R3Det and R3Det++ are based on Focal Loss for Dense Object Detection, and it is completed by YangXue.
Techniques:
- ResNet, MobileNetV2, EfficientNet
- RetinaNet-H, RetinaNet-R
- R3Det: Feature Refinement Module (FRM)
- R3Det++: Instance Level Denoising (InLD)
- IoU-Smooth L1 Loss
- Circular Smooth Label (CSL)
- Densely Coded Label (DCL)
- mmdetection version is released
- Dataset support: DOTA, HRSC2016, ICDAR2015, ICDAR2017 MLT, UCAS-AOD, FDDB, OHD-SJTU, SSDD++
- OHDet: Object Heading Detection
| Model | Backbone | Training data | Val data | mAP | Model Link | Anchor | Angle Pred. | Reg. Loss | Angle Range | lr schd | Data Augmentation | GPU | Image/GPU | Configs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RetinaNet-H | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 65.73 | Baidu Drive (jum2) | H | Reg. | smooth L1 | 90 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v4.py |
| CSL | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 65.69 | Baidu Drive (kgr3) | H | Cls.: Gaussian (r=6, w=1) | smooth L1 | 180 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v1.py |
| CSL | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 67.38 | Baidu Drive (g3wt) | H | Cls.: Gaussian (r=1, w=10) | smooth L1 | 180 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v45.py |
| CSL | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 68.73 | Baidu Drive (3a4t) | H | Cls.: Pulse (w=1) | smooth L1 | 180 | 2x | × | 2X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v41.py |
| DCL | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 67.39 | H | Cls.: BCL (w=180/256) | smooth L1 | 180 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | ||
| R3Det | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 70.40 | - | H + R | Reg. | smooth L1 | 90 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_r3det_v1.py |
| R3Det* | ResNet101_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 73.79 | - | H + R | Reg. | iou-smooth L1 | 90 | 3x | √ | 4X GeForce RTX 2080 Ti | 1 | cfgs_res101_dota_r3det_v19.py |
| R3Det* | ResNet152_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 74.54 | - | H + R | Reg. | iou-smooth L1 | 90 | 3x | √ | 4X GeForce RTX 2080 Ti | 1 | cfgs_res152_dota_r3det_v25.py |
| R3Det | ResNet152_v1d 600->MS (+Flip) | DOTA1.0 trainval | DOTA1.0 test | 76.23 (+0.24) | model | H + R | Reg. | iou-smooth L1 | 90 | 4x | √ | 3X GeForce RTX 2080 Ti | 1 | cfgs_res152_dota_r3det_v3.py |
R3Det*: R3Det with two refinement stages
Due to the improvement of the code, the performance of this repo is gradually improving, so the experimental results in other configuration files are for reference only.
docker images: docker pull yangxue2docker/yx-tf-det:tensorflow1.13.1-cuda10-gpu-py3
1、python3.5 (anaconda recommend)
2、cuda 10.0
3、opencv(cv2)
4、tfplot 0.2.0 (optional)
5、tensorflow-gpu 1.13
1、Please download resnet50_v1, resnet101_v1, resnet152_v1, efficientnet, mobilenet_v2 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、(Recommend in this repo) Or you can choose to use a better backbone (resnet_v1d), refer to gluon2TF.
- Baidu Drive, password: 5ht9.
- Google Drive
cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace (or make)
cd $PATH_ROOT/libs/box_utils/
python setup.py build_ext --inplace
1、If you want to train your own data, please note:
(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
(2) Add category information in $PATH_ROOT/libs/label_name_dict/label_dict.py
(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord_multi_gpu.py
2、Make tfrecord
For DOTA dataset:
cd $PATH_ROOT/data/io/DOTA
python data_crop.py
cd $PATH_ROOT/data/io/
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/DOTA/'
--xml_dir='labeltxt'
--image_dir='images'
--save_name='train'
--img_format='.png'
--dataset='DOTA'
3、Multi-gpu train
cd $PATH_ROOT/tools
python multi_gpu_train_r3det.py
cd $PATH_ROOT/tools
python test_dota_r3det_ms.py --test_dir='/PATH/TO/IMAGES/'
--gpus=0,1,2,3,4,5,6,7
-ms (multi-scale testing, optional)
-s (visualization, optional)
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
If this is useful for your research, please consider cite.
@article{yang2020arbitrary,
title={Arbitrary-Oriented Object Detection with Circular Smooth Label},
author={Yang, Xue and Yan, Junchi},
journal={European Conference on Computer Vision (ECCV)},
year={2020}
organization={Springer}
}
@article{yang2019r3det,
title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
author={Yang, Xue and Liu, Qingqing and Yan, Junchi and Li, Ang and Zhang, Zhiqiang and Yu, Gang},
journal={arXiv preprint arXiv:1908.05612},
year={2019}
}
@article{yang2020scrdet++,
title={SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing},
author={Yang, Xue and Yan, Junchi and Yang, Xiaokang and Tang, Jin and Liao, Wenglong and He, Tao},
journal={arXiv preprint arXiv:2004.13316},
year={2020}
}
@inproceedings{yang2019scrdet,
title={SCRDet: Towards more robust detection for small, cluttered and rotated objects},
author={Yang, Xue and Yang, Jirui and Yan, Junchi and Zhang, Yue and Zhang, Tengfei and Guo, Zhi and Sun, Xian and Fu, Kun},
booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
pages={8232--8241},
year={2019}
}
@inproceedings{xia2018dota,
title={DOTA: A large-scale dataset for object detection in aerial images},
author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={3974--3983},
year={2018}
}
1、https://github.com/endernewton/tf-faster-rcnn
2、https://github.com/zengarden/light_head_rcnn
3、https://github.com/tensorflow/models/tree/master/research/object_detection
4、https://github.com/fizyr/keras-retinanet