Performance Evaluation of Indian Food Image Classification system using Transfer Learning with MobileNetV3

Abstract

Food image classification and recognition is an emerging research area due to its growing importance in the medical and health industries. As India is growing digitally rapidly, an automated Indian food image classification system will help in the development of diet tracking, calorie estimation, and many other health and food consumption-related applications. In recent years many deep learning techniques evolved. Deep learning is a robust and low-cost method for extracting information from food images, though, challenges lie in extracting information from real-world food images due to various factors affecting image quality such as photos from different angles and positions, several objects appearing in the photo, etc. We used Non-Standard dataset for Indian Food images of 13 different classes consist more than 3500 images. In this paper, we use CNN as our base model to build our system, which gives an accuracy of 47% of the system. After that, we deployed the transfer learning technique with MobileNetV3 for improvement in accuracy, which resulted in an improvement in accuracy of up to 90%.