Welcome to RDK X5 Board repository

Model Training and Conversion

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

This tutorial only introduces CLI model training and conversion. You can refer to the official website for Python case modifications.
Due to performance reasons, model training on Raspberry Pi 5 main board takes a long time, please wait patiently.

1. Model Training

Use CLI commands to directly train the model: copy the yolo26n.pt file to the directory where the configuration file is located, then open a terminal in the configuration file directory:

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cd /home/pi/ultralytics26/data/yahboom_data/orange_data

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yolo detect train data=orange.yaml model=yolo26n.pt epochs=100 imgsz=640

data: Dataset configuration file

model: Pre-trained model file

epochs: Number of training epochs

imgsz: Input specified image size

2. Model Conversion

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

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/home/sunrise/ultralytics26/data/yahboom_data/orange_data/runs/detect/train/weights

Convert pt model to ncnn model:

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cd  /home/sunrise/ultralytics26/data/yahboom_data/orange_data/runs/detect/train/weights

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yolo export model=best.pt format=ncnn

References

Model Training with Ultralytics YOLO - Ultralytics YOLO Documentation