FootwearFusion: Convolutional Neural Network for Shoe, Sandal, and Boot Classification

A fundamental task in computer vision, footwear categorization finds many applications in the retail, security, and fashion industries. This paper employs Convolutional Neural Networks (CNNs) to compare and categorize three popular footwear types: shoes, boots, and sandals. The collection is made up of photos that were gathered from several sources and feature a range of footwear brands, colors, and styles. High accuracy rates on both training and validation sets show how effective CNNs are at differentiating between shoes, boots, and sandals based on experimental results. It also examines the effect of class imbalance and dataset size on model performance, offering solutions and insights into possible problems. The present study serves as a standard for assessing future developments in this field and delivers insightful information about the use of deep learning algorithms for footwear categorization in manufacturing technology. Promising outcomes and practical deployment in real-world applications, such as automated inventory management, e-commerce product categorization, and security screening systems, are possible with the created model.