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docs/doc/en/vision/customize_model_yolov8.md

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Then run `python export_onnx.py yolov8n.pt 320 224` to export the `onnx` model. Here, we have redefined the input resolution. The model was originally trained with `640x640`, but we use `320x224` to improve the processing speed and match the MaixCAM's screen aspect ratio for convenient display. You can set the resolution according to your own needs.
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Espetially for NVIDIA users. You need to check onnxruntime version at your desktop venv and in tpu-mlir docker container. If they don't match you will see several "unimplemeted" errors. Jut update runtime in container
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```shell
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pip install onnxruntime==<version>
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```
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`<version>` is version equals your desktop version
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## Converting to a Model Supported by MaixCAM and MUD File
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MaixPy/MaixCDK currently supports YOLOv8 / YOLO11 for object detection, YOLOv8-pose / YOLO11-pose for keypoint detection, and YOLOv8-seg / YOLO11-seg for segmentation (as of 2024-10-10).

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