AUTOMATED FASHION IMAGE TAGGING SYSTEM USING DEEP LEARNING

Abstract

The rapid growth of e-commerce and digital fashion platforms has led to an overwhelming volume of fashion images, creating a strong need for efficient organization, retrieval, and recommendation systems. This project, “Automated Fashion Image Tagging System Using Deep Learning,” aims to develop an intelligent solution that automatically generates descriptive tags for fashion images using advanced deep learning techniques. The system leverages Convolutional Neural Networks (CNNs) and transfer learning models such as ResNet and VGGNet to extract visual features like color, texture, pattern, and clothing type from input images. The proposed system processes fashion images through a trained deep learning model that identifies multiple attributes, including garment category (e.g., shirt, dress), style (casual, formal), color, and patterns (striped, floral). A multi-label classification approach is adopted to assign multiple relevant tags to a single image, improving the accuracy and usefulness of metadata. The dataset used for training consists of labeled fashion images, preprocessed through resizing, normalization, and augmentation techniques to enhance model performance and generalization. The system architecture integrates image preprocessing, feature extraction, model training, and tag generation modules, enabling real-time or batch-based tagging. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring reliable and consistent results. The automated tagging system significantly reduces manual effort, improves searchability, and enhances recommendation systems in online retail platforms. Overall, this deep learning-based approach provides a scalable, efficient, and intelligent solution for fashion image management, contributing to advancements in computer vision applications within the fashion industry.