Using Deep Learning for Food and Beverage Image Recognition

Recently, deep learning achieved the state of the art in the field of food image recognition. In this paper we describe our deep learning contributions to the field: NutriNet, a novel deep learning architecture, and a pixel-level classification solution for images of fake food. NutriNet was trained on a food image dataset of a larger size and containing more food classes than previous works, and was the first to recognize beverage images. Our work on fake-food image recognition includes the first automatic system for recognizing images of fake food, while the visual similarity of fake and real food makes it useful for fake-food experiments as well as real food recognition.