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Objectives <p>The FoodBD dataset was initially collected to address the dietary assessment of diabetic patients. However, it was later expanded to address the lack of culturally diverse food image datasets, particularly for Bangladeshi cuisine, which is underrepresented in food recognition research. It supports tasks in computer vision, nutrition estimation, and health monitoring by providing a resource for AI-driven dietary assessment tools.</p> Data description <p>FoodBD comprises 3,523 smartphone-captured meal images representing authentic Bangladeshi meals, with minimal preprocessing to preserve real-world complexity. Each image is annotated with polygon-based segmentation across 67 food categories. Additionally, among them 1,837 images include expert-estimated nutritional information (carbohydrate, protein, fat, fiber, calorie, and glycemic load). The dataset is split into training, validation, and test subsets, facilitating reproducibility in machine learning pipelines.</p>

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FoodBD: a polygon-annotated meal image dataset of Bangladeshi cuisines with visual and nutritional labels

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月9日

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  • Benzir Md Ahmed,
  • Md. Enamul Haque,
  • A. K. Obidul Huq,
  • Mohammad Mehedy Masud,
  • Mohammed Eunus Ali,
  • Mahmuda Naznin

摘要

Objectives

The FoodBD dataset was initially collected to address the dietary assessment of diabetic patients. However, it was later expanded to address the lack of culturally diverse food image datasets, particularly for Bangladeshi cuisine, which is underrepresented in food recognition research. It supports tasks in computer vision, nutrition estimation, and health monitoring by providing a resource for AI-driven dietary assessment tools.

Data description

FoodBD comprises 3,523 smartphone-captured meal images representing authentic Bangladeshi meals, with minimal preprocessing to preserve real-world complexity. Each image is annotated with polygon-based segmentation across 67 food categories. Additionally, among them 1,837 images include expert-estimated nutritional information (carbohydrate, protein, fat, fiber, calorie, and glycemic load). The dataset is split into training, validation, and test subsets, facilitating reproducibility in machine learning pipelines.