Source code to ICLR'19, 'A Closer Look at Few-shot Classification' (still under construction)

This is a PyTorch implementation of our paper A Closer Look at Few-shot Classification accepted by ICLR 2019.

A detailed empirical study in few-shot classification with an integrated testbed

• Python3
• Pytorch
• json

First check and modify the dirs in ./configs.py

#CUB

• Change directory to ./filelists/CUB
• Download CUB-200-2011 from http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz,
• run python ./write_CUB_filelist.py (check paths in it first)

#mini-ImageNet

• Change directory to ./filelists/miniImagenet
• Download Imagenet from http://image-net.org/image/ILSVRC2015/ILSVRC2015_CLS-LOC.tar.gz
• Download data-split from https://github.com/twitter/meta-learning-lstm/tree/master/data/miniImagenet
• run python ./write_miniImagenet_filelist.py (check paths in it first)

#mini-ImageNet->CUB

• Finish preparation for CUB and mini-ImageNet
• Change directory to ./filelists/miniImagenet
• run python ./write_cross_filelist.py (check paths in it first)

#self-defined setting

• Require 3 data split json file: 'base.json', 'val.json', 'novel.json' for each dataset
• The format should look like
  {"label_names": ["class0","class1",...], "image_names": ["filepath1","filepath2",...],"image_labels":[l1,l2,l3,...]}
  See test.json for reference
• Put these file in the same folder and change data_dir['DATASETNAME'] in configs.py to the folder path

Run python ./train.py --dataset [DATASETNAME] --model [BACKBONENAME] --method [METHODNAME] [--OPTIONARG]

For example, run python ./train.py --dataset miniImagenet --model Conv4 --method baseline --train_aug
Commands below follow this example, and please refer to io_utils.py for more options

Save feature before classifaction layer to increase test speed, not applicable to MAML, but required for other methods
Run python ./save_features.py --dataset miniImagenet --model Conv4 --method baseline --train_aug

Run python ./test.py --dataset miniImagenet --model Conv4 --method baseline --train_aug

This testbed has modified and integrated the following codes:

• Framework, Backbone, Method: Matching Network https://github.com/facebookresearch/low-shot-shrink-hallucinate
• Method: Prototypical Network https://github.com/jakesnell/prototypical-networks
• Method: Relational Network https://github.com/floodsung/LearningToCompare_FSL
• Method: MAML https://github.com/cbfinn/maml
  https://github.com/dragen1860/MAML-Pytorch
  https://github.com/katerakelly/pytorch-maml

Please cite the article:

"A Closer Look at Few-shot Classification" Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang, ICLR'19