Real Time Food Recognition and Calorie Estimation System

Abstract

Accurate dietary assessment is essential for enhancing health and managing nutrition-related issues. This paper outlines the creation of a Real-Time Food Recognition and Calorie Estimation System that employs deep learning methodologies, particularly Convolutional Neural Networks (CNN) and the MobileNetV2 framework. Users can upload images of food via a web-based platform, where the model classifies the food and estimates its caloric and macronutrient content. Utilizing transfer learning and data augmentation techniques, the model achieves an 85% accuracy rate in classifying 10 different food categories. With a lightweight and scalable design, the system guarantees real-time predictions and is compatible with various devices. Additionally, features such as dynamic feedback and historical tracking improve user interaction and system flexibility. By automating food recognition and nutritional assessment, this system offers a dependable, efficient, and user-friendly solution for personalized dietary monitoring.