run: python train.py --dataset deecamp --model deecamp --method maml --train_aug

python test.py --dataset deecamp --model deecamp --method maml --train_aug

##matchingnet save_feature.py test.py

##io_utils.py: setting(CwayKshot)

##method: ├── baselinefinetune.py ├── baselinetrain.py ├── init.py ├── maml.py ├── matchingnet.py ├── meta_template.py ├── protonet.py └── relationnet.py

##backbone: conv,simpleblock,bottleblock

##train.py optimizer lr size

A Closer Look at Few-shot Classification

This repo contains the reference source code for the paper A Closer Look at Few-shot Classification in International Conference on Learning Representations (ICLR 2019). In this project, we provide a integrated testbed for a detailed empirical study for few-shot classification.

Citation

If you find our code useful, please consider citing our work using the bibtex:

@inproceedings{
chen2019closerfewshot,
title={A Closer Look at Few-shot Classification},
author={Chen, Wei-Yu and Liu, Yen-Cheng and Kira, Zsolt and Wang, Yu-Chiang and  Huang, Jia-Bin},
booktitle={International Conference on Learning Representations},
year={2019}
}

Enviroment

• Python3
• Pytorch before 0.4 (for newer vesion, please see issue #3 )
• json

Getting started

CUB

• Change directory to ./filelists/CUB
• run source ./download_CUB.sh

mini-ImageNet

• Change directory to ./filelists/miniImagenet
• run source ./download_miniImagenet.sh

(WARNING: This would download the 155G ImageNet dataset. You can comment out correponded line 5-6 in download_miniImagenet.sh if you already have one.)

mini-ImageNet->CUB (cross)

• Finish preparation for CUB and mini-ImageNet and you are done!

Omniglot

• Change directory to ./filelists/omniglot
• run source ./download_omniglot.sh

Omniglot->EMNIST (cross_char)

• Finish preparation for omniglot first
• Change directory to ./filelists/emnist
• run source ./download_emnist.sh

Self-defined setting

• Require three data split json file: 'base.json', 'val.json', 'novel.json' for each dataset
• The format should follow
  {"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

References

Our testbed builds upon several existing publicly available code. Specifically, we have modified and integrated the following code into this project:

• Framework, Backbone, Method: Matching Network https://github.com/facebookresearch/low-shot-shrink-hallucinate
• Omniglot dataset, 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