Download src/train.py from Amitkumar001/Logo_watermark_detection: direct link, hf CLI and curl.
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https://huggingface.co/Amitkumar001/Logo_watermark_detection/resolve/main/src/train.py
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| """Train YOLOv8 model using ultralytics package.""" | |
| import argparse | |
| from ultralytics import YOLO | |
| import os | |
| def train(data: str, epochs: int=100, imgsz: int=640, batch: int=16, device: str=None, save_dir: str='runs'): | |
| model = YOLO('yolov8s.pt') # Use standard detection model for bounding box training | |
| os.makedirs(save_dir, exist_ok=True) | |
| model.train(data=data, epochs=epochs, imgsz=imgsz, batch=batch, device=device, save=True, project=save_dir, name='logo_watermark') | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--data', required=True) | |
| parser.add_argument('--epochs', type=int, default=100) | |
| parser.add_argument('--imgsz', type=int, default=640) | |
| parser.add_argument('--batch', type=int, default=16) | |
| parser.add_argument('--device', type=str, default=None) | |
| parser.add_argument('--save_dir', type=str, default='runs') | |
| args = parser.parse_args() | |
| train(args.data, args.epochs, args.imgsz, args.batch, args.device, args.save_dir) | |