Simple jQuery Dropdowns
Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/45625
Title: Thai fast food image classification using deep learning
Authors: Narit Hnoohom
Sumeth Yuenyong
Mahidol University
Keywords: Computer Science;Engineering
Issue Date: 8-Jun-2018
Citation: 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018. (2018), 116-119
Abstract: © 2018 IEEE. This paper presents a prediction model for classifying Thai fast food images. The model uses a deep learning process that was trained on natural images (GoogLeNet dataset) and was fine-tuned to generate the predictive Thai fast food model. The researchers created a dataset, called the Thai Fast Food (TFF) dataset, which contained 3,960 images. The dataset was divided into eleven groups of food images comprised of omelet on rice, rice topped with stir-fried chicken and basil, barbecued red pork in sauce with rice, stewed pork leg on rice, Thai fried noodle, rice with curried chicken, steamed chicken with rice, shrimp-paste fried rice, fried noodle with pork in soy sauce and vegetables, wide rice noodles with vegetables and meat. The final group comprised dishes which are not members of the other ten groups listed (non-ten-types), but which exist among Thai fast food. The classification average accuracy on a separate test set shows that Thai fast food can be predicted at 88.33%.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050003696&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/45625
Appears in Collections:Scopus 2018

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.