Cufflinks 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

Bullet-backWhale-backChain-linkFixed cufflinksNovelty and gemstone cufflinksCufflink sets

Not covered

Shirt buttons and studsStandalone tie clips and barsBroochesFastenersOther jewellery types

What it reads

The model reads cufflinks 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.

01Face shape and motif
02Backing mechanism
03Link and post form
04Metal finish
05Stone or enamel treatment
+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/cufflinksPreview

{
  "model": "detect-cufflinks",
  "listing": "site_a/8812-4410",
  "similarity": 0.94,
  "attributes": {
    "face_shape_and_motif": 0.91,
    "backing_mechanism": 0.92,
    "link_and_post_form": 0.93,
    "metal_finish": 0.94,
    "stone_or_enamel_treatment": 0.47
  },
  "evidence": "ev_9f3a.png"
}

How teams use it

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

Capabilities

Fine-grained attribute understanding: Reads cufflinks 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 cufflinks model find counterfeits without a logo or brand name?

Yes. It reads cufflinks by design, through features such as face shape and motif, backing mechanism and link and post form. 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 cufflinks model cover?

In scope: Bullet-back, Whale-back, Chain-link, Fixed cufflinks, Novelty and gemstone cufflinks and Cufflink sets. Not covered: Shirt buttons and studs, Standalone tie clips and bars, Brooches, Fasteners and Other jewellery types.

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.

Cufflinks vision model