Existing fashion recommendation systems encounter difficulties in using visual data for accurate and personalized recommendations. This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations. When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements. These captions guide retrieval from a global fashion product catalog to offer similar alternatives that fit the visual characteristics of the original image. On a dataset of over 100,000 categorized fashion photos, the pipeline was trained and evaluated. The F1-score for the object detection model was 0.97, exhibiting exact fashion object recognition capabilities optimized for recommendation. This visually-aware system represents a key advancement in customer engagement through personalized fashion recommendations.

Fashion is an ever-evolving industry that requires constant adaptation and innovation to stay relevant. One of the latest technological advancements in the industry is the use of deep learning algorithms for fashion recommendation systems. In this blog, we will explore how to use the ResNet50 model for building a fashion recommendation system. For this point of time, we create one streamlit webpage on localsystem to see the 10 recommended fashion product images which looks similar to query image. The ResNet50 is a deep convolutional neural network that was introduced by Microsoft Research in 2015.