LearningToCompare_FSL

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

For Zero-Shot Learning part, please visit here.

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 and maml.

For mini-Imagenet experiments, please download mini-Imagenet 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).

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

MAML-pytorch