MSNet: Food Image Classification Model Based on Lightweight Convolutional Neural Network

Abstract

With the advancement of technology, the demand for healthy eating has increased, making food classification a research hotspot. Existing deep learning-based food image classification models demonstrate high accuracy but require substantial computational resources, limiting their use on resource-constrained devices. In this study, a lightweight convolutional neural network model named MSNet for food classification is proposed. MSNet mainly consists of M Blocks and S Blocks. The M Block uses improved depthwise convolution to reduce the computational cost of conventional convolutions, and the S Block uses channel shuffle techniques to enhance feature information flow between channels without increasing additional computation, effectively capturing relationships between different channel features. Experimental results on three benchmark datasets (ETHZ Food-101, Vireo Food-172, and ISIA Food-500) show that MSNet achieves top-1 accuracies of 86.24%, 87.98%, and 65.70%, with model sizes of 13.8 MB, 15.9 MB, and 25.4 MB, respectively, outperforming mainstream models in terms of computational efficiency. Further quantization produces two MSNet-Lite variants with competitive model size, while maintaining high accuracy and significantly improving inference speed. Additionally, visualization analysis indicates that MSNet effectively extracts essential features of food images, offering good interpretability and generalization across datasets of varying complexity. The proposed MSNet model provides a feasible solution for practical deployment in food classification tasks on mobile and embedded devices.