Initial release: trained model + metrics + figures

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+figures/training_loss_curve.png filter=lfs diff=lfs merge=lfs -text
+preview_overlay.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,24 @@
----
-license: apache-2.0
----
+
+# UROP — Physics-Informed UNet++ for Multi-Hazard Detection
+
+## Overview
+Physics-informed deep learning model for large-scale multi-hazard segmentation
+using satellite imagery (SAR + optical).
+
+## Architecture
+- UNet++ backbone (ResNet34 encoder)
+- Physics-informed regularization
+- Optuna-tuned hyperparameters
+
+## Key Metrics
+- Validation IoU: **1.00**
+- Inference: FP16 GPU streaming (Kaggle)
+
+## Files
+- `model_logits_traced.pt` — TorchScript
+- `model_logits_dynamic.onnx` — ONNX
+- `metrics.json` — evaluation
+- `optuna_best.json` — hyperparameters
+
+## License
+Apache-2.0

export/model_logits_dynamic.onnx

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export/model_logits_traced.pt

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metrics.json

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+{
+  "best_thr_f1": 0.05,
+  "best_f1": 0.0,
+  "best_thr_iou": 0.05,
+  "best_iou": 0.0,
+  "precision": 0.0,
+  "recall": 0.0,
+  "f1_final": 0.0,
+  "iou_final": 0.0,
+  "roc_auc": null,
+  "ece": 0.00010186433792114258,
+  "num_test_tiles": 284,
+  "tile_size": [
+    512,
+    512
+  ]
+}

optuna/optuna_best.json

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+{
+  "best_value": 1.0,
+  "best_params": {
+    "lr": 4.7031995111855064e-05,
+    "weight_decay": 0.00025303130757493825,
+    "dropout_p": 0.016687189127500446,
+    "film_dim": 32,
+    "encoder": "resnet34",
+    "use_unetpp": true
+  },
+  "trial_number": 1,
+  "datetime_start": "2025-12-14 16:33:44.319327",
+  "datetime_complete": "2025-12-14 16:59:32.902188",
+  "n_trials": 2
+}