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Dry Fish Image dataset: Data-driven analysis and deep learning-based classification.

Amran Hossain1, Md Jakir Hossain1, Iffat Ara Arin1

  • 1Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh.

Data in Brief
|April 9, 2026
PubMed
Summary
This summary is machine-generated.

A new dry fish image dataset supports computer vision research, featuring diverse, real-world conditions. This dataset aids machine learning and deep learning applications in food recognition and supply chain digitization.

Keywords:
Computer visionDeep learningFood recognitionImage classificationMachine learning applicationsVisual inspection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning
  • Fisheries Informatics

Background:

  • Data-driven research in computer vision requires diverse, real-world datasets.
  • Existing datasets may not capture the complexities of food items like dry fish in various market conditions.
  • The need for specialized datasets to advance applications in food recognition and supply chain management is growing.

Purpose of the Study:

  • To present a comprehensive dry fish image dataset framework for computer vision and machine learning research.
  • To support data-driven investigations into dry fish classification, feature extraction, and related applications.
  • To facilitate advancements in automated food recognition, market automation, and supply chain digitization.

Main Methods:

  • Collected high-quality RGB images of twelve dry fish species from multiple markets in Dhaka.
  • Acquired images using mobile cameras under natural lighting, incorporating variations in background, angles, and obstructions.
  • Performed expert-verified manual classification, duplicate removal, and format standardization for data integrity.

Main Results:

  • A diverse dataset of 12 dry fish types, reflecting real-world variations in appearance, handling, and presentation.
  • Images captured under natural lighting with variations to enhance data diversity for practical deployment scenarios.
  • A structured dataset, preprocessed for immediate use with deep learning frameworks and further expansion.

Conclusions:

  • The dry fish dataset provides a valuable resource for computer vision, machine learning, and deep learning applications.
  • It enables research in areas such as image classification, feature extraction, data imbalance analysis, and explainable AI.
  • The dataset supports advancements in low-resource food recognition, market automation, supply chain digitization, and fisheries informatics.