# LearningToCompare_FSL

**Repository Path**: a1258/LearningToCompare_FSL

## Basic Information

- **Project Name**: LearningToCompare_FSL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LearningToCompare_FSL
PyTorch code for CVPR 2018 paper: [Learning to Compare: Relation Network for Few-Shot Learning](https://arxiv.org/abs/1711.06025) (Few-Shot Learning part)

For Zero-Shot Learning part, please visit [here](https://github.com/lzrobots/LearningToCompare_ZSL).

# Requirements

Python 2.7

Pytorch 0.3

# Data

For Omniglot experiments, I directly attach omniglot 28x28 resized images in the git, which is created based on [omniglot](https://github.com/brendenlake/omniglot) and [maml](https://github.com/cbfinn/maml).

For mini-Imagenet experiments, please download [mini-Imagenet](https://drive.google.com/open?id=0B3Irx3uQNoBMQ1FlNXJsZUdYWEE) and put it in ./datas/mini-Imagenet and run proc_image.py to preprocess generate train/val/test datasets. (This process method is based on [maml](https://github.com/cbfinn/maml)).

# Train

omniglot 5way 1 shot:

```
python omniglot_train_one_shot.py -w 5 -s 1 -b 19 
```

omniglot 5way 5 shot:

```
python omniglot_train_few_shot.py -w 5 -s 5 -b 15 
```

omniglot 20way 1 shot:

```
python omniglot_train_one_shot.py -w 20 -s 1 -b 10
```

omniglot 20way 5 shot:

```
python omniglot_train_few_shot.py -w 20 -s 5 -b 5
```

mini-Imagenet 5 way 1 shot:

```
python miniimagenet_train_one_shot.py -w 5 -s 1 -b 15
```

mini-Imagenet 5 way 5 shot:

```
python miniimagenet_train_few_shot.py -w 5 -s 5 -b 10
```

you can change -b parameter based on your GPU memory. Currently It will load my trained model, if you want to train from scratch, you can delete models by yourself.

## Test

omniglot 5way 1 shot:

```
python omniglot_test_one_shot.py -w 5 -s 1
```

Other experiments' testings are similar.


## Citing

If you use this code in your research, please use the following BibTeX entry.

```
@inproceedings{sung2018learning,
  title={Learning to Compare: Relation Network for Few-Shot Learning},
  author={Sung, Flood and Yang, Yongxin and Zhang, Li and Xiang, Tao and Torr, Philip HS and Hospedales, Timothy M},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2018}
}
```

## Reference

[MAML](https://github.com/cbfinn/maml)

[MAML-pytorch](https://github.com/katerakelly/pytorch-maml)