Klaimed's jackets vision model understands a jacket like a category expert — cut and length, collar and closure, pockets and fabric — to find the exact product from a reference and its closest matches. The vision foundation for counterfeit detection.
The model reads a jacket the way a specialist in the category would — body cut and length, collar or hood, front closure, sleeve and cuff construction, pocket layout, quilting or panelling, fabric and lining, hardware and trims — resolving the shape, geometry, material, colour and type of each, and how they combine into one design.
Reads jackets 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. The output is a structured record of what is actually visible, not a single label.
Given a reference product, returns the closest matches from a monitored set, ranked by design similarity rather than by colour, scene or filename. Two jackets that share construction sit together even in different colourways; two that merely share a palette do not.
Every match comes with its reasoning: which features agree between the reference and the candidate, which diverge, and how confident each comparison is. A reviewer sees why a pair was matched instead of an opaque similarity score.
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.
Every model is trained against a structured attribute taxonomy: the named visual features that separate one design in this category from another, and the values each of those features can take. The taxonomy is proprietary and the training dataset as well.
We will talk you through what the model is trained to look at for your category, and if a feature matters to your workflow that we have not covered yet, we add it to the taxonomy and retrain. The taxonomy grows with the categories our customers actually work in.
Tell us the category and the volume. We will show you the model running against your own listings before anything is signed.