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mmrotate is an open source rotating object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.6+. The compatibility to earlier versions of PyTorch is not fully tested.

Documentation: https://mmrotate.readthedocs.io/en/latest/.

• Support multiple angle representations

  MMRotate provides three mainstream angle representations to meet different paper settings.

• Modular Design

  We decompose the rotation detection framework into different components, which makes it much easy and flexible to build a new model by combining different modules.

• Strong baseline and State of the art

  The toolbox provides strong baselines and state-of-the-art methods in rotation detection.

This project is released under the Apache 2.0 license.

Supported algorithms:

Please refer to model_zoo for more details.

Please refer to install.md for installation of mmrotate and data preparation for dataset preparation.

If you are new of rotation detection, you can start with learn the basics. If you are familiar with it, check out getting_started.md for the basic usage of mmrotate.

Refer to the below tutorials to dive deeper:

• Overview
• Config
• Customize Dataset
• Customize Model

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

@article{mmrotate2022,
    title={MMRotate: A rotation detection benchmark using pytorch},
    author={Zhou, Yue and Yang, Xue and Zhang, Gefan},
    journal= {arXiv preprint arXiv:xxxx.xxxx},
    year={2022}
}

We appreciate all contributions to improve mmrotate. Please refer to CONTRIBUTING.md in MMRotate 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.

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