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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 1.5+.
Major features
-
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.
MMSelfSup v0.11.0 was released in 30/12/2022.
Highlights of the new version:
- Support
InterCLR - Fix some bugs
Please refer to changelog.md for details and release history.
Differences between MMSelfSup and OpenSelfSup codebases can be found in compatibility.md.
A brand new version of MMSelfSup v1.0.0rc4 was released in 07/12/2022.
Highlights of the new version:
- Based on MMEngine and MMCV.
- Refine all documents.
- Support
BEiT v1,BEiT v2,MILAN,MixMIM,EVA. - Support
MAE,SimMIM,MoCoV3with different pre-training epochs and backbones of different scales. - More concise APIs.
- More visualization tools.
- More powerful data pipeline.
- Higher accurcy for some algorithms.
Find more new features in 1.x branch. Issues and PRs are welcome!
MMSelfSup relies on PyTorch, MMCV and MMClassification.
Please refer to install.md for more detailed instruction.
Please refer to prepare_data.md for dataset preparation, get_started.md for the basic usage and benchmarks.md for running benchmarks.
We also provides more detailed tutorials:
Besides, we provide colab tutorial for basic usage.
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)
- InterCLR (IJCV'2022)
- MAE (CVPR'2022)
- SimMIM (CVPR'2022)
- MaskFeat (CVPR'2022)
- CAE (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 CONTRIBUTING.md 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.
- MMCV: 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.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
- 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 image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.