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MMRotate is an open-source toolbox for rotated object detection based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.6+.

Please refer to install.md for installation guide.

Please see get_started.md for the basic usage of MMRotate. There are also tutorials:

• learn the basics
• learn the config
• customize dataset
• customize model

Results and models are available in the README.md of each method's config directory. A summary can be found in the Model Zoo page.

We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in MMRotate Roadmap.

Please refer to data_preparation.md to prepare the data.

Please refer to FAQ for frequently asked questions.

We appreciate all contributions to improve MMRotate. Please refer to CONTRIBUTING.md for the contributing guideline.

MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.

If you find this project useful in your research, please consider cite:

@misc{mmrotate2022,
  title={MMRotate: A Rotated Object Detection Benchmark using PyTorch},
  author =       {Zhou, Yue and Yang, Xue and Zhang, Gefan and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi and Zhang, Wenwei, and Chen, Kai},
  howpublished = {\url{https://github.com/open-mmlab/mmrotate}},
  year =         {2022}
}

This project is released under the Apache 2.0 license.

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