README.md

---
license: apache-2.0
tags:
  - fashion
  - retrieval
  - text-to-image
  - open_clip
  - siglip2
pipeline_tag: feature-extraction
library_name: open_clip
base_model: HopitAI/moda-pro-lite
---

# MODA Pro Lite+

**The strongest open system at ≤250M parameters on catalogue and title search.**

MODA Pro Lite+ is [MODA Pro Lite](https://huggingface.co/HopitAI/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.

## 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.

## Serving cost

```
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.

## Use

```bash
pip install open_clip_torch pillow numpy hnswlib
python serving_ann.py --demo
```

```python
from serving_ann import load, encode_images, encode_queries, build_index, search

enc   = load()                          # open_clip, this repo's weights
docs  = encode_images(catalogue, enc)   # (n, 768) float32, one vector per item
index = build_index(docs)               # hnswlib, cosine via inner product
qry   = encode_queries(["black leather ankle boots"], enc)
ids, scores = search(index, qry, k=10)
```

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

```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.

## The recipe

```
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.

## 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) — the bare encoder these weights come from.
- [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.