# Augmentor: An Image Augmentation Library for Machine Learning

arXiv: https://arxiv.org/abs/1708.04680 · HTML: https://mlsanity.com/paper/1708.04680/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 14 of 14)

- [Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation](https://mlsanity.com/paper/2104.05647/) (2021)
- [The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding](https://mlsanity.com/paper/2109.13392/) (2021)
- [CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication](https://mlsanity.com/paper/2109.00899/) (2021)
- [Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages](https://mlsanity.com/paper/2208.00519/) (2022)
- [Efficient brain age prediction from 3D MRI volumes using 2D projections](https://mlsanity.com/paper/2211.05762/) (2022)
- [AutoML Systems For Medical Imaging](https://mlsanity.com/paper/2306.04750/) (2023)
- [Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data](https://mlsanity.com/paper/2107.03852/) (2021)
- [Simulating Realistic MRI variations to Improve Deep Learning model and visual explanations using GradCAM](https://mlsanity.com/paper/2111.00837/) (2021)
- [Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning](https://mlsanity.com/paper/2212.07527/) (2022)
- [Rotational augmentation techniques: a new perspective on ensemble learning for image classification](https://mlsanity.com/paper/2306.07027/) (2023)
- [Textureless Object Recognition: An Edge-based Approach](https://mlsanity.com/paper/2403.06107/) (2024)
- [Application of discrete Ricci curvature in pruning randomly wired neural networks: A case study with chest x-ray classification of COVID-19](https://mlsanity.com/paper/2509.05322/) (2025)
- [Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains](https://mlsanity.com/paper/2603.14764/) (2026)
- [Trusting Right Predictions for Wrong Reasons: A LIME Based Analysis of Deep Learning Interpretability in Lung Cancer Diagnosis](https://mlsanity.com/paper/2606.16036/) (2026)

## Abstract (arXiv)

The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.