Fashion style analysis and recommendation using Machine Learning & Deep Learning

Abstract

Fashion has always been an essential feature of our daily routine. It plays an important role in everyone's life. Today, the impact of deep learning on computer vision applications is increasing every day. Deep learning techniques are applied in many areas like clothing search, automatic product recommendation. The online fashion market continues to grow, and an algorithm capable of identifying clothing can help companies in the apparel industry understand the profile of potential buyers and focus sales on specific niches. As well as tailoring campaigns based on customer tastes and improving user experience. Artificial intelligence capable of understanding, recommending and labeling human clothing is essential, and can be used to improve sales or better understand users. In this paper, a new deep learning model based on Convolutional Neural Network (CNN) is proposed to solve the classification problem. These networks can extract features from images using convolutional layers, unlike traditional machine learning algorithms. In this paper, we used our own generated dataset, where the total number of data was 1000. The dataset contains total 10 categories such as shirt, punjabi, t-shirt, blazer, sweater, saree, salwar kameez, gown, western tops and party wear. All the data we have collected from online like social media, google, facebook, instagram, linkedin. The main goal of this project is that research findings can contribute to developing intelligent fashion style analysis and recommendation systems, improving users' fashion preferences and providing a personalized fashion experience. The topic combines the fields of fashion, style and machine learning to create a system that can analyze fashion images, classifying them into different styles. In this paper I have used the Customize CNN Algorithm, through which we have used the 7 architectures of CNN. The 7 custom CNN methods we used are MobileNetV2, MobileNetV3, EfficientNet B0, EfficientNet B3, Inception V3, DenseNet201 and VGG19. Here we can see that the accuracy of MobileNetV2 is 59%, the accuracy of MobileNetV3 is 75%, the accuracy of EfficientNet B0 is 80%, the accuracy of EfficientNet B3 is 86%, the accuracy of Inception V3 is 60%, the accuracy of DenseNet201 is 65% and the accuracy of VGG19 is 85%.