https://github.com/floodsung/LearningToCompare_FSL

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
https://github.com/floodsung/LearningToCompare_FSL

few-shot-learning meta-learning

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PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

• Host: GitHub
• URL: https://github.com/floodsung/LearningToCompare_FSL
• Owner: floodsung
• License: mit
• Created: 2018-03-28T12:14:14.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-10-22T03:19:44.000Z (about 5 years ago)
• Last Synced: 2024-10-15T11:03:04.640Z (about 2 months ago)
• Topics: few-shot-learning, meta-learning
• Language: Python
• Size: 8.19 MB
• Stars: 1,045
• Watchers: 32
• Forks: 268
• Open Issues: 33
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-few-shot-meta-learning - code - official (PyTorch fewshot)

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)