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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 |
|---|---|
 |
 |
[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 |
If you have any questions about this code, please do not hesitate to contact us.