SCL: Self-supervised contrastive learning for few-shot image classification

This repository contains the pytorch code for the paper: "SCL: Self-supervised contrastive learning for few-shot image classification" Jit Yan Lim, Kian Ming Lim, Chin Poo Lee, Yong Xuan Tan

Environment

The code is tested on Windows 10 with Anaconda3 and following packages:

• python 3.7.4
• pytorch 1.3.1

Preparation

1. Change the ROOT_PATH value in the following files to yours:

   • datasets/miniimagenet.py
   • datasets/tiered_imagenet.py
   • datasets/cifarfs.py
   • datasets/fc100.py

2. Download the datasets and put them into corresponding folders that mentioned in the ROOT_PATH:
   

   • miniImageNet: download from CSS and put in data/miniImageNet folder.

   • tieredImageNet: download from MetaOptNet and put in data/tieredImageNet folder.

   • CIFARFS: download from MetaOptNet and put in data/cifarfs folder.

   • FC100: download from MTL, extract them into train, val, and test folders and put in data/fc100 folder.

Pre-trained Models

[Optional] The pre-trained models can be downloaded from here. Extract and put the content in the save folder. To evaluate the model, run the test.py file with the proper save path as in the next section.

Experiments

To train on miniImageNet:

python train.py --dataset mini --gamma-rot 1.5 --gamma-dist 0.02 --save-path ./save/mini-exp1

To evaluate on 5-way 1-shot and 5-way 5-shot miniImageNet:

python test.py --dataset mini --shot 1 --save-path ./save/mini-exp1
python test.py --dataset mini --shot 5 --save-path ./save/mini-exp1

Citation

If you find this repo useful for your research, please consider citing the paper:

@article{LIM2023,
  title = {SCL: Self-supervised contrastive learning for few-shot image classification},
  journal = {Neural Networks},
  year = {2023},
  issn = {0893-6080},
  doi = {https://doi.org/10.1016/j.neunet.2023.05.037},
  url = {https://www.sciencedirect.com/science/article/pii/S0893608023002812},
  author = {Jit Yan Lim and Kian Ming Lim and Chin Poo Lee and Yong Xuan Tan}
}

Contacts

For any questions, please contact:

Jit Yan Lim (jityan95@gmail.com)
Kian Ming Lim (Kian-Ming.Lim@nottingham.edu.cn)

Acknowlegements

This repo is based on Prototypical Networks, RFS, and SKD.