Attention-Based Few-Shot Food Classification using Prototypical Networks

In the era of rapidly advancing technology, food classification has emerged as a pivotal application across various domains including health monitoring, dietary assessment, and culinary innovation. However, efficiently categorizing food items remains a challenge, particularly in scenarios with limited labeled data. This paper introduces a novel approach for few-shot food classification using Prototypical Networks with ResNet-50 and an attention mechanism as embedding network. Leveraging the inherent capability of Prototypical Networks to learn from scarce examples, our method demonstrates exceptional adaptability and accuracy in classifying food items. Through extensive experimentation on the Food-101 dataset, employing various CNN architectures, our findings underscore the effectiveness of our approach. In particular, ResNet-50 integrated with the attention mechanism surpasses other architectures, achieving superior classification accuracies of 91.5% and 95.2% for 1-shot and 5-shot learning scenarios, respectively. This integrated approach showcases the potential of Prototypical Networks in addressing the challenges of limited labeled data in food classification tasks, marking a significant advancement in the field.