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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 |
|---|---|
 |
 |
[Official Torchvision in Transform]
[Pytorch: Random Erasing for ImageNet]
[Person_reID_baseline + Random Erasing + Re-ranking]
Requirements for Pytorch （see Pytorch installation instructions）
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
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
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
For ResNet：
--arch resnet --depth (20， 32， 44， 56， 110)
For WRN：
--arch wrn --depth 28 --widen-factor 10
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 |
NOTE THAT, if you use the latest released Fashion-MNIST, the performance of Baseline and RE will slightly lower than the results reported in our paper. Please refer to the issue.
If you have any questions about this code, please do not hesitate to contact us.