I built an AI-powered apparel identification system designed to identify products even when they don't have a barcode. The core idea is to take an image of an apparel product, detect the relevant visual information, and retrieve the most visually similar products from a database of 10,000+ product images.

I used YOLOv8 for object detection, CLIP for generating visual embeddings, and FAISS for efficient similarity search. I also evaluated the system using precision/recall and Recall@K, and analyzed incorrect matches and edge cases to improve the reliability of the results.

The UI provides a practical way to interact with the system and see the identification/retrieval results rather than just exposing the underlying model. I built the project to explore how computer vision and similarity search can solve a real-world product identification problem where traditional barcode-based identification isn't available.