File size: 5,239 Bytes
5425c34 13d78f0 5425c34 13d78f0 5425c34 13d78f0 5425c34 13d78f0 5425c34 13d78f0 5425c34 13d78f0 5425c34 13d78f0 5425c34 5490cce 5425c34 13d78f0 5425c34 13d78f0 5425c34 13d78f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | ---
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
- applicability
model-index:
- name: MODA_NER(V) Full-body
results:
- task:
type: image-classification
name: Fashion attribute extraction
dataset:
type: moda-general-attribute-suite
name: MODA General Attribute Suite (fullbody track)
metrics:
- type: f1
value: 0.6917
name: Tier-1 macro-F1
- type: f1
value: 0.6637
name: Tier-2 N/A-F1
- type: f1
value: 0.5785
name: Tier-3 visible macro-F1
---
# MODA_NER(V) - Full-body
> **Fix, 2026-09-19. If you ran this model before this date, re-run it.** The repository's
> `models/inference.py` ran the full-body route with the wrong image preprocessing: OpenAI-CLIP
> normalisation and a centre crop, instead of the mean/std 0.5 squash this checkpoint was trained
> with. Its outputs did not match the published benchmark predictions, matching on none of 64
> held-out images. The weights in this repository are unchanged and correct. Pull the latest
> [Moda_ner](https://github.com/hopit-ai/Moda_ner) code and the Usage command below produces the
> published predictions. The benchmark numbers on this card were produced correctly.
*Tier `*` - open code, open weights. Licence: CC BY-NC 4.0.*
**Tier `*`** — open code, open weights. **Weights: CC BY-NC 4.0.**
Eighteen class-balanced heads with an explicit N/A class, on our own frozen encoder (see Provenance below).
**Input contract:** one full-body fashion photograph.
**Output:** upper, lower and outer fabric; upper, lower and outer pattern; neckline; sleeve
length; lower-garment length; cardigan; navel coverage; hat; glasses; neckwear; waist
accessories; wrist accessories; ring; socks or leggings. Every field can return N/A, and N/A
is scored as a real class rather than treated as a negative.
| Measure (fullbody track) | Published | Strongest external open baseline | Paired 95% CI |
|---|---:|---:|---:|
| Tier-1 macro-F1 | 0.6917 | 0.5943 | [+0.0891, +0.1053] |
| Tier-2 N/A-F1 | 0.6637 | 0.6088 | [+0.0433, +0.0657] |
| Tier-3 visible macro-F1 | 0.5785 | 0.4969 | [+0.0723, +0.0905] |
**These are the weights that produced the published numbers above** (run `...linear-probe-v7-20260810`, recorded in `training_commitment.json`). Evaluated on a fresh product-group-disjoint
split: 5,000 images, 1,751 groups, zero overlap with any prior experiment. Per-field range is
wide (0.3287 to 0.9187) — read the per-field table before relying on a single field.
**Why non-commercial.** Same as the catalog route: the evaluation corpus is research-only and
its terms extend to derived data. These weights are not in Hopit's hosted product. Commercial
deployments are fine-tuned on customer data instead.
## 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`).
## 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-fullbody --local-dir ./moda-ner-v-fullbody
python models/inference.py --route fullbody --model-dir ./moda-ner-v-fullbody --images photo.jpg
```
|