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🌟 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 to try it and discuss with us! :point_left:

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Introduction

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 1.8 or higher.

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.
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.
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.
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.

What's New

MMSelfSup v1.0.0 was released based on main branch. Please refer to Migration Guide for more details.

MMSelfSup v1.0.0 was released in 06/04/2023.

Support PixMIM.
Support DINO in projects/dino/.
Refactor file io interface.
Refine documentations.

MMSelfSup v1.0.0rc6 was released in 10/02/2023.

Support MaskFeat with video dataset in projects/maskfeat_video/
Translate documentation to Chinese.

MMSelfSup v1.0.0rc5 was released in 30/12/2022.

Support BEiT v2, MixMIM, EVA.
Support ShapeBias for 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.

Installation

MMSelfSup depends on PyTorch, MMCV, MMEngine and MMClassification.

Please refer to Installation for more detailed instruction.

Get Started

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.

Model Zoo

Please refer to Model Zoo.md for a comprehensive set of pre-trained models and benchmarks.

Supported algorithms:

[x] Relative Location (ICCV'2015)
[x] Rotation Prediction (ICLR'2018)
[x] DeepCluster (ECCV'2018)
[x] NPID (CVPR'2018)
[x] ODC (CVPR'2020)
[x] MoCo v1 (CVPR'2020)
[x] SimCLR (ICML'2020)
[x] MoCo v2 (arXiv'2020)
[x] BYOL (NeurIPS'2020)
[x] SwAV (NeurIPS'2020)
[x] DenseCL (CVPR'2021)
[x] SimSiam (CVPR'2021)
[x] Barlow Twins (ICML'2021)
[x] MoCo v3 (ICCV'2021)
[x] BEiT (ICLR'2022)
[x] MAE (CVPR'2022)
[x] SimMIM (CVPR'2022)
[x] [MaskFeat (CVPR'2022)](https://github.com/open-mmlab/mmselfsup

Mmselfsup

Install / Use

README