DelicacyNet for nutritional evaluation of recipes

Frontiers in Nutrition

Nutrition

Ruijie Li¹  Qing Kong²  Peihan Ji³ 

[1] College of Computer Science and Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, China;College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China;Haide College, Ocean University of China, Qingdao, Shandong, China;

关键词: recipe;    nutrition;    computer vision;    transformer;    environment features;   

DOI  :  10.3389/fnut.2023.1247631

 received in 2023-06-26, accepted in 2023-08-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

In this paper, we are interested in how computers can be used to better serve us humans, such as helping humans control their nutrient intake, with higher level shortcuts. Specifically, the neural network model was used to help humans identify and analyze the content and proportion of nutrients in daily food intake, so as to help humans autonomously choose and reasonably match diets. In this study, we formed the program we wanted to obtain by establishing four modules, in which the imagination module sampled the environment, then relied on the encoder to extract the implicit features of the image, and finally relied on the decoder to obtain the required feature vector from the implicit features, and converted it into the battalion formation table information through the semantic output module. Finally, the model achieved extremely high accuracy on recipe1M+ and food2K datasets.

【 授权许可】

Unknown   
Copyright © 2023 Li, Ji and Kong.

【 图 表 】

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