https://github.com/facebookresearch/mixup-cifar10

mixup: Beyond Empirical Risk Minimization
https://github.com/facebookresearch/mixup-cifar10

Last synced: about 2 months ago
JSON representation

mixup: Beyond Empirical Risk Minimization

• Host: GitHub
• URL: https://github.com/facebookresearch/mixup-cifar10
• Owner: facebookresearch
• License: other
• Archived: true
• Created: 2018-02-19T04:13:05.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2021-10-12T21:33:03.000Z (about 3 years ago)
• Last Synced: 2024-09-21T15:43:08.398Z (about 2 months ago)
• Language: Python
• Size: 28.3 KB
• Stars: 1,156
• Watchers: 21
• Forks: 225
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Mixup - [Code

README

        
# Mixup-CIFAR10

By [Hongyi Zhang](http://web.mit.edu/~hongyiz/www/), [Moustapha Cisse](https://mine.kaust.edu.sa/Pages/cisse.aspx), [Yann Dauphin](http://dauphin.io/), [David Lopez-Paz](https://lopezpaz.org/).

Facebook AI Research

## Introduction

Mixup is a generic and straightforward data augmentation principle.

In essence, mixup trains a neural network on convex combinations of pairs of

examples and their labels. By doing so, mixup regularizes the neural network to

favor simple linear behavior in-between training examples.

This repository contains the implementation used for the results in

our paper (https://arxiv.org/abs/1710.09412).

## Citation

If you use this method or this code in your paper, then please cite it:

```

@article{

zhang2018mixup,

title={mixup: Beyond Empirical Risk Minimization},

author={Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz},

journal={International Conference on Learning Representations},

year={2018},

url={https://openreview.net/forum?id=r1Ddp1-Rb},

}

```

## Requirements and Installation

* A computer running macOS or Linux

* For training new models, you'll also need a NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl)

* Python version 3.6

* A [PyTorch installation](http://pytorch.org/)

## Training

Use `python train.py` to train a new model.

Here is an example setting:

```

$ CUDA_VISIBLE_DEVICES=0 python train.py --lr=0.1 --seed=20170922 --decay=1e-4

```

## License

This project is CC-BY-NC-licensed.

## Acknowledgement

The CIFAR-10 reimplementation of _mixup_ is adapted from the [pytorch-cifar](https://github.com/kuangliu/pytorch-cifar) repository by [kuangliu](https://github.com/kuangliu).