Model training conversion

After completing the tutorial content of dataset annotation, we can use the motherboard to start training the model.

This tutorial only introduces the model training and conversion of CLI. You can refer to the official website to modify the Python case

1. Model training

Use CLI command to train the model directly: copy the yolo11n.pt file to the directory where the configuration file is located, and then open the terminal in the directory where the configuration file is located:

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cd /home/jetson/ultralytics/ultralytics/data/yahboom_data/orange_data

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yolo detect train data=orange.yaml model=yolo11n.pt epochs=100 imgsz=640

data: Dataset configuration file

model: Pre-trained model file

epochs: Number of training rounds

imgsz: Enter the specified image size

2. Model conversion

The final model will be generated in the runs folder: generally choose the best.pt file for use

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/home/jetson/ultralytics/ultralytics/data/yahboom_data/orange_data/runs/detect/train/weights

Convert the PyTorch model to TensorRT:

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cd     /home/jetson/ultralytics/ultralytics/data/yahboom_data/orange_data/runs/detect/train/weights

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yolo export model=best.pt format=engine

References

https://docs.ultralytics.com/modes/train/