mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-05-23 13:34:23 +08:00
57 lines
2.4 KiB
Python
57 lines
2.4 KiB
Python
from ultralytics import YOLOv10, YOLO
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# from ultralytics.engine.pgt_trainer import PGTTrainer
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import os
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from ultralytics.models.yolo.segment import PGTSegmentationTrainer
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import argparse
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from datetime import datetime
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def main(args):
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# model = YOLOv10()
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# If you want to finetune the model with pretrained weights, you could load the
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# pretrained weights like below
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# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
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# or
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# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
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# model = YOLOv10('yolov10n.pt', task='segment')
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args = dict(model='yolov10n.pt', data=args.data_yaml,
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epochs=args.epochs, batch=args.batch_size,
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# cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
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)
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trainer = PGTSegmentationTrainer(overrides=args)
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trainer.train(
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# debug=True,
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# args = dict(pgt_coeff=0.1), # Should add later to config
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)
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# Create a directory to save model weights if it doesn't exist
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model_weights_dir = 'model_weights'
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if not os.path.exists(model_weights_dir):
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os.makedirs(model_weights_dir)
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# Save the trained model with a unique name based on the current date and time
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current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
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data_yaml_base = os.path.splitext(os.path.basename(args.data_yaml))[0]
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model_save_path = os.path.join(model_weights_dir, f'yolov10_{data_yaml_base}_trained_{current_time}.pt')
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trainer.save(model_save_path)
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# Evaluate the model on the validation set
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results = trainer.val(data=args.data_yaml)
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# Print the evaluation results
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print(results)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Train YOLOv10 model with PGT segmentation.')
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parser.add_argument('--device', type=str, default='0', help='CUDA device number')
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parser.add_argument('--batch_size', type=int, default=64, help='Batch size for training')
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parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
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parser.add_argument('--data_yaml', type=str, default='coco.yaml', help='Path to the data YAML file')
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args = parser.parse_args()
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# Set CUDA device (only needed for multi-gpu machines)
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = args.device
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main(args) |