Smart Shoe Classification Using Artificial Intelligence on EfficientnetB3 Model

In both athletics and daily life, shoes are crucial. Recently, smart sneakers have also taken on a certain trend among both sportsmen and the general public. Therefore, it has actually become crucial to classify shoes in a way that enables industry specialists to do so in accordance with customer demands keeping medical and health point of view. Each shoe product has four distinct perspectives, front, left, right, and rear, but only a few of these views have the product information that allows researchers to create innovative transfer learning models to understand its qualities with precision. Selection, prediction, and categorization of the specific perspective from the provided dataset from many viewpoints are all made possible via the development of machine learning models and the explanation of the artificial intelligence method. In order to create a sound and useful framework for shoe categorization, several efforts have been made over the past few decades. The suggested model in this research exhibits an accuracy of 88.8% on the Adam optimizer utilizing the EfficientNetB3 model and illustrates the accurate classification of the shoe class as utilized in the dataset.