DESIGN AND IMPLEMENT A FOOD IMAGE CLASSIFICATION SYSTEM USING TRANSFER LEARNING

The growing demand for automated food recognition systems has inspired the development of advanced machine learning solutions capable of identifying food items with high accuracy. This project presents the design and implementation of a deep learning–based food Classification system that leverages transfer learning and Convolutional Neural Networks (CNNs) to classify images of food items into predefined categories. The system was developed using pre-trained CNN architectures such as VGG16, ResNet50, and EfficientNet, which were fine-tuned on the Food-101 dataset to improve performance and reduce training time. The application incorporates a Flask-based web interface where users can upload food images for prediction. Uploaded images are preprocessed through resizing, normalization, and data augmentation to ensure model consistency and robustness. The Detection engine then predicts the food category and displays the result along with a confidence score through the web interface. Experimental results demonstrate that the EfficientNet-based model achieved the highest accuracy (above 90%) while maintaining low computational cost. The web deployment enables real-time inference and provides an intuitive platform for users to interact with the model without requiring technical expertise. This system provides a foundation for applications in dietary assessment, nutrition tracking, and restaurant automation, highlighting the potential of deep learning and web technologies in food recognition and related domains.