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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+.
mmpose.demo.mp4
Major Features
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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.
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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.
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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.
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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.
- We are glad to support 3 new datasets:
- (CVPR 2023) Human-Art
- (CVPR 2022) Animal Kingdom
- (AAAI 2020) LaPa
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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:
- Become a contributors and make MMPose greater. Start your journey from the example project
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2023-07-04: MMPose v1.1.0 is officially released, with the main updates including:
- Support new datasets: Human-Art, Animal Kingdom and LaPa.
- Support new config type that is more user-friendly and flexible.
- Improve RTMPose with better performance.
- Migrate 3D pose estimation models on h36m.
- Inference speedup and webcam inference with all demo scripts.
Please refer to the release notes for more updates brought by MMPose v1.1.0!
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 the following list.
Migration Progress
Algorithm | Status |
---|---|
MTUT (CVPR 2019) | |
MSPN (ArXiv 2019) | done |
InterNet (ECCV 2020) | |
DEKR (CVPR 2021) | done |
HigherHRNet (CVPR 2020) | |
DeepPose (CVPR 2014) | done |
RLE (ICCV 2021) | done |
SoftWingloss (TIP 2021) | done |
VideoPose3D (CVPR 2019) | done |
Hourglass (ECCV 2016) | done |
LiteHRNet (CVPR 2021) | done |
AdaptiveWingloss (ICCV 2019) | done |
SimpleBaseline2D (ECCV 2018) | done |
PoseWarper (NeurIPS 2019) | |
SimpleBaseline3D (ICCV 2017) | done |
HMR (CVPR 2018) | |
UDP (CVPR 2020) | done |
VIPNAS (CVPR 2021) | done |
Wingloss (CVPR 2018) | done |
DarkPose (CVPR 2020) | done |
Associative Embedding (NIPS 2017) | in progress |
VoxelPose (ECCV 2020) | |
RSN (ECCV 2020) | done |
CID (CVPR 2022) | done |
CPM (CVPR 2016) | done |
HRNet (CVPR 2019) | done |
HRNetv2 (TPAMI 2019) | done |
SCNet (CVPR 2020) | done |
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:
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For the basic usage of MMPose:
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For developers who wish to develop based on MMPose:
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For researchers and developers who are willing to contribute to MMPose:
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For some common issues, we provide a FAQ list:
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)
- Human-Art [homepage] (CVPR'2023)
- LaPa [homepage] (AAAI'2020)
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.
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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