---
license: apache-2.0
tags:
  - fashion
  - retrieval
  - text-to-image
  - open_clip
  - siglip2
pipeline_tag: feature-extraction
library_name: open_clip
---

# 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+](https://huggingface.co/HopitAI/moda-pro-lite-plus)** — 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](https://huggingface.co/HopitAI/moda-duo), which routes per query.

## Use

```bash
pip install open_clip_torch pillow
```

```python
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](https://huggingface.co/HopitAI/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](https://github.com/hopit-ai/Moda).

## Related

- [MODA Pro Lite+](https://huggingface.co/HopitAI/moda-pro-lite-plus) — this encoder with its serving recipe.
- [MODA Duo](https://huggingface.co/HopitAI/moda-duo) — routes each query to Pro Lite+ or MODA by its shape; beats both on a mixed workload.
- [MODA](https://huggingface.co/HopitAI/moda-fashionsiglip-multiview-203m) — FashionSigLIP with a serving recipe. Stronger on long descriptions.
- [MODA-SigLIP-Distilled](https://huggingface.co/HopitAI/moda-fashion-distilled) — image-to-image retrieval.