NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene
  Dataset for Dietary Intake Estimation
Chi-en Amy Tai Saeejith Nair Olivia Markham Matthew Keller Yifan Wu Yuhao Chen Alexander Wong
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.