Furniture Product Image Dataset
Curated Furniture & Home Furnishings Image Library
A large-scale, professionally curated collection of furniture and home-furnishings product images extracted from major retail and brand sources (Ashley Furniture, RH, Pottery Barn, Maisons du Monde, and others). This dataset is optimized for AI training, visual search, e-commerce analytics, and catalog workflows.
Updates automatically as new sources are ingested
What This Dataset Includes
The Furniture Image Dataset contains a wide range of e-commerce-grade product photography, including:
🖼️ Multiple View Types
- Hero images on neutral or white backgrounds
- Alternate product angles (front, side, back, zoomed views)
- Detail and material close-ups
- Lifestyle and room-scene images
- Variations by color, fabric, finish, and configuration
All images are stored with provenance metadata (source site, folder path, product association) to support traceability, filtering, and controlled exports.
Why This Dataset Is Valuable
This collection is designed for teams working with visual data at scale:
🤖 AI & ML Training
Train and fine-tune models for furniture recognition, visual search, similarity matching, and recommendation systems.
📊 Model Evaluation
Compare performance across different retail domains (Ashley vs RH vs Pottery Barn).
🛒 E-commerce Intelligence
Improve product categorization, duplicate detection, and merchandising workflows.
🎨 Design Research
Analyze composition, lighting, staging, and presentation styles across brands.
Organization & Metadata Structure
We follow a folder-first labeling approach to ensure deterministic, auditable tags:
📁 Primary Labels
Derived directly from source folder structures (e.g., sofas/hero, chairs/detail, tables/lifestyle)
🏷️ Product Metadata
When available, includes:
- source_site — Originating retailer
- product title — Product name
- product identifier — SKU or ID
- original folder path — Full provenance
Optional AI-generated tags (object, color, style) are stored separately and never override canonical labels. This structure enables fast filtering, dataset slicing, and repeatable exports by category, brand, view type, or source.
Popular Categories
(Tag availability updates as the dataset grows.)
Common Use Cases
Teams typically use this dataset for:
- Visual search & similarity indexing — Generate embeddings and build image-based product discovery systems.
- Category & attribute classification — Train classifiers using folder-derived labels (sofas, chairs, tables).
- Object detection & segmentation — Curate subsets for bounding boxes and pixel-level masks.
- Duplicate & near-duplicate detection — Use perceptual hashing and metadata to identify redundant assets across retailers.
Getting Started
Recommended workflow:
- Review the live image count displayed above (15,647 images).
- Browse by source or category to identify relevant suppliers.
- Export a small evaluation sample (100–500 images).
- Validate quality and decide on annotation requirements.
- Create training / validation splits (by product or site where possible).
Data Governance & Licensing
Retail product imagery is typically owned by the originating brand or retailer. This collection preserves full provenance metadata to support internal audits and licensing reviews.
Important: For external redistribution or public release, confirm usage rights with the original content owners.
Notes & Caveats
- Some images may contain watermarks or composited elements — review before large-scale deployment.
- Folder-derived labels are the authoritative taxonomy on this page.
- AI-generated tags are supplemental and stored separately.
- Annotation (bounding boxes, masks, attributes) requires an additional labeling pass.
Need This Dataset Packaged or Licensed?
Contact us to request custom subsets, enterprise licensing, ongoing data updates, or annotation services.
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