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README.md

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-Edit this `README.md` markdown file to author your organization card.
+# GradEye
+
+Model checkpoints and code supporting our study on **per-threshold calibration 
+under domain shift** in diabetic retinopathy (DR) grading. We use a CORN 
+ordinal regression head across a leave-one-domain-out (LODO) protocol on four 
+public fundus photograph datasets, and show that calibration — not just 
+accuracy — degrades non-uniformly across ordinal thresholds under domain shift.
+
+🔗 Paper: coming soon
+🔗 Code: [github.com/yourname/gradeeye](https://github.com/yourname/gradeeye)
+📄 License: see below (per-repo, not MIT — derived from restricted-use source data)
+
+## What we publish
+
+Pretrained CNN/ViT backbones (ConvNeXt-Tiny, DeiT-3 Small/384, MaxViT-Tiny/384) 
+with a CORN ordinal regression head, trained under a strict LODO protocol 
+across four DR fundus photograph domains, plus auxiliary-channel variants 
+(learned segmentation masks, Sobel edge maps) and the U-Net segmentation 
+models used to generate them.
+
+## Data sources (LODO folds)
+
+| Domain | Role |
+|:--|:--|
+| EyePACS | held-out fold / training source |
+| APTOS | held-out fold / training source |
+| Messidor-2 | held-out fold / training source |
+| DDR | held-out fold / training source |
+
+Each model is trained on 3 of the 4 domains and evaluated on the 4th, 
+held-out domain — the model never sees that domain during training or 
+model selection.
+
+## Key findings
+
+| # | Finding | Headline number |
+|--:|:--|:--|
+| 1 | Per-threshold calibration diverges structurally under domain shift | ECE range 0.05–0.29 across thresholds (6× spread on Messidor-2) |
+| 2 | Post-hoc temperature scaling fails under shift — and fails the *opposite* way expected | T saturates at upper bound (5.0); model is under-confident, not overconfident |
+| 3 | Class balancing hurts cross-domain accuracy in this LODO setup | unbalanced beats balanced by +3.93% mean QWK |
+| 4 | Mild NPDR (grade 1) is the dominant cross-study failure mode | F1 < 0.13 on every fold, every variant |
+| 5 | Best cross-domain backbone (of 3 tested) | ConvNeXt-Tiny, mean QWK 0.686 ± 0.156 |
+
+Full per-fold, per-variant tables are in the paper and in each repo's model card.
+
+## Repository structure
+
+One repo per training configuration (backbone × channel variant × loss). 
+See the [Collection] *(link once live)* for the full index. Each repo's 
+model card documents architecture, LODO fold, training hyperparameters, 
+and metrics for that specific checkpoint set.
+
+## License
+
+Model weights are derived from datasets with **non-commercial, 
+research/academic-use restrictions** (EyePACS and APTOS Kaggle competition 
+terms prohibit redistribution and commercial use). Accordingly, all 
+checkpoints in this org are released under **`cc-by-nc-4.0`**, research use 
+only. See each repo's card for source-dataset attribution. Raw source 
+images are not redistributed — only trained model weights.
+
+## Status
+
+This is an active research project; some tables in the paper are still 
+being finalized (confidence intervals, seed sensitivity, full ablation 
+matrix). Findings above are from completed runs and are stable; exact 
+numbers may be refined before camera-ready.
+
+## Author
+
+[Your Name] · [GitHub](https://github.com/yourname) · [contact/socials]