| <div align="center"> | |
| <img src="resources/mmpose-logo.png" width="450"/> | |
| <div> </div> | |
| <div align="center"> | |
| <b>OpenMMLab website</b> | |
| <sup> | |
| <a href="https://openmmlab.com"> | |
| <i>HOT</i> | |
| </a> | |
| </sup> | |
| | |
| <b>OpenMMLab platform</b> | |
| <sup> | |
| <a href="https://platform.openmmlab.com"> | |
| <i>TRY IT OUT</i> | |
| </a> | |
| </sup> | |
| </div> | |
| <div> </div> | |
| [](https://mmpose.readthedocs.io/en/latest/?badge=latest) | |
| [](https://github.com/open-mmlab/mmpose/actions) | |
| [](https://codecov.io/gh/open-mmlab/mmpose) | |
| [](https://pypi.org/project/mmpose/) | |
| [](https://github.com/open-mmlab/mmpose/blob/main/LICENSE) | |
| [](https://github.com/open-mmlab/mmpose/issues) | |
| [](https://github.com/open-mmlab/mmpose/issues) | |
| [📘Documentation](https://mmpose.readthedocs.io/en/latest/) | | |
| [🛠️Installation](https://mmpose.readthedocs.io/en/latest/installation.html) | | |
| [👀Model Zoo](https://mmpose.readthedocs.io/en/latest/model_zoo.html) | | |
| [📜Papers](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html) | | |
| [🆕Update News](https://mmpose.readthedocs.io/en/latest/notes/changelog.html) | | |
| [🤔Reporting Issues](https://github.com/open-mmlab/mmpose/issues/new/choose) | | |
| [🔥RTMPose](/projects/rtmpose/) | |
| </div> | |
| <div align="center"> | |
| <a href="https://openmmlab.medium.com/" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a> | |
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| <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a> | |
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| <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a> | |
| <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> | |
| <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a> | |
| </div> | |
| ## Introduction | |
| English | [简体中文](README_CN.md) | |
| 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 main branch works with **PyTorch 1.8+**. | |
| https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4 | |
| <br/> | |
| <details close> | |
| <summary><b>Major Features</b></summary> | |
| - **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](demo/docs/en) 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/notes/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](docs/en/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. | |
| </details> | |
| ## What's New | |
| - We are glad to support 3 new datasets: | |
| - (CVPR 2023) [Human-Art](https://github.com/IDEA-Research/HumanArt) | |
| - (CVPR 2022) [Animal Kingdom](https://github.com/sutdcv/Animal-Kingdom) | |
| - (AAAI 2020) [LaPa](https://github.com/JDAI-CV/lapa-dataset/) | |
|  | |
| - Welcome to [*projects of MMPose*](/projects/README.md), 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: | |
| - [RTMPose](/projects/rtmpose/) | |
| - [YOLOX-Pose](/projects/yolox_pose/) | |
| - [MMPose4AIGC](/projects/mmpose4aigc/) | |
| - [Simple Keypoints](/projects/skps/) | |
| - Become a contributors and make MMPose greater. Start your journey from the [example project](/projects/example_project/) | |
| <br/> | |
| - 2023-07-04: MMPose [v1.1.0](https://github.com/open-mmlab/mmpose/releases/tag/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](https://github.com/open-mmlab/mmpose/releases/tag/v1.1.0) for more updates brought by MMPose v1.1.0! | |
| ## 0.x / 1.x Migration | |
| 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. | |
| <details close> | |
| <summary><b>Migration Progress</b></summary> | |
| | 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 | | |
| </details> | |
| If your algorithm has not been migrated, you can continue to use the [0.x branch](https://github.com/open-mmlab/mmpose/tree/0.x) and [old documentation](https://mmpose.readthedocs.io/en/0.x/). | |
| ## Installation | |
| Please refer to [installation.md](https://mmpose.readthedocs.io/en/latest/installation.html) for more detailed installation and dataset preparation. | |
| ## Getting Started | |
| We provided a series of tutorials about the basic usage of MMPose for new users: | |
| 1. For the basic usage of MMPose: | |
| - [A 20-minute Tour to MMPose](https://mmpose.readthedocs.io/en/latest/guide_to_framework.html) | |
| - [Demos](https://mmpose.readthedocs.io/en/latest/demos.html) | |
| - [Inference](https://mmpose.readthedocs.io/en/latest/user_guides/inference.html) | |
| - [Configs](https://mmpose.readthedocs.io/en/latest/user_guides/configs.html) | |
| - [Prepare Datasets](https://mmpose.readthedocs.io/en/latest/user_guides/prepare_datasets.html) | |
| - [Train and Test](https://mmpose.readthedocs.io/en/latest/user_guides/train_and_test.html) | |
| 2. For developers who wish to develop based on MMPose: | |
| - [Learn about Codecs](https://mmpose.readthedocs.io/en/latest/advanced_guides/codecs.html) | |
| - [Dataflow in MMPose](https://mmpose.readthedocs.io/en/latest/advanced_guides/dataflow.html) | |
| - [Implement New Models](https://mmpose.readthedocs.io/en/latest/advanced_guides/implement_new_models.html) | |
| - [Customize Datasets](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_datasets.html) | |
| - [Customize Data Transforms](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_transforms.html) | |
| - [Customize Optimizer](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_optimizer.html) | |
| - [Customize Logging](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_logging.html) | |
| - [How to Deploy](https://mmpose.readthedocs.io/en/latest/advanced_guides/how_to_deploy.html) | |
| - [Model Analysis](https://mmpose.readthedocs.io/en/latest/advanced_guides/model_analysis.html) | |
| - [Migration Guide](https://mmpose.readthedocs.io/en/latest/migration.html) | |
| 3. For researchers and developers who are willing to contribute to MMPose: | |
| - [Contribution Guide](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) | |
| 4. For some common issues, we provide a FAQ list: | |
| - [FAQ](https://mmpose.readthedocs.io/en/latest/faq.html) | |
| ## 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/model_zoo.html) page. | |
| <details close> | |
| <summary><b>Supported algorithms:</b></summary> | |
| - [x] [DeepPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014) | |
| - [x] [CPM](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#cpm-cvpr-2016) (CVPR'2016) | |
| - [x] [Hourglass](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hourglass-eccv-2016) (ECCV'2016) | |
| - [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017) | |
| - [ ] [Associative Embedding](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017) | |
| - [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018) | |
| - [x] [DSNT](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#dsnt-2018) (ArXiv'2021) | |
| - [x] [HRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019) | |
| - [x] [IPR](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#ipr-eccv-2018) (ECCV'2018) | |
| - [x] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019) | |
| - [x] [HRNetv2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019) | |
| - [x] [MSPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019) | |
| - [x] [SCNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#scnet-cvpr-2020) (CVPR'2020) | |
| - [ ] [HigherHRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020) | |
| - [x] [RSN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#rsn-eccv-2020) (ECCV'2020) | |
| - [ ] [InterNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#internet-eccv-2020) (ECCV'2020) | |
| - [ ] [VoxelPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020) | |
| - [x] [LiteHRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021) | |
| - [x] [ViPNAS](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021) | |
| - [x] [Debias-IPR](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#debias-ipr-iccv-2021) (ICCV'2021) | |
| - [x] [SimCC](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simcc-eccv-2022) (ECCV'2022) | |
| </details> | |
| <details close> | |
| <summary><b>Supported techniques:</b></summary> | |
| - [x] [FPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fpn-cvpr-2017) (CVPR'2017) | |
| - [x] [FP16](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017) | |
| - [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018) | |
| - [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019) | |
| - [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020) | |
| - [x] [UDP](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#udp-cvpr-2020) (CVPR'2020) | |
| - [x] [Albumentations](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#albumentations-information-2020) (Information'2020) | |
| - [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#softwingloss-tip-2021) (TIP'2021) | |
| - [x] [RLE](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#rle-iccv-2021) (ICCV'2021) | |
| </details> | |
| <details close> | |
| <summary><b>Supported datasets:</b></summary> | |
| - [x] [AFLW](https://mmpose.readthedocs.io/en/latest/model_zoo_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/model_zoo_papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013) | |
| - [x] [COFW](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013) | |
| - [x] [MPII](https://mmpose.readthedocs.io/en/latest/model_zoo_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/model_zoo_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/model_zoo_papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014) | |
| - [x] [CMU Panoptic](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015) | |
| - [x] [DeepFashion](https://mmpose.readthedocs.io/en/latest/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018) | |
| - [x] [OCHuman](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019) | |
| - [x] [CrowdPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019) | |
| - [x] [MPII-TRB](https://mmpose.readthedocs.io/en/latest/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) | |
| - [x] [ATRW](https://mmpose.readthedocs.io/en/latest/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_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/model_zoo_papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021) | |
| - [x] [Human-Art](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#human-art-cvpr-2023) \[[homepage](https://idea-research.github.io/HumanArt/)\] (CVPR'2023) | |
| - [x] [LaPa](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#lapa-aaai-2020) \[[homepage](https://github.com/JDAI-CV/lapa-dataset)\] (AAAI'2020) | |
| </details> | |
| <details close> | |
| <summary><b>Supported backbones:</b></summary> | |
| - [x] [AlexNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012) | |
| - [x] [VGG](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#vgg-iclr-2015) (ICLR'2015) | |
| - [x] [ResNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnet-cvpr-2016) (CVPR'2016) | |
| - [x] [ResNext](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnext-cvpr-2017) (CVPR'2017) | |
| - [x] [SEResNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018) | |
| - [x] [ShufflenetV1](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018) | |
| - [x] [ShufflenetV2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018) | |
| - [x] [MobilenetV2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018) | |
| - [x] [ResNetV1D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019) | |
| - [x] [ResNeSt](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020) | |
| - [x] [Swin](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#swin-cvpr-2021) (CVPR'2021) | |
| - [x] [HRFormer](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrformer-nips-2021) (NIPS'2021) | |
| - [x] [PVT](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#pvt-iccv-2021) (ICCV'2021) | |
| - [x] [PVTV2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022) | |
| </details> | |
| ### 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/2258). | |
| ## Contributing | |
| We appreciate all contributions to improve MMPose. Please refer to [CONTRIBUTING.md](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) 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 | |
| - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models. | |
| - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. | |
| - [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark. | |
| - [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox. | |
| - [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. | |
| - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception 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. | |
| - [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. | |
| - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. | |
| - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework. | |
| - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark. | |
| - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. | |
| - [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab. | |