This is a tensorflow-based rotation detection benchmark, also called UranusDet.
UranusDet is maintained by Xue Yang with Shanghai Jiao Tong University supervised by Prof. Junchi Yan.
Papers and codes related to remote sensing/aerial image detection: DOTA-DOAI 
Techniques:
- Dataset: DOTA, HRSC2016, ICDAR2015, ICDAR2017 MLT, MSRA-TD500, UCAS-AOD, FDDB, OHD-SJTU, SSDD++, Total-Text
- Baackbone: ResNet, MobileNetV2, EfficientNet, DarkNet53
- Neck: FPN, BiFPN
- Detectors:
- R2CNN (Faster-RCNN-H): R2CNN_Faster-RCNN_Tensorflow
, DOTA-DOAI
, R2CNN_FPN_Tensorflow (Deprecated)
- RRPN (Faster-RCNN-R): TF code
- SCRDet (ICCV19): R2CNN++
, IoU-Smooth L1 Loss
- RetinaNet-H, RetinaNet-R: TF code
- RefineRetinaNet (CascadeRetinaNet): TF code
- FCOS
- RSDet (AAAI21): TF code
- R3Det (AAAI21): TF code
, Pytorch code
- Circular Smooth Label (CSL, ECCV20): TF code
- Densely Coded Label (DCL, CVPR21): TF code
- GWD (ICML21): TF code
- KLD: TF code
- RIDet: Pytorch code
- Mixed method: R3Det-DCL, R3Det-GWD, R3Det-KLD, FCOS-RSDet
- R2CNN (Faster-RCNN-H): R2CNN_Faster-RCNN_Tensorflow
- Loss: CE, Focal Loss, Smooth L1 Loss, IoU-Smooth L1 Loss, Modulated Loss
- Others: SWA, exportPb, MMdnn
The above-mentioned rotation detectors are all modified based on the following horizontal detectors:
- Faster RCNN: TF code
- R-FCN: TF code
- FPN: TF code1
, TF code2 (Deprecated)
- Cascade RCNN: TF code
- Cascade FPN RCNN: TF code
- RetinaNet: TF code
- RefineDet: MxNet code
- FCOS: TF code
, MxNet code
More results and trained models are available in the MODEL_ZOO.md.
Base setting:
| Backbone | Neck | Training/test dataset | Data Augmentation | Epoch |
|---|---|---|---|---|
| ResNet50_v1d 600->800 | FPN | trainval/test | × | 13 (AP50) or 17 (AP50:95) is enough for baseline (default is 13) |
Note:
- Single GPU training: SAVE_WEIGHTS_INTE = iter_epoch * 1 (DOTA1.0: iter_epoch=27000, DOTA1.5: iter_epoch=32000, DOTA2.0: iter_epoch=40000)
- Multi-GPU training (better): SAVE_WEIGHTS_INTE = iter_epoch * 2
docker images: yangxue2docker/yx-tf-det:tensorflow1.13.1-cuda10-gpu-py3 or yangxue2docker/py3-tf1.15.2-nv-torch1.8.0-cuda11:v1.0
- python3.5 (anaconda recommend)
- cuda 10.0
- opencv-python 4.1.1.26
- tfplot 0.2.0 (optional)
- tensorflow-gpu 1.13
- tqdm 4.54.0
- Shapely 1.7.1
Note: For 30xx series graphics cards, I recommend this blog to install tf1.xx, or refer to ngc and tensorflow-release-notes to download docker image according to your environment, or just use my docker image (yangxue2docker/py3-tf1.15.2-nv-torch1.8.0-cuda11:v1.0)
Download a pretrain weight you need from the following three options, and then put it to $PATH_ROOT/dataloader/pretrained_weights.
- MxNet pretrain weights (recommend in this repo, default in NET_NAME): resnet_v1d, resnet_v1b, refer to gluon2TF.
- Tensorflow pretrain weights: resnet50_v1, resnet101_v1, resnet152_v1, efficientnet, mobilenet_v2, darknet53 (Baidu Drive (1jg2), Google Drive).
- Pytorch pretrain weights, refer to pretrain_zoo.py and Others.
- Please download trained models by this project, then put them to $PATH_ROOT/output/pretained_weights.
```
cd $PATH_ROOT/libs/utils/cython_utils
rm *.so
rm *.c
rm *.cpp
python setup.py build_ext --inplace (or make)
cd $PATH_ROOT/libs/utils/
rm *.so
rm *.c
rm *.cpp
python setup.py build_ext --inplace
```
-
If you want to train your own dataset, please note:
(1) Select the detector and dataset you want to use, and mark them as #DETECTOR and #DATASET (such as #DETECTOR=retinanet and #DATASET=DOTA) (2) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py (3) Copy $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py to $PATH_ROOT/libs/configs/cfgs.py (4) Add category information in $PATH_ROOT/libs/label_name_dict/label_dict.py (5) Add data_name to $PATH_ROOT/dataloader/dataset/read_tfrecord.py -
Make tfrecord
If image is very large (such as DOTA dataset), the image needs to be cropped. Take DOTA dataset as a example:cd $PATH_ROOT/dataloader/dataset/DOTA python data_crop.pyIf image does not need to be cropped, just convert the annotation file into xml format, refer to example.xml.
cd $PATH_ROOT/dataloader/dataset/ python convert_data_to_tfrecord.py --root_dir='/PATH/TO/DOTA/' --xml_dir='labeltxt' --image_dir='images' --save_name='train' --img_format='.png' --dataset='DOTA' -
Start training
cd $PATH_ROOT/tools/#DETECTOR python train.py
-
For large-scale image, take DOTA dataset as a example (the output file or visualization is in $PATH_ROOT/tools/#DETECTOR/test_dota/VERSION):
cd $PATH_ROOT/tools/#DETECTOR python test_dota.py --test_dir='/PATH/TO/IMAGES/' --gpus=0,1,2,3,4,5,6,7 -ms (multi-scale testing, optional) -s (visualization, optional) or (better than multi-scale testing) python test_dota_sota.py --test_dir='/PATH/TO/IMAGES/' --gpus=0,1,2,3,4,5,6,7 -s (visualization, optional)Notice: In order to set the breakpoint conveniently, the read and write mode of the file is' a+'. If the model of the same #VERSION needs to be tested again, the original test results need to be deleted.
-
For small-scale image, take HRSC2016 dataset as a example:
cd $PATH_ROOT/tools/#DETECTOR python test_hrsc2016.py --test_dir='/PATH/TO/IMAGES/' --gpu=0 --image_ext='bmp' --test_annotation_path='/PATH/TO/ANNOTATIONS' -s (visualization, optional)
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
If you find our code useful for your research, please consider cite.
@article{yang2021learning,
title={Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence},
author={Yang, Xue and Yang, Xiaojiang and Yang, Jirui and Ming, Qi and Wang, Wentao and Tian, Qi and Yan, Junchi},
journal={arXiv preprint arXiv:2106.01883},
year={2021}
}
@inproceedings{yang2021rethinking,
title={Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss},
author={Yang, Xue and Yan, Junchi and Qi, Ming and Wang, Wentao and Xiaopeng, Zhang and Qi, Tian},
booktitle={International Conference on Machine Learning (ICML)},
year={2021}
}
@article{ming2021optimization,
title={Optimization for Oriented Object Detection via Representation Invariance Loss},
author={Ming, Qi and Zhou, Zhiqiang and Miao, Lingjuan and Yang, Xue and Dong, Yunpeng},
journal={arXiv preprint arXiv:2103.11636},
year={2021}
}
@inproceedings{yang2021dense,
title={Dense Label Encoding for Boundary Discontinuity Free Rotation Detection},
author={Yang, Xue and Hou, Liping and Zhou, Yue and Wang, Wentao and Yan, Junchi},
booktitle={Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR)},
month={June},
year={2021},
pages={15819-15829}
}
@inproceedings{yang2020arbitrary,
title={Arbitrary-oriented object detection with circular smooth label},
author={Yang, Xue and Yan, Junchi},
booktitle={European Conference on Computer Vision (ECCV)},
pages={677--694},
year={2020},
organization={Springer}
}
@inproceedings{yang2021r3det,
title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
author={Yang, Xue and Yan, Junchi and Feng, Ziming and He, Tao},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2021}
}
@inproceedings{qian2021learning,
title={Learning modulated loss for rotated object detection},
author={Qian, Wen and Yang, Xue and Peng, Silong and Yan, Junchi and Guo, Yue },
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2021}
}
@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}
}
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