Uniforms 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

School uniformsWork and corporate uniformsMedical scrubsHospitality and service uniformsTeam kits sold as uniforms

Not covered

Individual shirtsTrousersJackets sold as separatesCostumesActivewearProtective safety gearStandalone insignia

What it reads

The model reads uniforms 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.

01Garment type and cut
02Collar and closure
03Pocket and panel layout
04Trim and piping
05Fabric type and insignia placement
+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/uniformsPreview

{
  "model": "detect-uniforms",
  "listing": "site_a/8812-4410",
  "similarity": 0.94,
  "attributes": {
    "garment_type_and_cut": 0.91,
    "collar_and_closure": 0.92,
    "pocket_and_panel_layout": 0.93,
    "trim_and_piping": 0.94,
    "fabric_type_and_insignia_pla": 0.47
  },
  "evidence": "ev_9f3a.png"
}

How teams use it

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

Capabilities

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

Yes. It reads uniforms by design, through features such as garment type and cut, collar and closure and pocket and panel layout. 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 uniforms model cover?

In scope: School uniforms, Work and corporate uniforms, Medical scrubs, Hospitality and service uniforms and Team kits sold as uniforms. Not covered: Individual shirts, Trousers, Jackets sold as separates, Costumes, Activewear, Protective safety gear and Standalone insignia.

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.

Uniforms vision model