Click and Cart Fashion Classification and Object Detection

Abstract

In the rapidly evolving e-commerce industry, fashion classification and object detection play pivotalrolesinenhancinguserexperienceandimproving operational efficiency. \"Click & Cart\" is an advanced systemdesignedtoaddresschallengesinfashionretail by combiningstate-of-the-artcomputervisiontechniquesfor object detection with machine learning algorithms for fashionclassification.Thesystemleveragesdeeplearning models, particularly Convolutional Neural Networks (CNNs), to classify and detect fashion items such as clothing,accessories,andfootwear fromproduct images. Additionally, it uses object detection algorithms like YOLO (You Only Look Once) and Faster R-CNN to pinpoint key components within images, enabling the extraction of detailed information such as color, fabric, andstyle.Byintegratingbothclassificationanddetection, Click & Cart offers personalized recommendations, assists in virtual try-ons, and streamlines inventory management. This approach is designed to improve customer satisfaction through more accurate searches, real-time suggestions, and a more intuitive shopping experience. Furthermore, Click & Cart presents a scalable solution that can be applied across various platforms,ensuringconsistencyinbothonlineandmobile shopping environments.

Introduction

The fashion industry’s shift to e-commerce has transformed how consumers shop, but accurately identifying and categorizing fashion products from images remains challenging. Traditional tagging and search methods often fail to deliver precise results, leading to a poor shopping experience. To solve this, Click & Cart integrates deep learning techniques—using Convolutional Neural Networks (CNNs) for classification and object detection models like YOLO and Faster R-CNN—to accurately classify fashion items by type, color, fabric, and style, and detect individual components within images.

This approach enhances search accuracy, personalization, virtual try-on features, and inventory management, revolutionizing online fashion retail by making it more intuitive and engaging.

The literature review covers key studies on clothing identification for forensic use, fashion intelligence systems combining computer vision and NLP for outfit interpretation, CNNs for fashion classification, clothing recognition for product suggestions in photos, and vision-language models (VLMs) for smart fitting rooms offering real-time similarity assessments.

Existing systems rely on large fashion datasets like Fashion MNIST, Deep Fashion, and ModaNet for training models. Object detection uses datasets such as COCO and Fashionpedia, supporting applications like virtual try-ons and personalized shopping.

The methodology involves building an intelligent e-commerce website using CNNs (e.g., VGG16, ResNet) for image-based search and NLP models (e.g., Word2Vec, BERT) for text-based search. Users can upload images or enter keywords to find visually or textually similar products. Sorting algorithms organize results by relevance, price, or popularity, and a secure login allows saving favorite items.

The project uses Keras and TensorFlow frameworks for developing and training deep learning models to improve product retrieval and recommendations. The website interface supports multiple search options, filtering, and image-based product discovery, enhancing the overall shopping experience.

Conclusion

We can conclude that, Leveraging CNNs for fashion classification and object detection enhances the online shopping experience by enabling search by image and text, improving product discovery and accuracy. Image-based search uses CNN models to find visually similar products, while text-based search employs NLP models for precise recommendations. Additional features like virtual try-ons, wishlists,andpersonalizedrecommendationsfurtherenhance userengagement.Virtualtry-onsallowcustomerstovisualize clothingbeforepurchasing,wishlistshelpsavefavoriteitems, and Neural Collaborative Filtering (NCF) ensures personalized product suggestions. For retailers, these technologies improve inventory management, sales forecasting,andcustomer insights,makingfashionretailmore data-driven, efficient, and customer-focused, ultimately driving better sales and user satisfaction.

References

[1] Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton. \"ImageNet Classification with Deep Convolutional Neural Networks.\" Advances in Neural Information Processing Systems. 2012. [2] Simonyan, Karen, and Andrew Zisserman. \"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" arXiv preprint arXiv:1409.1556 (2014) [3] He, Kaiming, et al. \"Deep Residual Learning for Image Recognition.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. [4] Szegedy, Christian, et al. \"Going Deeper with Convolutions.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [5] Van der Maaten, Laurens, and Geoffrey Hinton. \"Visualizing Data using t-SNE.\" Journal of Machine Learning Research 9.Nov (2008): 2579-2605. [6] Jégou, Hervé, et al. \"Product Quantization for Nearest Neighbor Search.\"IEEETransactionson Pattern Analysis and Machine Intelligence 33.1 (2011): 117-128. [7] Philbin, James, et al. \"Object retrieval with large vocabularies and fast spatial matching.\" Proceedings of the IEEE conference on Computer Vision and PatternRecognition2007.

Copyright

Copyright © 2025 Nandini Khandade, Utkarsha Dhanawade, Sapna Kedar, Pranjal Madhavi, Prof. Arti Devmane . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.