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DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision.

Sofia Amarù1, Davide Marelli1, Gianluigi Ciocca1

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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.

Keywords:
computer visiondata augmentationdeep learninglibraries

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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.