| --- |
| title: gradeeye |
| emoji: 📚 |
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| https://cdn-uploads.huggingface.co/production/uploads/68cef9e7195e8b94d3557b81/CAfWeW32yUYWygr9dMb4Y.png |
| --- |
| |
| # 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 / testing source | |
| | APTOS | held-out fold / testing source | |
| | Messidor-2 | held-out fold / testing source | |
| | DDR | held-out fold / testing 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 |
|
|
| [Ahmed Farhanur Rashid] · [GitHub](https://github.com/ahmed-farhanur-rashid) |
