05.325117 9857 solver.cpp:242] Iteration 96740, loss = 0.726037 +I0616 18:06:05.325156 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0976721 (* 1 = 0.0976721 loss) +I0616 18:06:05.325161 9857 solver.cpp:258] Train net output #1: loss_cls = 0.222464 (* 1 = 0.222464 loss) +I0616 18:06:05.325165 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0521662 (* 1 = 0.0521662 loss) +I0616 18:06:05.325170 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0204952 (* 1 = 0.0204952 loss) +I0616 18:06:05.325173 9857 solver.cpp:571] Iteration 96740, lr = 0.0001 +I0616 18:06:16.725723 9857 solver.cpp:242] Iteration 96760, loss = 0.609854 +I0616 18:06:16.725764 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184341 (* 1 = 0.184341 loss) +I0616 18:06:16.725769 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211318 (* 1 = 0.211318 loss) +I0616 18:06:16.725775 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0127147 (* 1 = 0.0127147 loss) +I0616 18:06:16.725777 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00846395 (* 1 = 0.00846395 loss) +I0616 18:06:16.725781 9857 solver.cpp:571] Iteration 96760, lr = 0.0001 +I0616 18:06:28.403678 9857 solver.cpp:242] Iteration 96780, loss = 0.603951 +I0616 18:06:28.403720 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.316286 (* 1 = 0.316286 loss) +I0616 18:06:28.403725 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248781 (* 1 = 0.248781 loss) +I0616 18:06:28.403729 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.247102 (* 1 = 0.247102 loss) +I0616 18:06:28.403733 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.109602 (* 1 = 0.109602 loss) +I0616 18:06:28.403738 9857 solver.cpp:571] Iteration 96780, lr = 0.0001 +speed: 0.595s / iter +I0616 18:06:39.949390 9857 solver.cpp:242] Iteration 96800, loss = 0.304638 +I0616 18:06:39.949431 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.100552 (* 1 = 0.100552 loss) +I0616 18:06:39.949436 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16359 (* 1 = 0.16359 loss) +I0616 18:06:39.949440 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0312813 (* 1 = 0.0312813 loss) +I0616 18:06:39.949445 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00174839 (* 1 = 0.00174839 loss) +I0616 18:06:39.949448 9857 solver.cpp:571] Iteration 96800, lr = 0.0001 +I0616 18:06:51.716925 9857 solver.cpp:242] Iteration 96820, loss = 0.802087 +I0616 18:06:51.716967 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.415857 (* 1 = 0.415857 loss) +I0616 18:06:51.716972 9857 solver.cpp:258] Train net output #1: loss_cls = 0.345296 (* 1 = 0.345296 loss) +I0616 18:06:51.716977 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.042027 (* 1 = 0.042027 loss) +I0616 18:06:51.716980 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00834779 (* 1 = 0.00834779 loss) +I0616 18:06:51.716984 9857 solver.cpp:571] Iteration 96820, lr = 0.0001 +I0616 18:07:03.331535 9857 solver.cpp:242] Iteration 96840, loss = 0.64291 +I0616 18:07:03.331574 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.204078 (* 1 = 0.204078 loss) +I0616 18:07:03.331579 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126226 (* 1 = 0.126226 loss) +I0616 18:07:03.331584 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.073549 (* 1 = 0.073549 loss) +I0616 18:07:03.331588 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.05107 (* 1 = 0.05107 loss) +I0616 18:07:03.331591 9857 solver.cpp:571] Iteration 96840, lr = 0.0001 +I0616 18:07:15.092617 9857 solver.cpp:242] Iteration 96860, loss = 0.594714 +I0616 18:07:15.092658 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197339 (* 1 = 0.197339 loss) +I0616 18:07:15.092664 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211861 (* 1 = 0.211861 loss) +I0616 18:07:15.092669 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0629001 (* 1 = 0.0629001 loss) +I0616 18:07:15.092671 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0252269 (* 1 = 0.0252269 loss) +I0616 18:07:15.092676 9857 solver.cpp:571] Iteration 96860, lr = 0.0001 +I0616 18:07:26.469640 9857 solver.cpp:242] Iteration 96880, loss = 1.00314 +I0616 18:07:26.469682 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.412286 (* 1 = 0.412286 loss) +I0616 18:07:26.469688 9857 solver.cpp:258] Train net output #1: loss_cls = 0.315651 (* 1 = 0.315651 loss) +I0616 18:07:26.469692 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.126253 (* 1 = 0.126253 loss) +I0616 18:07:26.469696 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.319119 (* 1 = 0.319119 loss) +I0616 18:07:26.469699 9857 solver.cpp:571] Iteration 96880, lr = 0.0001 +I0616 18:07:38.077730 9857 solver.cpp:242] Iteration 96900, loss = 0.674184 +I0616 18:07:38.077772 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.230199 (* 1 = 0.230199 loss) +I0616 18:07:38.077778 9857 solver.cpp:258] Train net output #1: loss_cls = 0.465703 (* 1 = 0.465703 loss) +I0616 18:07:38.077782 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.122128 (* 1 = 0.122128 loss) +I0616 18:07:38.077786 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.130406 (* 1 = 0.130406 loss) +I0616 18:07:38.077790 9857 solver.cpp:571] Iteration 96900, lr = 0.0001 +I0616 18:07:49.708580 9857 solver.cpp:242] Iteration 96920, loss = 0.629813 +I0616 18:07:49.708621 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161097 (* 1 = 0.161097 loss) +I0616 18:07:49.708626 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191675 (* 1 = 0.191675 loss) +I0616 18:07:49.708631 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166715 (* 1 = 0.166715 loss) +I0616 18:07:49.708633 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.205635 (* 1 = 0.205635 loss) +I0616 18:07:49.708637 9857 solver.cpp:571] Iteration 96920, lr = 0.0001 +I0616 18:08:01.329574 9857 solver.cpp:242] Iteration 96940, loss = 0.390723 +I0616 18:08:01.329617 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0955721 (* 1 = 0.0955721 loss) +I0616 18:08:01.329622 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0829854 (* 1 = 0.0829854 loss) +I0616 18:08:01.329627 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0194407 (* 1 = 0.0194407 loss) +I0616 18:08:01.329629 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00076057 (* 1 = 0.00076057 loss) +I0616 18:08:01.329635 9857 solver.cpp:571] Iteration 96940, lr = 0.0001 +I0616 18:08:12.724427 9857 solver.cpp:242] Iteration 96960, loss = 1.33053 +I0616 18:08:12.724469 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.140229 (* 1 = 0.140229 loss) +I0616 18:08:12.724474 9857 solver.cpp:258] Train net output #1: loss_cls = 0.272627 (* 1 = 0.272627 loss) +I0616 18:08:12.724479 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0809322 (* 1 = 0.0809322 loss) +I0616 18:08:12.724483 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.545327 (* 1 = 0.545327 loss) +I0616 18:08:12.724486 9857 solver.cpp:571] Iteration 96960, lr = 0.0001 +I0616 18:08:24.359881 9857 solver.cpp:242] Iteration 96980, loss = 0.283318 +I0616 18:08:24.359923 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.192116 (* 1 = 0.192116 loss) +I0616 18:08:24.359928 9857 solver.cpp:258] Train net output #1: loss_cls = 0.184289 (* 1 = 0.184289 loss) +I0616 18:08:24.359933 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.010242 (* 1 = 0.010242 loss) +I0616 18:08:24.359936 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0113968 (* 1 = 0.0113968 loss) +I0616 18:08:24.359942 9857 solver.cpp:571] Iteration 96980, lr = 0.0001 +speed: 0.595s / iter +I0616 18:08:35.611768 9857 solver.cpp:242] Iteration 97000, loss = 0.303947 +I0616 18:08:35.611810 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.128407 (* 1 = 0.128407 loss) +I0616 18:08:35.611816 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134209 (* 1 = 0.134209 loss) +I0616 18:08:35.611820 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0473589 (* 1 = 0.0473589 loss) +I0616 18:08:35.611824 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00655962 (* 1 = 0.00655962 loss) +I0616 18:08:35.611827 9857 solver.cpp:571] Iteration 97000, lr = 0.0001 +I0616 18:08:47.111402 9857 solver.cpp:242] Iteration 97020, loss = 0.237491 +I0616 18:08:47.111445 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0387408 (* 1 = 0.0387408 loss) +I0616 18:08:47.111450 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0848388 (* 1 = 0.0848388 loss) +I0616 18:08:47.111454 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0256585 (* 1 = 0.0256585 loss) +I0616 18:08:47.111459 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00716421 (* 1 = 0.00716421 loss) +I0616 18:08:47.111462 9857 solver.cpp:571] Iteration 97020, lr = 0.0001 +I0616 18:08:58.713150 9857 solver.cpp:242] Iteration 97040, loss = 0.52754 +I0616 18:08:58.713191 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0628717 (* 1 = 0.0628717 loss) +I0616 18:08:58.713197 9857 solver.cpp:258] Train net output #1: loss_cls = 0.187069 (* 1 = 0.187069 loss) +I0616 18:08:58.713201 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.047392 (* 1 = 0.047392 loss) +I0616 18:08:58.713206 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0107668 (* 1 = 0.0107668 loss) +I0616 18:08:58.713208 9857 solver.cpp:571] Iteration 97040, lr = 0.0001 +I0616 18:09:10.400355 9857 solver.cpp:242] Iteration 97060, loss = 0.683725 +I0616 18:09:10.400398 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.161841 (* 1 = 0.161841 loss) +I0616 18:09:10.400403 9857 solver.cpp:258] Train net output #1: loss_cls = 0.33139 (* 1 = 0.33139 loss) +I0616 18:09:10.400408 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0676811 (* 1 = 0.0676811 loss) +I0616 18:09:10.400411 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0751043 (* 1 = 0.0751043 loss) +I0616 18:09:10.400415 9857 solver.cpp:571] Iteration 97060, lr = 0.0001 +I0616 18:09:22.110770 9857 solver.cpp:242] Iteration 97080, loss = 0.230212 +I0616 18:09:22.110813 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0245452 (* 1 = 0.0245452 loss) +I0616 18:09:22.110818 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0958182 (* 1 = 0.0958182 loss) +I0616 18:09:22.110822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0345036 (* 1 = 0.0345036 loss) +I0616 18:09:22.110826 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00929305 (* 1 = 0.00929305 loss) +I0616 18:09:22.110831 9857 solver.cpp:571] Iteration 97080, lr = 0.0001 +I0616 18:09:33.541057 9857 solver.cpp:242] Iteration 97100, loss = 0.461526 +I0616 18:09:33.541100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.222928 (* 1 = 0.222928 loss) +I0616 18:09:33.541105 9857 solver.cpp:258] Train net output #1: loss_cls = 0.418982 (* 1 = 0.418982 loss) +I0616 18:09:33.541110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0460162 (* 1 = 0.0460162 loss) +I0616 18:09:33.541113 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0598663 (* 1 = 0.0598663 loss) +I0616 18:09:33.541117 9857 solver.cpp:571] Iteration 97100, lr = 0.0001 +I0616 18:09:45.263319 9857 solver.cpp:242] Iteration 97120, loss = 0.319873 +I0616 18:09:45.263361 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0965112 (* 1 = 0.0965112 loss) +I0616 18:09:45.263367 9857 solver.cpp:258] Train net output #1: loss_cls = 0.135089 (* 1 = 0.135089 loss) +I0616 18:09:45.263371 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00349419 (* 1 = 0.00349419 loss) +I0616 18:09:45.263375 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0122395 (* 1 = 0.0122395 loss) +I0616 18:09:45.263378 9857 solver.cpp:571] Iteration 97120, lr = 0.0001 +I0616 18:09:56.832628 9857 solver.cpp:242] Iteration 97140, loss = 0.79428 +I0616 18:09:56.832669 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.237153 (* 1 = 0.237153 loss) +I0616 18:09:56.832675 9857 solver.cpp:258] Train net output #1: loss_cls = 0.279938 (* 1 = 0.279938 loss) +I0616 18:09:56.832679 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0954194 (* 1 = 0.0954194 loss) +I0616 18:09:56.832682 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0635488 (* 1 = 0.0635488 loss) +I0616 18:09:56.832686 9857 solver.cpp:571] Iteration 97140, lr = 0.0001 +I0616 18:10:08.242476 9857 solver.cpp:242] Iteration 97160, loss = 0.603563 +I0616 18:10:08.242517 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12676 (* 1 = 0.12676 loss) +I0616 18:10:08.242522 9857 solver.cpp:258] Train net output #1: loss_cls = 0.172853 (* 1 = 0.172853 loss) +I0616 18:10:08.242527 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0418072 (* 1 = 0.0418072 loss) +I0616 18:10:08.242530 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0039255 (* 1 = 0.0039255 loss) +I0616 18:10:08.242534 9857 solver.cpp:571] Iteration 97160, lr = 0.0001 +I0616 18:10:19.661900 9857 solver.cpp:242] Iteration 97180, loss = 0.438948 +I0616 18:10:19.661943 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.172681 (* 1 = 0.172681 loss) +I0616 18:10:19.661948 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180106 (* 1 = 0.180106 loss) +I0616 18:10:19.661952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0503066 (* 1 = 0.0503066 loss) +I0616 18:10:19.661957 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0289865 (* 1 = 0.0289865 loss) +I0616 18:10:19.661960 9857 solver.cpp:571] Iteration 97180, lr = 0.0001 +speed: 0.595s / iter +I0616 18:10:31.427594 9857 solver.cpp:242] Iteration 97200, loss = 0.446019 +I0616 18:10:31.427636 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0935494 (* 1 = 0.0935494 loss) +I0616 18:10:31.427641 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0901479 (* 1 = 0.0901479 loss) +I0616 18:10:31.427646 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00190858 (* 1 = 0.00190858 loss) +I0616 18:10:31.427649 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00155476 (* 1 = 0.00155476 loss) +I0616 18:10:31.427654 9857 solver.cpp:571] Iteration 97200, lr = 0.0001 +I0616 18:10:42.773432 9857 solver.cpp:242] Iteration 97220, loss = 0.657178 +I0616 18:10:42.773474 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.35114 (* 1 = 0.35114 loss) +I0616 18:10:42.773480 9857 solver.cpp:258] Train net output #1: loss_cls = 0.17737 (* 1 = 0.17737 loss) +I0616 18:10:42.773484 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0696491 (* 1 = 0.0696491 loss) +I0616 18:10:42.773488 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.129108 (* 1 = 0.129108 loss) +I0616 18:10:42.773493 9857 solver.cpp:571] Iteration 97220, lr = 0.0001 +I0616 18:10:54.414017 9857 solver.cpp:242] Iteration 97240, loss = 0.261172 +I0616 18:10:54.414058 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131277 (* 1 = 0.131277 loss) +I0616 18:10:54.414065 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144173 (* 1 = 0.144173 loss) +I0616 18:10:54.414069 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0179601 (* 1 = 0.0179601 loss) +I0616 18:10:54.414073 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0070666 (* 1 = 0.0070666 loss) +I0616 18:10:54.414077 9857 solver.cpp:571] Iteration 97240, lr = 0.0001 +I0616 18:11:05.935075 9857 solver.cpp:242] Iteration 97260, loss = 0.522272 +I0616 18:11:05.935117 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193907 (* 1 = 0.193907 loss) +I0616 18:11:05.935122 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219905 (* 1 = 0.219905 loss) +I0616 18:11:05.935127 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.263204 (* 1 = 0.263204 loss) +I0616 18:11:05.935129 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.110372 (* 1 = 0.110372 loss) +I0616 18:11:05.935133 9857 solver.cpp:571] Iteration 97260, lr = 0.0001 +I0616 18:11:17.432616 9857 solver.cpp:242] Iteration 97280, loss = 0.235444 +I0616 18:11:17.432658 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121263 (* 1 = 0.121263 loss) +I0616 18:11:17.432664 9857 solver.cpp:258] Train net output #1: loss_cls = 0.133756 (* 1 = 0.133756 loss) +I0616 18:11:17.432668 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00148759 (* 1 = 0.00148759 loss) +I0616 18:11:17.432672 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0111912 (* 1 = 0.0111912 loss) +I0616 18:11:17.432675 9857 solver.cpp:571] Iteration 97280, lr = 0.0001 +I0616 18:11:28.680181 9857 solver.cpp:242] Iteration 97300, loss = 0.673602 +I0616 18:11:28.680222 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231733 (* 1 = 0.231733 loss) +I0616 18:11:28.680228 9857 solver.cpp:258] Train net output #1: loss_cls = 0.259606 (* 1 = 0.259606 loss) +I0616 18:11:28.680233 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0745007 (* 1 = 0.0745007 loss) +I0616 18:11:28.680235 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0173228 (* 1 = 0.0173228 loss) +I0616 18:11:28.680239 9857 solver.cpp:571] Iteration 97300, lr = 0.0001 +I0616 18:11:40.272483 9857 solver.cpp:242] Iteration 97320, loss = 0.466331 +I0616 18:11:40.272526 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.141833 (* 1 = 0.141833 loss) +I0616 18:11:40.272532 9857 solver.cpp:258] Train net output #1: loss_cls = 0.125865 (* 1 = 0.125865 loss) +I0616 18:11:40.272536 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.28156 (* 1 = 0.28156 loss) +I0616 18:11:40.272541 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.185806 (* 1 = 0.185806 loss) +I0616 18:11:40.272544 9857 solver.cpp:571] Iteration 97320, lr = 0.0001 +I0616 18:11:51.801642 9857 solver.cpp:242] Iteration 97340, loss = 0.460977 +I0616 18:11:51.801686 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.280759 (* 1 = 0.280759 loss) +I0616 18:11:51.801692 9857 solver.cpp:258] Train net output #1: loss_cls = 0.294481 (* 1 = 0.294481 loss) +I0616 18:11:51.801695 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0512745 (* 1 = 0.0512745 loss) +I0616 18:11:51.801699 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0197866 (* 1 = 0.0197866 loss) +I0616 18:11:51.801702 9857 solver.cpp:571] Iteration 97340, lr = 0.0001 +I0616 18:12:03.638677 9857 solver.cpp:242] Iteration 97360, loss = 0.482743 +I0616 18:12:03.638718 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0813493 (* 1 = 0.0813493 loss) +I0616 18:12:03.638723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180886 (* 1 = 0.180886 loss) +I0616 18:12:03.638727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389637 (* 1 = 0.0389637 loss) +I0616 18:12:03.638731 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.005732 (* 1 = 0.005732 loss) +I0616 18:12:03.638736 9857 solver.cpp:571] Iteration 97360, lr = 0.0001 +I0616 18:12:15.063597 9857 solver.cpp:242] Iteration 97380, loss = 0.261827 +I0616 18:12:15.063637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0705319 (* 1 = 0.0705319 loss) +I0616 18:12:15.063643 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145324 (* 1 = 0.145324 loss) +I0616 18:12:15.063647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00729722 (* 1 = 0.00729722 loss) +I0616 18:12:15.063652 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00488996 (* 1 = 0.00488996 loss) +I0616 18:12:15.063654 9857 solver.cpp:571] Iteration 97380, lr = 0.0001 +speed: 0.595s / iter +I0616 18:12:26.586937 9857 solver.cpp:242] Iteration 97400, loss = 0.811887 +I0616 18:12:26.586979 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.296002 (* 1 = 0.296002 loss) +I0616 18:12:26.586985 9857 solver.cpp:258] Train net output #1: loss_cls = 0.432891 (* 1 = 0.432891 loss) +I0616 18:12:26.586989 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0986577 (* 1 = 0.0986577 loss) +I0616 18:12:26.586993 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.187276 (* 1 = 0.187276 loss) +I0616 18:12:26.586997 9857 solver.cpp:571] Iteration 97400, lr = 0.0001 +I0616 18:12:38.297433 9857 solver.cpp:242] Iteration 97420, loss = 0.699548 +I0616 18:12:38.297475 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.248484 (* 1 = 0.248484 loss) +I0616 18:12:38.297482 9857 solver.cpp:258] Train net output #1: loss_cls = 0.431928 (* 1 = 0.431928 loss) +I0616 18:12:38.297485 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00947449 (* 1 = 0.00947449 loss) +I0616 18:12:38.297489 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00980663 (* 1 = 0.00980663 loss) +I0616 18:12:38.297492 9857 solver.cpp:571] Iteration 97420, lr = 0.0001 +I0616 18:12:50.034835 9857 solver.cpp:242] Iteration 97440, loss = 0.437993 +I0616 18:12:50.034889 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0420567 (* 1 = 0.0420567 loss) +I0616 18:12:50.034894 9857 solver.cpp:258] Train net output #1: loss_cls = 0.181405 (* 1 = 0.181405 loss) +I0616 18:12:50.034898 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.152162 (* 1 = 0.152162 loss) +I0616 18:12:50.034903 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.149896 (* 1 = 0.149896 loss) +I0616 18:12:50.034906 9857 solver.cpp:571] Iteration 97440, lr = 0.0001 +I0616 18:13:01.615990 9857 solver.cpp:242] Iteration 97460, loss = 0.801807 +I0616 18:13:01.616032 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.34826 (* 1 = 0.34826 loss) +I0616 18:13:01.616039 9857 solver.cpp:258] Train net output #1: loss_cls = 0.344485 (* 1 = 0.344485 loss) +I0616 18:13:01.616042 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0284669 (* 1 = 0.0284669 loss) +I0616 18:13:01.616046 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0968134 (* 1 = 0.0968134 loss) +I0616 18:13:01.616052 9857 solver.cpp:571] Iteration 97460, lr = 0.0001 +I0616 18:13:13.057664 9857 solver.cpp:242] Iteration 97480, loss = 0.598616 +I0616 18:13:13.057708 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.166917 (* 1 = 0.166917 loss) +I0616 18:13:13.057713 9857 solver.cpp:258] Train net output #1: loss_cls = 0.144348 (* 1 = 0.144348 loss) +I0616 18:13:13.057718 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0995556 (* 1 = 0.0995556 loss) +I0616 18:13:13.057721 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.181732 (* 1 = 0.181732 loss) +I0616 18:13:13.057724 9857 solver.cpp:571] Iteration 97480, lr = 0.0001 +I0616 18:13:24.514425 9857 solver.cpp:242] Iteration 97500, loss = 0.399252 +I0616 18:13:24.514467 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.201812 (* 1 = 0.201812 loss) +I0616 18:13:24.514472 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306873 (* 1 = 0.306873 loss) +I0616 18:13:24.514477 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0112417 (* 1 = 0.0112417 loss) +I0616 18:13:24.514480 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0206886 (* 1 = 0.0206886 loss) +I0616 18:13:24.514483 9857 solver.cpp:571] Iteration 97500, lr = 0.0001 +I0616 18:13:35.948678 9857 solver.cpp:242] Iteration 97520, loss = 0.534651 +I0616 18:13:35.948719 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0468678 (* 1 = 0.0468678 loss) +I0616 18:13:35.948724 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15296 (* 1 = 0.15296 loss) +I0616 18:13:35.948729 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0723063 (* 1 = 0.0723063 loss) +I0616 18:13:35.948734 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0388097 (* 1 = 0.0388097 loss) +I0616 18:13:35.948736 9857 solver.cpp:571] Iteration 97520, lr = 0.0001 +I0616 18:13:47.490998 9857 solver.cpp:242] Iteration 97540, loss = 0.290087 +I0616 18:13:47.491039 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0817019 (* 1 = 0.0817019 loss) +I0616 18:13:47.491044 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126707 (* 1 = 0.126707 loss) +I0616 18:13:47.491050 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0515471 (* 1 = 0.0515471 loss) +I0616 18:13:47.491053 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00424527 (* 1 = 0.00424527 loss) +I0616 18:13:47.491056 9857 solver.cpp:571] Iteration 97540, lr = 0.0001 +I0616 18:13:59.178699 9857 solver.cpp:242] Iteration 97560, loss = 0.701345 +I0616 18:13:59.178741 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.3369 (* 1 = 0.3369 loss) +I0616 18:13:59.178746 9857 solver.cpp:258] Train net output #1: loss_cls = 0.350558 (* 1 = 0.350558 loss) +I0616 18:13:59.178751 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.151043 (* 1 = 0.151043 loss) +I0616 18:13:59.178755 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.240278 (* 1 = 0.240278 loss) +I0616 18:13:59.178761 9857 solver.cpp:571] Iteration 97560, lr = 0.0001 +I0616 18:14:10.686548 9857 solver.cpp:242] Iteration 97580, loss = 0.508817 +I0616 18:14:10.686591 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.337417 (* 1 = 0.337417 loss) +I0616 18:14:10.686596 9857 solver.cpp:258] Train net output #1: loss_cls = 0.289076 (* 1 = 0.289076 loss) +I0616 18:14:10.686600 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.129833 (* 1 = 0.129833 loss) +I0616 18:14:10.686604 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0633727 (* 1 = 0.0633727 loss) +I0616 18:14:10.686607 9857 solver.cpp:571] Iteration 97580, lr = 0.0001 +speed: 0.595s / iter +I0616 18:14:22.177901 9857 solver.cpp:242] Iteration 97600, loss = 0.499302 +I0616 18:14:22.177942 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.241772 (* 1 = 0.241772 loss) +I0616 18:14:22.177947 9857 solver.cpp:258] Train net output #1: loss_cls = 0.26248 (* 1 = 0.26248 loss) +I0616 18:14:22.177952 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.157436 (* 1 = 0.157436 loss) +I0616 18:14:22.177955 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0833716 (* 1 = 0.0833716 loss) +I0616 18:14:22.177958 9857 solver.cpp:571] Iteration 97600, lr = 0.0001 +I0616 18:14:33.702165 9857 solver.cpp:242] Iteration 97620, loss = 0.319526 +I0616 18:14:33.702208 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.167173 (* 1 = 0.167173 loss) +I0616 18:14:33.702214 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169836 (* 1 = 0.169836 loss) +I0616 18:14:33.702217 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0377833 (* 1 = 0.0377833 loss) +I0616 18:14:33.702221 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.121893 (* 1 = 0.121893 loss) +I0616 18:14:33.702224 9857 solver.cpp:571] Iteration 97620, lr = 0.0001 +I0616 18:14:45.194031 9857 solver.cpp:242] Iteration 97640, loss = 0.297897 +I0616 18:14:45.194072 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121958 (* 1 = 0.121958 loss) +I0616 18:14:45.194078 9857 solver.cpp:258] Train net output #1: loss_cls = 0.108174 (* 1 = 0.108174 loss) +I0616 18:14:45.194082 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0209099 (* 1 = 0.0209099 loss) +I0616 18:14:45.194085 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160727 (* 1 = 0.0160727 loss) +I0616 18:14:45.194089 9857 solver.cpp:571] Iteration 97640, lr = 0.0001 +I0616 18:14:56.861347 9857 solver.cpp:242] Iteration 97660, loss = 0.815246 +I0616 18:14:56.861388 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.381288 (* 1 = 0.381288 loss) +I0616 18:14:56.861394 9857 solver.cpp:258] Train net output #1: loss_cls = 0.556703 (* 1 = 0.556703 loss) +I0616 18:14:56.861398 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.13523 (* 1 = 0.13523 loss) +I0616 18:14:56.861402 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0888885 (* 1 = 0.0888885 loss) +I0616 18:14:56.861405 9857 solver.cpp:571] Iteration 97660, lr = 0.0001 +I0616 18:15:08.266543 9857 solver.cpp:242] Iteration 97680, loss = 0.177144 +I0616 18:15:08.266585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0400072 (* 1 = 0.0400072 loss) +I0616 18:15:08.266592 9857 solver.cpp:258] Train net output #1: loss_cls = 0.112342 (* 1 = 0.112342 loss) +I0616 18:15:08.266595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00856409 (* 1 = 0.00856409 loss) +I0616 18:15:08.266599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00931438 (* 1 = 0.00931438 loss) +I0616 18:15:08.266602 9857 solver.cpp:571] Iteration 97680, lr = 0.0001 +I0616 18:15:19.882892 9857 solver.cpp:242] Iteration 97700, loss = 1.03265 +I0616 18:15:19.882936 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.387362 (* 1 = 0.387362 loss) +I0616 18:15:19.882941 9857 solver.cpp:258] Train net output #1: loss_cls = 0.422842 (* 1 = 0.422842 loss) +I0616 18:15:19.882946 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.366692 (* 1 = 0.366692 loss) +I0616 18:15:19.882948 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.104544 (* 1 = 0.104544 loss) +I0616 18:15:19.882952 9857 solver.cpp:571] Iteration 97700, lr = 0.0001 +I0616 18:15:31.555727 9857 solver.cpp:242] Iteration 97720, loss = 0.790029 +I0616 18:15:31.555768 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.223572 (* 1 = 0.223572 loss) +I0616 18:15:31.555773 9857 solver.cpp:258] Train net output #1: loss_cls = 0.518518 (* 1 = 0.518518 loss) +I0616 18:15:31.555778 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0949346 (* 1 = 0.0949346 loss) +I0616 18:15:31.555781 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0794407 (* 1 = 0.0794407 loss) +I0616 18:15:31.555785 9857 solver.cpp:571] Iteration 97720, lr = 0.0001 +I0616 18:15:42.926714 9857 solver.cpp:242] Iteration 97740, loss = 0.431521 +I0616 18:15:42.926759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.17796 (* 1 = 0.17796 loss) +I0616 18:15:42.926764 9857 solver.cpp:258] Train net output #1: loss_cls = 0.22847 (* 1 = 0.22847 loss) +I0616 18:15:42.926769 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0510671 (* 1 = 0.0510671 loss) +I0616 18:15:42.926772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0637741 (* 1 = 0.0637741 loss) +I0616 18:15:42.926776 9857 solver.cpp:571] Iteration 97740, lr = 0.0001 +I0616 18:15:54.254600 9857 solver.cpp:242] Iteration 97760, loss = 0.359074 +I0616 18:15:54.254642 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.283747 (* 1 = 0.283747 loss) +I0616 18:15:54.254647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.219984 (* 1 = 0.219984 loss) +I0616 18:15:54.254652 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0460529 (* 1 = 0.0460529 loss) +I0616 18:15:54.254654 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0205575 (* 1 = 0.0205575 loss) +I0616 18:15:54.254658 9857 solver.cpp:571] Iteration 97760, lr = 0.0001 +I0616 18:16:05.740820 9857 solver.cpp:242] Iteration 97780, loss = 0.589791 +I0616 18:16:05.740862 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.257805 (* 1 = 0.257805 loss) +I0616 18:16:05.740867 9857 solver.cpp:258] Train net output #1: loss_cls = 0.318095 (* 1 = 0.318095 loss) +I0616 18:16:05.740871 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.110275 (* 1 = 0.110275 loss) +I0616 18:16:05.740875 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.357352 (* 1 = 0.357352 loss) +I0616 18:16:05.740880 9857 solver.cpp:571] Iteration 97780, lr = 0.0001 +speed: 0.595s / iter +I0616 18:16:17.377984 9857 solver.cpp:242] Iteration 97800, loss = 0.257809 +I0616 18:16:17.378026 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.12811 (* 1 = 0.12811 loss) +I0616 18:16:17.378031 9857 solver.cpp:258] Train net output #1: loss_cls = 0.158356 (* 1 = 0.158356 loss) +I0616 18:16:17.378036 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0174203 (* 1 = 0.0174203 loss) +I0616 18:16:17.378039 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00613368 (* 1 = 0.00613368 loss) +I0616 18:16:17.378043 9857 solver.cpp:571] Iteration 97800, lr = 0.0001 +I0616 18:16:28.928966 9857 solver.cpp:242] Iteration 97820, loss = 0.267238 +I0616 18:16:28.929008 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0933029 (* 1 = 0.0933029 loss) +I0616 18:16:28.929013 9857 solver.cpp:258] Train net output #1: loss_cls = 0.095018 (* 1 = 0.095018 loss) +I0616 18:16:28.929016 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0621528 (* 1 = 0.0621528 loss) +I0616 18:16:28.929020 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00235199 (* 1 = 0.00235199 loss) +I0616 18:16:28.929023 9857 solver.cpp:571] Iteration 97820, lr = 0.0001 +I0616 18:16:40.358906 9857 solver.cpp:242] Iteration 97840, loss = 0.33296 +I0616 18:16:40.358947 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0538285 (* 1 = 0.0538285 loss) +I0616 18:16:40.358952 9857 solver.cpp:258] Train net output #1: loss_cls = 0.154474 (* 1 = 0.154474 loss) +I0616 18:16:40.358957 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0749719 (* 1 = 0.0749719 loss) +I0616 18:16:40.358960 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0249611 (* 1 = 0.0249611 loss) +I0616 18:16:40.358964 9857 solver.cpp:571] Iteration 97840, lr = 0.0001 +I0616 18:16:51.988713 9857 solver.cpp:242] Iteration 97860, loss = 0.336277 +I0616 18:16:51.988754 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.111204 (* 1 = 0.111204 loss) +I0616 18:16:51.988759 9857 solver.cpp:258] Train net output #1: loss_cls = 0.12434 (* 1 = 0.12434 loss) +I0616 18:16:51.988764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0215635 (* 1 = 0.0215635 loss) +I0616 18:16:51.988767 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0238629 (* 1 = 0.0238629 loss) +I0616 18:16:51.988770 9857 solver.cpp:571] Iteration 97860, lr = 0.0001 +I0616 18:17:03.689697 9857 solver.cpp:242] Iteration 97880, loss = 0.288943 +I0616 18:17:03.689739 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0637369 (* 1 = 0.0637369 loss) +I0616 18:17:03.689744 9857 solver.cpp:258] Train net output #1: loss_cls = 0.164213 (* 1 = 0.164213 loss) +I0616 18:17:03.689749 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00734491 (* 1 = 0.00734491 loss) +I0616 18:17:03.689752 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0141801 (* 1 = 0.0141801 loss) +I0616 18:17:03.689756 9857 solver.cpp:571] Iteration 97880, lr = 0.0001 +I0616 18:17:15.310755 9857 solver.cpp:242] Iteration 97900, loss = 0.28925 +I0616 18:17:15.310801 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.164374 (* 1 = 0.164374 loss) +I0616 18:17:15.310807 9857 solver.cpp:258] Train net output #1: loss_cls = 0.221386 (* 1 = 0.221386 loss) +I0616 18:17:15.310811 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00542052 (* 1 = 0.00542052 loss) +I0616 18:17:15.310816 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00926436 (* 1 = 0.00926436 loss) +I0616 18:17:15.310818 9857 solver.cpp:571] Iteration 97900, lr = 0.0001 +I0616 18:17:26.980046 9857 solver.cpp:242] Iteration 97920, loss = 0.238733 +I0616 18:17:26.980088 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0307636 (* 1 = 0.0307636 loss) +I0616 18:17:26.980094 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0858261 (* 1 = 0.0858261 loss) +I0616 18:17:26.980098 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0325292 (* 1 = 0.0325292 loss) +I0616 18:17:26.980101 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103365 (* 1 = 0.0103365 loss) +I0616 18:17:26.980105 9857 solver.cpp:571] Iteration 97920, lr = 0.0001 +I0616 18:17:38.476907 9857 solver.cpp:242] Iteration 97940, loss = 0.41901 +I0616 18:17:38.476948 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263085 (* 1 = 0.263085 loss) +I0616 18:17:38.476953 9857 solver.cpp:258] Train net output #1: loss_cls = 0.269288 (* 1 = 0.269288 loss) +I0616 18:17:38.476958 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.109475 (* 1 = 0.109475 loss) +I0616 18:17:38.476961 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0469163 (* 1 = 0.0469163 loss) +I0616 18:17:38.476964 9857 solver.cpp:571] Iteration 97940, lr = 0.0001 +I0616 18:17:50.170131 9857 solver.cpp:242] Iteration 97960, loss = 0.332124 +I0616 18:17:50.170173 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.118476 (* 1 = 0.118476 loss) +I0616 18:17:50.170178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11759 (* 1 = 0.11759 loss) +I0616 18:17:50.170183 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.033016 (* 1 = 0.033016 loss) +I0616 18:17:50.170186 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0114341 (* 1 = 0.0114341 loss) +I0616 18:17:50.170191 9857 solver.cpp:571] Iteration 97960, lr = 0.0001 +I0616 18:18:01.563159 9857 solver.cpp:242] Iteration 97980, loss = 0.233628 +I0616 18:18:01.563200 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0632927 (* 1 = 0.0632927 loss) +I0616 18:18:01.563206 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136765 (* 1 = 0.136765 loss) +I0616 18:18:01.563210 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0246297 (* 1 = 0.0246297 loss) +I0616 18:18:01.563215 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0117693 (* 1 = 0.0117693 loss) +I0616 18:18:01.563220 9857 solver.cpp:571] Iteration 97980, lr = 0.0001 +speed: 0.595s / iter +I0616 18:18:13.092675 9857 solver.cpp:242] Iteration 98000, loss = 0.283624 +I0616 18:18:13.092718 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.154499 (* 1 = 0.154499 loss) +I0616 18:18:13.092723 9857 solver.cpp:258] Train net output #1: loss_cls = 0.157163 (* 1 = 0.157163 loss) +I0616 18:18:13.092727 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0437195 (* 1 = 0.0437195 loss) +I0616 18:18:13.092730 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0556524 (* 1 = 0.0556524 loss) +I0616 18:18:13.092735 9857 solver.cpp:571] Iteration 98000, lr = 0.0001 +I0616 18:18:24.415441 9857 solver.cpp:242] Iteration 98020, loss = 0.547384 +I0616 18:18:24.415483 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0783077 (* 1 = 0.0783077 loss) +I0616 18:18:24.415489 9857 solver.cpp:258] Train net output #1: loss_cls = 0.11921 (* 1 = 0.11921 loss) +I0616 18:18:24.415493 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0149846 (* 1 = 0.0149846 loss) +I0616 18:18:24.415498 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00821855 (* 1 = 0.00821855 loss) +I0616 18:18:24.415503 9857 solver.cpp:571] Iteration 98020, lr = 0.0001 +I0616 18:18:35.700215 9857 solver.cpp:242] Iteration 98040, loss = 0.839863 +I0616 18:18:35.700258 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.268875 (* 1 = 0.268875 loss) +I0616 18:18:35.700264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.485449 (* 1 = 0.485449 loss) +I0616 18:18:35.700268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0758133 (* 1 = 0.0758133 loss) +I0616 18:18:35.700271 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0539673 (* 1 = 0.0539673 loss) +I0616 18:18:35.700275 9857 solver.cpp:571] Iteration 98040, lr = 0.0001 +I0616 18:18:47.055944 9857 solver.cpp:242] Iteration 98060, loss = 0.494439 +I0616 18:18:47.055987 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.180856 (* 1 = 0.180856 loss) +I0616 18:18:47.055994 9857 solver.cpp:258] Train net output #1: loss_cls = 0.171714 (* 1 = 0.171714 loss) +I0616 18:18:47.055997 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00442656 (* 1 = 0.00442656 loss) +I0616 18:18:47.056001 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00996095 (* 1 = 0.00996095 loss) +I0616 18:18:47.056005 9857 solver.cpp:571] Iteration 98060, lr = 0.0001 +I0616 18:18:58.729730 9857 solver.cpp:242] Iteration 98080, loss = 0.357998 +I0616 18:18:58.729773 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.207094 (* 1 = 0.207094 loss) +I0616 18:18:58.729779 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161745 (* 1 = 0.161745 loss) +I0616 18:18:58.729784 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0486419 (* 1 = 0.0486419 loss) +I0616 18:18:58.729786 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0447599 (* 1 = 0.0447599 loss) +I0616 18:18:58.729790 9857 solver.cpp:571] Iteration 98080, lr = 0.0001 +I0616 18:19:10.285743 9857 solver.cpp:242] Iteration 98100, loss = 0.230129 +I0616 18:19:10.285784 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.123263 (* 1 = 0.123263 loss) +I0616 18:19:10.285789 9857 solver.cpp:258] Train net output #1: loss_cls = 0.139786 (* 1 = 0.139786 loss) +I0616 18:19:10.285792 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0307528 (* 1 = 0.0307528 loss) +I0616 18:19:10.285796 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.018099 (* 1 = 0.018099 loss) +I0616 18:19:10.285800 9857 solver.cpp:571] Iteration 98100, lr = 0.0001 +I0616 18:19:22.020295 9857 solver.cpp:242] Iteration 98120, loss = 0.701812 +I0616 18:19:22.020339 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.187059 (* 1 = 0.187059 loss) +I0616 18:19:22.020344 9857 solver.cpp:258] Train net output #1: loss_cls = 0.182096 (* 1 = 0.182096 loss) +I0616 18:19:22.020347 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0644788 (* 1 = 0.0644788 loss) +I0616 18:19:22.020351 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0365034 (* 1 = 0.0365034 loss) +I0616 18:19:22.020355 9857 solver.cpp:571] Iteration 98120, lr = 0.0001 +I0616 18:19:33.498739 9857 solver.cpp:242] Iteration 98140, loss = 0.604977 +I0616 18:19:33.498783 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.263734 (* 1 = 0.263734 loss) +I0616 18:19:33.498790 9857 solver.cpp:258] Train net output #1: loss_cls = 0.303443 (* 1 = 0.303443 loss) +I0616 18:19:33.498793 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.166138 (* 1 = 0.166138 loss) +I0616 18:19:33.498797 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0657322 (* 1 = 0.0657322 loss) +I0616 18:19:33.498801 9857 solver.cpp:571] Iteration 98140, lr = 0.0001 +I0616 18:19:45.078887 9857 solver.cpp:242] Iteration 98160, loss = 0.341922 +I0616 18:19:45.078930 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0949488 (* 1 = 0.0949488 loss) +I0616 18:19:45.078935 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0877377 (* 1 = 0.0877377 loss) +I0616 18:19:45.078940 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0125656 (* 1 = 0.0125656 loss) +I0616 18:19:45.078943 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00667266 (* 1 = 0.00667266 loss) +I0616 18:19:45.078948 9857 solver.cpp:571] Iteration 98160, lr = 0.0001 +I0616 18:19:56.449820 9857 solver.cpp:242] Iteration 98180, loss = 0.301266 +I0616 18:19:56.449859 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0782298 (* 1 = 0.0782298 loss) +I0616 18:19:56.449864 9857 solver.cpp:258] Train net output #1: loss_cls = 0.248876 (* 1 = 0.248876 loss) +I0616 18:19:56.449868 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0837181 (* 1 = 0.0837181 loss) +I0616 18:19:56.449872 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00783107 (* 1 = 0.00783107 loss) +I0616 18:19:56.449877 9857 solver.cpp:571] Iteration 98180, lr = 0.0001 +speed: 0.595s / iter +I0616 18:20:08.244320 9857 solver.cpp:242] Iteration 98200, loss = 0.246724 +I0616 18:20:08.244362 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0495241 (* 1 = 0.0495241 loss) +I0616 18:20:08.244369 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0982062 (* 1 = 0.0982062 loss) +I0616 18:20:08.244372 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0247964 (* 1 = 0.0247964 loss) +I0616 18:20:08.244375 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0631782 (* 1 = 0.0631782 loss) +I0616 18:20:08.244379 9857 solver.cpp:571] Iteration 98200, lr = 0.0001 +I0616 18:20:19.858957 9857 solver.cpp:242] Iteration 98220, loss = 0.384762 +I0616 18:20:19.858999 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.115375 (* 1 = 0.115375 loss) +I0616 18:20:19.859004 9857 solver.cpp:258] Train net output #1: loss_cls = 0.107547 (* 1 = 0.107547 loss) +I0616 18:20:19.859009 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0436414 (* 1 = 0.0436414 loss) +I0616 18:20:19.859012 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00715837 (* 1 = 0.00715837 loss) +I0616 18:20:19.859016 9857 solver.cpp:571] Iteration 98220, lr = 0.0001 +I0616 18:20:31.606091 9857 solver.cpp:242] Iteration 98240, loss = 0.880693 +I0616 18:20:31.606132 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.384624 (* 1 = 0.384624 loss) +I0616 18:20:31.606137 9857 solver.cpp:258] Train net output #1: loss_cls = 0.519269 (* 1 = 0.519269 loss) +I0616 18:20:31.606142 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.249839 (* 1 = 0.249839 loss) +I0616 18:20:31.606145 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337238 (* 1 = 0.0337238 loss) +I0616 18:20:31.606149 9857 solver.cpp:571] Iteration 98240, lr = 0.0001 +I0616 18:20:42.948907 9857 solver.cpp:242] Iteration 98260, loss = 0.461261 +I0616 18:20:42.948945 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.16946 (* 1 = 0.16946 loss) +I0616 18:20:42.948951 9857 solver.cpp:258] Train net output #1: loss_cls = 0.218496 (* 1 = 0.218496 loss) +I0616 18:20:42.948956 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0378885 (* 1 = 0.0378885 loss) +I0616 18:20:42.948959 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0404833 (* 1 = 0.0404833 loss) +I0616 18:20:42.948963 9857 solver.cpp:571] Iteration 98260, lr = 0.0001 +I0616 18:20:54.464697 9857 solver.cpp:242] Iteration 98280, loss = 0.355015 +I0616 18:20:54.464737 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206778 (* 1 = 0.206778 loss) +I0616 18:20:54.464743 9857 solver.cpp:258] Train net output #1: loss_cls = 0.133237 (* 1 = 0.133237 loss) +I0616 18:20:54.464747 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0392103 (* 1 = 0.0392103 loss) +I0616 18:20:54.464751 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0713081 (* 1 = 0.0713081 loss) +I0616 18:20:54.464756 9857 solver.cpp:571] Iteration 98280, lr = 0.0001 +I0616 18:21:05.882453 9857 solver.cpp:242] Iteration 98300, loss = 0.435567 +I0616 18:21:05.882495 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.197761 (* 1 = 0.197761 loss) +I0616 18:21:05.882500 9857 solver.cpp:258] Train net output #1: loss_cls = 0.191023 (* 1 = 0.191023 loss) +I0616 18:21:05.882504 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0334811 (* 1 = 0.0334811 loss) +I0616 18:21:05.882508 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0211708 (* 1 = 0.0211708 loss) +I0616 18:21:05.882513 9857 solver.cpp:571] Iteration 98300, lr = 0.0001 +I0616 18:21:17.586164 9857 solver.cpp:242] Iteration 98320, loss = 0.6014 +I0616 18:21:17.586206 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217074 (* 1 = 0.217074 loss) +I0616 18:21:17.586211 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153411 (* 1 = 0.153411 loss) +I0616 18:21:17.586215 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.147292 (* 1 = 0.147292 loss) +I0616 18:21:17.586220 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0368452 (* 1 = 0.0368452 loss) +I0616 18:21:17.586225 9857 solver.cpp:571] Iteration 98320, lr = 0.0001 +I0616 18:21:29.208600 9857 solver.cpp:242] Iteration 98340, loss = 0.669386 +I0616 18:21:29.208642 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.168938 (* 1 = 0.168938 loss) +I0616 18:21:29.208647 9857 solver.cpp:258] Train net output #1: loss_cls = 0.193094 (* 1 = 0.193094 loss) +I0616 18:21:29.208652 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0495361 (* 1 = 0.0495361 loss) +I0616 18:21:29.208655 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0867554 (* 1 = 0.0867554 loss) +I0616 18:21:29.208658 9857 solver.cpp:571] Iteration 98340, lr = 0.0001 +I0616 18:21:40.565284 9857 solver.cpp:242] Iteration 98360, loss = 0.555651 +I0616 18:21:40.565326 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0670407 (* 1 = 0.0670407 loss) +I0616 18:21:40.565332 9857 solver.cpp:258] Train net output #1: loss_cls = 0.123158 (* 1 = 0.123158 loss) +I0616 18:21:40.565336 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.02598 (* 1 = 0.02598 loss) +I0616 18:21:40.565340 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00629888 (* 1 = 0.00629888 loss) +I0616 18:21:40.565345 9857 solver.cpp:571] Iteration 98360, lr = 0.0001 +I0616 18:21:52.008999 9857 solver.cpp:242] Iteration 98380, loss = 0.322694 +I0616 18:21:52.009042 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0661494 (* 1 = 0.0661494 loss) +I0616 18:21:52.009047 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0665291 (* 1 = 0.0665291 loss) +I0616 18:21:52.009052 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0448087 (* 1 = 0.0448087 loss) +I0616 18:21:52.009055 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00948979 (* 1 = 0.00948979 loss) +I0616 18:21:52.009059 9857 solver.cpp:571] Iteration 98380, lr = 0.0001 +speed: 0.595s / iter +I0616 18:22:03.861979 9857 solver.cpp:242] Iteration 98400, loss = 0.372731 +I0616 18:22:03.862020 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.165701 (* 1 = 0.165701 loss) +I0616 18:22:03.862025 9857 solver.cpp:258] Train net output #1: loss_cls = 0.229233 (* 1 = 0.229233 loss) +I0616 18:22:03.862030 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0509328 (* 1 = 0.0509328 loss) +I0616 18:22:03.862033 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0160669 (* 1 = 0.0160669 loss) +I0616 18:22:03.862037 9857 solver.cpp:571] Iteration 98400, lr = 0.0001 +I0616 18:22:15.503269 9857 solver.cpp:242] Iteration 98420, loss = 0.396774 +I0616 18:22:15.503311 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0411976 (* 1 = 0.0411976 loss) +I0616 18:22:15.503317 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0435013 (* 1 = 0.0435013 loss) +I0616 18:22:15.503321 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0445715 (* 1 = 0.0445715 loss) +I0616 18:22:15.503325 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00855791 (* 1 = 0.00855791 loss) +I0616 18:22:15.503330 9857 solver.cpp:571] Iteration 98420, lr = 0.0001 +I0616 18:22:27.132148 9857 solver.cpp:242] Iteration 98440, loss = 0.760426 +I0616 18:22:27.132189 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.361178 (* 1 = 0.361178 loss) +I0616 18:22:27.132195 9857 solver.cpp:258] Train net output #1: loss_cls = 0.635774 (* 1 = 0.635774 loss) +I0616 18:22:27.132200 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.323267 (* 1 = 0.323267 loss) +I0616 18:22:27.132202 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0624308 (* 1 = 0.0624308 loss) +I0616 18:22:27.132206 9857 solver.cpp:571] Iteration 98440, lr = 0.0001 +I0616 18:22:38.586544 9857 solver.cpp:242] Iteration 98460, loss = 0.535638 +I0616 18:22:38.586586 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151784 (* 1 = 0.151784 loss) +I0616 18:22:38.586591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.254302 (* 1 = 0.254302 loss) +I0616 18:22:38.586596 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0115638 (* 1 = 0.0115638 loss) +I0616 18:22:38.586599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0109291 (* 1 = 0.0109291 loss) +I0616 18:22:38.586603 9857 solver.cpp:571] Iteration 98460, lr = 0.0001 +I0616 18:22:50.174721 9857 solver.cpp:242] Iteration 98480, loss = 0.528988 +I0616 18:22:50.174765 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0515833 (* 1 = 0.0515833 loss) +I0616 18:22:50.174772 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120214 (* 1 = 0.120214 loss) +I0616 18:22:50.174775 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0703999 (* 1 = 0.0703999 loss) +I0616 18:22:50.174778 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0244919 (* 1 = 0.0244919 loss) +I0616 18:22:50.174782 9857 solver.cpp:571] Iteration 98480, lr = 0.0001 +I0616 18:23:01.878986 9857 solver.cpp:242] Iteration 98500, loss = 0.310759 +I0616 18:23:01.879012 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.122279 (* 1 = 0.122279 loss) +I0616 18:23:01.879019 9857 solver.cpp:258] Train net output #1: loss_cls = 0.168261 (* 1 = 0.168261 loss) +I0616 18:23:01.879022 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0578229 (* 1 = 0.0578229 loss) +I0616 18:23:01.879025 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0213229 (* 1 = 0.0213229 loss) +I0616 18:23:01.879029 9857 solver.cpp:571] Iteration 98500, lr = 0.0001 +I0616 18:23:13.326807 9857 solver.cpp:242] Iteration 98520, loss = 0.38831 +I0616 18:23:13.326848 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.13213 (* 1 = 0.13213 loss) +I0616 18:23:13.326854 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0887013 (* 1 = 0.0887013 loss) +I0616 18:23:13.326858 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0389098 (* 1 = 0.0389098 loss) +I0616 18:23:13.326863 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00546061 (* 1 = 0.00546061 loss) +I0616 18:23:13.326865 9857 solver.cpp:571] Iteration 98520, lr = 0.0001 +I0616 18:23:24.947252 9857 solver.cpp:242] Iteration 98540, loss = 0.454529 +I0616 18:23:24.947295 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114922 (* 1 = 0.114922 loss) +I0616 18:23:24.947302 9857 solver.cpp:258] Train net output #1: loss_cls = 0.110109 (* 1 = 0.110109 loss) +I0616 18:23:24.947309 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00962802 (* 1 = 0.00962802 loss) +I0616 18:23:24.947314 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0549277 (* 1 = 0.0549277 loss) +I0616 18:23:24.947321 9857 solver.cpp:571] Iteration 98540, lr = 0.0001 +I0616 18:23:36.441633 9857 solver.cpp:242] Iteration 98560, loss = 0.403689 +I0616 18:23:36.441675 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.184094 (* 1 = 0.184094 loss) +I0616 18:23:36.441680 9857 solver.cpp:258] Train net output #1: loss_cls = 0.245033 (* 1 = 0.245033 loss) +I0616 18:23:36.441684 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0524611 (* 1 = 0.0524611 loss) +I0616 18:23:36.441689 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0772471 (* 1 = 0.0772471 loss) +I0616 18:23:36.441691 9857 solver.cpp:571] Iteration 98560, lr = 0.0001 +I0616 18:23:48.175531 9857 solver.cpp:242] Iteration 98580, loss = 0.209021 +I0616 18:23:48.175573 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0747206 (* 1 = 0.0747206 loss) +I0616 18:23:48.175580 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0902945 (* 1 = 0.0902945 loss) +I0616 18:23:48.175583 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0434246 (* 1 = 0.0434246 loss) +I0616 18:23:48.175587 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0191759 (* 1 = 0.0191759 loss) +I0616 18:23:48.175591 9857 solver.cpp:571] Iteration 98580, lr = 0.0001 +speed: 0.595s / iter +I0616 18:23:59.769150 9857 solver.cpp:242] Iteration 98600, loss = 0.264699 +I0616 18:23:59.769193 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0764996 (* 1 = 0.0764996 loss) +I0616 18:23:59.769199 9857 solver.cpp:258] Train net output #1: loss_cls = 0.130798 (* 1 = 0.130798 loss) +I0616 18:23:59.769203 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00939657 (* 1 = 0.00939657 loss) +I0616 18:23:59.769207 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0223517 (* 1 = 0.0223517 loss) +I0616 18:23:59.769210 9857 solver.cpp:571] Iteration 98600, lr = 0.0001 +I0616 18:24:11.292331 9857 solver.cpp:242] Iteration 98620, loss = 0.181776 +I0616 18:24:11.292374 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0983271 (* 1 = 0.0983271 loss) +I0616 18:24:11.292379 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0550653 (* 1 = 0.0550653 loss) +I0616 18:24:11.292383 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0119968 (* 1 = 0.0119968 loss) +I0616 18:24:11.292387 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103598 (* 1 = 0.0103598 loss) +I0616 18:24:11.292390 9857 solver.cpp:571] Iteration 98620, lr = 0.0001 +I0616 18:24:22.865092 9857 solver.cpp:242] Iteration 98640, loss = 0.420763 +I0616 18:24:22.865130 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.145904 (* 1 = 0.145904 loss) +I0616 18:24:22.865135 9857 solver.cpp:258] Train net output #1: loss_cls = 0.217033 (* 1 = 0.217033 loss) +I0616 18:24:22.865140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0285704 (* 1 = 0.0285704 loss) +I0616 18:24:22.865144 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0424657 (* 1 = 0.0424657 loss) +I0616 18:24:22.865147 9857 solver.cpp:571] Iteration 98640, lr = 0.0001 +I0616 18:24:34.517208 9857 solver.cpp:242] Iteration 98660, loss = 0.409399 +I0616 18:24:34.517251 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.134953 (* 1 = 0.134953 loss) +I0616 18:24:34.517256 9857 solver.cpp:258] Train net output #1: loss_cls = 0.126553 (* 1 = 0.126553 loss) +I0616 18:24:34.517261 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0484911 (* 1 = 0.0484911 loss) +I0616 18:24:34.517264 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00487692 (* 1 = 0.00487692 loss) +I0616 18:24:34.517268 9857 solver.cpp:571] Iteration 98660, lr = 0.0001 +I0616 18:24:45.889297 9857 solver.cpp:242] Iteration 98680, loss = 0.192874 +I0616 18:24:45.889338 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.11848 (* 1 = 0.11848 loss) +I0616 18:24:45.889343 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0772535 (* 1 = 0.0772535 loss) +I0616 18:24:45.889348 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00865812 (* 1 = 0.00865812 loss) +I0616 18:24:45.889351 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00423948 (* 1 = 0.00423948 loss) +I0616 18:24:45.889355 9857 solver.cpp:571] Iteration 98680, lr = 0.0001 +I0616 18:24:57.189988 9857 solver.cpp:242] Iteration 98700, loss = 0.576841 +I0616 18:24:57.190031 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.249521 (* 1 = 0.249521 loss) +I0616 18:24:57.190050 9857 solver.cpp:258] Train net output #1: loss_cls = 0.247854 (* 1 = 0.247854 loss) +I0616 18:24:57.190054 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0593924 (* 1 = 0.0593924 loss) +I0616 18:24:57.190058 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0364205 (* 1 = 0.0364205 loss) +I0616 18:24:57.190062 9857 solver.cpp:571] Iteration 98700, lr = 0.0001 +I0616 18:25:08.573914 9857 solver.cpp:242] Iteration 98720, loss = 0.282331 +I0616 18:25:08.573954 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0453947 (* 1 = 0.0453947 loss) +I0616 18:25:08.573959 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0625279 (* 1 = 0.0625279 loss) +I0616 18:25:08.573963 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00444238 (* 1 = 0.00444238 loss) +I0616 18:25:08.573967 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00529971 (* 1 = 0.00529971 loss) +I0616 18:25:08.573971 9857 solver.cpp:571] Iteration 98720, lr = 0.0001 +I0616 18:25:20.138499 9857 solver.cpp:242] Iteration 98740, loss = 0.634458 +I0616 18:25:20.138540 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.394471 (* 1 = 0.394471 loss) +I0616 18:25:20.138546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.282273 (* 1 = 0.282273 loss) +I0616 18:25:20.138550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.142452 (* 1 = 0.142452 loss) +I0616 18:25:20.138553 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.187936 (* 1 = 0.187936 loss) +I0616 18:25:20.138557 9857 solver.cpp:571] Iteration 98740, lr = 0.0001 +I0616 18:25:31.769947 9857 solver.cpp:242] Iteration 98760, loss = 0.177708 +I0616 18:25:31.769989 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0562809 (* 1 = 0.0562809 loss) +I0616 18:25:31.769995 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0828273 (* 1 = 0.0828273 loss) +I0616 18:25:31.769999 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0267751 (* 1 = 0.0267751 loss) +I0616 18:25:31.770004 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00575828 (* 1 = 0.00575828 loss) +I0616 18:25:31.770006 9857 solver.cpp:571] Iteration 98760, lr = 0.0001 +I0616 18:25:43.644629 9857 solver.cpp:242] Iteration 98780, loss = 0.907794 +I0616 18:25:43.644671 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.217195 (* 1 = 0.217195 loss) +I0616 18:25:43.644676 9857 solver.cpp:258] Train net output #1: loss_cls = 0.476578 (* 1 = 0.476578 loss) +I0616 18:25:43.644680 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.162267 (* 1 = 0.162267 loss) +I0616 18:25:43.644685 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0447989 (* 1 = 0.0447989 loss) +I0616 18:25:43.644687 9857 solver.cpp:571] Iteration 98780, lr = 0.0001 +speed: 0.595s / iter +I0616 18:25:55.016715 9857 solver.cpp:242] Iteration 98800, loss = 0.298793 +I0616 18:25:55.016754 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.193207 (* 1 = 0.193207 loss) +I0616 18:25:55.016760 9857 solver.cpp:258] Train net output #1: loss_cls = 0.21382 (* 1 = 0.21382 loss) +I0616 18:25:55.016764 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0392512 (* 1 = 0.0392512 loss) +I0616 18:25:55.016768 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0231553 (* 1 = 0.0231553 loss) +I0616 18:25:55.016772 9857 solver.cpp:571] Iteration 98800, lr = 0.0001 +I0616 18:26:06.617190 9857 solver.cpp:242] Iteration 98820, loss = 0.940773 +I0616 18:26:06.617234 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0429957 (* 1 = 0.0429957 loss) +I0616 18:26:06.617239 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0633488 (* 1 = 0.0633488 loss) +I0616 18:26:06.617244 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0139449 (* 1 = 0.0139449 loss) +I0616 18:26:06.617247 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00627246 (* 1 = 0.00627246 loss) +I0616 18:26:06.617254 9857 solver.cpp:571] Iteration 98820, lr = 0.0001 +I0616 18:26:17.950558 9857 solver.cpp:242] Iteration 98840, loss = 0.460725 +I0616 18:26:17.950600 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.242389 (* 1 = 0.242389 loss) +I0616 18:26:17.950606 9857 solver.cpp:258] Train net output #1: loss_cls = 0.162471 (* 1 = 0.162471 loss) +I0616 18:26:17.950610 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0624272 (* 1 = 0.0624272 loss) +I0616 18:26:17.950614 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0302878 (* 1 = 0.0302878 loss) +I0616 18:26:17.950618 9857 solver.cpp:571] Iteration 98840, lr = 0.0001 +I0616 18:26:29.472707 9857 solver.cpp:242] Iteration 98860, loss = 0.481716 +I0616 18:26:29.472749 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.275747 (* 1 = 0.275747 loss) +I0616 18:26:29.472754 9857 solver.cpp:258] Train net output #1: loss_cls = 0.366222 (* 1 = 0.366222 loss) +I0616 18:26:29.472759 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.064421 (* 1 = 0.064421 loss) +I0616 18:26:29.472762 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0317737 (* 1 = 0.0317737 loss) +I0616 18:26:29.472766 9857 solver.cpp:571] Iteration 98860, lr = 0.0001 +I0616 18:26:41.106770 9857 solver.cpp:242] Iteration 98880, loss = 0.337648 +I0616 18:26:41.106812 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0653304 (* 1 = 0.0653304 loss) +I0616 18:26:41.106817 9857 solver.cpp:258] Train net output #1: loss_cls = 0.178104 (* 1 = 0.178104 loss) +I0616 18:26:41.106822 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0282987 (* 1 = 0.0282987 loss) +I0616 18:26:41.106824 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0103425 (* 1 = 0.0103425 loss) +I0616 18:26:41.106828 9857 solver.cpp:571] Iteration 98880, lr = 0.0001 +I0616 18:26:52.316057 9857 solver.cpp:242] Iteration 98900, loss = 0.262621 +I0616 18:26:52.316100 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0631893 (* 1 = 0.0631893 loss) +I0616 18:26:52.316107 9857 solver.cpp:258] Train net output #1: loss_cls = 0.120885 (* 1 = 0.120885 loss) +I0616 18:26:52.316110 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.033931 (* 1 = 0.033931 loss) +I0616 18:26:52.316114 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0137471 (* 1 = 0.0137471 loss) +I0616 18:26:52.316118 9857 solver.cpp:571] Iteration 98900, lr = 0.0001 +I0616 18:27:04.055420 9857 solver.cpp:242] Iteration 98920, loss = 1.00468 +I0616 18:27:04.055462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270185 (* 1 = 0.270185 loss) +I0616 18:27:04.055469 9857 solver.cpp:258] Train net output #1: loss_cls = 0.506837 (* 1 = 0.506837 loss) +I0616 18:27:04.055472 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.192033 (* 1 = 0.192033 loss) +I0616 18:27:04.055475 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.389993 (* 1 = 0.389993 loss) +I0616 18:27:04.055480 9857 solver.cpp:571] Iteration 98920, lr = 0.0001 +I0616 18:27:15.480134 9857 solver.cpp:242] Iteration 98940, loss = 0.328497 +I0616 18:27:15.480177 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0717537 (* 1 = 0.0717537 loss) +I0616 18:27:15.480182 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0748377 (* 1 = 0.0748377 loss) +I0616 18:27:15.480186 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0213009 (* 1 = 0.0213009 loss) +I0616 18:27:15.480190 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00695591 (* 1 = 0.00695591 loss) +I0616 18:27:15.480193 9857 solver.cpp:571] Iteration 98940, lr = 0.0001 +I0616 18:27:26.773524 9857 solver.cpp:242] Iteration 98960, loss = 0.513309 +I0616 18:27:26.773563 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.439283 (* 1 = 0.439283 loss) +I0616 18:27:26.773568 9857 solver.cpp:258] Train net output #1: loss_cls = 0.180639 (* 1 = 0.180639 loss) +I0616 18:27:26.773572 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.119437 (* 1 = 0.119437 loss) +I0616 18:27:26.773576 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0834656 (* 1 = 0.0834656 loss) +I0616 18:27:26.773579 9857 solver.cpp:571] Iteration 98960, lr = 0.0001 +I0616 18:27:38.267802 9857 solver.cpp:242] Iteration 98980, loss = 0.281918 +I0616 18:27:38.267843 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0780353 (* 1 = 0.0780353 loss) +I0616 18:27:38.267848 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0848558 (* 1 = 0.0848558 loss) +I0616 18:27:38.267853 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0324999 (* 1 = 0.0324999 loss) +I0616 18:27:38.267856 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0275296 (* 1 = 0.0275296 loss) +I0616 18:27:38.267859 9857 solver.cpp:571] Iteration 98980, lr = 0.0001 +speed: 0.595s / iter +I0616 18:27:49.848088 9857 solver.cpp:242] Iteration 99000, loss = 0.812387 +I0616 18:27:49.848129 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406793 (* 1 = 0.406793 loss) +I0616 18:27:49.848135 9857 solver.cpp:258] Train net output #1: loss_cls = 0.39646 (* 1 = 0.39646 loss) +I0616 18:27:49.848140 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.223865 (* 1 = 0.223865 loss) +I0616 18:27:49.848142 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.321309 (* 1 = 0.321309 loss) +I0616 18:27:49.848146 9857 solver.cpp:571] Iteration 99000, lr = 0.0001 +I0616 18:28:01.334019 9857 solver.cpp:242] Iteration 99020, loss = 0.332989 +I0616 18:28:01.334062 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0461671 (* 1 = 0.0461671 loss) +I0616 18:28:01.334067 9857 solver.cpp:258] Train net output #1: loss_cls = 0.192078 (* 1 = 0.192078 loss) +I0616 18:28:01.334072 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00798904 (* 1 = 0.00798904 loss) +I0616 18:28:01.334076 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00681842 (* 1 = 0.00681842 loss) +I0616 18:28:01.334081 9857 solver.cpp:571] Iteration 99020, lr = 0.0001 +I0616 18:28:13.059687 9857 solver.cpp:242] Iteration 99040, loss = 0.918443 +I0616 18:28:13.059731 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.270077 (* 1 = 0.270077 loss) +I0616 18:28:13.059736 9857 solver.cpp:258] Train net output #1: loss_cls = 0.57921 (* 1 = 0.57921 loss) +I0616 18:28:13.059741 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.188191 (* 1 = 0.188191 loss) +I0616 18:28:13.059744 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.14482 (* 1 = 0.14482 loss) +I0616 18:28:13.059748 9857 solver.cpp:571] Iteration 99040, lr = 0.0001 +I0616 18:28:24.302304 9857 solver.cpp:242] Iteration 99060, loss = 0.271705 +I0616 18:28:24.302345 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.183757 (* 1 = 0.183757 loss) +I0616 18:28:24.302351 9857 solver.cpp:258] Train net output #1: loss_cls = 0.179405 (* 1 = 0.179405 loss) +I0616 18:28:24.302356 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0097497 (* 1 = 0.0097497 loss) +I0616 18:28:24.302359 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167582 (* 1 = 0.0167582 loss) +I0616 18:28:24.302364 9857 solver.cpp:571] Iteration 99060, lr = 0.0001 +I0616 18:28:35.668314 9857 solver.cpp:242] Iteration 99080, loss = 0.758417 +I0616 18:28:35.668357 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.229511 (* 1 = 0.229511 loss) +I0616 18:28:35.668362 9857 solver.cpp:258] Train net output #1: loss_cls = 0.351774 (* 1 = 0.351774 loss) +I0616 18:28:35.668366 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.12432 (* 1 = 0.12432 loss) +I0616 18:28:35.668370 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.221045 (* 1 = 0.221045 loss) +I0616 18:28:35.668375 9857 solver.cpp:571] Iteration 99080, lr = 0.0001 +I0616 18:28:47.376797 9857 solver.cpp:242] Iteration 99100, loss = 0.321762 +I0616 18:28:47.376838 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.191308 (* 1 = 0.191308 loss) +I0616 18:28:47.376843 9857 solver.cpp:258] Train net output #1: loss_cls = 0.134092 (* 1 = 0.134092 loss) +I0616 18:28:47.376848 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0839486 (* 1 = 0.0839486 loss) +I0616 18:28:47.376852 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00402321 (* 1 = 0.00402321 loss) +I0616 18:28:47.376855 9857 solver.cpp:571] Iteration 99100, lr = 0.0001 +I0616 18:28:58.537681 9857 solver.cpp:242] Iteration 99120, loss = 0.317092 +I0616 18:28:58.537722 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0640134 (* 1 = 0.0640134 loss) +I0616 18:28:58.537729 9857 solver.cpp:258] Train net output #1: loss_cls = 0.16217 (* 1 = 0.16217 loss) +I0616 18:28:58.537732 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0912009 (* 1 = 0.0912009 loss) +I0616 18:28:58.537735 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00867188 (* 1 = 0.00867188 loss) +I0616 18:28:58.537739 9857 solver.cpp:571] Iteration 99120, lr = 0.0001 +I0616 18:29:09.999546 9857 solver.cpp:242] Iteration 99140, loss = 0.270703 +I0616 18:29:09.999585 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.114234 (* 1 = 0.114234 loss) +I0616 18:29:09.999591 9857 solver.cpp:258] Train net output #1: loss_cls = 0.169633 (* 1 = 0.169633 loss) +I0616 18:29:09.999595 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0417111 (* 1 = 0.0417111 loss) +I0616 18:29:09.999599 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0377783 (* 1 = 0.0377783 loss) +I0616 18:29:09.999603 9857 solver.cpp:571] Iteration 99140, lr = 0.0001 +I0616 18:29:21.493525 9857 solver.cpp:242] Iteration 99160, loss = 0.285719 +I0616 18:29:21.493567 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.121893 (* 1 = 0.121893 loss) +I0616 18:29:21.493573 9857 solver.cpp:258] Train net output #1: loss_cls = 0.161362 (* 1 = 0.161362 loss) +I0616 18:29:21.493577 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.014724 (* 1 = 0.014724 loss) +I0616 18:29:21.493582 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00554821 (* 1 = 0.00554821 loss) +I0616 18:29:21.493587 9857 solver.cpp:571] Iteration 99160, lr = 0.0001 +I0616 18:29:32.951989 9857 solver.cpp:242] Iteration 99180, loss = 0.688097 +I0616 18:29:32.952030 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.127041 (* 1 = 0.127041 loss) +I0616 18:29:32.952036 9857 solver.cpp:258] Train net output #1: loss_cls = 0.585918 (* 1 = 0.585918 loss) +I0616 18:29:32.952040 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0757115 (* 1 = 0.0757115 loss) +I0616 18:29:32.952044 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00678241 (* 1 = 0.00678241 loss) +I0616 18:29:32.952047 9857 solver.cpp:571] Iteration 99180, lr = 0.0001 +speed: 0.595s / iter +I0616 18:29:44.583499 9857 solver.cpp:242] Iteration 99200, loss = 0.292114 +I0616 18:29:44.583541 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.146627 (* 1 = 0.146627 loss) +I0616 18:29:44.583546 9857 solver.cpp:258] Train net output #1: loss_cls = 0.146362 (* 1 = 0.146362 loss) +I0616 18:29:44.583550 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0339284 (* 1 = 0.0339284 loss) +I0616 18:29:44.583554 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0180498 (* 1 = 0.0180498 loss) +I0616 18:29:44.583557 9857 solver.cpp:571] Iteration 99200, lr = 0.0001 +I0616 18:29:56.106865 9857 solver.cpp:242] Iteration 99220, loss = 0.419964 +I0616 18:29:56.106907 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.215182 (* 1 = 0.215182 loss) +I0616 18:29:56.106914 9857 solver.cpp:258] Train net output #1: loss_cls = 0.143214 (* 1 = 0.143214 loss) +I0616 18:29:56.106917 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0472742 (* 1 = 0.0472742 loss) +I0616 18:29:56.106921 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0503047 (* 1 = 0.0503047 loss) +I0616 18:29:56.106925 9857 solver.cpp:571] Iteration 99220, lr = 0.0001 +I0616 18:30:07.601207 9857 solver.cpp:242] Iteration 99240, loss = 0.618297 +I0616 18:30:07.601248 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.354704 (* 1 = 0.354704 loss) +I0616 18:30:07.601254 9857 solver.cpp:258] Train net output #1: loss_cls = 0.435199 (* 1 = 0.435199 loss) +I0616 18:30:07.601258 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0852827 (* 1 = 0.0852827 loss) +I0616 18:30:07.601263 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0621997 (* 1 = 0.0621997 loss) +I0616 18:30:07.601265 9857 solver.cpp:571] Iteration 99240, lr = 0.0001 +I0616 18:30:19.491825 9857 solver.cpp:242] Iteration 99260, loss = 0.36372 +I0616 18:30:19.491868 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.06714 (* 1 = 0.06714 loss) +I0616 18:30:19.491873 9857 solver.cpp:258] Train net output #1: loss_cls = 0.127165 (* 1 = 0.127165 loss) +I0616 18:30:19.491876 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0072427 (* 1 = 0.0072427 loss) +I0616 18:30:19.491880 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00566145 (* 1 = 0.00566145 loss) +I0616 18:30:19.491883 9857 solver.cpp:571] Iteration 99260, lr = 0.0001 +I0616 18:30:30.899492 9857 solver.cpp:242] Iteration 99280, loss = 0.49391 +I0616 18:30:30.899534 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177613 (* 1 = 0.177613 loss) +I0616 18:30:30.899539 9857 solver.cpp:258] Train net output #1: loss_cls = 0.439023 (* 1 = 0.439023 loss) +I0616 18:30:30.899544 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0646144 (* 1 = 0.0646144 loss) +I0616 18:30:30.899547 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0319938 (* 1 = 0.0319938 loss) +I0616 18:30:30.899551 9857 solver.cpp:571] Iteration 99280, lr = 0.0001 +I0616 18:30:42.336808 9857 solver.cpp:242] Iteration 99300, loss = 1.32797 +I0616 18:30:42.336849 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.343631 (* 1 = 0.343631 loss) +I0616 18:30:42.336855 9857 solver.cpp:258] Train net output #1: loss_cls = 0.647619 (* 1 = 0.647619 loss) +I0616 18:30:42.336859 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.23685 (* 1 = 0.23685 loss) +I0616 18:30:42.336863 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.484957 (* 1 = 0.484957 loss) +I0616 18:30:42.336868 9857 solver.cpp:571] Iteration 99300, lr = 0.0001 +I0616 18:30:53.966373 9857 solver.cpp:242] Iteration 99320, loss = 0.662873 +I0616 18:30:53.966415 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0906241 (* 1 = 0.0906241 loss) +I0616 18:30:53.966421 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0605863 (* 1 = 0.0605863 loss) +I0616 18:30:53.966426 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00904818 (* 1 = 0.00904818 loss) +I0616 18:30:53.966429 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0167331 (* 1 = 0.0167331 loss) +I0616 18:30:53.966434 9857 solver.cpp:571] Iteration 99320, lr = 0.0001 +I0616 18:31:05.219257 9857 solver.cpp:242] Iteration 99340, loss = 0.448368 +I0616 18:31:05.219297 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.297797 (* 1 = 0.297797 loss) +I0616 18:31:05.219302 9857 solver.cpp:258] Train net output #1: loss_cls = 0.353473 (* 1 = 0.353473 loss) +I0616 18:31:05.219307 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0286734 (* 1 = 0.0286734 loss) +I0616 18:31:05.219311 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.040016 (* 1 = 0.040016 loss) +I0616 18:31:05.219315 9857 solver.cpp:571] Iteration 99340, lr = 0.0001 +I0616 18:31:16.561817 9857 solver.cpp:242] Iteration 99360, loss = 0.621702 +I0616 18:31:16.561858 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.308774 (* 1 = 0.308774 loss) +I0616 18:31:16.561864 9857 solver.cpp:258] Train net output #1: loss_cls = 0.283325 (* 1 = 0.283325 loss) +I0616 18:31:16.561868 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0400278 (* 1 = 0.0400278 loss) +I0616 18:31:16.561872 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0115568 (* 1 = 0.0115568 loss) +I0616 18:31:16.561875 9857 solver.cpp:571] Iteration 99360, lr = 0.0001 +I0616 18:31:28.049873 9857 solver.cpp:242] Iteration 99380, loss = 0.316093 +I0616 18:31:28.049916 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0588299 (* 1 = 0.0588299 loss) +I0616 18:31:28.049921 9857 solver.cpp:258] Train net output #1: loss_cls = 0.115225 (* 1 = 0.115225 loss) +I0616 18:31:28.049926 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0066456 (* 1 = 0.0066456 loss) +I0616 18:31:28.049928 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0244321 (* 1 = 0.0244321 loss) +I0616 18:31:28.049932 9857 solver.cpp:571] Iteration 99380, lr = 0.0001 +speed: 0.595s / iter +I0616 18:31:39.629931 9857 solver.cpp:242] Iteration 99400, loss = 1.03962 +I0616 18:31:39.629973 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.186816 (* 1 = 0.186816 loss) +I0616 18:31:39.629979 9857 solver.cpp:258] Train net output #1: loss_cls = 0.292685 (* 1 = 0.292685 loss) +I0616 18:31:39.629983 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0562829 (* 1 = 0.0562829 loss) +I0616 18:31:39.629987 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0105967 (* 1 = 0.0105967 loss) +I0616 18:31:39.629992 9857 solver.cpp:571] Iteration 99400, lr = 0.0001 +I0616 18:31:51.109416 9857 solver.cpp:242] Iteration 99420, loss = 0.657023 +I0616 18:31:51.109458 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.233111 (* 1 = 0.233111 loss) +I0616 18:31:51.109464 9857 solver.cpp:258] Train net output #1: loss_cls = 0.607091 (* 1 = 0.607091 loss) +I0616 18:31:51.109468 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0428097 (* 1 = 0.0428097 loss) +I0616 18:31:51.109472 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.03883 (* 1 = 0.03883 loss) +I0616 18:31:51.109475 9857 solver.cpp:571] Iteration 99420, lr = 0.0001 +I0616 18:32:02.488605 9857 solver.cpp:242] Iteration 99440, loss = 0.647863 +I0616 18:32:02.488648 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.433467 (* 1 = 0.433467 loss) +I0616 18:32:02.488654 9857 solver.cpp:258] Train net output #1: loss_cls = 0.324105 (* 1 = 0.324105 loss) +I0616 18:32:02.488658 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.168421 (* 1 = 0.168421 loss) +I0616 18:32:02.488662 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.094843 (* 1 = 0.094843 loss) +I0616 18:32:02.488665 9857 solver.cpp:571] Iteration 99440, lr = 0.0001 +I0616 18:32:13.946130 9857 solver.cpp:242] Iteration 99460, loss = 0.344214 +I0616 18:32:13.946172 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0742639 (* 1 = 0.0742639 loss) +I0616 18:32:13.946178 9857 solver.cpp:258] Train net output #1: loss_cls = 0.128863 (* 1 = 0.128863 loss) +I0616 18:32:13.946182 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0155174 (* 1 = 0.0155174 loss) +I0616 18:32:13.946185 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00477375 (* 1 = 0.00477375 loss) +I0616 18:32:13.946189 9857 solver.cpp:571] Iteration 99460, lr = 0.0001 +I0616 18:32:25.471596 9857 solver.cpp:242] Iteration 99480, loss = 0.228291 +I0616 18:32:25.471637 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0614476 (* 1 = 0.0614476 loss) +I0616 18:32:25.471642 9857 solver.cpp:258] Train net output #1: loss_cls = 0.145303 (* 1 = 0.145303 loss) +I0616 18:32:25.471647 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0163259 (* 1 = 0.0163259 loss) +I0616 18:32:25.471650 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00979426 (* 1 = 0.00979426 loss) +I0616 18:32:25.471653 9857 solver.cpp:571] Iteration 99480, lr = 0.0001 +I0616 18:32:37.002498 9857 solver.cpp:242] Iteration 99500, loss = 0.4631 +I0616 18:32:37.002542 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0587355 (* 1 = 0.0587355 loss) +I0616 18:32:37.002547 9857 solver.cpp:258] Train net output #1: loss_cls = 0.153587 (* 1 = 0.153587 loss) +I0616 18:32:37.002552 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00709585 (* 1 = 0.00709585 loss) +I0616 18:32:37.002555 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00410104 (* 1 = 0.00410104 loss) +I0616 18:32:37.002558 9857 solver.cpp:571] Iteration 99500, lr = 0.0001 +I0616 18:32:48.500861 9857 solver.cpp:242] Iteration 99520, loss = 0.427218 +I0616 18:32:48.500905 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206421 (* 1 = 0.206421 loss) +I0616 18:32:48.500910 9857 solver.cpp:258] Train net output #1: loss_cls = 0.275576 (* 1 = 0.275576 loss) +I0616 18:32:48.500915 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0503425 (* 1 = 0.0503425 loss) +I0616 18:32:48.500918 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0271514 (* 1 = 0.0271514 loss) +I0616 18:32:48.500922 9857 solver.cpp:571] Iteration 99520, lr = 0.0001 +I0616 18:33:00.145758 9857 solver.cpp:242] Iteration 99540, loss = 0.496943 +I0616 18:33:00.145798 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.278422 (* 1 = 0.278422 loss) +I0616 18:33:00.145804 9857 solver.cpp:258] Train net output #1: loss_cls = 0.267393 (* 1 = 0.267393 loss) +I0616 18:33:00.145808 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.16831 (* 1 = 0.16831 loss) +I0616 18:33:00.145812 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.094805 (* 1 = 0.094805 loss) +I0616 18:33:00.145817 9857 solver.cpp:571] Iteration 99540, lr = 0.0001 +I0616 18:33:11.714586 9857 solver.cpp:242] Iteration 99560, loss = 0.705886 +I0616 18:33:11.714627 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.171756 (* 1 = 0.171756 loss) +I0616 18:33:11.714632 9857 solver.cpp:258] Train net output #1: loss_cls = 0.356386 (* 1 = 0.356386 loss) +I0616 18:33:11.714637 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.158637 (* 1 = 0.158637 loss) +I0616 18:33:11.714639 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.356958 (* 1 = 0.356958 loss) +I0616 18:33:11.714643 9857 solver.cpp:571] Iteration 99560, lr = 0.0001 +I0616 18:33:22.994000 9857 solver.cpp:242] Iteration 99580, loss = 0.420452 +I0616 18:33:22.994041 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.206995 (* 1 = 0.206995 loss) +I0616 18:33:22.994046 9857 solver.cpp:258] Train net output #1: loss_cls = 0.151327 (* 1 = 0.151327 loss) +I0616 18:33:22.994051 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.143734 (* 1 = 0.143734 loss) +I0616 18:33:22.994055 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0337944 (* 1 = 0.0337944 loss) +I0616 18:33:22.994058 9857 solver.cpp:571] Iteration 99580, lr = 0.0001 +speed: 0.595s / iter +I0616 18:33:34.208447 9857 solver.cpp:242] Iteration 99600, loss = 0.228994 +I0616 18:33:34.208489 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0744785 (* 1 = 0.0744785 loss) +I0616 18:33:34.208495 9857 solver.cpp:258] Train net output #1: loss_cls = 0.1647 (* 1 = 0.1647 loss) +I0616 18:33:34.208499 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.028098 (* 1 = 0.028098 loss) +I0616 18:33:34.208503 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0112913 (* 1 = 0.0112913 loss) +I0616 18:33:34.208508 9857 solver.cpp:571] Iteration 99600, lr = 0.0001 +I0616 18:33:46.048451 9857 solver.cpp:242] Iteration 99620, loss = 0.405554 +I0616 18:33:46.048493 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.139002 (* 1 = 0.139002 loss) +I0616 18:33:46.048499 9857 solver.cpp:258] Train net output #1: loss_cls = 0.209507 (* 1 = 0.209507 loss) +I0616 18:33:46.048503 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0821394 (* 1 = 0.0821394 loss) +I0616 18:33:46.048507 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0234199 (* 1 = 0.0234199 loss) +I0616 18:33:46.048511 9857 solver.cpp:571] Iteration 99620, lr = 0.0001 +I0616 18:33:57.511718 9857 solver.cpp:242] Iteration 99640, loss = 0.49277 +I0616 18:33:57.511759 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0779961 (* 1 = 0.0779961 loss) +I0616 18:33:57.511765 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0808566 (* 1 = 0.0808566 loss) +I0616 18:33:57.511768 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0190441 (* 1 = 0.0190441 loss) +I0616 18:33:57.511772 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0873564 (* 1 = 0.0873564 loss) +I0616 18:33:57.511776 9857 solver.cpp:571] Iteration 99640, lr = 0.0001 +I0616 18:34:08.921144 9857 solver.cpp:242] Iteration 99660, loss = 0.707454 +I0616 18:34:08.921185 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.406177 (* 1 = 0.406177 loss) +I0616 18:34:08.921191 9857 solver.cpp:258] Train net output #1: loss_cls = 0.460622 (* 1 = 0.460622 loss) +I0616 18:34:08.921195 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.070883 (* 1 = 0.070883 loss) +I0616 18:34:08.921198 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.234285 (* 1 = 0.234285 loss) +I0616 18:34:08.921202 9857 solver.cpp:571] Iteration 99660, lr = 0.0001 +I0616 18:34:20.248121 9857 solver.cpp:242] Iteration 99680, loss = 0.70836 +I0616 18:34:20.248163 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.117974 (* 1 = 0.117974 loss) +I0616 18:34:20.248168 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19709 (* 1 = 0.19709 loss) +I0616 18:34:20.248172 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.021502 (* 1 = 0.021502 loss) +I0616 18:34:20.248177 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0308322 (* 1 = 0.0308322 loss) +I0616 18:34:20.248180 9857 solver.cpp:571] Iteration 99680, lr = 0.0001 +I0616 18:34:32.091737 9857 solver.cpp:242] Iteration 99700, loss = 0.617038 +I0616 18:34:32.091778 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0957635 (* 1 = 0.0957635 loss) +I0616 18:34:32.091784 9857 solver.cpp:258] Train net output #1: loss_cls = 0.136318 (* 1 = 0.136318 loss) +I0616 18:34:32.091789 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0146542 (* 1 = 0.0146542 loss) +I0616 18:34:32.091792 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0104415 (* 1 = 0.0104415 loss) +I0616 18:34:32.091795 9857 solver.cpp:571] Iteration 99700, lr = 0.0001 +I0616 18:34:43.753793 9857 solver.cpp:242] Iteration 99720, loss = 0.347714 +I0616 18:34:43.753835 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0725551 (* 1 = 0.0725551 loss) +I0616 18:34:43.753840 9857 solver.cpp:258] Train net output #1: loss_cls = 0.092832 (* 1 = 0.092832 loss) +I0616 18:34:43.753845 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00640693 (* 1 = 0.00640693 loss) +I0616 18:34:43.753849 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0156148 (* 1 = 0.0156148 loss) +I0616 18:34:43.753852 9857 solver.cpp:571] Iteration 99720, lr = 0.0001 +I0616 18:34:55.450947 9857 solver.cpp:242] Iteration 99740, loss = 0.6354 +I0616 18:34:55.450989 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.231711 (* 1 = 0.231711 loss) +I0616 18:34:55.450994 9857 solver.cpp:258] Train net output #1: loss_cls = 0.232606 (* 1 = 0.232606 loss) +I0616 18:34:55.450999 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0953872 (* 1 = 0.0953872 loss) +I0616 18:34:55.451002 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0267244 (* 1 = 0.0267244 loss) +I0616 18:34:55.451006 9857 solver.cpp:571] Iteration 99740, lr = 0.0001 +I0616 18:35:06.881464 9857 solver.cpp:242] Iteration 99760, loss = 0.512202 +I0616 18:35:06.881505 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.151367 (* 1 = 0.151367 loss) +I0616 18:35:06.881510 9857 solver.cpp:258] Train net output #1: loss_cls = 0.211575 (* 1 = 0.211575 loss) +I0616 18:35:06.881515 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0224592 (* 1 = 0.0224592 loss) +I0616 18:35:06.881518 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0728542 (* 1 = 0.0728542 loss) +I0616 18:35:06.881522 9857 solver.cpp:571] Iteration 99760, lr = 0.0001 +I0616 18:35:18.656857 9857 solver.cpp:242] Iteration 99780, loss = 0.309272 +I0616 18:35:18.656900 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0509814 (* 1 = 0.0509814 loss) +I0616 18:35:18.656905 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0888469 (* 1 = 0.0888469 loss) +I0616 18:35:18.656910 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0560772 (* 1 = 0.0560772 loss) +I0616 18:35:18.656913 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.01262 (* 1 = 0.01262 loss) +I0616 18:35:18.656918 9857 solver.cpp:571] Iteration 99780, lr = 0.0001 +speed: 0.595s / iter +I0616 18:35:30.406213 9857 solver.cpp:242] Iteration 99800, loss = 0.616588 +I0616 18:35:30.406255 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.194671 (* 1 = 0.194671 loss) +I0616 18:35:30.406260 9857 solver.cpp:258] Train net output #1: loss_cls = 0.306219 (* 1 = 0.306219 loss) +I0616 18:35:30.406265 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0411927 (* 1 = 0.0411927 loss) +I0616 18:35:30.406268 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.17132 (* 1 = 0.17132 loss) +I0616 18:35:30.406272 9857 solver.cpp:571] Iteration 99800, lr = 0.0001 +I0616 18:35:41.858222 9857 solver.cpp:242] Iteration 99820, loss = 0.497819 +I0616 18:35:41.858259 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.196714 (* 1 = 0.196714 loss) +I0616 18:35:41.858264 9857 solver.cpp:258] Train net output #1: loss_cls = 0.208413 (* 1 = 0.208413 loss) +I0616 18:35:41.858268 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0806778 (* 1 = 0.0806778 loss) +I0616 18:35:41.858273 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0714112 (* 1 = 0.0714112 loss) +I0616 18:35:41.858276 9857 solver.cpp:571] Iteration 99820, lr = 0.0001 +I0616 18:35:53.362105 9857 solver.cpp:242] Iteration 99840, loss = 0.382006 +I0616 18:35:53.362146 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0453681 (* 1 = 0.0453681 loss) +I0616 18:35:53.362152 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0702499 (* 1 = 0.0702499 loss) +I0616 18:35:53.362156 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.00588503 (* 1 = 0.00588503 loss) +I0616 18:35:53.362159 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0166426 (* 1 = 0.0166426 loss) +I0616 18:35:53.362164 9857 solver.cpp:571] Iteration 99840, lr = 0.0001 +I0616 18:36:04.997422 9857 solver.cpp:242] Iteration 99860, loss = 0.199261 +I0616 18:36:04.997462 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0431997 (* 1 = 0.0431997 loss) +I0616 18:36:04.997468 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0665549 (* 1 = 0.0665549 loss) +I0616 18:36:04.997473 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.000414727 (* 1 = 0.000414727 loss) +I0616 18:36:04.997475 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.00583805 (* 1 = 0.00583805 loss) +I0616 18:36:04.997479 9857 solver.cpp:571] Iteration 99860, lr = 0.0001 +I0616 18:36:16.251775 9857 solver.cpp:242] Iteration 99880, loss = 0.339673 +I0616 18:36:16.251817 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0935651 (* 1 = 0.0935651 loss) +I0616 18:36:16.251823 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0421645 (* 1 = 0.0421645 loss) +I0616 18:36:16.251827 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0135467 (* 1 = 0.0135467 loss) +I0616 18:36:16.251830 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0436006 (* 1 = 0.0436006 loss) +I0616 18:36:16.251834 9857 solver.cpp:571] Iteration 99880, lr = 0.0001 +I0616 18:36:27.743038 9857 solver.cpp:242] Iteration 99900, loss = 0.232426 +I0616 18:36:27.743080 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.125781 (* 1 = 0.125781 loss) +I0616 18:36:27.743086 9857 solver.cpp:258] Train net output #1: loss_cls = 0.15137 (* 1 = 0.15137 loss) +I0616 18:36:27.743090 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0328616 (* 1 = 0.0328616 loss) +I0616 18:36:27.743094 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.000566984 (* 1 = 0.000566984 loss) +I0616 18:36:27.743098 9857 solver.cpp:571] Iteration 99900, lr = 0.0001 +I0616 18:36:39.232003 9857 solver.cpp:242] Iteration 99920, loss = 0.396103 +I0616 18:36:39.232046 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.0532796 (* 1 = 0.0532796 loss) +I0616 18:36:39.232053 9857 solver.cpp:258] Train net output #1: loss_cls = 0.0960585 (* 1 = 0.0960585 loss) +I0616 18:36:39.232056 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0351988 (* 1 = 0.0351988 loss) +I0616 18:36:39.232060 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.007271 (* 1 = 0.007271 loss) +I0616 18:36:39.232064 9857 solver.cpp:571] Iteration 99920, lr = 0.0001 +I0616 18:36:50.729141 9857 solver.cpp:242] Iteration 99940, loss = 0.403607 +I0616 18:36:50.729183 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.177732 (* 1 = 0.177732 loss) +I0616 18:36:50.729189 9857 solver.cpp:258] Train net output #1: loss_cls = 0.185772 (* 1 = 0.185772 loss) +I0616 18:36:50.729193 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.128588 (* 1 = 0.128588 loss) +I0616 18:36:50.729197 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.0137714 (* 1 = 0.0137714 loss) +I0616 18:36:50.729200 9857 solver.cpp:571] Iteration 99940, lr = 0.0001 +I0616 18:37:02.472246 9857 solver.cpp:242] Iteration 99960, loss = 0.595308 +I0616 18:37:02.472290 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.158351 (* 1 = 0.158351 loss) +I0616 18:37:02.472295 9857 solver.cpp:258] Train net output #1: loss_cls = 0.19247 (* 1 = 0.19247 loss) +I0616 18:37:02.472300 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.141097 (* 1 = 0.141097 loss) +I0616 18:37:02.472302 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.107264 (* 1 = 0.107264 loss) +I0616 18:37:02.472306 9857 solver.cpp:571] Iteration 99960, lr = 0.0001 +I0616 18:37:14.033203 9857 solver.cpp:242] Iteration 99980, loss = 0.384573 +I0616 18:37:14.033246 9857 solver.cpp:258] Train net output #0: loss_bbox = 0.131488 (* 1 = 0.131488 loss) +I0616 18:37:14.033252 9857 solver.cpp:258] Train net output #1: loss_cls = 0.268397 (* 1 = 0.268397 loss) +I0616 18:37:14.033255 9857 solver.cpp:258] Train net output #2: rpn_cls_loss = 0.0363423 (* 1 = 0.0363423 loss) +I0616 18:37:14.033259 9857 solver.cpp:258] Train net output #3: rpn_loss_bbox = 0.034098 (* 1 = 0.034098 loss) +I0616 18:37:14.033263 9857 solver.cpp:571] Iteration 99980, lr = 0.0001 +speed: 0.595s / iter +Wrote snapshot to: /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_100000.caffemodel +done solving + +real 992m9.493s +user 792m33.616s +sys 151m58.952s ++ set +x ++ ./tools/test_net_imagenet.py --gpu 3 --def models/VGG16/faster_rcnn_end2end/test.prototxt --net /home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_100000.caffemodel --imdb imagenet_val2 --cfg experiments/cfgs/faster_rcnn_end2end.yml +imagenet_train + at 0x7f6fda341488> +imagenet_val + at 0x7f6fda341500> +imagenet_val1 + at 0x7f6fda341578> +imagenet_val2 + at 0x7f6fda3415f0> +imagenet_test + at 0x7f6fda341668> +Called with args: +Namespace(caffemodel='/home/andrewliao11/py-faster-rcnn/output/faster_rcnn_end2end/val1/vgg16_faster_rcnn_iter_100000.caffemodel', cfg_file='experiments/cfgs/faster_rcnn_end2end.yml', comp_mode=False, gpu_id=3, imdb_name='imagenet_val2', prototxt='models/VGG16/faster_rcnn_end2end/test.prototxt', set_cfgs=None, wait=True) +Using config: +{'DEDUP_BOXES': 0.0625, + 'EPS': 1e-14, + 'EXP_DIR': 'faster_rcnn_end2end', + 'GPU_ID': 3, + 'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]), + 'RNG_SEED': 3, + 'ROOT_DIR': '/home/andrewliao11/py-faster-rcnn', + 'TEST': {'BBOX_REG': True, + 'HAS_RPN': True, + 'MAX_SIZE': 1000, + 'NMS': 0.3, + 'PROPOSAL_METHOD': 'selective_search', + 'RPN_MIN_SIZE': 16, + 'RPN_NMS_THRESH': 0.7, + 'RPN_POST_NMS_TOP_N': 300, + 'RPN_PRE_NMS_TOP_N': 6000, + 'SCALES': [600], + 'SVM': False}, + 'TRAIN': {'ASPECT_GROUPING': True, + 'BATCH_SIZE': 128, + 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], + 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0], + 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2], + 'BBOX_NORMALIZE_TARGETS': True, + 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True, + 'BBOX_REG': True, + 'BBOX_THRESH': 0.5, + 'BG_THRESH_HI': 0.5, + 'BG_THRESH_LO': 0.1, + 'FG_FRACTION': 0.25, + 'FG_THRESH': 0.5, + 'HAS_RPN': True, + 'IMS_PER_BATCH': 1, + 'MAX_SIZE': 1000, + 'PROPOSAL_METHOD': 'gt', + 'RPN_BATCHSIZE': 256, + 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], + 'RPN_CLOBBER_POSITIVES': False, + 'RPN_FG_FRACTION': 0.5, + 'RPN_MIN_SIZE': 16, + 'RPN_NEGATIVE_OVERLAP': 0.3, + 'RPN_NMS_THRESH': 0.7, + 'RPN_POSITIVE_OVERLAP': 0.7, + 'RPN_POSITIVE_WEIGHT': -1.0, + 'RPN_POST_NMS_TOP_N': 2000, + 'RPN_PRE_NMS_TOP_N': 12000, + 'SCALES': [600], + 'SNAPSHOT_INFIX': '', + 'SNAPSHOT_ITERS': 10000, + 'USE_FLIPPED': True, + 'USE_PREFETCH': False}, + 'USE_GPU_NMS': True} +WARNING: Logging before InitGoogleLogging() is written to STDERR +I0616 18:37:28.329444 12684 net.cpp:50] Initializing net from parameters: +name: "VGG_ILSVRC_16_layers" +input: "data" +input: "im_info" +state { + phase: TEST +} +input_shape { + dim: 1 + dim: 3 + dim: 224 + dim: 224 +} +input_shape { + dim: 1 + dim: 3 +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 18 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 36 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "rpn_cls_score_reshape" + type: "Reshape" + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + reshape_param { + shape { + dim: 0 + dim: 2 + dim: -1 + dim: 0 + } + } +} +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} +layer { + name: "rpn_cls_prob_reshape" + type: "Reshape" + bottom: "rpn_cls_prob" + top: "rpn_cls_prob_reshape" + reshape_param { + shape { + dim: 0 + dim: 18 + dim: -1 + dim: 0 + } + } +} +layer { + name: "proposal" + type: "Python" + bottom: "rpn_cls_prob_reshape" + bottom: "rpn_bbox_pred" + bottom: "im_info" + top: "rois" + python_param { + module: "rpn.proposal_layer" + layer: "ProposalLayer" + param_str: "\'feat_stride\': 16" + } +} +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_h: 7 + pooled_w: 7 + spatial_scale: 0.0625 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 201 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 804 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "cls_prob" + type: "Softmax" + bottom: "cls_score" + top: "cls_prob" +} +I0616 18:37:28.329586 12684 net.cpp:435] Input 0 -> data +I0616 18:37:28.329615 12684 net.cpp:435] Input 1 -> im_info +I0616 18:37:28.329624 12684 layer_factory.hpp:76] Creating layer conv1_1 +I0616 18:37:28.329630 12684 net.cpp:110] Creating Layer conv1_1 +I0616 18:37:28.329633 12684 net.cpp:477] conv1_1 <- data +I0616 18:37:28.329637 12684 net.cpp:433] conv1_1 -> conv1_1 +I0616 18:37:28.554134 12684 net.cpp:155] Setting up conv1_1 +I0616 18:37:28.554172 12684 net.cpp:163] Top shape: 1 64 224 224 (3211264) +I0616 18:37:28.554185 12684 layer_factory.hpp:76] Creating layer relu1_1 +I0616 18:37:28.554194 12684 net.cpp:110] Creating Layer relu1_1 +I0616 18:37:28.554198 12684 net.cpp:477] relu1_1 <- conv1_1 +I0616 18:37:28.554203 12684 net.cpp:419] relu1_1 -> conv1_1 (in-place) +I0616 18:37:28.554214 12684 net.cpp:155] Setting up relu1_1 +I0616 18:37:28.554216 12684 net.cpp:163] Top shape: 1 64 224 224 (3211264) +I0616 18:37:28.554219 12684 layer_factory.hpp:76] Creating layer conv1_2 +I0616 18:37:28.554224 12684 net.cpp:110] Creating Layer conv1_2 +I0616 18:37:28.554226 12684 net.cpp:477] conv1_2 <- conv1_1 +I0616 18:37:28.554229 12684 net.cpp:433] conv1_2 -> conv1_2 +I0616 18:37:28.554316 12684 net.cpp:155] Setting up conv1_2 +I0616 18:37:28.554322 12684 net.cpp:163] Top shape: 1 64 224 224 (3211264) +I0616 18:37:28.554340 12684 layer_factory.hpp:76] Creating layer relu1_2 +I0616 18:37:28.554343 12684 net.cpp:110] Creating Layer relu1_2 +I0616 18:37:28.554347 12684 net.cpp:477] relu1_2 <- conv1_2 +I0616 18:37:28.554349 12684 net.cpp:419] relu1_2 -> conv1_2 (in-place) +I0616 18:37:28.554354 12684 net.cpp:155] Setting up relu1_2 +I0616 18:37:28.554358 12684 net.cpp:163] Top shape: 1 64 224 224 (3211264) +I0616 18:37:28.554360 12684 layer_factory.hpp:76] Creating layer pool1 +I0616 18:37:28.554365 12684 net.cpp:110] Creating Layer pool1 +I0616 18:37:28.554368 12684 net.cpp:477] pool1 <- conv1_2 +I0616 18:37:28.554371 12684 net.cpp:433] pool1 -> pool1 +I0616 18:37:28.554378 12684 net.cpp:155] Setting up pool1 +I0616 18:37:28.554381 12684 net.cpp:163] Top shape: 1 64 112 112 (802816) +I0616 18:37:28.554384 12684 layer_factory.hpp:76] Creating layer conv2_1 +I0616 18:37:28.554389 12684 net.cpp:110] Creating Layer conv2_1 +I0616 18:37:28.554392 12684 net.cpp:477] conv2_1 <- pool1 +I0616 18:37:28.554395 12684 net.cpp:433] conv2_1 -> conv2_1 +I0616 18:37:28.555274 12684 net.cpp:155] Setting up conv2_1 +I0616 18:37:28.555281 12684 net.cpp:163] Top shape: 1 128 112 112 (1605632) +I0616 18:37:28.555289 12684 layer_factory.hpp:76] Creating layer relu2_1 +I0616 18:37:28.555294 12684 net.cpp:110] Creating Layer relu2_1 +I0616 18:37:28.555296 12684 net.cpp:477] relu2_1 <- conv2_1 +I0616 18:37:28.555299 12684 net.cpp:419] relu2_1 -> conv2_1 (in-place) +I0616 18:37:28.555305 12684 net.cpp:155] Setting up relu2_1 +I0616 18:37:28.555307 12684 net.cpp:163] Top shape: 1 128 112 112 (1605632) +I0616 18:37:28.555310 12684 layer_factory.hpp:76] Creating layer conv2_2 +I0616 18:37:28.555315 12684 net.cpp:110] Creating Layer conv2_2 +I0616 18:37:28.555317 12684 net.cpp:477] conv2_2 <- conv2_1 +I0616 18:37:28.555320 12684 net.cpp:433] conv2_2 -> conv2_2 +I0616 18:37:28.555421 12684 net.cpp:155] Setting up conv2_2 +I0616 18:37:28.555426 12684 net.cpp:163] Top shape: 1 128 112 112 (1605632) +I0616 18:37:28.555430 12684 layer_factory.hpp:76] Creating layer relu2_2 +I0616 18:37:28.555435 12684 net.cpp:110] Creating Layer relu2_2 +I0616 18:37:28.555438 12684 net.cpp:477] relu2_2 <- conv2_2 +I0616 18:37:28.555441 12684 net.cpp:419] relu2_2 -> conv2_2 (in-place) +I0616 18:37:28.555445 12684 net.cpp:155] Setting up relu2_2 +I0616 18:37:28.555449 12684 net.cpp:163] Top shape: 1 128 112 112 (1605632) +I0616 18:37:28.555450 12684 layer_factory.hpp:76] Creating layer pool2 +I0616 18:37:28.555454 12684 net.cpp:110] Creating Layer pool2 +I0616 18:37:28.555457 12684 net.cpp:477] pool2 <- conv2_2 +I0616 18:37:28.555460 12684 net.cpp:433] pool2 -> pool2 +I0616 18:37:28.555465 12684 net.cpp:155] Setting up pool2 +I0616 18:37:28.555469 12684 net.cpp:163] Top shape: 1 128 56 56 (401408) +I0616 18:37:28.555471 12684 layer_factory.hpp:76] Creating layer conv3_1 +I0616 18:37:28.555475 12684 net.cpp:110] Creating Layer conv3_1 +I0616 18:37:28.555477 12684 net.cpp:477] conv3_1 <- pool2 +I0616 18:37:28.555483 12684 net.cpp:433] conv3_1 -> conv3_1 +I0616 18:37:28.556074 12684 net.cpp:155] Setting up conv3_1 +I0616 18:37:28.556082 12684 net.cpp:163] Top shape: 1 256 56 56 (802816) +I0616 18:37:28.556090 12684 layer_factory.hpp:76] Creating layer relu3_1 +I0616 18:37:28.556094 12684 net.cpp:110] Creating Layer relu3_1 +I0616 18:37:28.556097 12684 net.cpp:477] relu3_1 <- conv3_1 +I0616 18:37:28.556102 12684 net.cpp:419] relu3_1 -> conv3_1 (in-place) +I0616 18:37:28.556105 12684 net.cpp:155] Setting up relu3_1 +I0616 18:37:28.556108 12684 net.cpp:163] Top shape: 1 256 56 56 (802816) +I0616 18:37:28.556112 12684 layer_factory.hpp:76] Creating layer conv3_2 +I0616 18:37:28.556115 12684 net.cpp:110] Creating Layer conv3_2 +I0616 18:37:28.556118 12684 net.cpp:477] conv3_2 <- conv3_1 +I0616 18:37:28.556121 12684 net.cpp:433] conv3_2 -> conv3_2 +I0616 18:37:28.557014 12684 net.cpp:155] Setting up conv3_2 +I0616 18:37:28.557023 12684 net.cpp:163] Top shape: 1 256 56 56 (802816) +I0616 18:37:28.557027 12684 layer_factory.hpp:76] Creating layer relu3_2 +I0616 18:37:28.557031 12684 net.cpp:110] Creating Layer relu3_2 +I0616 18:37:28.557034 12684 net.cpp:477] relu3_2 <- conv3_2 +I0616 18:37:28.557039 12684 net.cpp:419] relu3_2 -> conv3_2 (in-place) +I0616 18:37:28.557042 12684 net.cpp:155] Setting up relu3_2 +I0616 18:37:28.557045 12684 net.cpp:163] Top shape: 1 256 56 56 (802816) +I0616 18:37:28.557047 12684 layer_factory.hpp:76] Creating layer conv3_3 +I0616 18:37:28.557052 12684 net.cpp:110] Creating Layer conv3_3 +I0616 18:37:28.557055 12684 net.cpp:477] conv3_3 <- conv3_2 +I0616 18:37:28.557060 12684 net.cpp:433] conv3_3 -> conv3_3 +I0616 18:37:28.557953 12684 net.cpp:155] Setting up conv3_3 +I0616 18:37:28.557961 12684 net.cpp:163] Top shape: 1 256 56 56 (802816) +I0616 18:37:28.557966 12684 layer_factory.hpp:76] Creating layer relu3_3 +I0616 18:37:28.557971 12684 net.cpp:110] Creating Layer relu3_3 +I0616 18:37:28.557973 12684 net.cpp:477] relu3_3 <- conv3_3 +I0616 18:37:28.557976 12684 net.cpp:419] relu3_3 -> conv3_3 (in-place) +I0616 18:37:28.557979 12684 net.cpp:155] Setting up relu3_3 +I0616 18:37:28.557982 12684 net.cpp:163] Top shape: 1 256 56 56 (802816) +I0616 18:37:28.557986 12684 layer_factory.hpp:76] Creating layer pool3 +I0616 18:37:28.557991 12684 net.cpp:110] Creating Layer pool3 +I0616 18:37:28.557994 12684 net.cpp:477] pool3 <- conv3_3 +I0616 18:37:28.557998 12684 net.cpp:433] pool3 -> pool3 +I0616 18:37:28.558002 12684 net.cpp:155] Setting up pool3 +I0616 18:37:28.558007 12684 net.cpp:163] Top shape: 1 256 28 28 (200704) +I0616 18:37:28.558008 12684 layer_factory.hpp:76] Creating layer conv4_1 +I0616 18:37:28.558012 12684 net.cpp:110] Creating Layer conv4_1 +I0616 18:37:28.558014 12684 net.cpp:477] conv4_1 <- pool3 +I0616 18:37:28.558017 12684 net.cpp:433] conv4_1 -> conv4_1 +I0616 18:37:28.559984 12684 net.cpp:155] Setting up conv4_1 +I0616 18:37:28.559999 12684 net.cpp:163] Top shape: 1 512 28 28 (401408) +I0616 18:37:28.560005 12684 layer_factory.hpp:76] Creating layer relu4_1 +I0616 18:37:28.560011 12684 net.cpp:110] Creating Layer relu4_1 +I0616 18:37:28.560014 12684 net.cpp:477] relu4_1 <- conv4_1 +I0616 18:37:28.560019 12684 net.cpp:419] relu4_1 -> conv4_1 (in-place) +I0616 18:37:28.560024 12684 net.cpp:155] Setting up relu4_1 +I0616 18:37:28.560027 12684 net.cpp:163] Top shape: 1 512 28 28 (401408) +I0616 18:37:28.560030 12684 layer_factory.hpp:76] Creating layer conv4_2 +I0616 18:37:28.560034 12684 net.cpp:110] Creating Layer conv4_2 +I0616 18:37:28.560036 12684 net.cpp:477] conv4_2 <- conv4_1 +I0616 18:37:28.560053 12684 net.cpp:433] conv4_2 -> conv4_2 +I0616 18:37:28.563032 12684 net.cpp:155] Setting up conv4_2 +I0616 18:37:28.563053 12684 net.cpp:163] Top shape: 1 512 28 28 (401408) +I0616 18:37:28.563065 12684 layer_factory.hpp:76] Creating layer relu4_2 +I0616 18:37:28.563071 12684 net.cpp:110] Creating Layer relu4_2 +I0616 18:37:28.563074 12684 net.cpp:477] relu4_2 <- conv4_2 +I0616 18:37:28.563078 12684 net.cpp:419] relu4_2 -> conv4_2 (in-place) +I0616 18:37:28.563083 12684 net.cpp:155] Setting up relu4_2 +I0616 18:37:28.563087 12684 net.cpp:163] Top shape: 1 512 28 28 (401408) +I0616 18:37:28.563091 12684 layer_factory.hpp:76] Creating layer conv4_3 +I0616 18:37:28.563096 12684 net.cpp:110] Creating Layer conv4_3 +I0616 18:37:28.563098 12684 net.cpp:477] conv4_3 <- conv4_2 +I0616 18:37:28.563102 12684 net.cpp:433] conv4_3 -> conv4_3 +I0616 18:37:28.566105 12684 net.cpp:155] Setting up conv4_3 +I0616 18:37:28.566128 12684 net.cpp:163] Top shape: 1 512 28 28 (401408) +I0616 18:37:28.566135 12684 layer_factory.hpp:76] Creating layer relu4_3 +I0616 18:37:28.566143 12684 net.cpp:110] Creating Layer relu4_3 +I0616 18:37:28.566145 12684 net.cpp:477] relu4_3 <- conv4_3 +I0616 18:37:28.566150 12684 net.cpp:419] relu4_3 -> conv4_3 (in-place) +I0616 18:37:28.566156 12684 net.cpp:155] Setting up relu4_3 +I0616 18:37:28.566159 12684 net.cpp:163] Top shape: 1 512 28 28 (401408) +I0616 18:37:28.566162 12684 layer_factory.hpp:76] Creating layer pool4 +I0616 18:37:28.566167 12684 net.cpp:110] Creating Layer pool4 +I0616 18:37:28.566170 12684 net.cpp:477] pool4 <- conv4_3 +I0616 18:37:28.566174 12684 net.cpp:433] pool4 -> pool4 +I0616 18:37:28.566179 12684 net.cpp:155] Setting up pool4 +I0616 18:37:28.566184 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.566185 12684 layer_factory.hpp:76] Creating layer conv5_1 +I0616 18:37:28.566190 12684 net.cpp:110] Creating Layer conv5_1 +I0616 18:37:28.566192 12684 net.cpp:477] conv5_1 <- pool4 +I0616 18:37:28.566196 12684 net.cpp:433] conv5_1 -> conv5_1 +I0616 18:37:28.569231 12684 net.cpp:155] Setting up conv5_1 +I0616 18:37:28.569254 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.569262 12684 layer_factory.hpp:76] Creating layer relu5_1 +I0616 18:37:28.569269 12684 net.cpp:110] Creating Layer relu5_1 +I0616 18:37:28.569273 12684 net.cpp:477] relu5_1 <- conv5_1 +I0616 18:37:28.569278 12684 net.cpp:419] relu5_1 -> conv5_1 (in-place) +I0616 18:37:28.569283 12684 net.cpp:155] Setting up relu5_1 +I0616 18:37:28.569286 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.569288 12684 layer_factory.hpp:76] Creating layer conv5_2 +I0616 18:37:28.569293 12684 net.cpp:110] Creating Layer conv5_2 +I0616 18:37:28.569296 12684 net.cpp:477] conv5_2 <- conv5_1 +I0616 18:37:28.569300 12684 net.cpp:433] conv5_2 -> conv5_2 +I0616 18:37:28.572343 12684 net.cpp:155] Setting up conv5_2 +I0616 18:37:28.572365 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.572373 12684 layer_factory.hpp:76] Creating layer relu5_2 +I0616 18:37:28.572379 12684 net.cpp:110] Creating Layer relu5_2 +I0616 18:37:28.572383 12684 net.cpp:477] relu5_2 <- conv5_2 +I0616 18:37:28.572389 12684 net.cpp:419] relu5_2 -> conv5_2 (in-place) +I0616 18:37:28.572396 12684 net.cpp:155] Setting up relu5_2 +I0616 18:37:28.572398 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.572402 12684 layer_factory.hpp:76] Creating layer conv5_3 +I0616 18:37:28.572405 12684 net.cpp:110] Creating Layer conv5_3 +I0616 18:37:28.572408 12684 net.cpp:477] conv5_3 <- conv5_2 +I0616 18:37:28.572412 12684 net.cpp:433] conv5_3 -> conv5_3 +I0616 18:37:28.575486 12684 net.cpp:155] Setting up conv5_3 +I0616 18:37:28.575508 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.575515 12684 layer_factory.hpp:76] Creating layer relu5_3 +I0616 18:37:28.575522 12684 net.cpp:110] Creating Layer relu5_3 +I0616 18:37:28.575526 12684 net.cpp:477] relu5_3 <- conv5_3 +I0616 18:37:28.575531 12684 net.cpp:419] relu5_3 -> conv5_3 (in-place) +I0616 18:37:28.575537 12684 net.cpp:155] Setting up relu5_3 +I0616 18:37:28.575541 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.575542 12684 layer_factory.hpp:76] Creating layer conv5_3_relu5_3_0_split +I0616 18:37:28.575546 12684 net.cpp:110] Creating Layer conv5_3_relu5_3_0_split +I0616 18:37:28.575549 12684 net.cpp:477] conv5_3_relu5_3_0_split <- conv5_3 +I0616 18:37:28.575552 12684 net.cpp:433] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_0 +I0616 18:37:28.575557 12684 net.cpp:433] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_1 +I0616 18:37:28.575567 12684 net.cpp:155] Setting up conv5_3_relu5_3_0_split +I0616 18:37:28.575572 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.575573 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.575577 12684 layer_factory.hpp:76] Creating layer rpn_conv/3x3 +I0616 18:37:28.575582 12684 net.cpp:110] Creating Layer rpn_conv/3x3 +I0616 18:37:28.575585 12684 net.cpp:477] rpn_conv/3x3 <- conv5_3_relu5_3_0_split_0 +I0616 18:37:28.575588 12684 net.cpp:433] rpn_conv/3x3 -> rpn/output +I0616 18:37:28.627559 12684 net.cpp:155] Setting up rpn_conv/3x3 +I0616 18:37:28.627591 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.627599 12684 layer_factory.hpp:76] Creating layer rpn_relu/3x3 +I0616 18:37:28.627609 12684 net.cpp:110] Creating Layer rpn_relu/3x3 +I0616 18:37:28.627611 12684 net.cpp:477] rpn_relu/3x3 <- rpn/output +I0616 18:37:28.627616 12684 net.cpp:419] rpn_relu/3x3 -> rpn/output (in-place) +I0616 18:37:28.627622 12684 net.cpp:155] Setting up rpn_relu/3x3 +I0616 18:37:28.627625 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.627629 12684 layer_factory.hpp:76] Creating layer rpn/output_rpn_relu/3x3_0_split +I0616 18:37:28.627632 12684 net.cpp:110] Creating Layer rpn/output_rpn_relu/3x3_0_split +I0616 18:37:28.627635 12684 net.cpp:477] rpn/output_rpn_relu/3x3_0_split <- rpn/output +I0616 18:37:28.627638 12684 net.cpp:433] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_0 +I0616 18:37:28.627643 12684 net.cpp:433] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_1 +I0616 18:37:28.627648 12684 net.cpp:155] Setting up rpn/output_rpn_relu/3x3_0_split +I0616 18:37:28.627651 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.627653 12684 net.cpp:163] Top shape: 1 512 14 14 (100352) +I0616 18:37:28.627655 12684 layer_factory.hpp:76] Creating layer rpn_cls_score +I0616 18:37:28.627660 12684 net.cpp:110] Creating Layer rpn_cls_score +I0616 18:37:28.627662 12684 net.cpp:477] rpn_cls_score <- rpn/output_rpn_relu/3x3_0_split_0 +I0616 18:37:28.627667 12684 net.cpp:433] rpn_cls_score -> rpn_cls_score +I0616 18:37:28.627931 12684 net.cpp:155] Setting up rpn_cls_score +I0616 18:37:28.627936 12684 net.cpp:163] Top shape: 1 18 14 14 (3528) +I0616 18:37:28.627955 12684 layer_factory.hpp:76] Creating layer rpn_bbox_pred +I0616 18:37:28.627959 12684 net.cpp:110] Creating Layer rpn_bbox_pred +I0616 18:37:28.627962 12684 net.cpp:477] rpn_bbox_pred <- rpn/output_rpn_relu/3x3_0_split_1 +I0616 18:37:28.627965 12684 net.cpp:433] rpn_bbox_pred -> rpn_bbox_pred +I0616 18:37:28.628407 12684 net.cpp:155] Setting up rpn_bbox_pred +I0616 18:37:28.628412 12684 net.cpp:163] Top shape: 1 36 14 14 (7056) +I0616 18:37:28.628417 12684 layer_factory.hpp:76] Creating layer rpn_cls_score_reshape +I0616 18:37:28.628427 12684 net.cpp:110] Creating Layer rpn_cls_score_reshape +I0616 18:37:28.628430 12684 net.cpp:477] rpn_cls_score_reshape <- rpn_cls_score +I0616 18:37:28.628434 12684 net.cpp:433] rpn_cls_score_reshape -> rpn_cls_score_reshape +I0616 18:37:28.628440 12684 net.cpp:155] Setting up rpn_cls_score_reshape +I0616 18:37:28.628444 12684 net.cpp:163] Top shape: 1 2 126 14 (3528) +I0616 18:37:28.628446 12684 layer_factory.hpp:76] Creating layer rpn_cls_prob +I0616 18:37:28.628450 12684 net.cpp:110] Creating Layer rpn_cls_prob +I0616 18:37:28.628454 12684 net.cpp:477] rpn_cls_prob <- rpn_cls_score_reshape +I0616 18:37:28.628458 12684 net.cpp:433] rpn_cls_prob -> rpn_cls_prob +I0616 18:37:28.628481 12684 net.cpp:155] Setting up rpn_cls_prob +I0616 18:37:28.628485 12684 net.cpp:163] Top shape: 1 2 126 14 (3528) +I0616 18:37:28.628489 12684 layer_factory.hpp:76] Creating layer rpn_cls_prob_reshape +I0616 18:37:28.628492 12684 net.cpp:110] Creating Layer rpn_cls_prob_reshape +I0616 18:37:28.628494 12684 net.cpp:477] rpn_cls_prob_reshape <- rpn_cls_prob +I0616 18:37:28.628499 12684 net.cpp:433] rpn_cls_prob_reshape -> rpn_cls_prob_reshape +I0616 18:37:28.628504 12684 net.cpp:155] Setting up rpn_cls_prob_reshape +I0616 18:37:28.628506 12684 net.cpp:163] Top shape: 1 18 14 14 (3528) +I0616 18:37:28.628509 12684 layer_factory.hpp:76] Creating layer proposal +I0616 18:37:28.638039 12684 net.cpp:110] Creating Layer proposal +I0616 18:37:28.638049 12684 net.cpp:477] proposal <- rpn_cls_prob_reshape +I0616 18:37:28.638054 12684 net.cpp:477] proposal <- rpn_bbox_pred +I0616 18:37:28.638058 12684 net.cpp:477] proposal <- im_info +I0616 18:37:28.638063 12684 net.cpp:433] proposal -> rois +I0616 18:37:28.638475 12684 net.cpp:155] Setting up proposal +I0616 18:37:28.638484 12684 net.cpp:163] Top shape: 1 5 (5) +I0616 18:37:28.638489 12684 layer_factory.hpp:76] Creating layer roi_pool5 +I0616 18:37:28.638494 12684 net.cpp:110] Creating Layer roi_pool5 +I0616 18:37:28.638496 12684 net.cpp:477] roi_pool5 <- conv5_3_relu5_3_0_split_1 +I0616 18:37:28.638500 12684 net.cpp:477] roi_pool5 <- rois +I0616 18:37:28.638504 12684 net.cpp:433] roi_pool5 -> pool5 +I0616 18:37:28.638509 12684 roi_pooling_layer.cpp:30] Spatial scale: 0.0625 +I0616 18:37:28.638520 12684 net.cpp:155] Setting up roi_pool5 +I0616 18:37:28.638523 12684 net.cpp:163] Top shape: 1 512 7 7 (25088) +I0616 18:37:28.638526 12684 layer_factory.hpp:76] Creating layer fc6 +I0616 18:37:28.638530 12684 net.cpp:110] Creating Layer fc6 +I0616 18:37:28.638533 12684 net.cpp:477] fc6 <- pool5 +I0616 18:37:28.638537 12684 net.cpp:433] fc6 -> fc6 +I0616 18:37:28.765775 12684 net.cpp:155] Setting up fc6 +I0616 18:37:28.765812 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.765827 12684 layer_factory.hpp:76] Creating layer relu6 +I0616 18:37:28.765836 12684 net.cpp:110] Creating Layer relu6 +I0616 18:37:28.765838 12684 net.cpp:477] relu6 <- fc6 +I0616 18:37:28.765847 12684 net.cpp:419] relu6 -> fc6 (in-place) +I0616 18:37:28.765854 12684 net.cpp:155] Setting up relu6 +I0616 18:37:28.765858 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.765861 12684 layer_factory.hpp:76] Creating layer drop6 +I0616 18:37:28.765864 12684 net.cpp:110] Creating Layer drop6 +I0616 18:37:28.765867 12684 net.cpp:477] drop6 <- fc6 +I0616 18:37:28.765871 12684 net.cpp:419] drop6 -> fc6 (in-place) +I0616 18:37:28.765874 12684 net.cpp:155] Setting up drop6 +I0616 18:37:28.765877 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.765879 12684 layer_factory.hpp:76] Creating layer fc7 +I0616 18:37:28.765884 12684 net.cpp:110] Creating Layer fc7 +I0616 18:37:28.765887 12684 net.cpp:477] fc7 <- fc6 +I0616 18:37:28.765890 12684 net.cpp:433] fc7 -> fc7 +I0616 18:37:28.786797 12684 net.cpp:155] Setting up fc7 +I0616 18:37:28.786834 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.786842 12684 layer_factory.hpp:76] Creating layer relu7 +I0616 18:37:28.786851 12684 net.cpp:110] Creating Layer relu7 +I0616 18:37:28.786854 12684 net.cpp:477] relu7 <- fc7 +I0616 18:37:28.786859 12684 net.cpp:419] relu7 -> fc7 (in-place) +I0616 18:37:28.786869 12684 net.cpp:155] Setting up relu7 +I0616 18:37:28.786872 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.786875 12684 layer_factory.hpp:76] Creating layer drop7 +I0616 18:37:28.786880 12684 net.cpp:110] Creating Layer drop7 +I0616 18:37:28.786881 12684 net.cpp:477] drop7 <- fc7 +I0616 18:37:28.786885 12684 net.cpp:419] drop7 -> fc7 (in-place) +I0616 18:37:28.786890 12684 net.cpp:155] Setting up drop7 +I0616 18:37:28.786893 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.786895 12684 layer_factory.hpp:76] Creating layer fc7_drop7_0_split +I0616 18:37:28.786900 12684 net.cpp:110] Creating Layer fc7_drop7_0_split +I0616 18:37:28.786901 12684 net.cpp:477] fc7_drop7_0_split <- fc7 +I0616 18:37:28.786905 12684 net.cpp:433] fc7_drop7_0_split -> fc7_drop7_0_split_0 +I0616 18:37:28.786908 12684 net.cpp:433] fc7_drop7_0_split -> fc7_drop7_0_split_1 +I0616 18:37:28.786913 12684 net.cpp:155] Setting up fc7_drop7_0_split +I0616 18:37:28.786916 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.786919 12684 net.cpp:163] Top shape: 1 4096 (4096) +I0616 18:37:28.786921 12684 layer_factory.hpp:76] Creating layer cls_score +I0616 18:37:28.786927 12684 net.cpp:110] Creating Layer cls_score +I0616 18:37:28.786931 12684 net.cpp:477] cls_score <- fc7_drop7_0_split_0 +I0616 18:37:28.786934 12684 net.cpp:433] cls_score -> cls_score +I0616 18:37:28.805207 12684 net.cpp:155] Setting up cls_score +I0616 18:37:28.805217 12684 net.cpp:163] Top shape: 1 201 (201) +I0616 18:37:28.805222 12684 layer_factory.hpp:76] Creating layer bbox_pred +I0616 18:37:28.805228 12684 net.cpp:110] Creating Layer bbox_pred +I0616 18:37:28.805232 12684 net.cpp:477] bbox_pred <- fc7_drop7_0_split_1 +I0616 18:37:28.805234 12684 net.cpp:433] bbox_pred -> bbox_pred +I0616 18:37:28.877568 12684 net.cpp:155] Setting up bbox_pred +I0616 18:37:28.877589 12684 net.cpp:163] Top shape: 1 804 (804) +I0616 18:37:28.877598 12684 layer_factory.hpp:76] Creating layer cls_prob +I0616 18:37:28.877605 12684 net.cpp:110] Creating Layer cls_prob +I0616 18:37:28.877609 12684 net.cpp:477] cls_prob <- cls_score +I0616 18:37:28.877614 12684 net.cpp:433] cls_prob -> cls_prob +I0616 18:37:28.877640 12684 net.cpp:155] Setting up cls_prob +I0616 18:37:28.877645 12684 net.cpp:163] Top shape: 1 201 (201) +I0616 18:37:28.877647 12684 net.cpp:240] cls_prob does not need backward computation. +I0616 18:37:28.877650 12684 net.cpp:240] bbox_pred does not need backward computation. +I0616 18:37:28.877652 12684 net.cpp:240] cls_score does not need backward computation. +I0616 18:37:28.877655 12684 net.cpp:240] fc7_drop7_0_split does not need backward computation. +I0616 18:37:28.877658 12684 net.cpp:240] drop7 does not need backward computation. +I0616 18:37:28.877660 12684 net.cpp:240] relu7 does not need backward computation. +I0616 18:37:28.877662 12684 net.cpp:240] fc7 does not need backward computation. +I0616 18:37:28.877666 12684 net.cpp:240] drop6 does not need backward computation. +I0616 18:37:28.877668 12684 net.cpp:240] relu6 does not need backward computation. +I0616 18:37:28.877671 12684 net.cpp:240] fc6 does not need backward computation. +I0616 18:37:28.877673 12684 net.cpp:240] roi_pool5 does not need backward computation. +I0616 18:37:28.877676 12684 net.cpp:240] proposal does not need backward computation. +I0616 18:37:28.877679 12684 net.cpp:240] rpn_cls_prob_reshape does not need backward computation. +I0616 18:37:28.877681 12684 net.cpp:240] rpn_cls_prob does not need backward computation. +I0616 18:37:28.877684 12684 net.cpp:240] rpn_cls_score_reshape does not need backward computation. +I0616 18:37:28.877686 12684 net.cpp:240] rpn_bbox_pred does not need backward computation. +I0616 18:37:28.877689 12684 net.cpp:240] rpn_cls_score does not need backward computation. +I0616 18:37:28.877691 12684 net.cpp:240] rpn/output_rpn_relu/3x3_0_split does not need backward computation. +I0616 18:37:28.877694 12684 net.cpp:240] rpn_relu/3x3 does not need backward computation. +I0616 18:37:28.877696 12684 net.cpp:240] rpn_conv/3x3 does not need backward computation. +I0616 18:37:28.877699 12684 net.cpp:240] conv5_3_relu5_3_0_split does not need backward computation. +I0616 18:37:28.877702 12684 net.cpp:240] relu5_3 does not need backward computation. +I0616 18:37:28.877704 12684 net.cpp:240] conv5_3 does not need backward computation. +I0616 18:37:28.877707 12684 net.cpp:240] relu5_2 does not need backward computation. +I0616 18:37:28.877709 12684 net.cpp:240] conv5_2 does not need backward computation. +I0616 18:37:28.877712 12684 net.cpp:240] relu5_1 does not need backward computation. +I0616 18:37:28.877714 12684 net.cpp:240] conv5_1 does not need backward computation. +I0616 18:37:28.877717 12684 net.cpp:240] pool4 does not need backward computation. +I0616 18:37:28.877720 12684 net.cpp:240] relu4_3 does not need backward computation. +I0616 18:37:28.877722 12684 net.cpp:240] conv4_3 does not need backward computation. +I0616 18:37:28.877725 12684 net.cpp:240] relu4_2 does not need backward computation. +I0616 18:37:28.877727 12684 net.cpp:240] conv4_2 does not need backward computation. +I0616 18:37:28.877729 12684 net.cpp:240] relu4_1 does not need backward computation. +I0616 18:37:28.877732 12684 net.cpp:240] conv4_1 does not need backward computation. +I0616 18:37:28.877734 12684 net.cpp:240] pool3 does not need backward computation. +I0616 18:37:28.877737 12684 net.cpp:240] relu3_3 does not need backward computation. +I0616 18:37:28.877739 12684 net.cpp:240] conv3_3 does not need backward computation. +I0616 18:37:28.877743 12684 net.cpp:240] relu3_2 does not need backward computation. +I0616 18:37:28.877744 12684 net.cpp:240] conv3_2 does not need backward computation. +I0616 18:37:28.877748 12684 net.cpp:240] relu3_1 does not need backward computation. +I0616 18:37:28.877750 12684 net.cpp:240] conv3_1 does not need backward computation. +I0616 18:37:28.877753 12684 net.cpp:240] pool2 does not need backward computation. +I0616 18:37:28.877755 12684 net.cpp:240] relu2_2 does not need backward computation. +I0616 18:37:28.877758 12684 net.cpp:240] conv2_2 does not need backward computation. +I0616 18:37:28.877760 12684 net.cpp:240] relu2_1 does not need backward computation. +I0616 18:37:28.877763 12684 net.cpp:240] conv2_1 does not need backward computation. +I0616 18:37:28.877764 12684 net.cpp:240] pool1 does not need backward computation. +I0616 18:37:28.877766 12684 net.cpp:240] relu1_2 does not need backward computation. +I0616 18:37:28.877768 12684 net.cpp:240] conv1_2 does not need backward computation. +I0616 18:37:28.877771 12684 net.cpp:240] relu1_1 does not need backward computation. +I0616 18:37:28.877774 12684 net.cpp:240] conv1_1 does not need backward computation. +I0616 18:37:28.877776 12684 net.cpp:283] This network produces output bbox_pred +I0616 18:37:28.877779 12684 net.cpp:283] This network produces output cls_prob +I0616 18:37:28.877799 12684 net.cpp:297] Network initialization done. +I0616 18:37:28.877802 12684 net.cpp:298] Memory required for data: 117130668 +[libprotobuf WARNING google/protobuf/io/coded_stream.cc:505] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. +[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 563068199 +fc7 data +[[ 1.99270797 1.85343158 1.30490482 ..., -0. -0. 2.14815998] + [ 2.11360288 1.60754013 1.63168108 ..., -0. -0. 2.64853382] + [ 2.15054345 1.22248065 1.61407244 ..., -0. -0. 2.20639896] + ..., + [ 0.68315321 0.30353761 -0. ..., -0. -0. -0. ] + [ 1.24064517 0.40032357 -0. ..., 0.37769893 -0. -0. ] + [ 0.75826967 -0. 0.50197458 ..., 0.11738808 -0. 0.28230733]] +Traceback (most recent call last): + File "./tools/test_net_imagenet.py", line 85, in + test_net(net, imdb) + File "/home/andrewliao11/py-faster-rcnn/tools/../lib/fast_rcnn/test.py", line 304, in test_net + scores, boxes = im_detect(net, im, box_proposals) +ValueError: too many values to unpack From 3c07d1b81a79137c93e7c06296c0edfccfa5ad3f Mon Sep 17 00:00:00 2001 From: Andrew Date: 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Run the **$FRCNN/experiments/scripts/faster_rcnn_end2end_imagenet.sh**. The use of .sh file is just the same as the original [faster rcnn ](https://github.com/rbgirshick/py-faster-rcnn) + +## Experiment +This is the mean/median AP of different iterations.The highest mean AP falls in 90000 iterations. +![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_imagenet.png?raw=true) +The original Faster R-CNN states that they can achieve 59.9% mAP on PASCAL VOC 2007, which only contains 20 categories. The result of mine is relatively low compared to the original work. However, this is the trade-off since we increase the diversity of the object categories. My network can achieve 33.1% mAP. +So here I present the result of the overlapped category. My model achieves 48.7% mAP from the object category that appears in PASCAL VOC 2007 (12 categories), which is much higher than that of 200 categories. +![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_overlap.png?raw=true) +And I also present the mAP for each category in ImageNet +![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_200.png?raw=true) + ## Demo Just run the **demo.py** to visualize pictures! ![demo_02](https://github.com/andrewliao11/py-faster-rcnn/blob/master/tools/output_demo_02.jpg?raw=true) From 3b7f730923dffe766178fe2bbe03eccf6ef30786 Mon Sep 17 00:00:00 2001 From: Andrew Date: Mon, 20 Jun 2016 16:10:59 +0800 Subject: [PATCH 24/39] Update README.md --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 65aa1bb2c..777483a90 100644 --- a/README.md +++ b/README.md @@ -88,8 +88,11 @@ The use of .sh file is just the same as the original [faster rcnn ](https://gith ## Experiment This is the mean/median AP of different iterations.The highest mean AP falls in 90000 iterations. ![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_imagenet.png?raw=true) -The original Faster R-CNN states that they can achieve 59.9% mAP on PASCAL VOC 2007, which only contains 20 categories. The result of mine is relatively low compared to the original work. However, this is the trade-off since we increase the diversity of the object categories. My network can achieve 33.1% mAP. -So here I present the result of the overlapped category. My model achieves 48.7% mAP from the object category that appears in PASCAL VOC 2007 (12 categories), which is much higher than that of 200 categories. +The original Faster R-CNN states that they can achieve **59.9% mAP** on PASCAL VOC 2007, which only contains 20 categories. The result of mine is relatively low compared to the original work. However, this is the trade-off since we increase the diversity of the object categories. My network can achieve **33.1% mAP**. +The low accuracy is due to: +- Smaller dataset( ImageNet validation1 ) +- Diverse object category +So here I present the result of the overlapped category. My model achieves **48.7% mAP** from the object category that appears in PASCAL VOC 2007 (12 categories), which is much higher than that of 200 categories. ![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_overlap.png?raw=true) And I also present the mAP for each category in ImageNet ![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_200.png?raw=true) From d19c5db4bfab82ba52c49c07817c8fa71584c16a Mon Sep 17 00:00:00 2001 From: Andrew Date: Mon, 20 Jun 2016 16:12:10 +0800 Subject: [PATCH 25/39] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 777483a90..b667d39b4 100644 --- a/README.md +++ b/README.md @@ -88,12 +88,16 @@ The use of .sh file is just the same as the original [faster rcnn ](https://gith ## Experiment This is the mean/median AP of different iterations.The highest mean AP falls in 90000 iterations. ![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_imagenet.png?raw=true) -The original Faster R-CNN states that they can achieve **59.9% mAP** on PASCAL VOC 2007, which only contains 20 categories. The result of mine is relatively low compared to the original work. However, this is the trade-off since we increase the diversity of the object categories. My network can achieve **33.1% mAP**. + +The original Faster R-CNN states that they can achieve **59.9% mAP** on PASCAL VOC 2007, which only contains 20 categories. The result of mine is relatively low compared to the original work. However, this is the trade-off since we increase the diversity of the object categories. My network can achieve **33.1% mAP**. + The low accuracy is due to: - Smaller dataset( ImageNet validation1 ) - Diverse object category + So here I present the result of the overlapped category. My model achieves **48.7% mAP** from the object category that appears in PASCAL VOC 2007 (12 categories), which is much higher than that of 200 categories. -![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_overlap.png?raw=true) +![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_overlap.png?raw=true) + And I also present the mAP for each category in ImageNet ![](https://github.com/andrewliao11/py-faster-rcnn/blob/master/asset/mAP_200.png?raw=true) From 12cca09b06fadc012eb1b9f5843119b68919cb6c Mon Sep 17 00:00:00 2001 From: Andrew Date: Mon, 20 Jun 2016 16:12:39 +0800 Subject: [PATCH 26/39] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index b667d39b4..818d72480 100644 --- a/README.md +++ b/README.md @@ -104,7 +104,6 @@ And I also present the mAP for each category in ImageNet ## Demo Just run the **demo.py** to visualize pictures! ![demo_02](https://github.com/andrewliao11/py-faster-rcnn/blob/master/tools/output_demo_02.jpg?raw=true) -![demo_03](https://github.com/andrewliao11/py-faster-rcnn/blob/master/tools/output_demo_03.jpg?raw=true) ### faster rcnn with tracker on videos [![IMAGE ALT TEXT HERE](http://img.youtube.com/vi/wY7LADoEuFs/0.jpg)](http://www.youtube.com/watch?v=wY7LADoEuFs) From a332ef33559d1312ffd9fac1546f458c2c05ddd0 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Thu, 14 Jul 2016 12:31:51 +0800 Subject: [PATCH 27/39] Update README.md --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index 818d72480..238fdcb3c 100644 --- a/README.md +++ b/README.md @@ -111,6 +111,3 @@ Just run the **demo.py** to visualize pictures! Original video "https://www.jukinmedia.com/videos/view/5655" ## Reference [How to train fast rcnn on imagenet](http://sunshineatnoon.github.io/Train-fast-rcnn-model-on-imagenet-without-matlab/) - -## Others -If you have any advance question, feel free to contact me by andrewliao11@gmail.com From 5fd4a952780da308c92c098fab0e000380e73d08 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Tue, 19 Jul 2016 23:21:11 +0800 Subject: [PATCH 28/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 238fdcb3c..23e5ab4b9 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Training Faster RCNN on Imagenet - +If you want to know some basic ideas in faster rcnn, try to check [Video Object Detection using Faster R-CNN](http://andrewliao11.github.io/object_detection/faster_rcnn/) out! ## preparing data ``` From 6701c864293788221c90a62aa2853641c2bcb876 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:21:41 +0800 Subject: [PATCH 29/39] Create .keep --- misc/.keep | 1 + 1 file changed, 1 insertion(+) create mode 100644 misc/.keep diff --git a/misc/.keep b/misc/.keep new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/misc/.keep @@ -0,0 +1 @@ + From e5953e3dfa91f748c404eb2a8b80408ecfeabf2d Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:21:50 +0800 Subject: [PATCH 30/39] Add files via upload --- misc/ILSVRC2012_val_00037038.xml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 misc/ILSVRC2012_val_00037038.xml diff --git a/misc/ILSVRC2012_val_00037038.xml b/misc/ILSVRC2012_val_00037038.xml new file mode 100644 index 000000000..7c025ec27 --- /dev/null +++ b/misc/ILSVRC2012_val_00037038.xml @@ -0,0 +1,20 @@ + + ILSVRC2013_val + ILSVRC2012_val_00037038 + + ILSVRC_2013 + + + 500 + 375 + + + n02084071 + + 77 + 394 + 82 + 290 + + + From 9a43c70bf0f4702adc6374ea9f79fc1abf0bcb91 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:22:32 +0800 Subject: [PATCH 31/39] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 23e5ab4b9..9dea5978f 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,10 @@ If you want to know some basic ideas in faster rcnn, try to check [Video Object ``` ILSVRC13 -└─── LSVRC2013_DET_val +└─── ILSVRC2013_DET_val │ *.JPEG (Image files, ex:ILSVRC2013_val_00000565.JPEG) +└─── ILSVRC2013_DET_bbox_val + | *xml (you can find the example from ./misc/ILSVRC2012_val_00037038.xml under this repo) └─── data │ meta_det.mat └─── det_lists From 04a58221455ebe148b1455b91ba84be6439aaed5 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:23:16 +0800 Subject: [PATCH 32/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9dea5978f..bb30672dc 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ ILSVRC13 └─── ILSVRC2013_DET_val │ *.JPEG (Image files, ex:ILSVRC2013_val_00000565.JPEG) └─── ILSVRC2013_DET_bbox_val - | *xml (you can find the example from ./misc/ILSVRC2012_val_00037038.xml under this repo) + | *.xml (you can find the example from ./misc/ILSVRC2012_val_00037038.xml under this repo) └─── data │ meta_det.mat └─── det_lists From 8a9f8c8d3c8e341b12108c35bd454977bd513bc0 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:25:45 +0800 Subject: [PATCH 33/39] Delete ILSVRC2012_val_00037038.xml --- misc/ILSVRC2012_val_00037038.xml | 20 -------------------- 1 file changed, 20 deletions(-) delete mode 100644 misc/ILSVRC2012_val_00037038.xml diff --git a/misc/ILSVRC2012_val_00037038.xml b/misc/ILSVRC2012_val_00037038.xml deleted file mode 100644 index 7c025ec27..000000000 --- a/misc/ILSVRC2012_val_00037038.xml +++ /dev/null @@ -1,20 +0,0 @@ - - ILSVRC2013_val - ILSVRC2012_val_00037038 - - ILSVRC_2013 - - - 500 - 375 - - - n02084071 - - 77 - 394 - 82 - 290 - - - From f9a16cd3c7262c5b2069254ccbdaa2187dc05b32 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:26:31 +0800 Subject: [PATCH 34/39] Add files via upload --- misc/ILSVRC2012_val_00018464.xml | 47 ++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 misc/ILSVRC2012_val_00018464.xml diff --git a/misc/ILSVRC2012_val_00018464.xml b/misc/ILSVRC2012_val_00018464.xml new file mode 100644 index 000000000..7b4145667 --- /dev/null +++ b/misc/ILSVRC2012_val_00018464.xml @@ -0,0 +1,47 @@ + + ILSVRC2013_val + ILSVRC2012_val_00018464 + + ILSVRC_2013 + + + 640 + 554 + + + n02840245 + + 38 + 567 + 225 + 551 + + + + n02840245 + + 393 + 613 + 18 + 281 + + + + n02840245 + + 227 + 451 + 4 + 261 + + + + n02840245 + + 318 + 526 + 0 + 274 + + + From c73fee78e8aa634da9ab02d49ad8d194c33bf2f5 Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:33:29 +0800 Subject: [PATCH 35/39] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bb30672dc..b0f36b5ed 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,9 @@ ILSVRC13 └─── ILSVRC2013_DET_val │ *.JPEG (Image files, ex:ILSVRC2013_val_00000565.JPEG) └─── ILSVRC2013_DET_bbox_val - | *.xml (you can find the example from ./misc/ILSVRC2012_val_00037038.xml under this repo) + | *.xml (you can find the example from ./misc/ILSVRC2012_val_00018464.xml under this repo) └─── data - │ meta_det.mat + │ meta_det.mat (To load the category inside, like [here](https://github.com/andrewliao11/py-faster-rcnn-imagenet/blob/master/lib/datasets/imagenet.py#L26)) └─── det_lists │ val1.txt, val2.txt ``` From 4de5a22fbde4748e077142f881f5e3f01a25f77d Mon Sep 17 00:00:00 2001 From: Andrew Liao Date: Mon, 22 Aug 2016 14:37:59 +0800 Subject: [PATCH 36/39] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b0f36b5ed..c9985f798 100644 --- a/README.md +++ b/README.md @@ -10,10 +10,11 @@ ILSVRC13 └─── ILSVRC2013_DET_bbox_val | *.xml (you can find the example from ./misc/ILSVRC2012_val_00018464.xml under this repo) └─── data - │ meta_det.mat (To load the category inside, like [here](https://github.com/andrewliao11/py-faster-rcnn-imagenet/blob/master/lib/datasets/imagenet.py#L26)) + │ meta_det.mat └─── det_lists │ val1.txt, val2.txt ``` +meta_det.mat => Load the category inside, like [here](https://github.com/andrewliao11/py-faster-rcnn-imagenet/blob/master/lib/datasets/imagenet.py#L26/) Load the meta_det.mat file by ``` classes = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat')) From 6acd4bb8ffe36cf1798080df682392b3d6635ffe Mon Sep 17 00:00:00 2001 From: Andrew Date: Sat, 1 Apr 2017 19:55:54 +0800 Subject: [PATCH 37/39] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index c9985f798..2d1b3e27a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,9 @@ # Training Faster RCNN on Imagenet If you want to know some basic ideas in faster rcnn, try to check [Video Object Detection using Faster R-CNN](http://andrewliao11.github.io/object_detection/faster_rcnn/) out! + +Feel free to contact me via email, I'll try to give you a hand if I can, lol. + ## preparing data ``` From 3de4e4c05eac002b6df4b24c4c095469edcf0313 Mon Sep 17 00:00:00 2001 From: Andrew Date: Sat, 22 Apr 2017 16:53:37 +0800 Subject: [PATCH 38/39] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 2d1b3e27a..4f69d7c66 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Training Faster RCNN on Imagenet +[![Readme Score](http://readme-score-api.herokuapp.com/score.svg?url=andrewliao11/py-faster-rcnn-imagenet)](http://clayallsopp.github.io/readme-score?url=andrewliao11/py-faster-rcnn-imagenet) + If you want to know some basic ideas in faster rcnn, try to check [Video Object Detection using Faster R-CNN](http://andrewliao11.github.io/object_detection/faster_rcnn/) out! Feel free to contact me via email, I'll try to give you a hand if I can, lol. From e9f19bb36407317fd074141b0198c9c182026006 Mon Sep 17 00:00:00 2001 From: Andrew Date: Sat, 15 Jul 2017 17:50:49 +0800 Subject: [PATCH 39/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4f69d7c66..e3faff340 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ # Training Faster RCNN on Imagenet [![Readme Score](http://readme-score-api.herokuapp.com/score.svg?url=andrewliao11/py-faster-rcnn-imagenet)](http://clayallsopp.github.io/readme-score?url=andrewliao11/py-faster-rcnn-imagenet) -If you want to know some basic ideas in faster rcnn, try to check [Video Object Detection using Faster R-CNN](http://andrewliao11.github.io/object_detection/faster_rcnn/) out! +If you want to know some basic ideas in faster rcnn, try to check [Video Object Detection using Faster R-CNN](https://andrewliao11.github.io/object/detection/2016/07/23/detection/) out! Feel free to contact me via email, I'll try to give you a hand if I can, lol.

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