Prototypical Networks for Few-shot Learning

Name: jakesnell/prototypical-networks
Brand: jakesnell/prototypical-networks
SKU: project/jakesnell/prototypical-networks
Rating: 4.61 (524 reviews)

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.

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