Indian Food Image Classification and Recognition with Transfer Learning Technique Using MobileNetV3 and Data Augmentation

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 recognition 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 have 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. In this paper, we used CNN as our base model to build our system, which gives a system accuracy of 85%. After that, we deployed the transfer learning technique with MobileNetV3 for improvement in accuracy, which resulted in an improvement in accuracy of up to 93.3%. Furthermore, we applied data augmentation techniques in the preprocessing phase and we trained our model using transfer learning with MobileNetV3 and we obtained an accuracy of up to 95.3%. So, the accuracy of the model increases by applying the data augmentation technique in addition to transfer learning.

Keywords

• Indian food dataset
• MobileNetv3
• data augmentation
• CNN
• transfer learning
• deep learning