S2ANet模型

sudo nvidia-docker run -it --name paddle_s2anet -v $PWD:/paddle --network=host registry.baidubce.com/paddlepaddle/paddle:2.0.1-gpu-cuda10.1-cudnn7 /bin/bash

import paddle
print(paddle.__version__)
paddle.utils.run_check()

python3.7 setup.py install

cd PaddleDetecetion/ppdet/ext_op
python3.7 test.py

python3.7 tools/infer.py -c configs/dota/s2anet_1x_dota.yml -o weights=./weights/s2anet_1x_dota.pdparams  --infer_dir=dota_test_images --draw_threshold=0.05 --save_txt=True --output_dir=output

# 预测
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/dota/s2anet_1x_dota.yml -o weights=model.pdparams --infer_img=demo/P0072__1.0__0___0.png

@article{han2021align,  
  author={J. {Han} and J. {Ding} and J. {Li} and G. -S. {Xia}},  
  journal={IEEE Transactions on Geoscience and Remote Sensing},  
  title={Align Deep Features for Oriented Object Detection},  
  year={2021},
  pages={1-11},  
  doi={10.1109/TGRS.2021.3062048}}

@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},
  pages={3974--3983},
  year={2018}
}