Used by companies like Syte.ai, Visenze, Snap, Pinterest Lens, and Google Lens, as well as in-house teams at retailers building "shop the look" and "find similar" features as part of an ecommerce visual search dataset strategy.
Problem statement
Visual search models require image similarity training data that spans the same product category across many different visual styles — different colors, materials, backgrounds, and shooting angles.
Training a visual search engine dataset on a single retailer's images produces a model that works well for that retailer and poorly everywhere else.
How ImageHub solves it
- Same product category (e.g. sofas) represented across 10-20 different retailer photography styles.
- CLIP-derived semantic tags for fine-grained attribute labels (material, color, style, pattern).
- Primary image flag distinguishes the main product view — the most important for similarity indexing.
- Category hierarchy for hard-negative mining (sofa vs chair vs ottoman — close but distinct).
What to request
- Request cross-retailer bundles for the same category — this is what makes it a genuine product matching image dataset rather than a single-brand set.
- Use CLIP tags for attribute filtering when building attribute-aware similarity models.
- Request primary images only for catalog-matching use cases; primary + carousel for multi-angle embedding, ideal for a shop by image training dataset.
Key fields / metadata delivered
- clip_tags: semantic attributes (color, material, style, pattern)
- category_path: hierarchical category for hard-negative mining
- primary_image_flag: TRUE = main product image for similarity indexing
- site_name: retailer source for domain diversity analysis
Related datasets
Retail AI Dataset, Ecommerce Image Dataset, Furniture Dataset