inferencer_config{ # Defining input node of the model # defining target class names for the experiment. # Note: This must be mentioned in order of the networks classes. target_classes: "Person" target_classes: "Bag" target_classes: "Face" # Inference dimensions. image_width: 960 image_height: 544 # Must match what the model was trained for. image_channels: 3 batch_size: 1 gpu_index: 0 # model handler config tensorrt_config{ trt_engine: "peoplenet.engine" } } bbox_handler_config{ kitti_dump: true disable_overlay: false overlay_linewidth: 2 classwise_bbox_handler_config{ key:"Person" value: { confidence_model: "aggregate_cov" output_map: "Person" confidence_threshold: 0.9 bbox_color{ R: 0 G: 255 B: 0 } clustering_config{ coverage_threshold: 0.00 dbscan_eps: 0.3 dbscan_min_samples: 0.05 minimum_bounding_box_height: 4 } } } classwise_bbox_handler_config{ key:"Bag" value: { confidence_model: "aggregate_cov" output_map: "Bag" confidence_threshold: 0.9 bbox_color{ R: 0 G: 255 B: 255 } clustering_config{ coverage_threshold: 0.00 dbscan_eps: 0.3 dbscan_min_samples: 0.05 minimum_bounding_box_height: 4 } } } classwise_bbox_handler_config{ key:"Face" value: { confidence_model: "aggregate_cov" output_map: "Face" confidence_threshold: 0.9 bbox_color{ R: 255 G: 0 B: 0 } clustering_config{ coverage_threshold: 0.00 dbscan_eps: 0.3 dbscan_min_samples: 0.05 minimum_bounding_box_height: 4 } } } }