PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning

Last update: Dec 28, 2022

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Overview

Crafting Better Contrastive Views for Siamese Representation Learning

This is the official PyTorch implementation of the ContrastiveCrop paper:

@article{peng2022crafting,
  title={Crafting Better Contrastive Views for Siamese Representation Learning},
  author={Peng, Xiangyu and Wang, Kai and Zhu, Zheng and You, Yang},
  journal={arXiv preprint arXiv:2202.03278},
  year={2022}
}

This repo includes PyTorch implementation of SimCLR, MoCo, BYOL and SimSiam, as well as their DDP training code.

Preparation

Create a python enviroment with pytorch >= 1.8.1.
pip install -r requirements.txt
Modify dataset root in the config file.

Pre-train

# MoCo, CIFAR-10
python DDP_moco_ccrop.py configs/small/cifar10/moco_alpha0.1_th0.1.py

# SimSiam, CIFAR-100
python DDP_simsiam_ccrop.py configs/small/cifar100/simsiam_alpha0.1_th0.1.py

Linear Evaluation

# CIFAR-10
python DDP_linear.py configs/linear/cifar10_res18.py --load ./checkpoints/small/cifar10/moco_alpha0.1_th0.1/last.pth

# CIFAR-100
python DDP_linear.py configs/linear/cifar100_res18.py --load ./checkpoints/small/cifar100/simsiam_alpha0.1_th0.1/last.pth

More models and datasets coming soon.

PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning

Related tags

Overview

Crafting Better Contrastive Views for Siamese Representation Learning

Preparation

Pre-train

Linear Evaluation

Owner

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