README.md

---
license: cc-by-nc-4.0
library_name: open_clip
pipeline_tag: image-classification
base_model: HopitAI/moda-fashion-distilled
language:
  - en
metrics:
  - f1
  - accuracy
tags:
  - fashion
  - attribute-extraction
  - product-attributes
  - catalog-enrichment
  - e-commerce
  - computer-vision
  - image-classification
  - multi-label-classification
  - siglip
  - moda-ner
  - benchmark
  - reproducibility
  - non-commercial
  - color-detection
  - fit-prediction
model-index:
  - name: MODA_NER(V) Catalog
    results:
      - task:
          type: image-classification
          name: Fashion attribute extraction
        dataset:
          type: moda-general-attribute-suite
          name: MODA General Attribute Suite (catalog track)
        metrics:
          - type: f1
            value: 0.8292
            name: Field-macro set F1
---


# MODA_NER(V) - Catalog

*Tier `*` - open code, open weights. Licence: CC BY-NC 4.0.*

**Tier `*`** — open code, open weights. **Weights: CC BY-NC 4.0.**

Ten field-specific supervised heads on our own frozen encoder (see Provenance below).

**Input contract:** one clean catalogue product image.
**Output:** category, collar, **colour**, fabric, fastening, **fit**, neckline, pattern,
pocket, sleeve length.

| | Field-macro set F1 (catalog track) |
|---|---:|
| **This released checkpoint** | **0.8292** |
| Strongest external open baseline | 0.6657 |

Selected best-internal fields: colour 0.7053, fit 0.6708, fabric 0.6693, pocket 0.9491.

**Why non-commercial.** This track is evaluated against a research-only corpus whose terms do
not permit commercial use of models trained on it. We honour those terms, and they bind us as
well: **these weights are not part of Hopit's hosted product.** For commercial deployment we
fine-tune on the customer's own catalogue, which raises accuracy on their taxonomy and
produces a model with no dependency on research-licensed data.

## Provenance

The encoder these heads run on is ours: [`HopitAI/moda-fashion-distilled`](https://huggingface.co/HopitAI/moda-fashion-distilled), MIT, already public. Nothing from another vendor is loaded at inference time. (Recorded in the programme documentation; we re-confirm it against this track's frozen artifacts before release.)

That is worth stating plainly, because the comparator on this track is a FashionSigLIP-based system and it would be easy to assume this model is that system with heads attached. It is not. FashionSigLIP appears in two other roles:

- **As the distillation teacher.** An earlier ladder of checkpoints put conditional heads on frozen Marqo-FashionSigLIP. We distilled that system into our own encoder; the teacher is used during training and is not needed to serve.
- **As the baseline we measure against.** The comparator figure quoted above is that same FashionSigLIP-based system.

Lineage, stated once rather than implied: `moda-fashion-distilled` is itself a distilled student built on ViT-B/16-SigLIP, from a teacher ensemble that included our own DeepFashion2 fine-tune. Marqo-FashionSigLIP is Apache-2.0. The DeepFashion2 corpus is research-only, so we do not describe this pipeline as provenance-clean end to end.

**Credit for this model.** CC BY-NC requires attribution. Cite the MODA General Attribute
Suite (`CITATION.cff`) when reporting numbers from this track.

## Links

- Benchmark tables and protocol: <https://hopit-ai.github.io/Moda_ner/>
- Code, scorers and prediction files: <https://github.com/hopit-ai/Moda_ner>
- All Hopit AI benchmarks: <https://hopit-ai.github.io/>

## Usage

The heads are not a `transformers` architecture, so load them through the suite
repository rather than `AutoModel`:

```bash
git clone https://github.com/hopit-ai/Moda_ner && cd Moda_ner
pip install -r requirements-inference.txt
huggingface-cli download HopitAI/moda-ner-v-catalog --local-dir ./moda-ner-v-catalog
python models/inference.py --route catalog --model-dir ./moda-ner-v-catalog --images photo.jpg
```