Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/wyharveychen/CloserLookFewShot
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
https://github.com/wyharveychen/CloserLookFewShot
Last synced: 13 days ago
JSON representation
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
- Host: GitHub
- URL: https://github.com/wyharveychen/CloserLookFewShot
- Owner: wyharveychen
- License: other
- Created: 2019-01-14T21:09:58.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-12-14T23:56:23.000Z (almost 3 years ago)
- Last Synced: 2024-10-21T12:35:40.885Z (19 days ago)
- Language: Python
- Homepage:
- Size: 166 KB
- Stars: 1,127
- Watchers: 19
- Forks: 268
- Open Issues: 64
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-few-shot-meta-learning - code - official (PyTorch)
README
# A Closer Look at Few-shot Classification
This repo contains the reference source code for the paper [A Closer Look at Few-shot Classification](https://arxiv.org/abs/1904.04232) 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](http://pytorch.org/) 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
## Train
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 additional options.
## Save features
Save the extracted feature before the classifaction layer to increase test speed. This is not applicable to MAML, but are required for other methods.
Run
```python ./save_features.py --dataset miniImagenet --model Conv4 --method baseline --train_aug```
## Test
Run
```python ./test.py --dataset miniImagenet --model Conv4 --method baseline --train_aug```
## Results
* The test results will be recorded in `./record/results.txt`
* For all the pre-computed results, please see `./record/few_shot_exp_figures.xlsx`. This will be helpful for including your own results for a fair comparison.
## 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
## FAQ
* Q1 Why some of my reproduced results for CUB dataset are around 4~5% with you reported result? (#31, #34, #42)
* A1 Sorry about my reported the results on the paper may run in different epochs or episodes, please see each issue for details.
* Q2 Why some of my reproduced results for mini-ImageNet dataset are around 1~2% different with your reported results? (#17, #40, #41 #43)
* A2 Due to random initialization, each training process could lead to different accuracy. Also, each test time could lead to different accuracy.
* Q3 How do you decided the mean and the standard variation for dataset normalization? (#18, #39)
* A3 I use the mean and standard variation from ImageNet, but you can use the ones calculated from your own dataset.
* Q4 Do you have the mini-ImageNet dataset available without downloading the whole ImageNet? (#45 #29)
* A4 You can use the dataset here https://github.com/oscarknagg/few-shot, but you will need to modify filelists/miniImagenet/write_miniImagenet_filelist.py.