HopitAI/moda-pro-lite

MODA Pro Lite

A 213M open-weights fashion encoder that beats MODA — the strongest open fashion retrieval system — on catalog search, at full corpus and with statistical significance, from one checkpoint with no serving machinery.

By Hopit AI. Weights are open (Apache-2.0); the training and data pipeline are proprietary.

vs MODA, full corpus	MODA Pro Lite	gain
KAGL (44K products)	0.3185 vs 0.2890	+10.2%
Polyvore (94K)	0.3997 vs 0.3726	+7.3%
ZooClaw-Fashion short	0.4994 vs 0.4775	+4.6%

The KAGL and Polyvore gains are significant under a paired bootstrap (10,000 resamples, 95% CI excludes zero); on Atlas the difference is positive but inconclusive, and on Fashion200K and In-Shop MODA is significantly better — see the full table. Every number in this card is a full-corpus measurement (no subsampled galleries) through one shared harness, alongside FashionSigLIP, SigLIP-SO400M, and ZooClaw under identical protocol. Full six-system table: benchmark page.

The MODA family

model	what it is	availability
MODA	frozen FashionSigLIP + zero-parameter multi-view serving recipe	open source + open weights
MODA Pro Lite (this model)	trained 213M encoder — the drop-in single-checkpoint option	open weights
MODA Pro	our hosted retrieval system. Rank 1 or 2 on 9 of 10 benchmark cells; +6.9% mean over MODA on the six academic sets	closed · hosted by Hopit AI

Which to pick: already running MODA's recipe → keep MODA (it still leads on caption-style benchmarks). Want one checkpoint, standard CLIP usage, strongest catalog/title search → Pro Lite. Want the strongest overall → MODA Pro (hosted).

Usage

import torch, open_clip
from PIL import Image

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()

image = preprocess(Image.open("product.jpg")).unsqueeze(0)
text = tokenizer(["red floral maxi dress", "slim-fit denim jacket"])

with torch.no_grad(), torch.autocast("cuda"):
    img_emb = model.encode_image(image)
    txt_emb = model.encode_text(text)
    img_emb /= img_emb.norm(dim=-1, keepdim=True)
    txt_emb /= txt_emb.norm(dim=-1, keepdim=True)
    scores = txt_emb @ img_emb.T

Plain bi-encoder: one 768-d vector per image, one per query, cosine similarity. Works with any vector database; no reranker, no prompt template required.

What's inside (summary)

• Base: SigLIP2-base-384 (timm/ViT-B-16-SigLIP2-384), full fine-tune of both towers.
• Size — 213,159,938 params: the base's 256K-token multilingual vocabulary is trimmed to the 44,646 pieces fashion text actually uses (BPE merge-closure preserved). Verified: 100.00% bit-identical outputs on 12,000 benchmark queries and 6,000 held-out fashion texts vs the untrimmed 375M model — every benchmark number in this card transfers exactly. Out-of-vocabulary input (emoji, non-Latin scripts, rare typos) re-segments gracefully into kept pieces (cosine 0.82–0.97 to full model), never a silent failure.
• Data: 93K commercial product images (from Marqo-GS-10M, Apache-2.0), re-annotated by a frontier VLM with graded relevance labels (99.9% coverage).
• Training: a proprietary fine-tuning recipe; the pipeline and annotated dataset are not released.
• Distinctive property: unlike typical fashion fine-tunes, Pro Lite does not collapse out of domain — it adds fashion skill while keeping generalist behavior (image-tower drift from base: 0.36%).

Evaluation protocol

Measured on six academic fashion benchmarks (DeepFashion In-Shop, DeepFashion Multimodal, Fashion200K, Polyvore, Atlas, KAGL) at full corpus — no gallery subsampling — plus H&M (10K, MRR) and ZooClaw-Fashion (12K, MAP@10), all through one open harness with identical preprocessing per model. Where Pro Lite loses (instance retrieval, caption-style sets), the card for MODA shows the stronger pick — see the full comparison rather than a cherry-picked subset.

Using this in production

MODA Pro Lite is free to use under Apache-2.0. If you want the hosted MODA Pro, help evaluating on your own catalog, or a pilot, book a call — we bring specific examples from your category and a proposed pilot shape.

Citation

@misc{modaprolite2026,
  title  = {MODA Pro Lite: a fashion encoder that does not collapse out of domain},
  author = {Hopit AI},
  year   = {2026},
  url    = {https://huggingface.co/HopitAI/moda-pro-lite}
}