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📘Documentation | 🛠️Installation | 👀Model Zoo | 📜Papers | 🆕Update News | 🤔Reporting Issues
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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 1.5+.
mmpose.demo.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.md for more information.
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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.
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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.
- 2022-06-07: MMPose v0.27.0 is released. Major updates include:
- Support hand gesture recognition
- Try the demo for gesture recognition
- Learn more about the algorithm, dataset and experiment results
- Major upgrade to MMPose Webcam API towards simpler and more efficient development of pose-empowered applications
- Tutorials (EN|zh_CN)
- API Reference
- Demo
- Support hand gesture recognition
- 2022-04: MMPose is available on Gitee
- 2022-02-28: MMPose model deployment is supported by MMDeploy v0.3.0 MMPose Webcam API is a simple yet powerful tool to develop interactive webcam applications with MMPose features.
- 2021-12-29: OpenMMLab Open Platform is online! Try our pose estimation demo
MMPose depends on PyTorch and MMCV. Below are quick steps for installation. Please refer to install.md for detailed installation guide.
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
git clone https://github.com/open-mmlab/mmpose.git
cd mmpose
pip3 install -e .
Please see get_started.md for the basic usage of MMPose. There are also tutorials:
- learn about configs
- finetune model
- add new dataset
- customize data pipelines
- add new modules
- export a model to ONNX
- customize runtime settings
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)
- HMR (CVPR'2018)
- SimpleBaseline2D (ECCV'2018)
- HRNet (CVPR'2019)
- 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)
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)
- SmoothNet (arXiv'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)
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.
MMPose achieves superior of training speed and accuracy on the standard keypoint detection benchmarks like COCO. See more details at benchmark.md.
We summarize the model complexity and inference speed of major models in MMPose, including FLOPs, parameter counts and inference speeds on both CPU and GPU devices with different batch sizes. Please refer to inference_speed_summary.md for more details.
Please refer to data_preparation.md for a general knowledge of data preparation.
Please refer to FAQ for frequently asked questions.
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.
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM installs OpenMMLab packages.
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- MMPose: OpenMMLab pose estimation toolbox and benchmark.
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