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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
cd $PATH_ROOT/eval_devkit
sudo apt-get install swig
swig -c++ -python polyiou.i
python setup.py build_ext --inplace
Train
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/lable_dict.py
(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord_multi_gpu_ohdet.py
2、Make tfrecord
For OHD-SJTU dataset:
cd $PATH_ROOT/data/io/OHD-SJTU
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='OHD-SJTU'
3、Multi-gpu train
cd $PATH_ROOT/tools
python multi_gpu_train_r3det_csl_ohdet.py
Test
cd $PATH_ROOT/tools
python test_dota_r3det_csl_ohdet.py --test_dir='/PATH/TO/IMAGES/'
--gpus=0,1,2,3,4,5,6,7
cd $PATH_ROOT/eval_devkit
python OHD_SJTU_evaluation_OHD.py
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.
Tensorboard
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
Citation
If this is useful for your research, please consider cite.
@article{yang2020on,
title={On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited},
author={Yang, Xue and Yan, Junchi and He, Tao},
year={2020}
}
@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}
}
@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}
}