mmpose 1.0.0rc1

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 RTMPose, a real-time pose estimation framework including:

  • A family of lightweight pose estimation models with state-of-the-art performance
  • Inference APIs for Python, C++, C#, Java, etc. Easy to integrate into your applications and empower real-time stable pose estimation
  • Cross-platform deployment with various backends
  • A step-by-step guide to training and deploying your own models

  Checkout our project page and technical report for more information!

• 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 (coming soon)
    • MMPose4AIGC (coming soon)
  • Become a contributors and make MMPose greater. Start your journey from the example project

• 2022-03-15: MMPose v1.0.0rc1 is released. Major updates include:

  • Release RTMPose, a high-performance real-time pose estimation framework based on MMPose
  • Support ViTPose (NeurIPS'22), CID (CVPR'22) and DEKR (CVPR'21)
  • Add Inferencer, a convenient interface for inference and visualization

  See the full release note for more exciting updates brought by MMPose v1.0.0rc1!

Installation

Below are quick steps for installation:

conda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate open-mmlab
pip install openmim
git clone -b 1.x https://github.com/open-mmlab/mmpose.git
cd mmpose
mim install -e .

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:

• About Configs
• Add New Dataset
• Keypoint Encoding & Decoding
• Inference with Existing Models
• Train and Test
• Visualization Tools
• Other Useful Tools

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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