62 lines
2.1 KiB
YAML
62 lines
2.1 KiB
YAML
# YOLO Annotator Configuration - CPU Only (ONNX)
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#
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# This configuration uses ONNX Runtime for CPU-only inference.
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# No GPU required - works on any system.
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#
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# Usage:
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# python scripts/annotate.py --config configs/annotator_cpu.yaml
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model:
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path: "models/yolov9t.onnx" # ONNX model file
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device: "cpu" # cpu (ONNX uses CPU by default)
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backend: "onnx" # Force ONNX backend
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conf_threshold: 0.25 # Confidence threshold
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iou_threshold: 0.45 # NMS IoU threshold
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# ONNX specific options
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onnx:
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num_threads: 0 # CPU threads (0 = auto)
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optimization_level: "all" # Graph optimization level
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video:
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source: "input/video.mp4" # Video file path
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sample_fps: 2 # Frames per second to extract
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max_frames: null # Max frames (null = all)
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start_time: 0 # Start time in seconds
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end_time: null # End time (null = end of video)
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resize: null # [width, height] or null
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detection:
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classes: null # Class IDs to keep (null = all)
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min_confidence: 0.3 # Minimum confidence to save
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min_area: 100 # Minimum bbox area in pixels
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max_area: null # Maximum bbox area (null = no limit)
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min_size: 0.01 # Minimum bbox dimension (normalized)
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output:
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directory: "output/annotations" # Output directory
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save_snapshots: true # Save clean images
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save_labels: true # Save YOLO labels
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save_debug: true # Save debug visualizations
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save_manifest: true # Save JSON manifest
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image_format: "jpg" # jpg or png
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image_quality: 95 # JPEG quality (1-100)
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# Class names (COCO subset - common objects)
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class_names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: airplane
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5: bus
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6: train
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7: truck
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8: boat
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14: bird
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15: cat
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16: dog
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17: horse
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18: sheep
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19: cow
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