inferencer_config{
  # defining target class names for the experiment.
  # Note: This must be mentioned in order of the networks classes.
  target_classes: "damage"
  target_classes: "healthy"
  # Inference dimensions.
  image_width: 912
  image_height: 352
  # Must match what the model was trained for.
  image_channels: 3
  batch_size: 1
  gpu_index: 0
  # model handler config
  tlt_config{
    model: "/home/peter/TAO_toolkit/jp_detectnet/detectnet_v2/experiment_dir_pruned/weights/resnet50_detector_pruned.tlt"
    $KEY: OHJpNGVjazdxMzgyYzlydmRjZHB0YWNnYWo6ZTQ2ZWM3YjMtNDY0MC00NmE5LWE3OTUtYTdjOWI5NWQxOTMw
  }
}
bbox_handler_config{
  kitti_dump: true
  disable_overlay: false
  overlay_linewidth: 2
  classwise_bbox_handler_config{
    key:"damage"
    value: {
      confidence_model: "aggregate_cov"
      output_map: "damage"
      bbox_color{
        R: 255
        G: 0
        B: 0
      }
      clustering_config{
        clustering_algorithm: DBSCAN
        coverage_threshold: 0.025
        dbscan_eps: 0.5
        dbscan_min_samples: 0.5
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 20
      }
    }
  }
  classwise_bbox_handler_config{
    key:"healthy"
    value: {
      confidence_model: "aggregate_cov"
      output_map: "healthy"
      bbox_color{
        R: 0
        G: 200
        B: 200
      }
      clustering_config{
        clustering_algorithm: DBSCAN
        coverage_threshold: 0.025
        dbscan_eps: 0.5
        dbscan_min_samples: 0.5
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 50
      }
    }
  }
}