Sneaker Visual Retrieval System
AlwaysLegit needed a way for their team at expos to instantly identify shoes from photos and match them against a massive 150K+ SKU database, often in challenging real-world lighting and backgrounds.
The Challenge
The primary challenge was the scale and speed required. Matching a single photo against 150,000 products in under a second is computationally intensive. Furthermore, photos taken at busy expos often have cluttered backgrounds, varying lighting, and multiple angles, making traditional image matching unreliable. The system needed to be robust enough to handle these real-world variations while maintaining extreme accuracy to ensure the correct SKU was identified.
Our Solution
We used a multi-stage approach as follows:
Object Detection
The system first performs real-time object detection to perfectly isolate the sneaker from the background, even in busy expo environments.
Feature Extraction
Deep learning models extract high-dimensional embeddings representing structural features and specific color distributions.
Vector Search
A high-performance vector database enables similarity matching against 150K+ SKUs in under one second.
The Impact
The activation transformed how AlwaysLegit interacted with enthusiasts at events. Attendees could take a photo of any sneaker, and the system would instantly provide the exact product details, pricing, and availability. This not only provided a 'wow' factor but also significantly improved the efficiency of their on-site team, leading to higher engagement and a larger volume of SKU lookups than previously possible with manual search.
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