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Can anyone explain the following code for training Yolov5 (training.py) Thank you so much def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument(--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial

Can anyone explain the following code for training Yolov5 (training.py)

Thank you so much

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def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument("--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default="', help='model.yaml path') parser.add_argument('--data', type=str, default=ROOT / 'data/my_yaml.yaml', help='dataset.yaml path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs, -1 for autobatch') ### parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') parser.add_argument('--rect', action='store_true', help="rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument(' --noval', action='store_true', help='only validate final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check") parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') parser.add_argument(' -image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument -device', default='', help='cuda device, i.e. o or 0,1,2,3 or cpu') parser.add_argument(' -multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help="train multi-class data as single-class') parser.add_argument('--adam', action='store_true', help='use torch.optim. Adam() optimizer') parser.add_argument(" -sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--workers', type=int, default=2, help='max dataloader workers (per RANK in DDP mode)') ### parser.add_argument('--project', default=ROOT / 'runs/train', help='save to projectame') parser.add_argument('--name', default='exp', help='save to projectame') parser.add_argument('--exist-ok', action='store_true', help='existing projectame ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--linear-lr', action='store_true', help='linear LR') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--patience', type=int, default=100, help='Earlystopping patience (epochs without improvement)') parser.add_argument -freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24') parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if

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