{
  "model_type": "light-level-anomaly-detection",
  "library_name": "opencv",
  "category": "Image-Quality Analytics (classical computer vision)",
  "base_model": null,
  "source_framework": "OpenCV",
  "supported_precisions": [],
  "inference_engine": "OpenCV (CPU)",
  "hardware": [
    "CPU",
    "GPU"
  ],
  "detected_classes": "Underexposure, overexposure, sudden light change",
  "hf_repo_id": "Intel/light-level-anomaly-detection",
  "description": "Light-level anomaly detection use case using luminance statistics to flag underexposure, overexposure, and sudden light changes without a deep-learning model"
}