mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.

Created at: 2020-07-08 14:02:55
Language: Python
License: Apache-2.0
OpenMMLab website HOT      OpenMMLab platform TRY IT OUT      MMPose 1.0 Open Beta JOIN

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📘Documentation | 🛠️Installation | 👀Model Zoo | 📜Papers | 🆕Update News | 🤔Reporting Issues

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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 1.5+.

Major Features
  • 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 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 for more information.

  • Support for various datasets

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

  • 2022-10-14: MMPose v0.29.0 is released. Major updates include:
  • 2022-09-01: MMPose v1.0.0 beta has been released [ Code | Docs ]. Welcome to try it and your feedback will be greatly appreciated!
  • 2022-02-28: MMPose model deployment is supported by MMDeploy v0.3.0 MMPose Webcam API is a simple yet powerful tool to develop interactive webcam applications with MMPose features.
  • 2021-12-29: OpenMMLab Open Platform is online! Try our pose estimation demo


MMPose depends on PyTorch and MMCV. Below are quick steps for installation. Please refer to for detailed installation guide.

conda create -n openmmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate openmmlab
pip3 install openmim
mim install mmcv-full
git clone
cd mmpose
pip3 install -e .

Getting Started

Please see for the basic usage of MMPose. There are also tutorials:

Model Zoo

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

Supported algorithms:
Supported techniques:
Supported datasets:
Supported backbones:

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.


Accuracy and Training Speed

MMPose achieves superior of training speed and accuracy on the standard keypoint detection benchmarks like COCO. See more details at

Inference Speed

We summarize the model complexity and inference speed of major models in MMPose, including FLOPs, parameter counts and inference speeds on both CPU and GPU devices with different batch sizes. Please refer to for more details.

Data Preparation

Please refer to for a general knowledge of data preparation.


Please refer to FAQ for frequently asked questions.


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


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.


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

    title={OpenMMLab Pose Estimation Toolbox and Benchmark},
    author={MMPose Contributors},
    howpublished = {\url{}},


This project is released under the Apache 2.0 license.

Projects in OpenMMLab

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