{
  "model_info": {
    "name": "plant-detector",
    "type": "ConvolutionalAutoencoder",
    "framework": "PyTorch Lightning",
    "task": "anomaly_detection",
    "input_shape": [
      3,
      224,
      224
    ],
    "latent_dim": 128
  },
  "training": {
    "learning_rate": 0.0001,
    "batch_size": 32,
    "epochs": "N/A",
    "latent_dim": 128,
    "dataset_size": "N/A"
  },
  "metrics": {
    "threshold": 0.5687,
    "val_loss": "N/A",
    "mean_reconstruction_error": "N/A",
    "std_reconstruction_error": "N/A",
    "anomaly_rate": "N/A"
  },
  "normalization": {
    "mean": [
      0.4682,
      0.4865,
      0.305
    ],
    "std": [
      0.2064,
      0.1995,
      0.1961
    ]
  },
  "usage": {
    "threshold": 0.5687,
    "input_preprocessing": "Resize to 224x224, normalize with mean/std",
    "output_interpretation": "Lower reconstruction error = more plant-like"
  }
}