# prototypical-networks

**Repository Path**: zhou8827/prototypical-networks

## Basic Information

- **Project Name**: prototypical-networks
- **Description**: Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-14
- **Last Updated**: 2021-03-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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`.