matrixgame2018 / mmrotate

OpenMMLab Rotated Object Detection Toolbox and Benchmark

Home Page:https://mmrotate.readthedocs.io/en/latest/

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📘Documentation | 🛠️Installation | 👀Model Zoo | 🤔Reporting Issues

Introduction

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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+.

video.MP4
Major Features
  • Support multiple angle representations

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

  • Modular Design

    We decompose the rotated object 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 rotated object detection.

Installation

Please refer to install.md for installation guide.

Get Started

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

Model Zoo

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.

Supported algorithms:

Model Request

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.

Data Preparation

Please refer to data_preparation.md to prepare the data.

FAQ

Please refer to FAQ for frequently asked questions.

Contributing

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

Acknowledgement

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.

Citation

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}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM Installs OpenMMLab Packages.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab next-generation platform for general 3D object detection.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMOCR: A comprehensive toolbox for text detection, recognition and understanding.
  • MMGeneration: OpenMMLab next-generation toolbox for generative models.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
  • MMRazor: OpenMMLab model compression toolbox and benchmark.
  • MMDeploy: OpenMMLab model deployment framework.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.

About

OpenMMLab Rotated Object Detection Toolbox and Benchmark

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

License:Apache License 2.0


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