{
  "model_type": "resnet_oil_slick_detection",
  "task": "binary_classification",
  "framework": "pytorch",
  "architecture": {
    "base_model": "ResNet",
    "input_size": [224, 224],
    "num_channels": 1,
    "num_classes": 2,
    "output_type": "confidence_score"
  },
  "training": {
    "batch_size": 8,
    "num_epochs": 15,
    "learning_rate": 0.001,
    "optimizer": "Adam",
    "loss_function": "CrossEntropyLoss",
    "training_samples": 1574,
    "validation_samples": 1615
  },
  "performance": {
    "final_train_loss": 0.344,
    "final_val_loss": 0.189,
    "final_train_accuracy": 0.851,
    "final_val_accuracy": 0.948
  },
  "data": {
    "source": "Sentinel-1 SAR",
    "preprocessing": ["pyroSAR", "snappy"],
    "image_format": "GeoTIFF",
    "normalization": {
      "mean": [0.485],
      "std": [0.229]
    }
  },
  "deployment": {
    "input_format": "PIL Image or numpy array",
    "output_format": "confidence score (0-1)",
    "recommended_threshold": 0.5,
    "inference_time": "< 100ms per image"
  },
  "tags": [
    "sar",
    "oil-slick-detection",
    "maritime-monitoring",
    "environmental-monitoring",
    "satellite-imagery",
    "resnet",
    "pytorch"
  ]
}