Lightweight Vision Transformer with Window and Spatial Attention for Food Image Classification
This work addresses food image classification for automated quality control and safety in the food industry, representing an incremental improvement in efficiency for resource-constrained environments.
The paper tackled the challenge of high computational complexity in Vision Transformers for food image classification by proposing a lightweight algorithm integrating Window Multi-Head Attention and Spatial Attention, achieving accuracies of 95.24% on Food-101 and 94.33% on Vireo Food-172 while reducing parameters and FLOPs.
With the rapid development of society and continuous advances in science and technology, the food industry increasingly demands higher production quality and efficiency. Food image classification plays a vital role in enabling automated quality control on production lines, supporting food safety supervision, and promoting intelligent agricultural production. However, this task faces challenges due to the large number of parameters and high computational complexity of Vision Transformer models. To address these issues, we propose a lightweight food image classification algorithm that integrates a Window Multi-Head Attention Mechanism (WMHAM) and a Spatial Attention Mechanism (SAM). The WMHAM reduces computational cost by capturing local and global contextual features through efficient window partitioning, while the SAM adaptively emphasizes key spatial regions to improve discriminative feature representation. Experiments conducted on the Food-101 and Vireo Food-172 datasets demonstrate that our model achieves accuracies of 95.24% and 94.33%, respectively, while significantly reducing parameters and FLOPs compared with baseline methods. These results confirm that the proposed approach achieves an effective balance between computational efficiency and classification performance, making it well-suited for deployment in resource-constrained environments.