Enhancing Fashion Choices: AI-Powered Style Analysis and Recommendations

Sheikh Sadi Bandan., MD. Samiul Islam Sabbir., Md Sharuf Hossain. and Khadiza Tul Kobra
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Sheikh Sadi Bandan.: Dept. of Computer Science & Engineering Daffodil International University Dhaka, Bangladesh
MD. Samiul Islam Sabbir.: Dept. of Computer Science & Engineering Daffodil International University Dhaka, Bangladesh
Md Sharuf Hossain.: Dept. of Data Science Loyola University Chicago, USA
Khadiza Tul Kobra: Dept. of Information Technology and Management Illinois Institute of Technology Chicago, USA

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 8, 478-490

Abstract: Fashion has always been an essential feature of our daily routine. It plays an important role in everyone’s life. 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. 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, 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 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%.

Date: 2024
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