A re-implementation of Prototypical Network.

With ConvNet-4 backbone on miniImageNet.

For deep backbones (ResNet), see Meta-Baseline.

1-shot: 49.1% (49.4% in the paper)

5-shot: 66.9% (68.2% in the paper)

• python 3
• pytorch 0.4.0

1. Download the images: https://drive.google.com/open?id=0B3Irx3uQNoBMQ1FlNXJsZUdYWEE

2. Make a folder materials/images and put those images into it.

--gpu to specify device for program.

python train.py

python test.py

python train.py --shot 5 --train-way 20 --save-path ./save/proto-5

python test.py --load ./save/proto-5/max-acc.pth --shot 5