文章基本信息

标题：An Intelligent Mobile Application to Automate the Analysis of Food Calorie using Artificial Intelligence and Deep Learning
本地全文：下载
作者：Yongqing Yu ; Yishan Zou ; Yu Sun 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：14
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：As obesity becomes increasingly common worldwide [9], more and more people want to lose weight – for both their health and their image. According to the Centers for Disease Control and Prevention (CDC), long-term changes in daily eating habits (such as regarding food/nutrition type, calorie intake) are successful at keeping weights off [10]. Therefore, it would be helpful to have an AI mobile program that identifies the types of food the user consumes and automatically calculates the total calories. This paper examines the development and optimization of an 11-categorical food classification model based on the MobileNet neural network using Python. Specifically, it classifies any food image as one of bread, dairy, dessert, egg product, fried food, meat, noodles, rice, seafood, soup, or fruit/vegetables. Methods of optimization include data preprocessing and learning rate and batch size adjustments. Experimental results show that scaling image inputs to standard size (Python Numpy resize() function), 300 training epochs, dynamic learning rate (start with 0.001 and *0.1 for every 30 epochs), and a batch size of 16 yields our best model of 83.44% accuracy.
关键词：Food classification;Python;Data preprocessing;MobileNet;Epochs;Overfitting;Learning rate;Batch size