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Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Screen Shot 2021-04-29 at 6 26 48 AM

PyTorch implementation of Barlow Twins.

  title={Barlow Twins: Self-Supervised Learning via Redundancy Reduction},
  author={Zbontar, Jure and Jing, Li and Misra, Ishan and LeCun, Yann and Deny, St{\'e}phane},
  journal={arXiv preprint arXiv:2103.03230},

Pretrained Model

epochs batch size acc1 acc5 download
1000 2048 73.5% 91.0% ResNet-50 full checkpoint train logs val logs

You can choose to download either the weights of the pretrained ResNet-50 network or the full checkpoint, which also contains the weights of the projector network and the state of the optimizer.

The pretrained model is also available on PyTorch Hub.

import torch
model = torch.hub.load('facebookresearch/barlowtwins:main', 'resnet50')

Barlow Twins Training

Install PyTorch and download ImageNet by following the instructions in the requirements section of the PyTorch ImageNet training example. The code has been developed for PyTorch version 1.7.1 and torchvision version 0.8.2, but it should work with other versions just as well.

Our best model is obtained by running the following command:

python /path/to/imagenet/

Training time is approximately 7 days on 16 v100 GPUs.

Evaluation: Linear Classification

Train a linear probe on the representations learned by Barlow Twins. Freeze the weights of the resnet and use the entire ImageNet training set.

python /path/to/imagenet/ /path/to/checkpoint/resnet50.pth --lr-classifier 0.3

Evaluation: Semi-supervised Learning

Train a linear probe on the representations learned by Barlow Twins. Finetune the weights of the resnet and use a subset of the ImageNet training set.

python /path/to/imagenet/ /path/to/checkpoint/resnet50.pth --weights finetune --train-perc 1 --epochs 20 --lr-backbone 0.005 --lr-classifier 0.5 --weight-decay 0 --checkpoint-dir ./checkpoint/semisup/

Community Updates

Let us know about all the cool stuff you are able to do with Barlow Twins so that we can advertise it here!


This project is released under MIT License, which allows commercial use. See LICENSE for details.