bentengma / learningtocompare-pytorch

Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning

LearningToCompare

Pytorch Implementation for Paper: Learning to Compare: Relation Network for Few-Shot Learning

Howto

download mini-imagenet and make it looks like:

mini-imagenet/
├── images
	├── n0210891500001298.jpg  
	├── n0287152500001298.jpg 
	...
├── test.csv
├── val.csv
└── train.csv

LearningToCompare-Pytorch/
├── compare.py
├── MiniImagenet.py
├── Readme.md
├── repnet.py
├── train.py
└── utils.py

python train.py

NOTICE

current code support multi-gpus on single machine training, to disable it and train on single machine, just set device_ids=[0] and downsize batch size according to your gpu memory capacity. make sure ckpt directory exists, otherwise mkdir ckpt.

mini-Imagenet

Model	Fine Tune	5-way Acc.		20-way Acc
		1-shot	5-shot	1-shot	5-shot
Matching Nets	N	43.56%	55.31%	17.31%	22.69%
Meta-LSTM		43.44%	60.60%	16.70%	26.06%
MAML	Y	48.7%	63.11%	16.49%	19.29%
Meta-SGD		50.49%	64.03%	17.56%	28.92%
TCML		55.71%	68.88%	-	-
Learning to Compare	N	57.02%	71.07%	-	-
Ours, similarity ensemble	N	55.2%	68.8%
Ours, feature ensemble	N	55.2%	70.1%