BIGWangYuDong / mmpose

OpenMMLab Pose Estimation Toolbox and Benchmark.

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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.


  • 2022-01-05: MMPose v0.22.0 was released. Major updates includes:
    • Support VoxelPose (ECCV'2020)
    • Support Soft Wing Loss (TIP'2021)
    • Support Adaptive Wing Loss (ICCV'2019)
  • 2021-12-29: OpenMMLab Open Platform is online! Try our pose estimation demo


Please refer to for installation guide.

Get 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 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

  • 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.


OpenMMLab Pose Estimation Toolbox and Benchmark.

License:Apache License 2.0


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