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DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision
Sofia Amarù1, Davide Marelli1, Gianluigi Ciocca1
1Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.
Journal of Imaging
|October 27, 2023
Summary
Data augmentation is key for enhancing machine learning models by expanding datasets. This study surveys popular computer vision data augmentation libraries, offering a guide for practitioners.
Area of Science:
- Computer Vision
- Machine Learning
- Artificial Intelligence
Background:
- Data augmentation is essential for improving machine learning model generalization and robustness by increasing training dataset size.
- Various libraries now simplify the implementation of diverse data augmentation strategies across different machine learning tasks.
Purpose of the Study:
- To survey widely adopted data augmentation libraries specifically for computer vision tasks.
- To provide practitioners with a comprehensive guide for navigating and utilizing these resources effectively.
Main Methods:
- A curated taxonomy is developed to classify different data augmentation approaches used by libraries.
- Application examples accompany the classification to illustrate practical usage.
- A public website, DALib, is created as a centralized repository for the taxonomy, methods, and examples.
Main Results:
- The survey identifies and categorizes key data augmentation libraries available for computer vision.
- The developed taxonomy offers a structured overview of augmentation techniques.
- The DALib website provides an accessible platform for exploring these resources.
Conclusions:
- Informed selection of data augmentation techniques for computer vision projects is facilitated by this survey and resource.
- The comprehensive resource aims to empower practitioners and advance computer vision research through effective data augmentation.
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