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https://github.com/zhunzhong07/Random-Erasing
Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
https://github.com/zhunzhong07/Random-Erasing
aaai2020 data-augmentation image-classification object-detection person-re-identification pytorch
Last synced: 3 months ago
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Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
- Host: GitHub
- URL: https://github.com/zhunzhong07/Random-Erasing
- Owner: zhunzhong07
- License: apache-2.0
- Created: 2017-09-15T08:45:42.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-11-08T12:56:54.000Z (about 1 year ago)
- Last Synced: 2024-08-03T17:14:41.513Z (6 months ago)
- Topics: aaai2020, data-augmentation, image-classification, object-detection, person-re-identification, pytorch
- Language: Python
- Homepage:
- Size: 15.6 MB
- Stars: 716
- Watchers: 16
- Forks: 156
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-cv - Random-Erasing
README
# Random Erasing Data Augmentation
===============================================================
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### This code has the source code for the paper "[Random Erasing Data Augmentation](https://arxiv.org/abs/1708.04896)".
If you find this code useful in your research, please consider citing:
@inproceedings{zhong2020random,
title={Random Erasing Data Augmentation},
author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020}
}
### Other re-implementations
[\[Official Torchvision in Transform\]](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomErasing)
[\[Pytorch: Random Erasing for ImageNet\]](https://github.com/rwightman/pytorch-image-models)
[\[Python Augmentor\]](http://augmentor.readthedocs.io/en/master/code.html#Augmentor.Pipeline.Pipeline.random_erasing)
[\[Person_reID CamStyle\]](https://github.com/zhunzhong07/CamStyle)
[\[Person_reID_baseline + Random Erasing + Re-ranking\]](https://github.com/layumi/Person_reID_baseline_pytorch)
[\[Keras re-implementation\]](https://github.com/yu4u/cutout-random-erasing)
### Installation
Requirements for Pytorch （see [Pytorch](http://pytorch.org/) 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 |
### 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](https://github.com/zhunzhong07/Random-Erasing/issues/9).
If you have any questions about this code, please do not hesitate to contact us.
[Zhun Zhong](http://zhunzhong.site)
[Liang Zheng](http://liangzheng.com.cn)