Food Recognition with ResNet-50

Zharfan Zahisham, Chin Poo Lee, Kian Ming Lim

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

49 Citations (Scopus)

Abstract

Object recognition has spurred much attention in recent years. The fact that computers are now able to detect and recognize objects has made Artificial Intelligence field, especially machine learning grow very rapidly. The proposed framework uses Deep Convolutional Neural Network (DCNN) that is based on ResNet 50 architecture. Due to the limited computational resources to train the whole model, the ResNet model is imitated and the pre-trained weights are imported. Thereafter, the last few layers of the model are trained on three datasets that have been acquired online. This process is called fine-tuning a pre-trained model. It is one of the most common approaches in building a DCNN architecture. The dataset that was used to evaluate the performance of the model are ETHZ-FOOD101, UECFOOD100 and UECFOOD256. The parameter setting and results of the proposed method are also presented in this paper.

Original languageEnglish
Title of host publicationIEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169460
DOIs
Publication statusPublished - 26 Sept 2020
Externally publishedYes
Event2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020 - Kota Kinabalu, Sabah, Malaysia
Duration: 26 Sept 202027 Sept 2020

Publication series

NameIEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020

Conference

Conference2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020
Country/TerritoryMalaysia
CityKota Kinabalu, Sabah
Period26/09/2027/09/20

Keywords

  • Convolutional Neural Network (CNN)
  • Deep Learning
  • Food Recognition
  • ResNet-50

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Information Systems and Management
  • Instrumentation

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