Earrings 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

StudsHoopsDrop and dangle earringsHuggiesEar cuffsClip-on earrings

Not covered

NecklacesRingsBraceletsBroochesEarbuds or hearing-aids worn in the ear

What it reads

The model reads earrings 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.

01Form and fitting type
02Drop and motif structure
03Setting and stone arrangement
04Metal finish
05Size and proportion
+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/earringsPreview

{
  "model": "detect-earrings",
  "listing": "site_a/8812-4410",
  "similarity": 0.94,
  "attributes": {
    "form_and_fitting_type": 0.97,
    "drop_and_motif_structure": 0.86,
    "setting_and_stone_arrangemen": 0.87,
    "metal_finish": 0.88,
    "size_and_proportion": 0.41
  },
  "evidence": "ev_9f3a.png"
}

How teams use it

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

Capabilities

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

Yes. It reads earrings by design, through features such as form and fitting type, drop and motif structure and setting and stone arrangement. 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 earrings model cover?

In scope: Studs, Hoops, Drop and dangle earrings, Huggies, Ear cuffs and Clip-on earrings. Not covered: Necklaces, Rings, Bracelets, Brooches and Earbuds or hearing-aids worn in the ear.

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.

Earrings vision model