Package: mmtrack
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🤔Reporting IssuesEnglish |简体中文IntroductionMMTracking is an open source video perception toolbox by PyTorch. It is a part ofOpenMMLabproject.The master branch works withPyTorch1.5+.Major featuresThe First Unified Video Perception PlatformWe are the first open source toolbox that unifies versatile video perception tasks include video object detection, multiple object tracking, single object tracking and video instance segmentation.Modular DesignWe decompose the video perception framework into different components and one can easily construct a customized method by combining different modules.Simple, Fast and StrongSimple: MMTracking interacts with other OpenMMLab projects. It is built uponMMDetectionthat we can capitalize any detector only through modifying the configs.Fast: All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations.Strong: We reproduce state-of-the-art models and some of them even outperform the official implementations.What's NewWe release MMTracking 1.0.0rc0, the first version of MMTracking 1.x.Built upon the newtraining engine, MMTracking 1.x unifies the interfaces of datasets, models, evaluation, and visualization.We also support more methods in MMTracking 1.x, such asStrongSORTfor MOT,Mask2Formerfor VIS,PrDiMPfor SOT.Please refer todev-1.xbranch for the using of MMTracking 1.x.InstallationPlease refer toinstall.mdfor install instructions.Getting StartedPlease seedataset.mdandquick_run.mdfor the basic usage of MMTracking.A Colab tutorial is provided. You may preview the notebookhereor directly run it onColab.There are also usagetutorials, such aslearning about configs,an example about detailed description of vid config,an example about detailed description of mot config,an example about detailed description of sot config,customizing dataset,customizing data pipeline,customizing vid model,customizing mot model,customizing sot model,customizing runtime settingsanduseful tools.Benchmark and model zooResults and models are available in themodel zoo.Video Object DetectionSupported MethodsDFF(CVPR 2017)FGFA(ICCV 2017)SELSA(ICCV 2019)Temporal RoI Align(AAAI 2021)Supported DatasetsILSVRCSingle Object TrackingSupported MethodsSiameseRPN++(CVPR 2019)STARK(ICCV 2021)PrDiMP(CVPR2020) (WIP)Supported DatasetsLaSOTUAV123TrackingNetOTB100GOT10kVOT2018Multi-Object TrackingSupported MethodsSORT/DeepSORT(ICIP 2016/2017)Tracktor(ICCV 2019)QDTrack(CVPR 2021)ByteTrack(ECCV 2022)OC-SORT(arXiv 2022)Supported DatasetsMOT ChallengeCrowdHumanLVISTAODanceTrackVideo Instance SegmentationSupported MethodsMaskTrack R-CNN(ICCV 2019)Supported DatasetsYouTube-VISContributingWe appreciate all contributions to improve MMTracking. Please refer toCONTRIBUTING.mdfor the contributing guideline andthis discussionfor development roadmap.AcknowledgementMMTracking is an open source project that welcome any contribution and feedback.
We wish that the toolbox and benchmark could serve the growing research
community by providing a flexible as well as standardized toolkit to reimplement existing methods
and develop their own new video perception methods.CitationIf you find this project useful in your research, please consider cite:@misc{mmtrack2020,
    title={{MMTracking: OpenMMLab}video perception toolbox and benchmark},
    author={MMTracking Contributors},
    howpublished ={\url{https://github.com/open-mmlab/mmtracking}},
    year={2020}}LicenseThis project is released under theApache 2.0 license.Projects in OpenMMLabMMCV: OpenMMLab foundational library for computer vision.MIM: MIM installs OpenMMLab packages.MMClassification: OpenMMLab image classification toolbox and benchmark.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.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.MMSelfSup: OpenMMLab self-supervised learning Toolbox and Benchmark.MMRazor: OpenMMLab Model Compression Toolbox and Benchmark.MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.MMTracking: OpenMMLab video perception toolbox and benchmark.MMFlow: OpenMMLab optical flow toolbox and benchmark.MMEditing: OpenMMLab image and video editing toolbox.MMGeneration:  OpenMMLab Generative Model toolbox and benchmark.MMDeploy: OpenMMlab deep learning model deployment toolset.