E-Commerce Visual Search
Increasing product discovery by 34% with a highly contextual, attribute-rich visual search dataset.
The Challenge
A major e-commerce platform wanted to revolutionize their shopping experience by allowing users to search for products using their smartphone cameras. However, user-uploaded photos are notoriously blurry, poorly lit, and cluttered, making it difficult for standard models to accurately retrieve the correct product.
Our Solution
Dserve AI created a highly specialized dataset of 300,000 product images. We didn't just use clean studio shots; we actively sourced lifestyle images showing products in natural, cluttered contexts. Every item across 80+ categories was annotated with fine-grained attributes: color, texture, material, shape, and style tags.
The Impact
"The platform launched their visual search feature with an astonishing 99% retrieval accuracy. By accurately bridging the gap between messy user photos and clean catalog items, they increased user product discovery and engagement by 34% in the very first quarter."
Taxonomy & Ontology
Bridging the Reality Gap
01. Catalog Ingestion
Processing clean, high-resolution studio shots of the product inventory.
02. Wild Sourcing
Collecting user-generated, smartphone-quality lifestyle images of identical products.
03. Vector Alignment
Mapping the 'wild' images to the clean catalog representations using contrastive learning tags.
04. Attribute Extraction
Isolating specific features (e.g., 'v-neck', 'floral') to improve granular search.