Augmentor: An Image Augmentation Library for Machine Learning. 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.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Jain, Niharika; Olmo, Alberto; Sengupta, Sailik; Manikonda, Lydia; Kambhampati, Subbarao: Imperfect imaGANation: implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses (2022)
- Özcan, Hakan; Emiroğlu, Bülent Gürsel; Sabuncuoğlu, Hakan; Özdoğan, Selçuk; Soyer, Ahmet; Saygı, Tahsin: A comparative study for glioma classification using deep convolutional neural networks (2021)
- Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
- Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
- Marcus D. Bloice, Christof Stocker, Andreas Holzinger: Augmentor: An Image Augmentation Library for Machine Learning (2017) arXiv