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OpenMMLab Pose Estimation Toolbox and Benchmark.

Project description

Introduction

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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 1.3+.


COCO 17-keypoint pose estimation

133-keypoint whole-body pose estimation (full HD version)

Major Features

  • Support diverse tasks

    We support a wide spectrum of mainstream human 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, fashion landmark detection and 3d human mesh recovery.

  • 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 data_preparation.md 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.

Model Zoo

Supported algorithms:

(click to collapse)
  • <input type="checkbox" checked="" disabled="" /> DeepPose (CVPR'2014)
  • <input type="checkbox" checked="" disabled="" /> Wingloss (CVPR'2018)
  • <input type="checkbox" checked="" disabled="" /> CPM (CVPR'2016)
  • <input type="checkbox" checked="" disabled="" /> Hourglass (ECCV'2016)
  • <input type="checkbox" checked="" disabled="" /> SimpleBaseline (ECCV'2018)
  • <input type="checkbox" checked="" disabled="" /> HRNet (CVPR'2019)
  • <input type="checkbox" checked="" disabled="" /> HRNetv2 (TPAMI'2019)
  • <input type="checkbox" checked="" disabled="" /> SCNet (CVPR'2020)
  • <input type="checkbox" checked="" disabled="" /> Associative Embedding (NeurIPS'2017)
  • <input type="checkbox" checked="" disabled="" /> HigherHRNet (CVPR'2020)
  • <input type="checkbox" checked="" disabled="" /> DarkPose (CVPR'2020)
  • <input type="checkbox" checked="" disabled="" /> UDP (CVPR'2020)
  • <input type="checkbox" checked="" disabled="" /> MSPN (ArXiv'2019)
  • <input type="checkbox" checked="" disabled="" /> RSN (ECCV'2020)
  • <input type="checkbox" checked="" disabled="" /> HMR (CVPR'2018)

Supported datasets:

(click to collapse)
  • <input type="checkbox" checked="" disabled="" /> COCO (ECCV'2014)
  • <input type="checkbox" checked="" disabled="" /> COCO-WholeBody (ECCV'2020)
  • <input type="checkbox" checked="" disabled="" /> MPII (CVPR'2014)
  • <input type="checkbox" checked="" disabled="" /> MPII-TRB (ICCV'2019)
  • <input type="checkbox" checked="" disabled="" /> AI Challenger (ArXiv'2017)
  • <input type="checkbox" checked="" disabled="" /> OCHuman (CVPR'2019)
  • <input type="checkbox" checked="" disabled="" /> CrowdPose (CVPR'2019)
  • <input type="checkbox" checked="" disabled="" /> PoseTrack18 (CVPR'2018)
  • <input type="checkbox" checked="" disabled="" /> MHP (ACM MM'2018)
  • <input type="checkbox" checked="" disabled="" /> sub-JHMDB (ICCV'2013)
  • <input type="checkbox" checked="" disabled="" /> OneHand10K (TCSVT'2019)
  • <input type="checkbox" checked="" disabled="" /> FreiHand (ICCV'2019)
  • <input type="checkbox" checked="" disabled="" /> RHD (ICCV'2017)
  • <input type="checkbox" checked="" disabled="" /> CMU Panoptic HandDB (CVPR'2017)
  • <input type="checkbox" checked="" disabled="" /> InterHand2.6M (ECCV'2020)
  • <input type="checkbox" checked="" disabled="" /> Human3.6M (TPAMI'2014)
  • <input type="checkbox" checked="" disabled="" /> DeepFashion (CVPR'2016)
  • <input type="checkbox" checked="" disabled="" /> 300W (IMAVIS'2016)
  • <input type="checkbox" checked="" disabled="" /> WFLW (CVPR'2018)
  • <input type="checkbox" checked="" disabled="" /> AFLW (ICCVW'2011)
  • <input type="checkbox" checked="" disabled="" /> COFW (ICCV'2013)

Supported backbones:

(click to expand)
  • <input type="checkbox" checked="" disabled="" /> AlexNet (NeurIPS'2012)
  • <input type="checkbox" checked="" disabled="" /> VGG (ICLR'2015)
  • <input type="checkbox" checked="" disabled="" /> HRNet (CVPR'2019)
  • <input type="checkbox" checked="" disabled="" /> ResNet (CVPR'2016)
  • <input type="checkbox" checked="" disabled="" /> ResNetV1D (CVPR'2019)
  • <input type="checkbox" checked="" disabled="" /> ResNeSt (ArXiv'2020)
  • <input type="checkbox" checked="" disabled="" /> ResNext (CVPR'2017)
  • <input type="checkbox" checked="" disabled="" /> SCNet (CVPR'2020)
  • <input type="checkbox" checked="" disabled="" /> SEResNet (CVPR'2018)
  • <input type="checkbox" checked="" disabled="" /> ShufflenetV1 (CVPR'2018)
  • <input type="checkbox" checked="" disabled="" /> ShufflenetV2 (ECCV'2018)
  • <input type="checkbox" checked="" disabled="" /> MobilenetV2 (CVPR'2018)

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 Issues.

Benchmark

We demonstrate the superiority of our MMPose framework in terms of speed and accuracy on the standard COCO keypoint detection benchmark.

Model Input size MMPose (s/iter) HRNet (s/iter) MMPose (mAP) HRNet (mAP)
resnet_50 256x192 0.28 0.64 0.718 0.704
resnet_50 384x288 0.81 1.24 0.731 0.722
resnet_101 256x192 0.36 0.84 0.726 0.714
resnet_101 384x288 0.79 1.53 0.748 0.736
resnet_152 256x192 0.49 1.00 0.735 0.720
resnet_152 384x288 0.96 1.65 0.750 0.743
hrnet_w32 256x192 0.54 1.31 0.746 0.744
hrnet_w32 384x288 0.76 2.00 0.760 0.758
hrnet_w48 256x192 0.66 1.55 0.756 0.751
hrnet_w48 384x288 1.23 2.20 0.767 0.763

More details about the benchmark are available on benchmark.md.

Installation

Please refer to install.md for installation.

Data Preparation

Please refer to data_preparation.md for a general knowledge of data preparation.

Get Started

Please see getting_started.md for the basic usage of MMPose. There are also tutorials:

FAQ

Please refer to FAQ for frequently asked questions.

License

This project is released under the Apache 2.0 license.

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}
}

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.

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab's 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.

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