Package: mmselfsup
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🤔Reporting Issues🌟 MMPreTrain is a newly upgraded open-source framework for visual pre-training. It has set out to provide multiple powerful pre-trained backbones and support different pre-training strategies.:point_right:MMPreTrain 1.0 branch is in trial, welcome every totry itanddiscuss with us!:point_left:English |简体中文IntroductionMMSelfSup is an open source self-supervised representation learning toolbox based on PyTorch. It is a part of theOpenMMLabproject.The master branch works withPyTorch 1.8or higher.Major featuresMethods All in OneMMSelfsup provides state-of-the-art methods in self-supervised learning. For comprehensive comparison in all benchmarks, most of the pre-training methods are under the same setting.Modular DesignMMSelfSup follows a similar code architecture of OpenMMLab projects with modular design, which is flexible and convenient for users to build their own algorithms.Standardized BenchmarksMMSelfSup standardizes the benchmarks including logistic regression, SVM / Low-shot SVM from linearly probed features, semi-supervised classification, object detection and semantic segmentation.CompatibilitySince MMSelfSup adopts similar design of modulars and interfaces as those in other OpenMMLab projects, it supports smooth evaluation on downstream tasks with other OpenMMLab projects like object detection and segmentation.What's NewMMSelfSup v1.0.0 was released based onmainbranch. Please refer toMigration Guidefor more details.MMSelfSupv1.0.0was released in 06/04/2023.SupportPixMIM.SupportDINOinprojects/dino/.Refactor file io interface.Refine documentations.MMSelfSupv1.0.0rc6was released in 10/02/2023.SupportMaskFeatwith video dataset inprojects/maskfeat_video/Translate documentation to Chinese.MMSelfSupv1.0.0rc5was released in 30/12/2022.SupportBEiT v2,MixMIM,EVA.SupportShapeBiasfor model analysisAdd Solution of FGIA ACCV 2022 (1st Place)Refactor t-SNEPlease refer toChangelogfor details and release history.Differences between MMSelfSup 1.x and 0.x can be found inMigration.InstallationMMSelfSup depends onPyTorch,MMCV,MMEngineandMMClassification.Please refer toInstallationfor more detailed instruction.Get StartedFor tutorials, we provideUser Guidesfor basic usage:PretrainConfigPrepare DatasetPretrain with Existing ModelsDownetream TasksClassificationDetectionSegmentationUseful ToolsVisualizationAnalysis ToolsAdvanced GuidesandColab Tutorialsare also provided.Please refer toFAQfor frequently asked questions.Model ZooPlease refer toModel Zoo.mdfor a comprehensive set of pre-trained models and benchmarks.Supported algorithms:Relative Location (ICCV'2015)Rotation Prediction (ICLR'2018)DeepCluster (ECCV'2018)NPID (CVPR'2018)ODC (CVPR'2020)MoCo v1 (CVPR'2020)SimCLR (ICML'2020)MoCo v2 (arXiv'2020)BYOL (NeurIPS'2020)SwAV (NeurIPS'2020)DenseCL (CVPR'2021)SimSiam (CVPR'2021)Barlow Twins (ICML'2021)MoCo v3 (ICCV'2021)BEiT (ICLR'2022)MAE (CVPR'2022)SimMIM (CVPR'2022)MaskFeat (CVPR'2022)CAE (arXiv'2022)MILAN (arXiv'2022)BEiT v2 (arXiv'2022)EVA (CVPR'2023)MixMIM (ArXiv'2022)PixMIM (ArXiv'2023)More algorithms are in our plan.BenchmarkBenchmarksSettingImageNet Linear Classification (Multi-head)Goyal2019ImageNet Linear Classification (Last)ImageNet Semi-Sup ClassificationPlaces205 Linear Classification (Multi-head)Goyal2019iNaturalist2018 Linear Classification (Multi-head)Goyal2019PASCAL VOC07 SVMGoyal2019PASCAL VOC07 Low-shot SVMGoyal2019PASCAL VOC07+12 Object DetectionMoCoCOCO17 Object DetectionMoCoCityscapes SegmentationMMSegPASCAL VOC12 Aug SegmentationMMSegContributingWe appreciate all contributions improving MMSelfSup. Please refer toContribution Guidesfor more details about the contributing guideline.AcknowledgementMMSelfSup 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 algorithms.MMSelfSup originates from OpenSelfSup, and we appreciate all early contributions made to OpenSelfSup. A few contributors are listed here: Xiaohang Zhan (@XiaohangZhan), Jiahao Xie (@Jiahao000), Enze Xie (@xieenze), Xiangxiang Chu (@cxxgtxy), Zijian He (@scnuhealthy).CitationIf you use this toolbox or benchmark in your research, please cite this project.@misc{mmselfsup2021,title={{MMSelfSup}: OpenMMLab Self-Supervised Learning Toolbox and Benchmark},author={MMSelfSup Contributors},howpublished={\url{https://github.com/open-mmlab/mmselfsup}},year={2021}}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.MMEval: A unified evaluation library for multiple machine learning libraries.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.MMYOLO: OpenMMLab YOLO series 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 image and video generative models toolbox.MMDeploy: OpenMMLab model deployment framework.