| --- |
| license: mit |
| library_name: open_clip |
| pipeline_tag: image-classification |
| base_model: HopitAI/moda-fashion-distilled |
| language: |
| - en |
| metrics: |
| - f1 |
| - accuracy |
| datasets: |
| - detection-datasets/fashionpedia |
| tags: |
| - fashion |
| - attribute-extraction |
| - product-attributes |
| - catalog-enrichment |
| - e-commerce |
| - computer-vision |
| - image-classification |
| - multi-label-classification |
| - siglip |
| - moda-ner |
| - benchmark |
| - reproducibility |
| - fashionpedia |
| - mit |
| model-index: |
| - name: MODA_NER(V) Crop |
| results: |
| - task: |
| type: image-classification |
| name: Fashion attribute extraction |
| dataset: |
| type: moda-general-attribute-suite |
| name: MODA General Attribute Suite (crop track) |
| metrics: |
| - type: f1 |
| value: 0.63 |
| name: Attribute micro-F1 |
| - type: f1 |
| value: 0.6074 |
| name: Field-macro F1 |
| - type: accuracy |
| value: 0.8825 |
| name: Category accuracy |
| - type: accuracy |
| value: 0.9215 |
| name: Master-category accuracy |
| --- |
| |
|
|
| # MODA_NER(V) - Crop |
| |
| > **Fix, 2026-09-19. If you ran this model before this date, re-run it.** The repository's |
| > `models/inference.py` ran the crop 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 16 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.* |
| |
| **Tier `*`** — open code, open weights. **Weights: MIT.** |
| |
| Conditional attribute heads on our own frozen encoder (see Provenance below). |
| |
| **Input contract:** one localized garment crop. |
| **Output:** 15 sparse fields — master category, category, sub-category, silhouette, hemline, |
| sleeve length and shape, neckline, collar presence and style, waist, material, surface |
| treatment, pattern, closure. No colour, no fit; the evaluation corpus for this track carries |
| no equivalent labels. |
| |
| | | Attribute micro-F1 (crop track) | |
| |---|---:| |
| | **This released checkpoint** | **0.6300** | |
| | Same architecture on a frozen FashionSigLIP encoder | 0.6245 | |
| | Zero-shot open VLM reference (Qwen3-VL-8B) | 0.1805 | |
| | Zero-shot FashionSigLIP text prototypes | 0.1817 | |
| |
| These are the weights that produced our published number: nothing is held back on this route. |
| Field-macro F1 is 0.6074, category accuracy 0.8825, master-category accuracy 0.9215. |
| |
| Weak fields, stated because the micro average hides them: material 0.4148, plus surface |
| treatment, collar style, neckline and waist type. A headline of 0.6300 does not mean every |
| field is usable. |
| |
| **Attribution.** The annotations behind this track are licensed CC BY 4.0 and require credit: |
| Jia et al., *Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset*, |
| ECCV 2020. No images from that corpus are redistributed in the weights or the repository. |
| |
| ## 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. |
| |
| 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.** If you use these weights or report numbers from this track, cite |
| the MODA General Attribute Suite (see `CITATION.cff`). Scorers stamp the suite name and |
| version into every result file for exactly this reason. |
| |
| ## 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-crop --local-dir ./moda-ner-v-crop |
| python models/inference.py --route crop --model-dir ./moda-ner-v-crop --images photo.jpg |
| ``` |
| |
