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250-prototypical-networks-for-few-shot-learning

https://github.com/szu-advtech-2024/250-prototypical-networks-for-few-shot-learning

Science Score: 31.0%

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  • Owner: SZU-AdvTech-2024
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Citation

https://github.com/SZU-AdvTech-2024/250-Prototypical-Networks-for-Few-shot-Learning/blob/main/ 

# 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`.

Owner

  • Name: SZU-AdvTech-2024
  • Login: SZU-AdvTech-2024
  • Kind: organization

Citation (citation.txt) 

@article{REPO250,
    author = "Snell, Jake and Swersky, Kevin and Zemel, Richard",
    journal = "Advances in neural information processing systems",
    title = "{Prototypical networks for few-shot learning}",
    volume = "30",
    year = "2017"
}

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