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

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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+.

• Release RTMW3D, a real-time model for 3D wholebody pose estimation.

• Release RTMO, a state-of-the-art real-time method for multi-person pose estimation.

  

• Release RTMW models in various sizes ranging from RTMW-m to RTMW-x. The input sizes include 256x192 and 384x288. This provides flexibility to select the right model for different speed and accuracy requirements.

• Support inference of PoseAnything. Web demo is available here.

• Support for new datasets:

  • (ICCV 2015) 300VW

• 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:
    • RTMPose
    • RTMO
    • RTMPose3D
    • PoseAnything
    • YOLOX-Pose
    • MMPose4AIGC
    • Simple Keypoints
    • Just Dance
    • Uniformer
  • Start your journey as an MMPose contributor with a simple example project, and let's build a better MMPose together!

• January 4, 2024: MMPose v1.3.0 has been officially released, with major updates including:

  • Support for new datasets: ExLPose, H3WB
  • Release of new RTMPose series models: RTMO, RTMW
  • Support for new algorithm PoseAnything
  • Enhanced Inferencer with optional progress bar and improved affinity for one-stage methods

  Please check the complete release notes for more details on the updates brought by MMPose v1.3.0!

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 installation.md for more detailed installation and dataset preparation.

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

1. For the basic usage of MMPose:

   • A 20-minute Tour to MMPose
   • Demos
   • Inference
   • Configs
   • Prepare Datasets
   • Train and Test
   • Deployment
   • Model Analysis
   • Dataset Annotation and Preprocessing

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

   • Learn about Codecs
   • Dataflow in MMPose
   • Implement New Models
   • Customize Datasets
   • Customize Data Transforms
   • Customize Evaluation
   • Customize Optimizer
   • Customize Logging
   • How to Deploy
   • Model Analysis
   • Migration Guide

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

   • Contribution Guide

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

   • FAQ

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.

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 CONTRIBUTING.md 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:

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

This project is released under the Apache 2.0 license.

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