HopitAI/moda-pro-lite

MODA Pro Lite

A 213M fashion retrieval encoder (SigLIP2-base-384 backbone, fashion-vocabulary build). Open weights.

Served with its calibrated recipe it becomes MODA Pro Lite+ — the strongest open system at ≤250M parameters on catalogue and title search. The recipe lives in that repository; the weights live here, and Pro Lite+ pulls them at load time.

Results

MAP@10, full corpus, all ground-truth queries, one evaluator (pytrec_eval map_cut.10). MODA is FashionSigLIP with its own serving recipe, shown for reference.

benchmark	MODA	Pro Lite (bare)	Pro Lite+ (with recipe)
KAGL	0.2887	0.3055	0.3201
Polyvore	0.3726	0.3952	0.4049
Atlas	0.1862	0.1814	0.1904
Fashion200K	0.1946	0.1758	0.1846
DeepFashion In-Shop	0.1642	0.0930	0.1026
DeepFashion Multimodal	0.0147	0.0118	0.0133

Pro Lite+ leads the ≤250M class on KAGL, Polyvore and Atlas — +10.9% over MODA on KAGL, +8.7% on Polyvore, both significant under a paired bootstrap (10,000 resamples).

The recipe is worth +2.5% to +12.8% over the bare encoder on every benchmark, and costs nothing at query time: the views are fused into a single vector before indexing.

Where this model is weak, stated plainly. Pro Lite is tuned for short catalogue titles. On long natural-language descriptions it trails FashionSigLIP substantially — DeepFashion In-Shop queries average 75 words, and Pro Lite+ scores 0.1026 there against MODA's 0.1642. If your queries are descriptions rather than titles, use MODA Duo, which routes per query.

Use

pip install open_clip_torch pillow

import open_clip, torch

model, _, preprocess = open_clip.create_model_and_transforms("hf-hub:HopitAI/moda-pro-lite")
tokenizer = open_clip.get_tokenizer("hf-hub:HopitAI/moda-pro-lite")
model.eval()

with torch.no_grad():
    image = torch.nn.functional.normalize(model.encode_image(preprocess(img).unsqueeze(0)), dim=-1)
    text  = torch.nn.functional.normalize(model.encode_text(tokenizer(["black leather ankle boots"])), dim=-1)
    score = (text @ image.T).item()

768-d embeddings, cosine similarity, one vector per item. Index them in any vector database. For the recipe that lifts these numbers to the Pro Lite+ column, use moda-pro-lite-plus.

Evaluation

All figures are full corpus, all ground-truth queries, MAP@10 under one evaluator (pytrec_eval map_cut.10), float32. Per-query results and confidence intervals are in the repository.

Related

• MODA Pro Lite+ — this encoder with its serving recipe.
• MODA Duo — routes each query to Pro Lite+ or MODA by its shape; beats both on a mixed workload.
• MODA — FashionSigLIP with a serving recipe. Stronger on long descriptions.
• MODA-SigLIP-Distilled — image-to-image retrieval.