NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation

TL;DR

NutritionVerse-Real is a comprehensive, manually collected 2D food scene dataset designed to improve dietary intake estimation through detailed images, segmentation masks, and nutritional metadata, supporting machine learning research.

Contribution

The paper introduces NutritionVerse-Real, a new open access dataset with detailed food images, segmentation masks, and nutritional data, created through manual collection and labeling for dietary estimation tasks.

Findings

Dataset contains 889 images of 251 dishes and 45 food types.

Includes manually labeled segmentation masks and nutritional metadata.

Highlights data diversity and potential biases for model development.

Abstract

Dietary intake estimation plays a crucial role in understanding the nutritional habits of individuals and populations, aiding in the prevention and management of diet-related health issues. Accurate estimation requires comprehensive datasets of food scenes, including images, segmentation masks, and accompanying dietary intake metadata. In this paper, we introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation with 889 images of 251 distinct dishes and 45 unique food types. The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish using the ingredient weights and nutritional information from the food packaging or the Canada Nutrient File. Segmentation masks were then generated through human labelling of the images. We provide further analysis on the data diversity to highlight potential biases when using this data to develop models for dietary intake estimation. NutritionVerse-Real is publicly available at https://www.kaggle.com/datasets/nutritionverse/nutritionverse-real as part of an open initiative to accelerate machine learning for dietary sensing.