config.json

{
  "model_name": "Physics-Informed UNet++",
  "task": "Disaster Risk Segmentation",
  "architecture": {
    "backbone": "UNet++",
    "encoder": "resnet34",
    "in_channels": 32,
    "out_channels": 1,
    "film_dim": 32,
    "dropout_p": 0.016687189127500446
  },
  "training": {
    "best_val_iou": 1.0,
    "optimizer": "AdamW",
    "learning_rate": 4.7031995111855064e-05,
    "weight_decay": 0.00025303130757493825,
    "batch_size": 8,
    "epochs": 50,
    "mixed_precision": "FP16"
  },
  "inference": {
    "input_shape": [
      1,
      32,
      512,
      512
    ],
    "output": "probability_map",
    "recommended_threshold": 0.000175
  },
  "framework": {
    "library": "PyTorch",
    "torch_version": "2.x",
    "export_formats": [
      "TorchScript",
      "ONNX"
    ]
  },
  "license": "Apache-2.0",
  "author": "Lokesh Reddy Poreddy",
  "repository": "https://huggingface.co/loki200519/urop"
}