# awesome-data-augmentation

**Repository Path**: barrylee9527/awesome-data-augmentation

## Basic Information

- **Project Name**: awesome-data-augmentation
- **Description**: This is a list of awesome methods about data augmentation.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-10
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


This library helps you with augmenting images for your machine learning projects. It converts a set of input images into a new, much larger set of slightly altered images. Many very popular projects have been integrated. New methods like augmix,cutmix，are being tracked. Whether you're a researcher or an engineer, just enjoy it!


# Popular Projects


## imgaug

<img align="center" src="assets/imgaug.png" width="600">


- intro: 2019
- github star: 7.8k
- github: <https://github.com/aleju/imgaug>


## Albumentations
**Albumentations: fast and flexible image augmentations**

<img align="center" src="assets/Albumentations.jpg" width="600">

- intro: ArXiv 2018
- github star: 4.1k
- arxiv: <https://arxiv.org/abs/1809.06839v1>
- github: <https://github.com/albumentations-team/albumentations>



## Augmentor
**Biomedical image augmentation using Augmentor**

<img align="center" src="assets/Augmentor.gif" width="400">

- intro: Bioinformatics
- github star: 3.7k
- arxiv: <https://github.com/mdbloice/Augmentor>
- github: <https://github.com/mdbloice/Augmentor>
- docs: <https://augmentor.readthedocs.io/en/master/>

Augmentor is a Python package designed to aid the augmentation and artificial generation of image data for machine learning tasks. It is primarily a data augmentation tool, but will also incorporate basic image pre-processing functionality.


# Papers&Codes


<img align="center" src="assets/augmix.gif" width="650">

## mixup

**Mixup: BEYOND EMPIRICAL RISK MINIMIZATION**

- intro: ICLR2018
- arxiv: <https://arxiv.org/abs/1710.09412>
- github: <https://github.com/facebookresearch/mixup-cifar10>

Mixup is a generic and straightforward data augmentation principle. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples.

## Cutout
**Improved Regularization of Convolutional Neural Networks with Cutout**

- intro: arXiv 2017
- arxiv: <https://arxiv.org/abs/1708.04552>
- github: <https://github.com/uoguelph-mlrg/Cutout>

## Cutmix
**CutMix:Regularization Strategy to Train Strong Classifiers with Localizable Features**

<img align="center" src="assets/CutMix.png" width="500">

- intro: ICCV 2019 (oral talk)
- arxiv: <https://arxiv.org/pdf/1905.04899.pdf>
- github: <https://github.com/clovaai/CutMix-PyTorch>

## Augmix
**AUGMIX: A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY**

- intro: ICLR 2020
- arxiv: <https://arxiv.org/pdf/1912.02781.pdf>
- github: <https://github.com/google-research/augmix>



## fast-autoaugment
**Fast AutoAugment**

- intro: NeurIPS 2019 
- github star: 671
- arxiv: <https://arxiv.org/abs/1905.00397>
- github: <https://github.com/kakaobrain/fast-autoaugment>


## AutoAugment
**AutoAugment:Learning Augmentation Strategies from Data**

- intro: CVPR 2019
- provider: google
- arxiv: <https://arxiv.org/pdf/1805.09501v3.pdf>
- github: <https://github.com/DeepVoltaire/AutoAugment>


## RandAugment
**RandAugment: Practical automated data augmentation with a reduced search space**

<img align="center" src="assets/RandAugment.png" width="550">

- intro: ICLR 2020
- provider: google
- arxiv: <https://arxiv.org/pdf/1912.02781.pdf>
- github: <https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet>


## GridMask
**GridMaskDataAugmentation**

<img align="center" src="assets/GridMask.png" width="650">

- intro:  2020.01
- arxiv: <https://arxiv.org/abs/2001.04086>
- github: <https://github.com/akuxcw/GridMask>



## imagecorruptions
**Benchmarking Robustness in Object Detection:Autonomous Driving when Winter is Coming**

<img align="center" src="assets/corruptions_sev_3.png" width="750">

- intro: arXiv  2019
- arxiv: <://arxiv.org/pdf/1912.02781.pdf>
- provider: UC Berkeley
- github: <https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix>
- github: <https://github.com/junyanz/CycleGAN>

## CycleGAN
**Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss**


<img align="center" src="assets/teaser_high_res.jpg" width="750">


- intro: ICCV 2017
- arxiv: <https://arxiv.org/pdf/1912.02781.pdf>
- provider: UC Berkeley
- github: <https://github.com/CrazyVertigo/imagecorruptions>



## Small Object Augmentation
**Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss**

<img align="center" src="assets/SmallObjectAugmentation.png" width="750">

- intro:  2017
- arxiv: <https://arxiv.org/pdf/1902.07296.pdf>
- github: <https://github.com/gmayday1997/SmallObjectAugmentation>


# Continuous updating...


If you find this library useful for your research, please consider starring the GitHub repository.