VICReg: Variance-Invariance-Covariance Regularization For Self-Supervised Learning

This repository provides a PyTorch implementation and pretrained models for VICReg, as described in the paper VICReg: Variance-Invariance-Covariance Regularization For Self-Supervised Learning
Adrien Bardes, Jean Ponce and Yann LeCun
Meta AI, Inria

Pre-trained Models

You can choose to download only the weights of the pretrained backbone used for downstream tasks, or the full checkpoint which contains backbone and projection head weights.

arch

params

accuracy

download

ResNet-50

23M

73.2%

backbone only

full ckpt

ResNet-50 (x2)

93M

75.5%

backbone only

full ckpt

ResNet-200 (x2)

250M

77.3%

backbone only

full ckpt

Pretrained models on PyTorch Hub

import torch
resnet50 = torch.hub.load('facebookresearch/vicreg:main', 'resnet50')
resnet50x2 = torch.hub.load('facebookresearch/vicreg:main', 'resnet50x2')
resnet200x2 = torch.hub.load('facebookresearch/vicreg:main', 'resnet200x2')

Training

Install PyTorch (pytorch.org) and download ImageNet. The code has been developed for PyTorch version 1.8.1 and torchvision version 0.9.1, but should work with other versions just as well.

Single-node local training

To pretrain VICReg with ResNet-50 on a single node with 8 GPUs for 100 epochs, run:

python -m torch.distributed.launch --nproc_per_node=8 main_vicreg.py --data-dir /path/to/imagenet/ --exp-dir /path/to/experiment/ --arch resnet50 --epochs 100 --batch-size 512 --base-lr 0.3

Multi-node training with SLURM

To pretrain VICReg with submitit (pip install submitit) and SLURM on 4 nodes with 8 GPUs each for 1000 epochs, run:

python run_with_submitit.py --nodes 4 --ngpus 8 --data-dir /path/to/imagenet --exp-dir /path/to/experiment/ --arch resnet50 --epochs 1000 --batch-size 2048 --base-lr 0.2

Evaluation

Linear evaluation

To evaluate a pretrained ResNet-50 backbone on linear classification on ImageNet, run:

python evaluate.py --data-dir /path/to/imagenet/ --pretrained /path/to/checkpoint/resnet50.pth --exp-dir /path/to/experiment/ --lr-head 0.02

Semi-supervised evaluation

To evaluate a pretrained ResNet50-model on semi-supervised fine-tunning on 1% of ImageNet labels, run:

python evaluate.py --data-dir /path/to/imagenet/ --pretrained /path/to/checkpoint/resnet50.pth --exp-dir /path/to/experiment/ --weights finetune --train-perc 1 --epochs 20 --lr-backbone 0.03 --lr-classifier 0.08 --weight-decay 0

To evaluate a pretrained ResNet50-model on semi-supervised fine-tunning on 10% of ImageNet labels, run:

python evaluate.py --data-dir /path/to/imagenet/ --pretrained /path/to/checkpoint/resnet50.pth --exp-dir /path/to/checkpoint/resnet.pth --weights finetune --train-perc 10 --epochs 20 --lr-backbone 0.01 --lr-classifier 0.1 --weight-decay 0

Acknowledgement

This repository is built using the Barlow Twins repository.

License

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

Citation

If you find this repository useful, please consider giving a star :star: and citation:

@inproceedings{bardes2022vicreg,
  author  = {Adrien Bardes and Jean Ponce and Yann LeCun},
  title   = {VICReg: Variance-Invariance-Covariance Regularization For Self-Supervised Learning},
  booktitle = {ICLR},
  year    = {2022},
}