  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 catalogue 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
