{
  "model_name": "Residual Convolutional Autoencoder Ensemble",
  "version": "v32",
  "architecture": {
    "type": "Convolutional Autoencoder",
    "encoder_layers": 6,
    "decoder_layers": 6,
    "residual_blocks": true,
    "input_size": [
      3,
      256,
      256
    ],
    "output_size": [
      3,
      256,
      256
    ]
  },
  "models": {
    "model_a": {
      "latent_dim": 512,
      "dropout": 0.15,
      "parameters": "~50M",
      "val_loss": 0.025486
    },
    "model_b": {
      "latent_dim": 768,
      "dropout": 0.2,
      "parameters": "~65M",
      "val_loss": 0.025033
    }
  },
  "training": {
    "loss_function": "MSE",
    "optimizer": "AdamW",
    "image_range": "[-1, 1]",
    "input_resolution": "256x256",
    "normalization": "x * 2 - 1"
  },
  "usage": {
    "preprocessing": "Resize to 256x256, normalize to [-1, 1]",
    "input_format": "RGB image tensor",
    "output_format": "Reconstructed image + latent code"
  },
  "limitations": {
    "note": "Model functions as image enhancer/denoiser, not anomaly detector",
    "not_suitable_for": [
      "Fake image detection",
      "Anomaly detection with modern AI images"
    ],
    "suitable_for": [
      "Image denoising",
      "Feature extraction",
      "Image reconstruction"
    ]
  }
}