MODA Pro Lite+ is MODA Pro Lite — a 213M fashion retrieval encoder — served with a calibrated multi-view recipe. This repository holds the recipe, as runnable code; the weights are pulled from moda-pro-lite at load time.

Zero added parameters. One stored vector per item. The uplift is paid once at index time and costs nothing at query time.

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.

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.

stored vectors per item : 1
ANN queries per search  : 1
image forwards at index : 3x   offline, paid once
text forwards per query : 2x   negligible beside the ANN probe

The recipe is a rule for what you encode, not a model change. Views are combined into one unit vector before indexing, so nearest-neighbour search costs exactly what the bare encoder costs — same index, same probe, no extra routes and no re-ranking.

Bare encoder, if you would rather not use the recipe:

768-d embeddings, cosine similarity, one vector per item. Index them in any vector database.

document = normalize(official + 0.25 * square_pad + 0.25 * foreground_pad)
query    = normalize(raw      + 0.25 * "a photo of {query}")

serving_ann.py implements it. Zero added parameters, one stored vector.

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.