mmclassification/configs/simsiam

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	N/A	N/A	config	model | log
simsiam_resnet50_8xb32-coslr-200e_in1k	N/A	N/A	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	N/A	N/A	68.30	config	model | log
resnet50_simsiam-200e-pre_8xb512-linear-coslr-90e_in1k	SIMSIAM 200-Epochs	N/A	N/A	69.80	config	model | log

Citation

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