/MAML-Pytorch

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

Primary LanguagePythonMIT LicenseMIT

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
  1. 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.

  1. 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}
}