Beer 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

LagersAlesStoutsIPAsCraft and mass-market beersCannedBottledMultipack beer

Not covered

Wine and spiritsNon-alcoholic and 0.0% beersCiders sold separatelySoft-drinksBeer glassware or brewing kits

What it reads

The model reads beer 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.

01Bottle geometry and shoulder profile
02Closure and capsule construction
03Neck and collar detail
04Label panel architecture
05Glass colour and finish
+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/beerPreview

{
  "model": "detect-beer",
  "listing": "site_a/8812-4410",
  "similarity": 0.94,
  "attributes": {
    "bottle_geometry_and_shoulder": 0.86,
    "closure_and_capsule_construc": 0.87,
    "neck_and_collar_detail": 0.88,
    "label_panel_architecture": 0.89,
    "glass_colour_and_finish": 0.42
  },
  "evidence": "ev_9f3a.png"
}

How teams use it

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

Capabilities

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

Yes. It reads beer by design, through features such as bottle geometry and shoulder profile, closure and capsule construction and neck and collar detail. 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 beer model cover?

In scope: Lagers, Ales, Stouts, IPAs, Craft and mass-market beers, Canned, Bottled and Multipack beer. Not covered: Wine and spirits, Non-alcoholic and 0.0% beers, Ciders sold separately, Soft-drinks and Beer glassware or brewing kits.

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.

Beer vision model