Code for the NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
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README.md

Prototypical Networks for Few-shot Learning

Code for the NIPS 2017 paper Prototypical Networks for Few-shot Learning.

If you use this code, please cite our paper:

@inproceedings{snell2017prototypical,
  title={Prototypical Networks for Few-shot Learning},
  author={Snell, Jake and Swersky, Kevin and Zemel, Richard},
  booktitle={Advances in Neural Information Processing Systems},
  year={2017}
 }

Training a prototypical network

Install dependencies

  • This code has been tested on Ubuntu 16.04 with Python 3.6 and PyTorch 0.4.
  • Install PyTorch and torchvision.
  • Install torchnet by running pip install git+https://github.com/pytorch/tnt.git@master.
  • Install the protonets package by running python setup.py install or python setup.py develop.

Set up the Omniglot dataset

  • Run sh download_omniglot.sh.

Train the model

  • Run python scripts/train/few_shot/run_train.py. This will run training and place the results into results.
    • You can specify a different output directory by passing in the option --log.exp_dir EXP_DIR, where EXP_DIR is your desired output directory.
    • If you are running on a GPU you can pass in the option --data.cuda.
  • Re-run in trainval mode python scripts/train/few_shot/run_trainval.py. This will save your model into results/trainval by default.

Evaluate

  • Run evaluation as: python scripts/predict/few_shot/run_eval.py --model.model_path results/trainval/best_model.pt.