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""" |
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Train YOLOv8 on the TypoRef historical document dataset. |
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This script wraps the Ultralytics YOLO API with a simple command-line |
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interface. It allows you to specify the dataset configuration file, |
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model backbone, image size, number of epochs, batch size, project |
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directory, and experiment name. Additional hyper-parameters can be |
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passed via --hyp to override defaults in `configs/hyp_augment.yaml`. |
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""" |
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import argparse |
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from ultralytics import YOLO |
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def parse_args(): |
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p = argparse.ArgumentParser(description="Train YOLOv8 for TypoRef document detection") |
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p.add_argument("--data", type=str, default="configs/ornaments.yaml", help="Path to data config") |
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p.add_argument("--model", type=str, default="yolov8s.pt", help="YOLOv8 backbone model") |
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p.add_argument("--imgsz", type=int, default=1024, help="Input image size") |
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p.add_argument("--epochs", type=int, default=100, help="Number of training epochs") |
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p.add_argument("--batch", type=int, default=8, help="Batch size") |
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p.add_argument("--workers", type=int, default=8, help="Number of dataloader workers") |
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p.add_argument("--project", type=str, default="runs/typoref", help="Project directory") |
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p.add_argument("--name", type=str, default="exp", help="Experiment name") |
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p.add_argument("--hyp", type=str, default="configs/hyp_augment.yaml", help="Hyper-parameter file") |
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p.add_argument("--patience", type=int, default=30, help="Early stopping patience") |
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p.add_argument("--seed", type=int, default=42, help="Random seed for reproducibility") |
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return p.parse_args() |
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def main(): |
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args = parse_args() |
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model = YOLO(args.model) |
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results = model.train( |
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data=args.data, |
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imgsz=args.imgsz, |
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epochs=args.epochs, |
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batch=args.batch, |
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workers=args.workers, |
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project=args.project, |
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name=args.name, |
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cache=True, |
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amp=True, |
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deterministic=True, |
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patience=args.patience, |
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seed=args.seed, |
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cfg=args.hyp, |
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) |
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print(results) |
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if __name__ == "__main__": |
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main() |
