NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

ABSTRACT: Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an

SUBMITTER: Mezgec S 

PROVIDER: S-EPMC5537777 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature