A Comparative Study of Deep Learning Methods on Food Classification Problem
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper gives a comparative study on the performances of several deep learning methods for the food images recognition challenge. The experiments were conducted on the UEC Food-100 dataset using ResNet-18, Inception-V3, Resnet-50, Densenet-121, Wide Resnet-50 and ResNext-50 with images of size 320x320 and 299x299. The limited size of the database required the transfer learning approach; that is, all models were trained with pretrained ImageNet weights. The best classification result was obtained using ResNext-50 with 87.7 % accuracy. © 2020 IEEE.
Açıklama
2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 -- 15 October 2020 through 17 October 2020 -- -- 165305
Anahtar Kelimeler
Classification, Deep Learning, Densenet, Inception, Machine Learning, Resnet, Resnext, Uec Food 100, Intelligent Systems, Learning Systems, Transfer Learning, Classification Results, Comparative Studies, Food İmage, Learning Methods, Deep Learning
Kaynak
Proceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
WoS Q Değeri
Scopus Q Değeri
N/A
