# Random Erasing Data Augmentation

This code has the source code for the paper "Random Erasing Data Augmentation".

If you find this code useful in your research, please consider citing:

@article{zhong2017random,
title={Random Erasing Data Augmentation},
author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
journal={arXiv preprint arXiv:1708.04896},
year={2017}
}

Thanks for Marcus D. Bloice, Marcus D. Bloice reproduces our method in Augmentor. Augmentor is an image augmentation library in Python for machine learning.

| Original image | Random Erasing | |----------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------| | Original | |

Other re-implementations

[Python Augmentor]

[CamStyle]

[Keras]

[Person_reID_baseline + Random Erasing + Re-ranking]

Installation

Requirements for Pytorch see Pytorch installation instructions

Examples:

CIFAR10

ResNet-20 baseline on CIFAR10 python cifar.py --dataset cifar10 --arch resnet --depth 20

ResNet-20 + Random Erasing on CIFAR10 python cifar.py --dataset cifar10 --arch resnet --depth 20 --p 0.5

CIFAR100

ResNet-20 baseline on CIFAR100 python cifar.py --dataset cifar100 --arch resnet --depth 20

ResNet-20 + Random Erasing on CIFAR100 python cifar.py --dataset cifar100 --arch resnet --depth 20 --p 0.5

Fashion-MNIST

ResNet-20 baseline on Fashion-MNIST python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20

ResNet-20 + Random Erasing on Fashion-MNIST python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20 --p 0.5

Other architectures

For ResNet --arch resnet --depth (20 32 44 56 110)

For WRN --arch wrn --depth 28 --widen-factor 10

Our results

You can reproduce the results in our paper:

| | CIFAR10 | CIFAR10| CIFAR100 | CIFAR100| Fashion-MNIST | Fashion-MNIST| | ----- | ----- | ---- | ----- | ---- | ----- | ---- | |Models | Base. | +RE | Base. | +RE | Base. | +RE | |ResNet-20 | 7.21 | 6.73 | 30.84 | 29.97 | 4.39 | 4.02 | |ResNet-32 | 6.41 | 5.66 | 28.50 | 27.18 | 4.16 | 3.80 | |ResNet-44 | 5.53 | 5.13 | 25.27 | 24.29 | 4.41 | 4.01 | |ResNet-56 | 5.31 | 4.89| 24.82 | 23.69 | 4.39 | 4.13 | |ResNet-110 | 5.10 | 4.61 | 23.73 | 22.10 | 4.40 | 4.01 | |WRN-28-10 | 3.80 | 3.08 | 18.49 | 17.73 | 4.01 | 3.65 |