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Official code of the paper "ReDet: A Rotation-Equivariant Detector for Aerial Object Detection" (CVPR 2021)

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ReDet_mmclassification

This branch contains the codes for training ReResNet. We make minor modifications on the mmclassification. The specific version is 4e6875d.

Benchmark and model zoo

Model Group Top-1 (%) Top-5 (%) Download
ReResNet-50 C8 71.20 90.28 raw | publish | log
ReResNet-101 C8 74.92 92.22 raw | publish | log

Note:

  • Alternative download link: baiduyun with extracting code ABCD.
  • The raw checkpoint is used to test the accuracy on ImageNet. The publish model is used for downstream tasks, e.g., object detection. We convert the raw model to publish model by tools/publish_model.py.

Installation

Please refer to install.md for installation and dataset preparation.

Get Started

Please see getting_started.md for the basic usage of MMClassification. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, and adding new modules.

Convert ReResNet to standard Pytorch layers

We can export ReResNet to a standard ResNet by tools/convert_re_resnet_to_torch.py.

First, download the checkpoint from here and put it to work_dirs/re_resnet50_c8_batch256/epoch_100.pth.

Then, convert the raw checkpoint to a standard checkpoint for ResNet.

python tools/convert_re_resnet_to_torch.py configs/re_resnet/re_resnet50_c8_batch256.py \
        work_dirs/re_resnet50_c8_batch256/epoch_100.pth work_dirs/re_resnet50_c8_batch256/epoch_100_torch.pth

Now, we can test the accuracy with a standard ResNet.

bash tools/dist_test.sh configs/imagenet/resnet50_batch256.py work_dirs/re_resnet50_c8_batch256/epoch_100_torch.pth 8

Citation

If you use this toolbox or benchmark in your research, please cite:

@misc{mmclassification,
  author =       {Yang, Lei and Li, Xiaojie and Lou, Zan and Yang, Mingmin and
                  Wang, Fei and Qian, Chen and Chen, Kai and Lin, Dahua},
  title =        {{MMClassification}},
  howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
  year =         {2020}
}

@inproceedings{han2021ReDet,
  title={ReDet: A Rotation-equivariant Detector for Aerial Object Detection},
  author={Han, Jiaming and Ding, Jian and Xue, Nan and Xia, Gui-Song},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}

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Official code of the paper "ReDet: A Rotation-Equivariant Detector for Aerial Object Detection" (CVPR 2021)

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