Package: mmpose
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|📜Papers|🆕Update News|🤔Reporting Issues|🔥RTMPoseIntroductionEnglish |简体中文MMPose is an open-source toolbox for pose estimation based on PyTorch.
It is a part of theOpenMMLab project.The main branch works withPyTorch 1.8+.https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4Major FeaturesSupport diverse tasksWe 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.
SeeDemofor more information.Higher efficiency and higher accuracyMMPose 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 asHRNet.
Seebenchmark.mdfor more information.Support for various datasetsThe toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc.
Seedataset_zoofor more information.Well designed, tested and documentedWe 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 NewReleaseRTMO, a state-of-the-art real-time method for multi-person pose estimation.ReleaseRTMWmodels in various sizes ranging from RTMW-m to RTMW-x. The input sizes include256x192and384x288. This provides flexibility to select the right model for different speed and accuracy requirements.Support inference ofPoseAnything. Web demo is availablehere.Support for two new datasets:(CVPR 2023)ExLPose(ICCV 2023)H3WBWelcome to use theMMPose project. Here, you can discover the latest features and algorithms in MMPose and quickly share your ideas and code implementations with the community. Adding new features to MMPose has become smoother:Provides a simple and fast way to add new algorithms, features, and applications to MMPose.More flexible code structure and style, fewer restrictions, and a shorter code review process.Utilize the powerful capabilities of MMPose in the form of independent projects without being constrained by the code framework.Newly added projects include:RTMPoseRTMOPoseAnythingYOLOX-PoseMMPose4AIGCSimple KeypointsJust DanceUniformerStart your journey as an MMPose contributor with a simpleexample project, and let's build a better MMPose together!January 4, 2024: MMPosev1.3.0has been officially released, with major updates including:Support for new datasets: ExLPose, H3WBRelease of new RTMPose series models: RTMO, RTMWSupport for new algorithm PoseAnythingEnhanced Inferencer with optional progress bar and improved affinity for one-stage methodsPlease check the completerelease notesfor more details on the updates brought by MMPose v1.3.0!0.x / 1.x MigrationMMPose 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 thisRoadmap.If your algorithm has not been migrated, you can continue to use the0.x branchandold documentation.InstallationPlease refer toinstallation.mdfor more detailed installation and dataset preparation.Getting StartedWe provided a series of tutorials about the basic usage of MMPose for new users:For the basic usage of MMPose:A 20-minute Tour to MMPoseDemosInferenceConfigsPrepare DatasetsTrain and TestDeploymentModel AnalysisDataset Annotation and PreprocessingFor developers who wish to develop based on MMPose:Learn about CodecsDataflow in MMPoseImplement New ModelsCustomize DatasetsCustomize Data TransformsCustomize EvaluationCustomize OptimizerCustomize LoggingHow to DeployModel AnalysisMigration GuideFor researchers and developers who are willing to contribute to MMPose:Contribution GuideFor some common issues, we provide a FAQ list:FAQModel ZooResults and models are available in theREADME.mdof each method's config directory.
A summary can be found in theModel Zoopage.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)UBody[homepage] (CVPR'2023)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)Model RequestWe 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 inMMPose Roadmap.ContributingWe appreciate all contributions to improve MMPose. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementMMPose 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.CitationIf 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}}LicenseThis project is released under theApache 2.0 license.Projects in OpenMMLabMMEngine: OpenMMLab foundational library for training deep learning models.MMCV: OpenMMLab foundational library for computer vision.MMPreTrain: OpenMMLab pre-training toolbox and benchmark.MMagic: OpenMMLabAdvanced,Generative andIntelligentCreation toolbox.MMDetection: OpenMMLab detection toolbox and benchmark.MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.MMRotate: OpenMMLab rotated object detection toolbox and benchmark.MMTracking: OpenMMLab video perception toolbox and benchmark.MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.MMPose: OpenMMLab pose estimation toolbox and benchmark.MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.MMFlow: OpenMMLab optical flow toolbox and benchmark.MMDeploy: OpenMMLab Model Deployment Framework.MMRazor: OpenMMLab model compression toolbox and benchmark.MIM: MIM installs OpenMMLab packages.Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.