Fashion Meets Machine Learning: An Optimized Approach to Clothes Categorization

Today, deep learning (DL) has become a prominent trend within machine learning (ML), offering more advanced pattern recognition and image classification capabilities than traditional ML approaches. Among various DL methods, Convolutional Neural Networks (CNNs) are particularly significant. In this study, we developed an automated classifier capable of categorising a large dataset of fashion clothing items using both DL and ML techniques. Initially, we performed classification tasks employing multiple ML algorithms. Subsequently, we introduced a novel CNN model comprising multiple convolutional layers, a max-pooling layer, and a fully connected layer. Finally, we conducted a comparative analysis of different algorithms. Our programming tools of choice included Python, TensorFlow, and Keras, which are widely used in this field.