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MMSelfSup is an open source self-supervised representation learning toolbox based on PyTorch. It is a part of the OpenMMLab project.
The master branch works with PyTorch 1.6 or higher.
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Methods All in One
MMSelfsup 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.
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Modular Design
MMSelfSup follows a similar code architecture of OpenMMLab projects with modular design, which is flexible and convenient for users to build their own algorithms.
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Standardized Benchmarks
MMSelfSup standardizes the benchmarks including logistic regression, SVM / Low-shot SVM from linearly probed features, semi-supervised classification, object detection and semantic segmentation.
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Compatibility
Since 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.
The default branch has been switched to 1.x from master, and we encourage users to migrate to the latest version, though it comes with some cost. Please refer to Migration Guide for more details.
MMSelfSup v1.0.0rc6 was released in 10/02/2023.
- Support
MaskFeatwith video dataset inprojects/maskfeat_video/ - Translate documentation to Chinese.
MMSelfSup v1.0.0rc5 was released in 30/12/2022.
- Support
BEiT v2,MixMIM,EVA. - Support
ShapeBiasfor model analysis - Add Solution of FGIA ACCV 2022 (1st Place)
- Refactor t-SNE
Please refer to Changelog for details and release history.
Differences between MMSelfSup 1.x and 0.x can be found in Migration.
MMSelfSup depends on PyTorch, MMCV, MMEngine and MMClassification.
Please refer to Installation for more detailed instruction.
For tutorials, we provide User Guides for basic usage:
Pretrain
Downetream Tasks
Useful Tools
Advanced Guides and Colab Tutorials are also provided.
Please refer to FAQ for frequently asked questions.
Please refer to Model Zoo.md for 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 (arXiv'2022)
- MixMIM (ArXiv'2022)
More algorithms are in our plan.
| Benchmarks | Setting |
|---|---|
| ImageNet Linear Classification (Multi-head) | Goyal2019 |
| ImageNet Linear Classification (Last) | |
| ImageNet Semi-Sup Classification | |
| Places205 Linear Classification (Multi-head) | Goyal2019 |
| iNaturalist2018 Linear Classification (Multi-head) | Goyal2019 |
| PASCAL VOC07 SVM | Goyal2019 |
| PASCAL VOC07 Low-shot SVM | Goyal2019 |
| PASCAL VOC07+12 Object Detection | MoCo |
| COCO17 Object Detection | MoCo |
| Cityscapes Segmentation | MMSeg |
| PASCAL VOC12 Aug Segmentation | MMSeg |
We appreciate all contributions improving MMSelfSup. Please refer to Contribution Guides for more details about the contributing guideline.
MMSelfSup 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).
If 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}
}This project is released under the Apache 2.0 license.
- MMEngine: 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.