{
  "model_id": "dummy_discriminator_v1_uid_1",
  "model_type": "detection",
  "description": "Dummy discriminator model for testing",
  "version": "1.0.0",
  "author": "kenjon",
  "miner_identity": {
    "uid": 1,
    "coldkey": "5Cvk3JRphVXXrwtJXP3xnDz9UF371P8ndAKfFA4JDxmTucQV",
    "hotkey": "5FsPe1tZym7PgP9NqzEsiSG2bvuGCR9fPDBBFqUY1Hm56gwe",
    "netuid": 379,
    "network": "test",
    "subnet": "BitMind"
  },
  "architecture": {
    "base_model": "custom_cnn",
    "num_classes": 3,
    "input_shape": [
      3,
      224,
      224
    ],
    "output_shape": [
      3
    ],
    "model_type": "detection"
  },
  "preprocessing": {
    "normalization": "imagenet",
    "resize": [
      224,
      224
    ],
    "augmentation": [
      "random_horizontal_flip"
    ]
  },
  "training": {
    "optimizer": "adam",
    "learning_rate": 0.001,
    "batch_size": 32,
    "epochs": 10,
    "loss_function": "cross_entropy"
  },
  "performance": {
    "accuracy": 0.85,
    "precision": 0.83,
    "recall": 0.87,
    "f1_score": 0.85
  },
  "dependencies": {
    "onnxruntime": ">=1.15.0",
    "numpy": ">=1.21.0",
    "torch": ">=2.0.0"
  },
  "usage": {
    "input_format": "numpy array (3, 224, 224)",
    "output_format": "numpy array (3,) - probabilities for [real, synthetic, semisynthetic]",
    "example": "model.predict(image_array)"
  },
  "submission_info": {
    "submitted_at": "2024-01-01T00:00:00Z",
    "submission_block": 0,
    "model_hash": "adf03e2aca622b5ec63e93af27f58a04f2dcd3e20229f1135099d36fe31e5e18",
    "file_size": 22274
  }
}