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

## TL;DR

FoodBD is a dataset of Bangladeshi meal images with detailed annotations and nutritional data to support AI tools for dietary assessment and health monitoring.

## Contribution

The dataset introduces a culturally diverse, polygon-annotated resource for Bangladeshi cuisine with nutritional labels.

## Key findings

- The dataset includes 3,523 smartphone-captured meal images with minimal preprocessing.
- 1,837 images are annotated with expert-estimated nutritional information across six categories.
- The dataset is split into training, validation, and test subsets for reproducible machine learning experiments.

## Abstract

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.

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.