Skip to content

Latest commit

 

History

History

swav

README.md

SwAV

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Abstract

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or “views”) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a “swapped” prediction mechanism where we predict the code of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('resnet50_swav-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('swav_resnet50_8xb32-mcrop-coslr-200e_in1k-224px-96px', 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/swav/swav_resnet50_8xb32-mcrop-coslr-200e_in1k-224px-96px.py

Test:

python tools/test.py configs/swav/benchmarks/resnet50_8xb512-linear-coslr-90e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96/resnet50_linear-8xb32-coslr-100e_in1k/resnet50_linear-8xb32-coslr-100e_in1k_20220825-80341e08.pth

Models and results

Pretrained models

Model Params (M) Flops (G) Config Download
swav_resnet50_8xb32-mcrop-coslr-200e_in1k-224px-96px 28.35 4.11 config model | log

Image Classification on ImageNet-1k

Model Pretrain Params (M) Flops (G) Top-1 (%) Config Download
resnet50_swav-pre_8xb32-linear-coslr-100e_in1k SWAV 25.56 4.11 70.50 config model | log

Citation

@article{caron2020unsupervised,
  title={Unsupervised Learning of Visual Features by Contrasting Cluster Assignments},
  author={Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand},
  booktitle={NeurIPS},
  year={2020}
}