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OpenMMLab Pose Estimation Toolbox and Benchmark.

Project description


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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 main branch works with PyTorch 1.8+.

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 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 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

  • We have added support for two new datasets:

  • Support for four new algorithms:

  • Released the first whole-body pose estimation model, RTMW, with accuracy exceeding 70 AP on COCO-Wholebody. For details, refer to RTMPose. Try it now!


  • Welcome to use the MMPose project. Here, you can discover the latest features and algorithms in MMPose and quickly share your ideas and code implementations with the community. Adding new features to MMPose has become smoother:

    • Provides a simple and fast way to add new algorithms, features, and applications to MMPose.
    • More flexible code structure and style, fewer restrictions, and a shorter code review process.
    • Utilize the powerful capabilities of MMPose in the form of independent projects without being constrained by the code framework.
    • Newly added projects include:
    • Start your journey as an MMPose contributor with a simple example project, and let's build a better MMPose together!

  • October 12, 2023: MMPose v1.2.0 has been officially released, with major updates including:

    • Support for new datasets: UBody, 300W-LP.
    • Support for new algorithms: MotionBERT, DWPose, EDPose, Uniformer.
    • Migration of Associate Embedding, InterNet, YOLOX-Pose algorithms.
    • Migration of the DeepFashion2 dataset.
    • Support for Badcase visualization analysis, multi-dataset evaluation, and keypoint visibility prediction features.

    Please check the complete release notes for more details on the updates brought by MMPose v1.2.0!

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 this Roadmap.

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


Please refer to 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 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.


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

  • MMEngine: OpenMMLab foundational library for training deep learning models.
  • MMCV: OpenMMLab foundational library for computer vision.
  • MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
  • MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMDeploy: OpenMMLab Model Deployment Framework.
  • MMRazor: OpenMMLab model compression toolbox and benchmark.
  • MIM: MIM installs OpenMMLab packages.
  • Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

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