Vietnamese Food Recognition System Using Convolutional Neural Networks Based Features.

Food image recognition has been extensively investigated during the last decade and had multiple useful applications for monitoring food calories and analyzing people’s eating habits to ensure better health. In this paper, we study a Vietnamese food recognition system using Convolutional Neural Networks (CNNs) based features. We manually collect one dataset for Vietnamese food classification with 13 categories and 8903 images. For learning a proper food classifier, we conduct brief analytics by comparing hand-crafted features and CNNs based features (including AlexNet, GoogleNet, ResNet50, ResNet101v2, and InceptionResnetv2) and choosing top K accuracy for measuring the performance of each model. The experimental results show that InceptionResnetv2 can achieve the best performance among all these techniques. We aim at publishing our codes and datasets for giving and additional contribution to the research community related to the Vietnamese food recognition problem.