Necklaces vision model: counterfeit detection

What it covers

The product types the model is trained on. Anything outside this list is handled by a neighbouring model.

In scope

ChainsPendantsChokersBeaded and pearl necklacesLocketsStatement necklaces

Not covered

BraceletsEarringsRingsBrooches sold under their own modelsNecklace cords sold as components

What it reads

The model reads necklaces through a structured taxonomy. Below are its top-level feature families. Each one breaks down into detailed parts, attributes and possible values, and some of our taxonomies go beyond 200 labels. The full taxonomy is shared during a pilot.

01Chain or cord type
02Pendant form and setting
03Clasp mechanism
04Length and layering
05Metal finish and stones
+Detailed parts, attributes and values within each family

Output format

Once live, the model returns the same record as every Klaimed model: the design similarity, a score for each feature, and the evidence image.

POST /v1/detect/necklacesPreview

{
  "model": "detect-necklaces",
  "listing": "site_a/8812-4410",
  "similarity": 0.94,
  "attributes": {
    "chain_or_cord_type": 0.97,
    "pendant_form_and_setting": 0.86,
    "clasp_mechanism": 0.87,
    "length_and_layering": 0.88,
    "metal_finish_and_stones": 0.41
  },
  "evidence": "ev_9f3a.png"
}

How teams use it

IP firmsMonitor necklaces copies for your clients, under your brand Design search firmsSearch necklaces designs across brand websites and registries Brand-protection platformsAdd necklaces recognition to your pipeline by API

Capabilities

Fine-grained attribute understanding: Reads necklaces 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.
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.
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.
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.

Questions

Can the necklaces model find counterfeits without a logo or brand name?

Yes. It reads necklaces by design, through features such as chain or cord type, pendant form and setting and clasp mechanism. A listing with no logo, no brand name and a different photo still matches its reference when the design is the same.

Which product types does the necklaces model cover?

In scope: Chains, Pendants, Chokers, Beaded and pearl necklaces, Lockets and Statement necklaces. Not covered: Bracelets, Earrings, Rings, Brooches sold under their own models and Necklace cords sold as components.

When will it be available?

It is in development. Early-access partners help shape the model on their own products. Tell us the product type and your volume and we'll share the timeline.

What images does it accept?

Studio packshots, seller phone photos and in-use images. Results stay consistent across angle, lighting, background and partial occlusion.

Is the attribute taxonomy public?

No. The taxonomy and training data are proprietary. We walk you through it during a pilot, and can add features and retrain the model on request.

How do I access it?

Request early access. Once it is live, it runs through the same Klaimed API and interface as every other model.

Necklaces vision model