Introduction

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

The master branch works with PyTorch 1.8+ .

Support diverse tasks

We support a wide spectrum of mainstream pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, 3d human mesh recovery, fashion landmark detection and animal pose estimation. See Demo for more information.

Higher efficiency and higher accuracy

MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet . See benchmark.md for more information.

Support for various datasets

The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc. See dataset_zoo for more information.

Well designed, tested and documented

We decompose MMPose into different components and one can easily construct a customized pose estimation framework by combining different modules. We provide detailed documentation and API reference, as well as unittests.

What's New

Welcome to projects of MMPose , where you can access to the latest features of MMPose, and share your ideas and codes with the community at once. Contribution to MMPose will be simple and smooth:

2022-04-06: MMPose v1.0.0 is officially released, with the main updates including:

Please refer to the release notes for more updates brought by MMPose v1.0.0!

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 the following list.

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:

For the basic usage of MMPose:

For developers who wish to develop based on MMPose:

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

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.

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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