mmpose 1.0.0

OpenMMLab Pose Estimation Toolbox and Benchmark.

 

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

Introduction

English | 简体中文

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 1.6+.

https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4

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 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

• We are excited to release YOLOX-Pose, a One-Stage multi-person pose estimation model based on YOLOX. Checkout our project page for more details.

• 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:

  • Provide an easy and agile way to integrate algorithms, features and applications into MMPose
  • Allow flexible code structure and style; only need a short code review process
  • Build individual projects with full power of MMPose but not bound up with heavy frameworks
  • Checkout new projects:
    • RTMPose
    • YOLOX-Pose
    • MMPose4AIGC
  • Become a contributors and make MMPose greater. Start your journey from the example project

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

  • Release of YOLOX-Pose, a One-Stage multi-person pose estimation model based on YOLOX
  • Development of MMPose for AIGC based on RTMPose, generating high-quality skeleton images for Pose-guided AIGC projects
  • Support for OpenPose-style skeleton visualization
  • More complete and user-friendly documentation and tutorials

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

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:

1. For the basic usage of MMPose:

   • A 20-minute Tour to MMPose
   • Demos
   • Inference
   • Configs
   • Prepare Datasets
   • Train and Test

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 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

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.

Supported algorithms:

• DeepPose (CVPR'2014)
• CPM (CVPR'2016)
• Hourglass (ECCV'2016)
• SimpleBaseline3D (ICCV'2017)
• Associative Embedding (NeurIPS'2017)
• SimpleBaseline2D (ECCV'2018)
• DSNT (ArXiv'2021)
• HRNet (CVPR'2019)
• IPR (ECCV'2018)
• VideoPose3D (CVPR'2019)
• HRNetv2 (TPAMI'2019)
• MSPN (ArXiv'2019)
• SCNet (CVPR'2020)
• HigherHRNet (CVPR'2020)
• RSN (ECCV'2020)
• InterNet (ECCV'2020)
• VoxelPose (ECCV'2020)
• LiteHRNet (CVPR'2021)
• ViPNAS (CVPR'2021)
• Debias-IPR (ICCV'2021)
• SimCC (ECCV'2022)

Supported techniques:

• FPN (CVPR'2017)
• FP16 (ArXiv'2017)
• Wingloss (CVPR'2018)
• AdaptiveWingloss (ICCV'2019)
• DarkPose (CVPR'2020)
• UDP (CVPR'2020)
• Albumentations (Information'2020)
• SoftWingloss (TIP'2021)
• RLE (ICCV'2021)

Supported datasets:

• AFLW [homepage] (ICCVW'2011)
• sub-JHMDB [homepage] (ICCV'2013)
• COFW [homepage] (ICCV'2013)
• MPII [homepage] (CVPR'2014)
• Human3.6M [homepage] (TPAMI'2014)
• COCO [homepage] (ECCV'2014)
• CMU Panoptic [homepage] (ICCV'2015)
• DeepFashion [homepage] (CVPR'2016)
• 300W [homepage] (IMAVIS'2016)
• RHD [homepage] (ICCV'2017)
• CMU Panoptic HandDB [homepage] (CVPR'2017)
• AI Challenger [homepage] (ArXiv'2017)
• MHP [homepage] (ACM MM'2018)
• WFLW [homepage] (CVPR'2018)
• PoseTrack18 [homepage] (CVPR'2018)
• OCHuman [homepage] (CVPR'2019)
• CrowdPose [homepage] (CVPR'2019)
• MPII-TRB [homepage] (ICCV'2019)
• FreiHand [homepage] (ICCV'2019)
• Animal-Pose [homepage] (ICCV'2019)
• OneHand10K [homepage] (TCSVT'2019)
• Vinegar Fly [homepage] (Nature Methods'2019)
• Desert Locust [homepage] (Elife'2019)
• Grévy’s Zebra [homepage] (Elife'2019)
• ATRW [homepage] (ACM MM'2020)
• Halpe [homepage] (CVPR'2020)
• COCO-WholeBody [homepage] (ECCV'2020)
• MacaquePose [homepage] (bioRxiv'2020)
• InterHand2.6M [homepage] (ECCV'2020)
• AP-10K [homepage] (NeurIPS'2021)
• Horse-10 [homepage] (WACV'2021)

Supported backbones:

• AlexNet (NeurIPS'2012)
• VGG (ICLR'2015)
• ResNet (CVPR'2016)
• ResNext (CVPR'2017)
• SEResNet (CVPR'2018)
• ShufflenetV1 (CVPR'2018)
• ShufflenetV2 (ECCV'2018)
• MobilenetV2 (CVPR'2018)
• ResNetV1D (CVPR'2019)
• ResNeSt (ArXiv'2020)
• Swin (CVPR'2021)
• HRFormer (NIPS'2021)
• PVT (ICCV'2021)
• PVTV2 (CVMJ'2022)

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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