Skip to content

Latest commit

 

History

History
85 lines (58 loc) · 5.38 KB

README.md

File metadata and controls

85 lines (58 loc) · 5.38 KB
Raw

BarlowTwins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Abstract

Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant solutions. Most current methods avoid such solutions by careful implementation details. We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors. The method is called Barlow Twins, owing to neuroscientist H. Barlow's redundancy-reduction principle applied to a pair of identical networks. Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors. Barlow Twins outperforms previous methods on ImageNet for semi-supervised classification in the low-data regime, and is on par with current state of the art for ImageNet classification with a linear classifier head, and for transfer tasks of classification and object detection.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('resnet50_barlowtwins-pre_8xb32-linear-coslr-100e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('barlowtwins_resnet50_8xb256-coslr-300e_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Train/Test Command

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k.py

Test:

python tools/test.py configs/barlowtwins/benchmarks/resnet50_8xb32-linear-coslr-100e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k/resnet50_linear-8xb32-coslr-100e_in1k/resnet50_linear-8xb32-coslr-100e_in1k_20220825-52fde35f.pth

Models and results

Pretrained models

Model Params (M) Flops (G) Config Download
barlowtwins_resnet50_8xb256-coslr-300e_in1k 174.54 4.11 config model | log

Image Classification on ImageNet-1k

Model Pretrain Params (M) Flops (G) Top-1 (%) Config Download
resnet50_barlowtwins-pre_8xb32-linear-coslr-100e_in1k BARLOWTWINS 25.56 4.11 71.80 config model | log

Citation

@inproceedings{zbontar2021barlow,
  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},
  booktitle={International Conference on Machine Learning},
  year={2021},
}