# Random-Erasing

**Repository Path**: greitzmann/Random-Erasing

## Basic Information

- **Project Name**: Random-Erasing
- **Description**: Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-27
- **Last Updated**: 2021-01-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Random Erasing Data Augmentation
===============================================================

![Examples](all_examples-page-001.jpg)

| black  | white | random |
|----------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|
|![i1](img/001-black.gif)|![i2](img/001-white.gif)| ![i3](img/001-random.gif)|
|![i4](img/002-black.gif)|![i5](img/002-white.gif)| ![i6](img/002-random.gif)|

### 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/docs/master/torchvision/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)