DelicacyNet for nutritional evaluation of recipes

Ruijie Li; Peihan Ji; Qing Kong

doi:10.3389/fnut.2023.1247631

DelicacyNet for nutritional evaluation of recipes

Front Nutr. 2023 Sep 14:10:1247631. doi: 10.3389/fnut.2023.1247631. eCollection 2023.

Authors

Ruijie Li¹, Peihan Ji², Qing Kong³

Affiliations

¹ College of Computer Science and Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, China.
² Haide College, Ocean University of China, Qingdao, Shandong, China.
³ College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China.

Abstract

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.

Keywords: computer vision; environment features; nutrition; recipe; transformer.