Food classification using transfer learning technique
G. VijayaKumari, Priyanka Vutkur, P Vishwanath
Abstract
In the subject of object detection using computer vision, image classification is becoming a prominent and promising aspect. However, studies have just scratched the surface. Till now, the superficials of food image classification in order to assess the nutritional abilities of people of different nationalities, The categorization of their traditional cuisine has a significant influence. Existing models categorize different sorts of foods. These models can only categorize a small number of meals at a given time. However, in a single model, the maximum number of foods must be recognized. This work focuses on the creation of a recognition model that uses transfer learning techniques to categorize various food products into their appropriate categories. Using Efficientnetb0, a transfer learning technique, the developed model classified 101 distinct food kinds with an accuracy of 80%. When compared to other state of art models, our model performed with best accuracy.