Can deep learning models recognize chilean diet

Abstract

The emergence of deep learning models has made it possible to address several real-life problems and, in particular, those in which computer vision plays a key role. In this sense, the food recognition task from images is one of the beneficiaries of this machine learning method. Its importance lies in its usefulness to become aware of the food eaten and in this way help us to lead a healthy lifestyle. In recent years, food image recognition has gained great prominence in the literature, providing novel models and datasets to address it. However, public data generally correspond to American, Asian, and European foods, therefore the methods developed cannot be directly applied to the Chilean diet. In this article we will publish a new dataset to recognize the foods present in the Chilean diet. In addition, we will perform a comparison with public popular food datasets to analyze the similarity in the dishes of the proposed dataset with respect to the exciting ones in the literature. Moreover, we will establish a baseline using the state-of-the-arts Convolutional Neural Network architectures and the novel Swin Transformer approach.