Upload released checkpoint (regression loss), ONNX export, and model card

README.md

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+---
+license: other
+license_name: research-use-only
+license_link: https://github.com/adarshcod30/Diabetic-Retinopathy-Detection/blob/main/MODEL_CARD.md
+tags:
+  - diabetic-retinopathy
+  - medical-imaging
+  - fundus
+  - explainable-ai
+  - efficientnet
+  - pytorch
+  - onnx
+library_name: pytorch
+pipeline_tag: image-classification
+---
+
+# drdetect: Diabetic Retinopathy Screening Model
+
+**Research prototype. NOT a medical device. NOT a substitute for clinical judgement.**
+
+EfficientNet-B0 (regression-loss ordinal head) for 5-class ICDR diabetic retinopathy grading,
+trained on APTOS 2019 and evaluated once, honestly, on a locked external test set (Messidor-2 +
+IDRiD). Full code, every experiment, and the complete evidence trail:
+[github.com/adarshcod30/Diabetic-Retinopathy-Detection](https://github.com/adarshcod30/Diabetic-Retinopathy-Detection).
+
+## Why this checkpoint
+
+This is **not** the checkpoint with the best internal-validation accuracy — a plain
+cross-entropy baseline scored higher there. It is the checkpoint that won a **pre-registered,
+one-time external evaluation** on data neither model was tuned on: referable-DR AUC 0.9242 vs.
+0.8878 for the CE baseline (DeLong test, p=6.1×10⁻¹⁰). See the full write-up:
+[`docs/22_PHASE8_VALIDATION_RESULTS.md`](https://github.com/adarshcod30/Diabetic-Retinopathy-Detection/blob/main/docs/22_PHASE8_VALIDATION_RESULTS.md).
+
+## Files
+
+- `best.ckpt` — PyTorch Lightning checkpoint (EfficientNet-B0, regression head, 512×512 input).
+- `efficientnet_b0_regression_512px.onnx` — ONNX export, parity-verified against the PyTorch
+  module (max abs diff 2.4×10⁻⁷).
+
+## Headline results (locked external test, run once)
+
+| | QWK | Sensitivity | Specificity | Referable AUC |
+|---|---:|---:|---:|---:|
+| This model | 0.6995 | 0.441 | 0.976 | 0.9242 |
+
+**Read the limitation, not just the AUC**: referable-DR sensitivity is 44.1% against a >=90%
+target — well below every published comparator. This is diagnosed (not just disclosed) as a
+threshold-transfer/calibration failure, not a pure discrimination failure: the frozen operating
+threshold from internal validation does not transfer to this external population, even though the
+model's ranking ability (AUC) held up in a range comparable to a cited external-validation drop in
+the literature. **Any real use of this model's binary referable/non-referable output requires
+fitting a new threshold on a local calibration set first.** Full detail:
+[`MODEL_CARD.md`](https://github.com/adarshcod30/Diabetic-Retinopathy-Detection/blob/main/MODEL_CARD.md).
+
+## Usage
+
+```python
+import torch
+from drdetect.grading.model import build_model  # from the GitHub repo's src/
+
+model = build_model("efficientnet_b0", num_outputs=1, pretrained=False, freeze_bn=True)
+ckpt = torch.load("best.ckpt", map_location="cpu", weights_only=False)
+state = ckpt.get("state_dict", ckpt)
+state = {k.removeprefix("model."): v for k, v in state.items() if k.startswith("model.")}
+model.load_state_dict(state)
+model.eval()
+```
+
+Or with the repo's own pipeline (handles preprocessing, quality gating, and decoding):
+
+```python
+from drdetect.serve.pipeline import load_grader, run_pipeline
+
+model = load_grader("best.ckpt", backbone="efficientnet_b0", loss_name="regression", device="cpu")
+result = run_pipeline(image_rgb, model, loss_name="regression", size=512, device="cpu")
+```
+
+## License
+
+**Research use only.** Derived from training data under mixed licenses that restrict
+redistribution (APTOS/Kaggle competition rules, Messidor-2's ADCIS terms) — see
+[`DATASET_CARD.md`](https://github.com/adarshcod30/Diabetic-Retinopathy-Detection/blob/main/DATASET_CARD.md).
+Not licensed for any clinical, diagnostic, or commercial product.

best.ckpt

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efficientnet_b0_regression_512px.onnx

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