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📘Documentation |
🛠️Installation |
👀Model Zoo |
📜Papers |
🆕Update News |
🤔Reporting Issues |
🔥RTMPose
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+.
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.
We are excited to release RTMPose, a real-time pose estimation framework including:
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:
2022-03-15: MMPose v1.0.0rc1 is released. Major updates include:
See the full release note for more exciting updates brought by MMPose v1.0.0rc1!
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 .
# If you have an older version of mmdet installed in your current environment,
# please upgrade to version 3.x
mim install "mmdet>=3.0.0rc6"
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:
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 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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