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fixed val and saving weights
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@ -14,7 +14,7 @@ def main(args):
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# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
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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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# 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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# 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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# model = YOLOv10('yolov10n.pt', task='segment')
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args = dict(model='yolov10n.pt', data=args.data_yaml,
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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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epochs=args.epochs, batch=args.batch_size,
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@ -35,10 +35,10 @@ def main(args):
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current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
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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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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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model_save_path = os.path.join(model_weights_dir, f'yolov10_{data_yaml_base}_trained_{current_time}.pt')
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model.save(model_save_path)
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trainer.save(model_save_path)
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# Evaluate the model on the validation set
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# Evaluate the model on the validation set
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results = model.val(data='coco.yaml')
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results = trainer.val(data=args.data_yaml)
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# Print the evaluation results
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# Print the evaluation results
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print(results)
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print(results)
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