JingweiZhang12 / OpenSelfSup

Self-Supervised Learning Toolbox and Benchmark

OpenSelfSup

News

• Downstream tasks now support more methods(Mask RCNN-FPN, RetinaNet, Keypoints RCNN) and more datasets(Cityscapes).
• 'GaussianBlur' is replaced from Opencv to PIL, and MoCo v2 training speed doubles!
  (time/iter 0.35s-->0.16s, SimCLR and BYOL are also affected.)
• OpenSelfSup now supports Mixed Precision Training (apex AMP)!
• A bug of MoCo v2 has been fixed and now the results are reproducible.
• OpenSelfSup now supports BYOL!

Introduction

The master branch works with PyTorch 1.1 or higher.

OpenSelfSup is an open source unsupervised representation learning toolbox based on PyTorch.

What does this repo do?

Below is the relations among Unsupervised Learning, Self-Supervised Learning and Representation Learning. This repo focuses on the shadow area, i.e., Unsupervised Representation Learning. Self-Supervised Representation Learning is the major branch of it. Since in many cases we do not distingush between Self-Supervised Representation Learning and Unsupervised Representation Learning strictly, we still name this repo as OpenSelfSup.

Major features

• All methods in one repository

  For comprehensive comparison in all benchmarks, refer to MODEL_ZOO.md. Most of the selfsup pretraining methods are under the batch_size=256, epochs=200 setting.

  Method	VOC07 SVM (best layer)	ImageNet (best layer)
  ImageNet	87.17	76.17
  Random	30.54	16.21
  Relative-Loc	64.78	49.31
  Rotation-Pred	67.38	54.99
  DeepCluster	74.26	57.71
  NPID	74.50	56.61
  ODC	78.42	57.70
  MoCo	79.18	60.60
  MoCo v2	84.26	67.69
  SimCLR	78.95	61.57
  BYOL (epoch=300)	86.58	72.35

  • Flexibility & Extensibility

    OpenSelfSup follows a similar code architecture of MMDetection while is even more flexible than MMDetection, since OpenSelfSup integrates various self-supervised tasks including classification, joint clustering and feature learning, contrastive learning, tasks with a memory bank, etc.

    For existing methods in this repo, you only need to modify config files to adjust hyper-parameters. It is also simple to design your own methods, please refer to GETTING_STARTED.md.

  • Efficiency

    All methods support multi-machine multi-gpu distributed training.

  • Standardized Benchmarks

    We standardize the benchmarks including logistic regression, SVM / Low-shot SVM from linearly probed features, semi-supervised classification, and object detection. Below are the setting of these benchmarks.

    Benchmarks	Setting	Remarks
    ImageNet Linear Classification (Multi)	goyal2019scaling	Evaluate different layers.
    ImageNet Linear Classification (Last)	MoCo	Evaluate the last layer after global pooling.
    Places205 Linear Classification	goyal2019scaling	Evaluate different layers.
    ImageNet Semi-Sup Classification
    PASCAL VOC07 SVM	goyal2019scaling	Costs="1.0,10.0,100.0" to save evaluation time w/o change of results.
    PASCAL VOC07 Low-shot SVM	goyal2019scaling	Costs="1.0,10.0,100.0" to save evaluation time w/o change of results.
    PASCAL VOC07+12 Object Detection	MoCo
    COCO17 Object Detection	MoCo

  Change Log

  Please refer to CHANGELOG.md for details and release history.

  [2020-10-14] OpenSelfSup v0.3.0 is released with some bugs fixed and support of new features.

  [2020-06-26] OpenSelfSup v0.2.0 is released with benchmark results and support of new features.

  [2020-06-16] OpenSelfSup v0.1.0 is released.

  Installation

  Please refer to INSTALL.md for installation and dataset preparation.

  Get Started

  Please see GETTING_STARTED.md for the basic usage of OpenSelfSup.

  Benchmark and Model Zoo

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

  License

  This project is released under the Apache 2.0 license.

  Acknowledgement

  • This repo borrows the architecture design and part of the code from MMDetection.
  • The implementation of MoCo and the detection benchmark borrow the code from moco.
  • The SVM benchmark borrows the code from fair_self_supervision_benchmark.
  • openselfsup/third_party/clustering.py is borrowed from deepcluster.

  Contributors

  We encourage researchers interested in Self-Supervised Learning to contribute to OpenSelfSup. Your contributions, including implementing or transferring new methods to OpenSelfSup, performing experiments, reproducing of results, parameter studies, etc, will be recorded in MODEL_ZOO.md. For now, the contributors include: Xiaohang Zhan (@XiaohangZhan), Jiahao Xie (@Jiahao000), Enze Xie (@xieenze), Xiangxiang Chu (@cxxgtxy), Zijian He (@scnuhealthy).

  Contact

  This repo is currently maintained by Xiaohang Zhan (@XiaohangZhan), Jiahao Xie (@Jiahao000) and Enze Xie (@xieenze).