106 lines
2.6 KiB
YAML
106 lines
2.6 KiB
YAML
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# Evaluation Configuration — ROI1
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# Same as eval_config_yolov26s-roi0.yaml but evaluates only roi_id="1" detections
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# ROI1: narrow crop 768×352, centered on vanishing point (crop_center_mode=vxvy)
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dataset:
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det_path: "/data1/dongying/Mono3d/G1Q3/model_inference/KPI/DL_KPI_SCENE/model_20260403" # Root directory containing case folders with txt_results
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gt_path: "/data1/dongying/Mono3d/G1Q3/dataset_for_evaluation/DL_KPI_SCENE" # Root directory containing case folders with labels
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path_depth: 1
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det_format: "json"
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gt_format: "json"
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det_subdir: "predictions/roi1" # Relative to each case directory; used before generic json_results/predictions probing
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det_roi_filter: "1" # Only evaluate detections with roi_id="1"; set to null to evaluate all ROIs together
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# Image properties
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image:
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width: 1920
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height: 1080
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# Model configuration for GT filtering
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model:
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input_size: 768 # Model input width (used for GT min box size calculation)
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min_box_size_at_input_scale: 8
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# GT filtering threshold: min_box_size = 8 * 768 / 768 = 8.0 pixels at original scale (ROI1 is 768×352)
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# Performance settings
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performance:
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num_workers: 32
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# ROI Ground Truth Processing
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roi_gt:
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enabled: true
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calib_root: "/data1/dongying/Mono3d/G1Q3/dataset_for_evaluation/DL_KPI_SCENE" # Root path to calibration files (camera4.json)
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roi_config: [768, 352] # ROI1 crop size (narrow, centered on VP, crop_center_mode=vxvy)
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roi_bottom_offset: 0
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# Class definitions
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classes:
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3d_classes: [0, 1, 2, 3, 4, 5, 6, 7, 8]
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2d_classes: [9, 10, 11, 12]
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class_names:
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0: "vehicle"
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1: "bus"
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2: "truck"
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3: "tanker"
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4: "unknown"
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5: "pedestrian"
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6: "bicycle"
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7: "motorcyclist"
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8: "tricycle"
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9: "traffic_sign"
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10: "wheel"
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11: "plate"
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12: "face"
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# Matching parameters
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matching:
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iou_threshold: 0.5
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# 2D metrics configuration
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metrics_2d:
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enabled: true
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conf_threshold: 0.27
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ap_method: "voc2010"
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distance_ranges:
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- [0, 30]
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- [30, 60]
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- [60, 100]
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- [100, 999]
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lateral_roi: [-15, 15]
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# 3D metrics configuration
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metrics_3d:
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enabled: true
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coordinate_system: "camera"
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heading_tolerance: "both"
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distance_ranges:
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- [0, 10]
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- [10, 20]
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- [20, 30]
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- [30, 40]
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- [40, 50]
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- [50, 60]
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- [60, 70]
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- [70, 80]
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- [80, 90]
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- [90, 100]
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- [100, 999]
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lateral_distance_ranges:
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- [-50, -40]
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- [-40, -30]
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- [-30, -20]
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- [-20, -10]
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- [-10, 0]
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- [0, 10]
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- [10, 20]
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- [20, 30]
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- [30, 40]
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- [40, 50]
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# Output configuration
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output:
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save_path: "eval_results_multiprocess/yolov5s/{timestamp}"
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formats: ["json", "txt"]
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print_details: true
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per_case_reports: true
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