https://github.com/jakesnell/prototypical-networks

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
https://github.com/jakesnell/prototypical-networks

deep-learning few-shot metric-learning nips-2017 omniglot pytorch

Last synced: 11 days ago
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Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

• Host: GitHub
• URL: https://github.com/jakesnell/prototypical-networks
• Owner: jakesnell
• License: mit
• Created: 2017-11-05T04:38:19.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-01-28T09:22:04.000Z (over 3 years ago)
• Last Synced: 2024-04-17T14:09:17.213Z (3 months ago)
• Topics: deep-learning, few-shot, metric-learning, nips-2017, omniglot, pytorch
• Language: Python
• Homepage:
• Size: 201 KB
• Stars: 1,074
• Watchers: 13
• Forks: 251
• Open Issues: 25
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-few-shot-meta-learning - code - official (PyTorch)

README

        
# Prototypical Networks for Few-shot Learning

Code for the NIPS 2017 paper [Prototypical Networks for Few-shot Learning](http://papers.nips.cc/paper/6996-prototypical-networks-for-few-shot-learning.pdf).

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](http://pytorch.org/).

* Install [torchnet](https://github.com/pytorch/tnt) 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`.