Home decor / Furniture

Sofas vision model

Klaimed's sofas vision model understands a sofa like a category expert — silhouette and seat count, arm and back shape, cushion, tufting and upholstery — to find the exact product from a reference and its closest matches. The vision foundation for brand protection.

Availabledetect-sofasv1.5.2
POST/v1/detect/sofas

What this model covers

In scope
  • Two- and three-seat sofas
  • Sectional and corner sofas
  • Loveseats
  • Sofa beds
  • Modular settees
Out of scope
  • Single-seat chairs
  • Benches
  • Ottomans and footstools
  • Upholstery-fabrics
  • Outdoor patio sets

What the model recognises

The model reads a sofa the way a specialist in the category would — overall silhouette and seat count, arm and back shape, leg and base type, cushion construction, seam and tufting layout, upholstery material and colour — resolving the shape, geometry, material, colour and type of each, and how they combine into one design.

Capabilities

Fine-grained attribute understanding

Reads sofas the way a category expert would: overall shape and proportion, the geometry of each component, material and surface character, and the specific construction features that define one design against another. The output is a structured record of what is actually visible, not a single label.

Design-level similarity retrieval

Given a reference product, returns the closest matches from a monitored set, ranked by design similarity rather than by colour, scene or filename. Two sofas that share construction sit together even in different colourways; two that merely share a palette do not.

Feature-by-feature match explanation

Every match comes with its reasoning: which features agree between the reference and the candidate, which diverge, and how confident each comparison is. A reviewer sees why a pair was matched instead of an opaque similarity score.

Stable across how the product was photographed

The same design retrieves consistently whether it was shot as a studio packshot, a seller's phone photo or an in-use image, and holds up under changes of angle, lighting, background and partial occlusion.

Attribute taxonomy

Every model is trained against a structured attribute taxonomy: the named visual features that separate one design in this category from another, and the values each of those features can take. The taxonomy is proprietary and the training dataset as well.

We will talk you through what the model is trained to look at for your category, and if a feature matters to your workflow that we have not covered yet, we add it to the taxonomy and retrain. The taxonomy grows with the categories our customers actually work in.

Proprietary
Not published. Walked through with you directly.

Put this model to work

Tell us the category and the volume. We will show you the model running against your own listings before anything is signed.