hight-head rcnn on caffe #119

chengshuai · 2017-11-30T02:49:46Z

I modify the light-head rcnn on resnet-50 though caffe py-R-FCN. Then, i train the model on voc data.
But, i get bad result. The prototxt is below:
###################train.prototxt#####################################
name: "ResNet-50"
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': 21"
}
}

------------------------ conv1 -----------------------------

layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
stride: 2
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}

}

layer {
bottom: "conv1"
top: "conv1"
name: "bn_conv1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "conv1"
top: "conv1"
name: "scale_conv1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "conv1"
top: "conv1"
name: "conv1_relu"
type: "ReLU"
}

layer {
bottom: "conv1"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
kernel_size: 3
stride: 2
pool: MAX
}
}

layer {
bottom: "pool1"
top: "res2a_branch1"
name: "res2a_branch1"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "bn2a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "scale2a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "pool1"
top: "res2a_branch2a"
name: "res2a_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "bn2a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "scale2a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "res2a_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res2a_branch2a"
top: "res2a_branch2b"
name: "res2a_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "bn2a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "scale2a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "res2a_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res2a_branch2b"
top: "res2a_branch2c"
name: "res2a_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "bn2a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "scale2a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a_branch1"
bottom: "res2a_branch2c"
top: "res2a"
name: "res2a"
type: "Eltwise"
}

layer {
bottom: "res2a"
top: "res2a"
name: "res2a_relu"
type: "ReLU"
}

layer {
bottom: "res2a"
top: "res2b_branch2a"
name: "res2b_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "bn2b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "scale2b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "res2b_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res2b_branch2a"
top: "res2b_branch2b"
name: "res2b_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "bn2b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "scale2b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "res2b_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res2b_branch2b"
top: "res2b_branch2c"
name: "res2b_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2b_branch2c"
top: "res2b_branch2c"
name: "bn2b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2b_branch2c"
top: "res2b_branch2c"
name: "scale2b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2a"
bottom: "res2b_branch2c"
top: "res2b"
name: "res2b"
type: "Eltwise"
}

layer {
bottom: "res2b"
top: "res2b"
name: "res2b_relu"
type: "ReLU"
}

layer {
bottom: "res2b"
top: "res2c_branch2a"
name: "res2c_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2c_branch2a"
top: "res2c_branch2a"
name: "bn2c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2c_branch2a"
top: "res2c_branch2a"
name: "scale2c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2c_branch2a"
top: "res2c_branch2a"
name: "res2c_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res2c_branch2a"
top: "res2c_branch2b"
name: "res2c_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2c_branch2b"
top: "res2c_branch2b"
name: "bn2c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2c_branch2b"
top: "res2c_branch2b"
name: "scale2c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2c_branch2b"
top: "res2c_branch2b"
name: "res2c_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res2c_branch2b"
top: "res2c_branch2c"
name: "res2c_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}

layer {
bottom: "res2c_branch2c"
top: "res2c_branch2c"
name: "bn2c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2c_branch2c"
top: "res2c_branch2c"
name: "scale2c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2b"
bottom: "res2c_branch2c"
top: "res2c"
name: "res2c"
type: "Eltwise"
}

layer {
bottom: "res2c"
top: "res2c"
name: "res2c_relu"
type: "ReLU"
}

layer {
bottom: "res2c"
top: "res3a_branch1"
name: "res3a_branch1"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3a_branch1"
top: "res3a_branch1"
name: "bn3a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch1"
top: "res3a_branch1"
name: "scale3a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res2c"
top: "res3a_branch2a"
name: "res3a_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "bn3a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "scale3a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "res3a_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res3a_branch2a"
top: "res3a_branch2b"
name: "res3a_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "bn3a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "scale3a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "res3a_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res3a_branch2b"
top: "res3a_branch2c"
name: "res3a_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3a_branch2c"
top: "res3a_branch2c"
name: "bn3a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch2c"
top: "res3a_branch2c"
name: "scale3a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a_branch1"
bottom: "res3a_branch2c"
top: "res3a"
name: "res3a"
type: "Eltwise"
}

layer {
bottom: "res3a"
top: "res3a"
name: "res3a_relu"
type: "ReLU"
}

layer {
bottom: "res3a"
top: "res3b_branch2a"
name: "res3b_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "bn3b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "scale3b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "res3b_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res3b_branch2a"
top: "res3b_branch2b"
name: "res3b_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "bn3b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "scale3b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "res3b_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res3b_branch2b"
top: "res3b_branch2c"
name: "res3b_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3b_branch2c"
top: "res3b_branch2c"
name: "bn3b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3b_branch2c"
top: "res3b_branch2c"
name: "scale3b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3a"
bottom: "res3b_branch2c"
top: "res3b"
name: "res3b"
type: "Eltwise"
}

layer {
bottom: "res3b"
top: "res3b"
name: "res3b_relu"
type: "ReLU"
}

layer {
bottom: "res3b"
top: "res3c_branch2a"
name: "res3c_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3c_branch2a"
top: "res3c_branch2a"
name: "bn3c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3c_branch2a"
top: "res3c_branch2a"
name: "scale3c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3c_branch2a"
top: "res3c_branch2a"
name: "res3c_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res3c_branch2a"
top: "res3c_branch2b"
name: "res3c_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3c_branch2b"
top: "res3c_branch2b"
name: "bn3c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3c_branch2b"
top: "res3c_branch2b"
name: "scale3c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3c_branch2b"
top: "res3c_branch2b"
name: "res3c_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res3c_branch2b"
top: "res3c_branch2c"
name: "res3c_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3c_branch2c"
top: "res3c_branch2c"
name: "bn3c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3c_branch2c"
top: "res3c_branch2c"
name: "scale3c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3b"
bottom: "res3c_branch2c"
top: "res3c"
name: "res3c"
type: "Eltwise"
}

layer {
bottom: "res3c"
top: "res3c"
name: "res3c_relu"
type: "ReLU"
}

layer {
bottom: "res3c"
top: "res3d_branch2a"
name: "res3d_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3d_branch2a"
top: "res3d_branch2a"
name: "bn3d_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3d_branch2a"
top: "res3d_branch2a"
name: "scale3d_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3d_branch2a"
top: "res3d_branch2a"
name: "res3d_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res3d_branch2a"
top: "res3d_branch2b"
name: "res3d_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3d_branch2b"
top: "res3d_branch2b"
name: "bn3d_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3d_branch2b"
top: "res3d_branch2b"
name: "scale3d_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3d_branch2b"
top: "res3d_branch2b"
name: "res3d_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res3d_branch2b"
top: "res3d_branch2c"
name: "res3d_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res3d_branch2c"
top: "res3d_branch2c"
name: "bn3d_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3d_branch2c"
top: "res3d_branch2c"
name: "scale3d_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3c"
bottom: "res3d_branch2c"
top: "res3d"
name: "res3d"
type: "Eltwise"
}

layer {
bottom: "res3d"
top: "res3d"
name: "res3d_relu"
type: "ReLU"
}

layer {
bottom: "res3d"
top: "res4a_branch1"
name: "res4a_branch1"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4a_branch1"
top: "res4a_branch1"
name: "bn4a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch1"
top: "res4a_branch1"
name: "scale4a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res3d"
top: "res4a_branch2a"
name: "res4a_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "bn4a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "scale4a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "res4a_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res4a_branch2a"
top: "res4a_branch2b"
name: "res4a_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "bn4a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "scale4a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "res4a_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res4a_branch2b"
top: "res4a_branch2c"
name: "res4a_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4a_branch2c"
top: "res4a_branch2c"
name: "bn4a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch2c"
top: "res4a_branch2c"
name: "scale4a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a_branch1"
bottom: "res4a_branch2c"
top: "res4a"
name: "res4a"
type: "Eltwise"
}

layer {
bottom: "res4a"
top: "res4a"
name: "res4a_relu"
type: "ReLU"
}

layer {
bottom: "res4a"
top: "res4b_branch2a"
name: "res4b_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "bn4b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "scale4b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "res4b_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res4b_branch2a"
top: "res4b_branch2b"
name: "res4b_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "bn4b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "scale4b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "res4b_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res4b_branch2b"
top: "res4b_branch2c"
name: "res4b_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4b_branch2c"
top: "res4b_branch2c"
name: "bn4b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4b_branch2c"
top: "res4b_branch2c"
name: "scale4b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4a"
bottom: "res4b_branch2c"
top: "res4b"
name: "res4b"
type: "Eltwise"
}

layer {
bottom: "res4b"
top: "res4b"
name: "res4b_relu"
type: "ReLU"
}

layer {
bottom: "res4b"
top: "res4c_branch2a"
name: "res4c_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4c_branch2a"
top: "res4c_branch2a"
name: "bn4c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4c_branch2a"
top: "res4c_branch2a"
name: "scale4c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4c_branch2a"
top: "res4c_branch2a"
name: "res4c_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res4c_branch2a"
top: "res4c_branch2b"
name: "res4c_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4c_branch2b"
top: "res4c_branch2b"
name: "bn4c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4c_branch2b"
top: "res4c_branch2b"
name: "scale4c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4c_branch2b"
top: "res4c_branch2b"
name: "res4c_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res4c_branch2b"
top: "res4c_branch2c"
name: "res4c_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4c_branch2c"
top: "res4c_branch2c"
name: "bn4c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4c_branch2c"
top: "res4c_branch2c"
name: "scale4c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4b"
bottom: "res4c_branch2c"
top: "res4c"
name: "res4c"
type: "Eltwise"
}

layer {
bottom: "res4c"
top: "res4c"
name: "res4c_relu"
type: "ReLU"
}

layer {
bottom: "res4c"
top: "res4d_branch2a"
name: "res4d_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4d_branch2a"
top: "res4d_branch2a"
name: "bn4d_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4d_branch2a"
top: "res4d_branch2a"
name: "scale4d_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4d_branch2a"
top: "res4d_branch2a"
name: "res4d_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res4d_branch2a"
top: "res4d_branch2b"
name: "res4d_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4d_branch2b"
top: "res4d_branch2b"
name: "bn4d_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4d_branch2b"
top: "res4d_branch2b"
name: "scale4d_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4d_branch2b"
top: "res4d_branch2b"
name: "res4d_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res4d_branch2b"
top: "res4d_branch2c"
name: "res4d_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4d_branch2c"
top: "res4d_branch2c"
name: "bn4d_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4d_branch2c"
top: "res4d_branch2c"
name: "scale4d_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4c"
bottom: "res4d_branch2c"
top: "res4d"
name: "res4d"
type: "Eltwise"
}

layer {
bottom: "res4d"
top: "res4d"
name: "res4d_relu"
type: "ReLU"
}

layer {
bottom: "res4d"
top: "res4e_branch2a"
name: "res4e_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4e_branch2a"
top: "res4e_branch2a"
name: "bn4e_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4e_branch2a"
top: "res4e_branch2a"
name: "scale4e_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4e_branch2a"
top: "res4e_branch2a"
name: "res4e_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res4e_branch2a"
top: "res4e_branch2b"
name: "res4e_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4e_branch2b"
top: "res4e_branch2b"
name: "bn4e_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4e_branch2b"
top: "res4e_branch2b"
name: "scale4e_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4e_branch2b"
top: "res4e_branch2b"
name: "res4e_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res4e_branch2b"
top: "res4e_branch2c"
name: "res4e_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4e_branch2c"
top: "res4e_branch2c"
name: "bn4e_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4e_branch2c"
top: "res4e_branch2c"
name: "scale4e_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4d"
bottom: "res4e_branch2c"
top: "res4e"
name: "res4e"
type: "Eltwise"
}

layer {
bottom: "res4e"
top: "res4e"
name: "res4e_relu"
type: "ReLU"
}

layer {
bottom: "res4e"
top: "res4f_branch2a"
name: "res4f_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4f_branch2a"
top: "res4f_branch2a"
name: "bn4f_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4f_branch2a"
top: "res4f_branch2a"
name: "scale4f_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4f_branch2a"
top: "res4f_branch2a"
name: "res4f_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res4f_branch2a"
top: "res4f_branch2b"
name: "res4f_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4f_branch2b"
top: "res4f_branch2b"
name: "bn4f_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4f_branch2b"
top: "res4f_branch2b"
name: "scale4f_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4f_branch2b"
top: "res4f_branch2b"
name: "res4f_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res4f_branch2b"
top: "res4f_branch2c"
name: "res4f_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res4f_branch2c"
top: "res4f_branch2c"
name: "bn4f_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4f_branch2c"
top: "res4f_branch2c"
name: "scale4f_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4e"
bottom: "res4f_branch2c"
top: "res4f"
name: "res4f"
type: "Eltwise"
}

layer {
bottom: "res4f"
top: "res4f"
name: "res4f_relu"
type: "ReLU"
}

layer {
bottom: "res4f"
top: "res5a_branch1"
name: "res5a_branch1"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5a_branch1"
top: "res5a_branch1"
name: "bn5a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch1"
top: "res5a_branch1"
name: "scale5a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res4f"
top: "res5a_branch2a"
name: "res5a_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "bn5a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "scale5a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "res5a_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res5a_branch2a"
top: "res5a_branch2b"
name: "res5a_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
dilation: 2
pad: 2
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "bn5a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "scale5a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "res5a_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res5a_branch2b"
top: "res5a_branch2c"
name: "res5a_branch2c"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5a_branch2c"
top: "res5a_branch2c"
name: "bn5a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch2c"
top: "res5a_branch2c"
name: "scale5a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a_branch1"
bottom: "res5a_branch2c"
top: "res5a"
name: "res5a"
type: "Eltwise"
}

layer {
bottom: "res5a"
top: "res5a"
name: "res5a_relu"
type: "ReLU"
}

layer {
bottom: "res5a"
top: "res5b_branch2a"
name: "res5b_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "bn5b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "scale5b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "res5b_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res5b_branch2a"
top: "res5b_branch2b"
name: "res5b_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
dilation: 2
pad: 2
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "bn5b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "scale5b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "res5b_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res5b_branch2b"
top: "res5b_branch2c"
name: "res5b_branch2c"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5b_branch2c"
top: "res5b_branch2c"
name: "bn5b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5b_branch2c"
top: "res5b_branch2c"
name: "scale5b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5a"
bottom: "res5b_branch2c"
top: "res5b"
name: "res5b"
type: "Eltwise"
}

layer {
bottom: "res5b"
top: "res5b"
name: "res5b_relu"
type: "ReLU"
}

layer {
bottom: "res5b"
top: "res5c_branch2a"
name: "res5c_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5c_branch2a"
top: "res5c_branch2a"
name: "bn5c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5c_branch2a"
top: "res5c_branch2a"
name: "scale5c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5c_branch2a"
top: "res5c_branch2a"
name: "res5c_branch2a_relu"
type: "ReLU"
}

layer {
bottom: "res5c_branch2a"
top: "res5c_branch2b"
name: "res5c_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
dilation: 2
pad: 2
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5c_branch2b"
top: "res5c_branch2b"
name: "bn5c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5c_branch2b"
top: "res5c_branch2b"
name: "scale5c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5c_branch2b"
top: "res5c_branch2b"
name: "res5c_branch2b_relu"
type: "ReLU"
}

layer {
bottom: "res5c_branch2b"
top: "res5c_branch2c"
name: "res5c_branch2c"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}

layer {
bottom: "res5c_branch2c"
top: "res5c_branch2c"
name: "bn5c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5c_branch2c"
top: "res5c_branch2c"
name: "scale5c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}

layer {
bottom: "res5b"
bottom: "res5c_branch2c"
top: "res5c"
name: "res5c"
type: "Eltwise"
}

layer {
bottom: "res5c"
top: "res5c"
name: "res5c_relu"
type: "ReLU"
}

#========= RPN ============

layer {
name: "rpn_conv/3x3"
type: "Convolution"
bottom: "res4f"
top: "rpn/output"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 512
kernel_size: 3 pad: 1 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_voc"
type: "Convolution"
bottom: "rpn/output"
top: "rpn_cls_score_voc"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 18 # 2(bg/fg) * 9(anchors)
kernel_size: 1 pad: 0 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}

layer {
name: "rpn_bbox_pred_voc"
type: "Convolution"
bottom: "rpn/output"
top: "rpn_bbox_pred_voc"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 36 # 4 * 9(anchors)
kernel_size: 1 pad: 0 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}

layer {
bottom: "rpn_cls_score_voc"
top: "rpn_cls_score_reshape_voc"
name: "rpn_cls_score_reshape_voc"
type: "Reshape"
reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }#118wh-->12*(9*w)*h
}

layer {
name: 'rpn-data'
type: 'Python'
bottom: 'rpn_cls_score_voc'
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"
#param_str: "{'feat_stride': 16, 'scales': [3, 6, 9, 16, 32], 'ratios': [0.5, 0.667, 1.0, 1.5, 2.0]}"
}
}

layer {
name: "rpn_loss_cls"
type: "SoftmaxWithLoss"
bottom: "rpn_cls_score_reshape_voc"
bottom: "rpn_labels"
propagate_down: 1#back the rpn shape layer
propagate_down: 0#rpn label layer not change
top: "rpn_cls_loss"
loss_weight: 1
loss_param {
ignore_label: -1
normalize: true
}
}

layer {
name: "rpn_loss_bbox"
type: "SmoothL1Loss"
bottom: "rpn_bbox_pred_voc"
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: 3.0 }
}

#========= RoI Proposal ============

layer {
name: "rpn_cls_prob_voc"
type: "Softmax"
bottom: "rpn_cls_score_reshape_voc"
top: "rpn_cls_prob_voc"
}

layer {
name: 'rpn_cls_prob_reshape_voc'
type: 'Reshape'
bottom: 'rpn_cls_prob_voc'
top: 'rpn_cls_prob_reshape_voc'
reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
}

layer {
name: 'proposal'
type: 'Python'
bottom: 'rpn_cls_prob_reshape_voc'
bottom: 'rpn_bbox_pred_voc'
bottom: 'im_info'
top: 'rpn_rois'

top: 'rpn_scores'

python_param {
module: 'rpn.proposal_layer'
layer: 'ProposalLayer'
param_str: "'feat_stride': 16"
#param_str: "{'feat_stride': 16, 'scales': [3, 6, 9, 16, 32], 'ratios': [0.5, 0.667, 1.0, 1.5, 2.0]}"
}
}

#layer {

name: 'debug-data'

type: 'Python'

bottom: 'data'

bottom: 'rpn_rois'

bottom: 'rpn_scores'

python_param {

module: 'rpn.debug_layer'

layer: 'RPNDebugLayer'

}

#}

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': 2"
}
}

#----------------------light conv layer------------------
#-------------conv_new1-------------
layer {
bottom: "res5c"
top: "conv_new_1"
name: "conv_new_1"
type: "Convolution"
convolution_param {
num_output: 256
kernel_h : 15
kernel_w : 1
pad_h : 7
pad_w : 0
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 3.0
}
}

layer {
bottom: "conv_new_1"
top: "conv_new_1"
name: "conv_new_1_relu"
type: "ReLU"
}
####-----------------conv_new2
layer {
bottom: "conv_new_1"
top: "conv_new_2"
name: "conv_new_2"
type: "Convolution"
convolution_param {
num_output: 490
kernel_h : 1
kernel_w : 15
pad_h : 0
pad_w : 7
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 3.0
}
}
layer {
bottom: "conv_new_2"
top: "conv_new_2"
name: "conv_new_2_relu"
type: "ReLU"
}
#-------------conv_new3-------------
layer {
bottom: "conv_new_1"
top: "conv_new_3"
name: "conv_new_3"
type: "Convolution"
convolution_param {
num_output: 256
kernel_h : 1
kernel_w : 15
pad_h : 0
pad_w : 7
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 3.0
}
}

layer {
bottom: "conv_new_3"
top: "conv_new_3"
name: "conv_new_3_relu"
type: "ReLU"
}
####-----------------conv_new4
layer {
bottom: "conv_new_3"
top: "conv_new_4"
name: "conv_new_4"
type: "Convolution"
convolution_param {
num_output: 490
kernel_h : 15
kernel_w : 1
pad_h : 7
pad_w : 0
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 3.0
}
}
layer {
bottom: "conv_new_4"
top: "conv_new_4"
name: "conv_new_4_relu"
type: "ReLU"
}

layer {
bottom: "conv_new_2"
bottom: "conv_new_4"
top: "light_head"
name: "light_head"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
#####################################################

#--------------position sensitive RoI pooling--------------
layer {
bottom: "light_head"
bottom: "rois"
top: "psroipooled"
name: "psroipooled"
type: "PSROIPooling"
psroi_pooling_param {
spatial_scale: 0.0625
output_dim: 10
group_size: 7
}
}

###fc
layer {
name: "fc6"
type: "InnerProduct"
bottom: "psroipooled"
top: "fc6"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2048
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}

layer {
name: "cls_score_voc"
type: "InnerProduct"
bottom: "fc6"
top: "cls_score"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 81
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bbox_pred_voc"
type: "InnerProduct"
bottom: "fc6"
top: "bbox_pred"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 8
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}

#--------------online hard example mining--------------
layer {
name: "per_roi_loss_cls"
type: "SoftmaxWithLossOHEM"
bottom: "cls_score_voc"
bottom: "labels"
top: "temp_loss_cls"
top: "temp_prob_cls"
top: "per_roi_loss_cls"
loss_weight: 0
loss_weight: 0
loss_weight: 0
propagate_down: false
propagate_down: false
}

layer {
name: "per_roi_loss_bbox"
type: "SmoothL1LossOHEM"
bottom: "bbox_pred_voc"
bottom: "bbox_targets"
bottom: "bbox_inside_weights"
top: "temp_loss_bbox"
top: "per_roi_loss_bbox"
loss_weight: 0
loss_weight: 0
propagate_down: false
propagate_down: false
propagate_down: false
}

layer {
name: "per_roi_loss"
type: "Eltwise"
bottom: "per_roi_loss_cls"
bottom: "per_roi_loss_bbox"
top: "per_roi_loss"
propagate_down: false
propagate_down: false
}

layer {
bottom: "rois"
bottom: "per_roi_loss"
bottom: "labels"
bottom: "bbox_inside_weights"
top: "labels_ohem"
top: "bbox_loss_weights_ohem"
name: "annotator_detector"
type: "BoxAnnotatorOHEM"
box_annotator_ohem_param {
roi_per_img: 128
ignore_label: -1
}
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
}

layer {
name: "silence"
type: "Silence"
bottom: "bbox_outside_weights"
bottom: "temp_loss_cls"
bottom: "temp_prob_cls"
bottom: "temp_loss_bbox"
}

#-----------------------output------------------------
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "cls_score_voc"
bottom: "labels_ohem"
top: "loss_cls"
loss_weight: 1
loss_param {
ignore_label: -1
}
propagate_down: true
propagate_down: false
}

layer {
name: "accuarcy"
type: "Accuracy"
bottom: "cls_score_voc"
bottom: "labels_ohem"
top: "accuarcy"
#include: { phase: TEST }
accuracy_param {
ignore_label: -1
}
propagate_down: false
propagate_down: false
}

layer {
name: "loss_bbox"
type: "SmoothL1LossOHEM"
bottom: "bbox_pred_voc"
bottom: "bbox_targets"
bottom: "bbox_loss_weights_ohem"
top: "loss_bbox"
loss_weight: 1
loss_param {
normalization: PRE_FIXED
pre_fixed_normalizer: 128
}
propagate_down: true
propagate_down: false
propagate_down: false
}

##################################test.prototxt################
name: "ResNet50"

input: "data"
input_shape {
dim: 1
dim: 3
dim: 224
dim: 224
}

input: "im_info"
input_shape {
dim: 1
dim: 3
}

layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
stride: 2
}
param {
lr_mult: 0.0
}