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

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