Food recognition via an efficient neural network with transformer grouping
Guorui Sheng, Shuqi Sun, Chengxu Liu, Yancun Yang
Abstract
Recently, considerable research efforts have been devoted to food recognition for its great potential applications in human health. Much work so far has focused on directly extracted deep visual features via Convolutional Neural Networks, which require significant computational resources and training time. The high requirements on hardware resources severely limit the application of food recognition in mobile devices and the sustainable extension on the server side. Therefore, how to design an efficient and high-performance lightweight neural network for food recognition is the key to solve the problem. In this paper, we propose a Lightweight Transformer-Based Deep Neural Network for food image recognition, which can achieve effective recognition of food images with fewer parameters and lower computational cost. Through Transformer Grouping and Token Shuffling, we construct an efficient food image recognition network that effectively combines the advantages of Transformer to extract global features and MobileNet to extract local features. The proposed network architecture effectively copes with the particularly scattered distribution of salient features in food images, and improves the recognition rate. We conduct extensive experiments on three popular food data sets, demonstrating that our method achieves state-of-the-art performance in applying lightweight neural networks to food image recognition.