Next Article in Journal
Constructive Alignment in Game Design for Learning Activities in Higher Education
Next Article in Special Issue
A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing
Previous Article in Journal
Innovation in the Era of IoT and Industry 5.0: Absolute Innovation Management (AIM) Framework
Previous Article in Special Issue
Fastai: A Layered API for Deep Learning
Open AccessArticle

Albumentations: Fast and Flexible Image Augmentations

1
Mapbox, Minsk 220030, Belarus
2
Lyft Level 5, Palo Alto, CA 94304, USA
3
ODS.ai, Odessa 65000, Ukraine
4
X5 Retail Group, Moscow 119049, Russia
5
Simicon, Saint Petersburg 195009, Russia
6
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
7
Shenzhen Research Institute of Big Data, Shenzhen 518172, Guangdong, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(2), 125; https://doi.org/10.3390/info11020125
Received: 31 December 2019 / Revised: 20 February 2020 / Accepted: 20 February 2020 / Published: 24 February 2020
(This article belongs to the Special Issue Machine Learning with Python)
Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels. In computer vision, image augmentations have become a common implicit regularization technique to combat overfitting in deep learning models and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations of flipping, rotating, scaling, and cropping. Moreover, image processing speed varies in existing image augmentation libraries. We present Albumentations, a fast and flexible open source library for image augmentation with many various image transform operations available that is also an easy-to-use wrapper around other augmentation libraries. We discuss the design principles that drove the implementation of Albumentations and give an overview of the key features and distinct capabilities. Finally, we provide examples of image augmentations for different computer vision tasks and demonstrate that Albumentations is faster than other commonly used image augmentation tools on most image transform operations. View Full-Text
Keywords: data augmentation; computer vision; deep learning data augmentation; computer vision; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Buslaev, A.; Iglovikov, V.I.; Khvedchenya, E.; Parinov, A.; Druzhinin, M.; Kalinin, A.A. Albumentations: Fast and Flexible Image Augmentations. Information 2020, 11, 125.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop