| import argparse |
| import subprocess |
|
|
| def run_transfer_learning(dataset, epochs, batch_size, imgsz, patience, cache, pretrained, cos_lr, profile, plots, resume, augment, model, run): |
| |
| command = [ |
| "python", "./experiment/transfer_learning_train&test.py", |
| "--dataset", str(dataset), |
| "--epochs", str(epochs), |
| "--batch", str(batch_size), |
| "--imgsz", str(imgsz), |
| "--patience", str(patience), |
| "--cache", cache, |
| "--model", model, |
| "--run", run |
| ] |
|
|
| |
| if pretrained: |
| command.append("--pretrained") |
| if cos_lr: |
| command.append("--cos_lr") |
| if profile: |
| command.append("--profile") |
| if plots: |
| command.append("--plots") |
| if resume: |
| command.append("--resume") |
| if augment: |
| command.append("--augment") |
|
|
| |
| subprocess.run(command, check=True) |
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser(description="Run transfer learning with YOLO model.") |
|
|
| |
| parser.add_argument('--dataset', type=str, choices=["Birds-Nest", "Common-VALID", "Electric-Substation", "InsPLAD-det"], help='Dataset name to be used') |
| parser.add_argument("--epochs", type=int, default=1000, help="Number of epochs") |
| parser.add_argument("--batch", type=int, default=16, help="Batch size") |
| parser.add_argument("--imgsz", type=int, default=640, help="Image size") |
| parser.add_argument("--patience", type=int, default=30, help="Patience for early stopping") |
| parser.add_argument("--cache", type=str, default='ram', help="Cache option") |
| parser.add_argument("--pretrained", action="store_true", help="Use pretrained weights") |
| parser.add_argument("--cos_lr", action="store_true", help="Use cosine learning rate") |
| parser.add_argument("--profile", action="store_true", help="Profile the training") |
| parser.add_argument("--plots", action="store_true", help="Generate plots") |
| parser.add_argument("--resume", action="store_true", help="Resume run") |
| parser.add_argument("--augment", action="store_true", help="Apply augmentation techniques during training") |
| parser.add_argument("--model", type=str, choices=["yolov8n", "yolov8s", "yolov8m", "yolov8l", "yolov10n", "yolov10s", "yolov10m", "yolov10l"], help="Model to use") |
| parser.add_argument("--run", type=str, choices=["From_Scratch", "Finetuning", "freeze_[P1-P3]", "freeze_Backbone", "freeze_[P1-23]"], help="Run mode") |
|
|
| args = parser.parse_args() |
|
|
| |
| run_transfer_learning( |
| dataset=args.dataset, |
| epochs=args.epochs, |
| batch_size=args.batch, |
| imgsz=args.imgsz, |
| patience=args.patience, |
| cache=args.cache, |
| pretrained=args.pretrained, |
| cos_lr=args.cos_lr, |
| profile=args.profile, |
| plots=args.plots, |
| resume=args.resume, |
| augment=args.augment, |
| model=args.model, |
| run=args.run |
| ) |
