Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,61 @@
 ---
 license: mit
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+  - medical-imaging
+  - radiology
+  - rheumatoid-arthritis
+  - dinov2
+  - ordinal-regression
 ---
+
+# OrdinalSvdH — released checkpoints
+
+Weights for the paper *Ordinal DINOv2 Transfer for Joint-Level SvdH Scoring: What Helps
+and What Does Not* (Cannito, Renzulli, Grangetto — RHEUMAI Workshop, MICCAI 2026).
+Code: [github.com/EIDOSLAB/OrdinalSvdH](https://github.com/EIDOSLAB/OrdinalSvdH).
+
+Backbone: DINOv2 ViT-S/14 (`vit_small_patch14_dinov2.lvd142m`), last four transformer
+blocks fine-tuned, cumulative ordinal BCE head, trained on 100% of the RAM-H1200
+training labels. Each run selects its checkpoint on best validation QWK.
+
+| Folder | Task | Seed | Test QWK | Test accuracy |
+|---|---|---|---|---|
+| `be_ordinal_bce_pct100_seed42`   | bone erosion (BE)          | 42  | 0.626 | 0.739 |
+| `jsn_ordinal_bce_pct100_seed123` | joint-space narrowing (JSN) | 123 | 0.640 | 0.755 |
+
+**These are single runs, not the numbers reported in the paper.** The paper reports the
+mean ± standard deviation over seeds 42, 123 and 456: **BE 0.591 ± 0.028** and
+**JSN 0.634 ± 0.006**. Released here is the best-performing seed per task, so its test QWK
+sits above that mean. Retrain all three seeds with the code in the repository to reproduce
+the reported means.
+
+Each folder contains `best_model.pt` (~85 MB) and `metrics.json` (full configuration and
+test metrics of that run). Checkpoint keys: `model_state_dict`, `config`, `score_to_idx`,
+`best_epoch`, `best_val_qwk`.
+
+## Usage
+
+```bash
+git clone https://github.com/EIDOSLAB/OrdinalSvdH.git && cd OrdinalSvdH
+pip install -r requirements.txt
+export RAM_H1200_ROOT=/path/to/RAM-H1200-v1
+
+python scripts/download_checkpoints.py
+python scripts/evaluate.py --checkpoint checkpoints/be_ordinal_bce_pct100_seed42
+```
+
+The RAM-H1200 scoring crops are required and are distributed separately by the dataset
+authors: [TokyoTechMagicYang/RAM-H1200-v1](https://huggingface.co/datasets/TokyoTechMagicYang/RAM-H1200-v1).
+
+## Intended use and limitations
+
+Research use only. The models grade predefined joint crops from posteroanterior hand
+radiographs; they do not localise joints and are not a diagnostic device. They were
+trained on a single benchmark (1,200 radiographs, six centres in Sapporo, Japan) and have
+not been validated on external or longitudinal cohorts. Both tasks are strongly imbalanced
+towards grade 0, and high grades remain the hardest cases. For BE the model predicts the
+clinical grades {0, 1, 2, 3, 5}; grade 4 does not occur in the benchmark.
+
+Code: MIT. Data: RAM-H1200, under its own terms.