d-li14 / few-shot-gnn

Few-Shot Learning with Graph Neural Networks

Implementation of Few-Shot Learning with Graph Neural Networks on Python3, Pytorch 0.3.1

Mini-Imagenet

Download the dataset

Create images.zip file and copy it inside mini_imagenet directory:

.
├── ...
└── datasets                    
   └── compressed                
      └── mini_imagenet
         └── images.zip

The images.zip file must contain the splits and images in the following format:

── images.zip
   ├── test.csv                
   ├── train.csv 
   ├── val.csv 
   └── images
      ├── n0153282900000006.jpg
      ├── ...
      └── n1313361300001299.jpg

The splits {test.csv, train.csv, val.csv} can be downloaded from Ravi and Larochelle - splits. For more information on how to obtain the images check the original source Ravi and Larochelle - github

Training

# 5-Way 1-shot | Few-shot
EXPNAME=minimagenet_N5_S1
python3 main.py --exp_name $EXPNAME --dataset mini_imagenet --test_N_way 5 --train_N_way 5 --train_N_shots 1 --test_N_shots 1 --batch_size 100 --dec_lr=15000 --iterations 80000

# 5-Way 5-shot | Few-shot
EXPNAME=minimagenet_N5_S5
python3 main.py --exp_name $EXPNAME --dataset mini_imagenet --test_N_way 5 --train_N_way 5 --train_N_shots 5 --test_N_shots 5 --batch_size 40 --dec_lr=15000 --iterations 90000

# 5-Way 5-shot 20%-labeled | Semi-supervised
EXPNAME=minimagenet_N5_S5_U4
python3 main.py --exp_name $EXPNAME --dataset mini_imagenet --test_N_way 5 --train_N_way 5 --train_N_shots 5 --test_N_shots 5  --unlabeled_extra 4 --batch_size 40 --dec_lr=15000 --iterations 100000

Omniglot

Download the dataset

Download images_background.zip and images_evaluation.zip files from brendenlake/omniglot and copy it inside the omniglot directory:

.
├── ...
└── datasets                    
   └── compressed                
      └── omniglot
         ├── images_background.zip
         └── images_evaluation.zip

Training

# 5-Way 1-shot | Few-shot
EXPNAME=omniglot_N5_S1_v2
python3 main.py --exp_name $EXPNAME --dataset omniglot --test_N_way 5 --train_N_way 5 --train_N_shots 1 --test_N_shots 1 --batch_size 300  --dec_lr=10000  --iterations 100000

# 5-Way 5-shot | Few-shot
EXPNAME=omniglot_N5_S5
python3 main.py --exp_name $EXPNAME --dataset omniglot --test_N_way 5 --train_N_way 5 --train_N_shots 5 --test_N_shots 5 --batch_size 100  --dec_lr=10000  --iterations 80000

# 20-Way 1-shot | Few-shot
EXPNAME=omniglot_N20_S1
python3 main.py --exp_name $EXPNAME --dataset omniglot --test_N_way 20 --train_N_way 20 --train_N_shots 1 --test_N_shots 1 --batch_size 100  --dec_lr=10000  --iterations 80000

# 5-Way 5-shot 20%-labeled | Semi-supervised
EXPNAME=omniglot_N5_S5_U4
python3 main.py --exp_name $EXPNAME --dataset omniglot --test_N_way 5 --train_N_way 5 --train_N_shots 5 --test_N_shots 5 --unlabeled_extra 4 --batch_size 100  --dec_lr=10000  --iterations 80000

Citation

If you find this code useful you can cite us using the following bibTex:

@article{garcia2017few,
  title={Few-Shot Learning with Graph Neural Networks},
  author={Garcia, Victor and Bruna, Joan},
  journal={arXiv preprint arXiv:1711.04043},
  year={2017}
}