Data Augmentation and Hyperparameter Tuning Boost Food Image Recognition

Food image classification has garnered significant interest in health and medical domains due to its potential for accurate calorie calculation and the development of sophisticated nutrition monitoring systems. Specifically, the study emphasizes the utilization of SqueezeNet, MobileNet, CNN, and NASNet architectures in the task of classifying food images. Among these architectures, SqueezeNet stands out for its lightweight construction, making it a practical and appealing choice for food classification tasks. Despite its simplicity, SqueezeNet demonstrates impressive performance in categorizing food images. Similarly, MobileNet achieves remarkable accuracy in food image categorization, even with fewer parameters, owing to its efficient depthwise separable convolutions. The traditional CNN architecture also contributes to the study, providing a baseline for comparison with more specialized models. Additionally, NASNet, with its sophisticated architecture discovered through neural architecture search techniques, showcases its ability to extract complex features from food photographs. In the comparison between these architectures, SqueezeNet and MobileNet show competitive performance, outperforming traditional CNN and NASNet in the field of classifying food images. Moreover, the systems demonstrate proficiency in associating food item names with their corresponding visuals, facilitating approximate recognition of these products. By exploring the intricacies of automated food classification and leveraging deep learning models such as SqueezeNet, MobileNet, CNN, and NASNet, this study makes a significant contribution to the field. It opens doors to more precise and efficient applications in health and medical sectors, offering promising avenues for enhanced nutrition monitoring and calorie calculation systems.