Skip to content

Latest commit

 

History

History
 
 

simsiam

README.md

SimSiam

Exploring simple siamese representation learning

Abstract

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our “SimSiam” method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('resnet50_simsiam-100e-pre_8xb512-linear-coslr-90e_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('simsiam_resnet50_8xb32-coslr-100e_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/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py

Test:

python tools/test.py configs/simsiam/benchmarks/resnet50_8xb512-linear-coslr-90e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-f53ba400.pth

Models and results

Pretrained models

Model Params (M) Flops (G) Config Download
simsiam_resnet50_8xb32-coslr-100e_in1k 38.20 4.11 config model | log
simsiam_resnet50_8xb32-coslr-200e_in1k 38.20 4.11 config model | log

Image Classification on ImageNet-1k

Model Pretrain Params (M) Flops (G) Top-1 (%) Config Download
resnet50_simsiam-100e-pre_8xb512-linear-coslr-90e_in1k SIMSIAM 100-Epochs 25.56 4.11 68.30 config model | log
resnet50_simsiam-200e-pre_8xb512-linear-coslr-90e_in1k SIMSIAM 200-Epochs 25.56 4.11 69.80 config model | log

Citation

@inproceedings{chen2021exploring,
  title={Exploring simple siamese representation learning},
  author={Chen, Xinlei and He, Kaiming},
  booktitle={CVPR},
  year={2021}
}