Enhancing Fashion Classification with Vision Transformer (ViT) and Developing Recommendation Fashion Systems Using DINOVA2

Abstract

As e-commerce platforms grow, consumers increasingly purchase clothes online; however, they often need clarification on clothing choices. Consumers and stores interact through the clothing recommendation system. A recommendation system can help customers to find clothing that they are interested in and can improve turnover. This work has two main goals: enhancing fashion classification and developing a fashion recommendation system. The main objective of fashion classification is to apply a Vision Transformer (ViT) to enhance performance. ViT is a set of transformer blocks; each transformer block consists of two layers: a multi-head self-attention layer and a multilayer perceptron (MLP) layer. The hyperparameters of ViT are configured based on the fashion images dataset. CNN models have different layers, including multi-convolutional layers, multi-max pooling layers, multi-dropout layers, multi-fully connected layers, and batch normalization layers. Furthermore, ViT is compared with different models, i.e., deep CNN models, VGG16, DenseNet-121, Mobilenet, and ResNet50, using different evaluation methods and two fashion image datasets. The ViT model performs the best on the Fashion-MNIST dataset (accuracy = 95.25, precision = 95.20, recall = 95.25, F1-score = 95.20). ViT records the highest performance compared to other models in the fashion product dataset (accuracy = 98.53, precision = 98.42, recall = 98.53, F1-score = 98.46). A recommendation fashion system is developed using Learning Robust Visual Features without Supervision (DINOv2) and a nearest neighbor search that is built in the FAISS library to obtain the top five similarity results for specific images.