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English | [简体中文](README_CN.md)
## Introduction MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the [OpenMMLab project](https://github.com/open-mmlab). The master branch works with **PyTorch 1.5+**. 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.md](demo/README.md) 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](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch). See [benchmark.md](docs/en/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](docs/en/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.
## What's New - 2022-07-06: MMPose [v0.28.0](https://github.com/open-mmlab/mmpose/releases/tag/v0.28.0) is released. Major updates include: - Support [TCFormer](https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.html) (CVPR'2022). See the [model page](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/tcformer_coco-wholebody.md) - Add [RLE](https://arxiv.org/abs/2107.11291) pre-trained model on COCO dataset. See the [model page](/configs/body/2d_kpt_sview_rgb_img/deeppose/coco/resnet_rle_coco.md) - Update [Swin](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/swin_coco.md) models with better performance - 2022-02-28: MMPose model deployment is supported by [MMDeploy](https://github.com/open-mmlab/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](https://platform.openmmlab.com/web-demo/demo/poseestimation) ## Installation MMPose depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv). Below are quick steps for installation. Please refer to [install.md](docs/en/install.md) for detailed installation guide. ```shell 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 . ``` ## Getting Started Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMPose. There are also tutorials: - [learn about configs](docs/en/tutorials/0_config.md) - [finetune model](docs/en/tutorials/1_finetune.md) - [add new dataset](docs/en/tutorials/2_new_dataset.md) - [customize data pipelines](docs/en/tutorials/3_data_pipeline.md) - [add new modules](docs/en/tutorials/4_new_modules.md) - [export a model to ONNX](docs/en/tutorials/5_export_model.md) - [customize runtime settings](docs/en/tutorials/6_customize_runtime.md) ## 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](https://mmpose.readthedocs.io/en/latest/modelzoo.html) page.
Supported algorithms: - [x] [DeepPose](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014) - [x] [CPM](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#cpm-cvpr-2016) (CVPR'2016) - [x] [Hourglass](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hourglass-eccv-2016) (ECCV'2016) - [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017) - [x] [Associative Embedding](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017) - [x] [HMR](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#hmr-cvpr-2018) (CVPR'2018) - [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018) - [x] [HRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019) - [x] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019) - [x] [HRNetv2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019) - [x] [MSPN](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019) - [x] [SCNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#scnet-cvpr-2020) (CVPR'2020) - [x] [HigherHRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020) - [x] [RSN](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#rsn-eccv-2020) (ECCV'2020) - [x] [InterNet](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#internet-eccv-2020) (ECCV'2020) - [x] [VoxelPose](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020) - [x] [LiteHRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021) - [x] [ViPNAS](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021)
Supported techniques: - [x] [FPN](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fpn-cvpr-2017) (CVPR'2017) - [x] [FP16](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017) - [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018) - [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019) - [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020) - [x] [UDP](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020) - [x] [Albumentations](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#albumentations-information-2020) (Information'2020) - [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021) - [x] [SmoothNet](/configs/_base_/filters/smoothnet_h36m.md) (arXiv'2021) - [x] [RLE](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#rle-iccv-2021) (ICCV'2021)
Supported datasets: - [x] [AFLW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#aflw-iccvw-2011) \[[homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011) - [x] [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013) - [x] [COFW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013) - [x] [MPII](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mpii-cvpr-2014) \[[homepage](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014) - [x] [Human3.6M](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#human3-6m-tpami-2014) \[[homepage](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014) - [x] [COCO](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014) - [x] [CMU Panoptic](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015) - [x] [DeepFashion](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#deepfashion-cvpr-2016) \[[homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016) - [x] [300W](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#300w-imavis-2016) \[[homepage](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016) - [x] [RHD](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#rhd-iccv-2017) \[[homepage](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017) - [x] [CMU Panoptic HandDB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cmu-panoptic-handdb-cvpr-2017) \[[homepage](http://domedb.perception.cs.cmu.edu/handdb.html)\] (CVPR'2017) - [x] [AI Challenger](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ai-challenger-arxiv-2017) \[[homepage](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017) - [x] [MHP](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mhp-acm-mm-2018) \[[homepage](https://lv-mhp.github.io/dataset)\] (ACM MM'2018) - [x] [WFLW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#wflw-cvpr-2018) \[[homepage](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018) - [x] [PoseTrack18](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018) - [x] [OCHuman](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019) - [x] [CrowdPose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019) - [x] [MPII-TRB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mpii-trb-iccv-2019) \[[homepage](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019) - [x] [FreiHand](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#freihand-iccv-2019) \[[homepage](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019) - [x] [Animal-Pose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#animal-pose-iccv-2019) \[[homepage](https://sites.google.com/view/animal-pose/)\] (ICCV'2019) - [x] [OneHand10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#onehand10k-tcsvt-2019) \[[homepage](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019) - [x] [Vinegar Fly](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#vinegar-fly-nature-methods-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019) - [x] [Desert Locust](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#desert-locust-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) - [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) - [x] [ATRW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#atrw-acm-mm-2020) \[[homepage](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020) - [x] [Halpe](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#halpe-cvpr-2020) \[[homepage](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020) - [x] [COCO-WholeBody](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#coco-wholebody-eccv-2020) \[[homepage](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020) - [x] [MacaquePose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#macaquepose-biorxiv-2020) \[[homepage](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020) - [x] [InterHand2.6M](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020) - [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021) - [x] [Horse-10](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021)
Supported backbones: - [x] [AlexNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012) - [x] [VGG](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#vgg-iclr-2015) (ICLR'2015) - [x] [ResNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnet-cvpr-2016) (CVPR'2016) - [x] [ResNext](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnext-cvpr-2017) (CVPR'2017) - [x] [SEResNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018) - [x] [ShufflenetV1](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018) - [x] [ShufflenetV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018) - [x] [MobilenetV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018) - [x] [ResNetV1D](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019) - [x] [ResNeSt](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020) - [x] [Swin](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#swin-cvpr-2021) (CVPR'2021) - [x] [HRFormer](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrformer-nips-2021) (NIPS'2021) - [x] [PVT](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#pvt-iccv-2021) (ICCV'2021) - [x] [PVTV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#pvtv2-cvmj-2022) (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](https://github.com/open-mmlab/mmpose/issues/9). ### Benchmark #### Accuracy and Training Speed MMPose achieves superior of training speed and accuracy on the standard keypoint detection benchmarks like COCO. See more details at [benchmark.md](docs/en/benchmark.md). #### Inference Speed 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](docs/en/inference_speed_summary.md) for more details. ## Data Preparation Please refer to [data_preparation.md](docs/en/data_preparation.md) for a general knowledge of data preparation. ## FAQ Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. ## Contributing We appreciate all contributions to improve MMPose. Please refer to [CONTRIBUTING.md](.github/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: ```bibtex @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](LICENSE). ## Projects in OpenMMLab - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark. - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox. - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark. - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark. - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark. - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark. - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox. - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.