  Many aging individuals encounter challenges in effectively tracking their
dietary intake, exacerbating their susceptibility to nutrition-related health
complications. Self-reporting methods are often inaccurate and suffer from
substantial bias; however, leveraging intelligent prediction methods can
automate and enhance precision in this process. Recent work has explored using
computer vision prediction systems to predict nutritional information from food
images. Still, these methods are often tailored to specific situations, require
other inputs in addition to a food image, or do not provide comprehensive
nutritional information.
  This paper aims to enhance the efficacy of dietary intake estimation by
leveraging various neural network architectures to directly predict a meal's
nutritional content from its image. Through comprehensive experimentation and
evaluation, we present NutritionVerse-Direct, a model utilizing a vision
transformer base architecture with three fully connected layers that lead to
five regression heads predicting calories (kcal), mass (g), protein (g), fat
(g), and carbohydrates (g) present in a meal. NutritionVerse-Direct yields a
combined mean average error score on the NutritionVerse-Real dataset of 412.6,
an improvement of 25.5% over the Inception-ResNet model, demonstrating its
potential for improving dietary intake estimation accuracy.
