@inproceedings{5eb90ddc8b874ce494ba1e30a53c086d,
title = "Food Recognition with ResNet-50",
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.",
keywords = "Convolutional Neural Network (CNN), Deep Learning, Food Recognition, ResNet-50",
author = "Zharfan Zahisham and Lee, {Chin Poo} and Lim, {Kian Ming}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020 ; Conference date: 26-09-2020 Through 27-09-2020",
year = "2020",
month = sep,
day = "26",
doi = "10.1109/IICAIET49801.2020.9257825",
language = "English",
series = "IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020",
address = "United States",
}