Hopit AI

World models for fashion: search, retrieval, ranking, and multimodal reasoning.

We publish two tracks. Each one ships its weights, its evaluation protocol, and the prediction files behind every number we quote, so our claims can be checked rather than taken on trust.

Moda: search and retrieval

Fashion image encoders for search, recommendation, and visual similarity. The distilled encoder is the one we serve, and it is the backbone under the attribute models below.

Benchmarks: hopit-ai.github.io/Moda · Code: github.com/hopit-ai/Moda

MODA_NER: attribute extraction

Turning a fashion image into structured product data: category, colour, fit, neckline, sleeve length, pattern, material. Each model is scored on one frozen track of the MODA General Attribute Suite, and the tracks are never averaged together, because a model can be strong on clean product shots and weak on full-body photos.

Two of these are evaluated against research-only corpora whose terms reach derived data, so their weights are non-commercial. That binds us too: those weights are not part of our paid product.

MODA_NER Pro beats FashionCLIP on all 10 catalogue attributes and 17 of 18 on full-body — 31% and 19% better on average — trained on your catalogue and mapped to your taxonomy, not the benchmarks'. Talk to us.

Benchmarks: hopit-ai.github.io/Moda_ner · Code: github.com/hopit-ai/Moda_ner

How we evaluate

We freeze the test set before running a model, keep related images out of both training and test data, hash every prediction file before scoring it, score every row instead of quietly dropping the awkward ones, and require a real gain on each track rather than a good average. We publish the losing runs alongside the winning ones.

Contact: hopit.ai