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Introduction

English | 简体中文

MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project.

The main branch works with PyTorch 1.8+.

https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4

<br/> <details close> <summary><b>Major Features</b></summary> </details>

What's New

<br/>

0.x / 1.x Migration

MMPose v1.0.0 is a major update, including many API and config file changes. Currently, a part of the algorithms have been migrated to v1.0.0, and the remaining algorithms will be completed in subsequent versions. We will show the migration progress in this Roadmap.

If your algorithm has not been migrated, you can continue to use the 0.x branch and old documentation.

Installation

Please refer to installation.md for more detailed installation and dataset preparation.

Getting Started

We provided a series of tutorials about the basic usage of MMPose for new users:

  1. For the basic usage of MMPose:

  2. For developers who wish to develop based on MMPose:

  3. For researchers and developers who are willing to contribute to MMPose:

  4. For some common issues, we provide a FAQ list:

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.

<details open> <summary><b>Supported algorithms:</b></summary> </details> <details open> <summary><b>Supported techniques:</b></summary> </details> <details open> <summary><b>Supported datasets:</b></summary> </details> <details open> <summary><b>Supported backbones:</b></summary> </details>

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 MMPose Roadmap.

Contributing

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

Acknowledgement

MMPose 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 models.

Citation

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

@misc{mmpose2020,
    title={OpenMMLab Pose Estimation Toolbox and Benchmark},
    author={MMPose Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmpose}},
    year={2020}
}

License

This project is released under the Apache 2.0 license.

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