animesh-007 / MAML-Pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

MAML-Pytorch

Ominiglot

Howto

• Run python omniglot_train.py, the program will download omniglot dataset automatically.

• For 5-way 1-shot exp., it allocates nearly 3GB GPU memory.

MiniImagenet

Howto

For 5-way 1-shot exp., it allocates nearly 6GB GPU memory.

1. download MiniImagenet dataset from here, splitting: train/val/test.csv from here.
2. extract it like:

miniimagenet/
├── images
	├── n0210891500001298.jpg  
	├── n0287152500001298.jpg 
	...
├── test.csv
├── val.csv
└── train.csv

3. modify the path in miniimagenet_train.py:

        mini = MiniImagenet('miniimagenet/', mode='train', n_way=args.n_way, k_shot=args.k_spt,
                    k_query=args.k_qry,
                    batchsz=10000, resize=args.imgsz)
		...
        mini_test = MiniImagenet('miniimagenet/', mode='test', n_way=args.n_way, k_shot=args.k_spt,
                    k_query=args.k_qry,
                    batchsz=100, resize=args.imgsz)

to your actual data path.

4. Run python miniimagenet_train.py

Benchmark

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%
Ours	Y	46.2%	60.3%	-	-

Reference taken from the following repository.

@misc{MAML_Pytorch,
  author = {Liangqu Long},
  title = {MAML-Pytorch Implementation},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/dragen1860/MAML-Pytorch}},
  commit = {master}
}