8000 Yolo docs minimalist by samfreund · Pull Request #1723 · PhotonVision/photonvision · GitHub
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Yolo docs minimalist #1723

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Jan 28, 2025
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Yolo docs minimalist #1723

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Update about-object-detection.md
samfreund Jan 13, 2025
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finish it up
samfreund Jan 13, 2025
3e66fee
Merge branch 'main' into yolo-docs-minimalist
samfreund Jan 13, 2025
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Merge branch 'main' into yolo-docs-minimalist
samfreund Jan 17, 2025
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6 changes: 5 additions & 1 deletion docs/source/docs/objectDetection/about-object-detection.md
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Expand Up @@ -35,7 +35,11 @@ Photonvision will letterbox your camera frame to 640x640. This means that if you

## Training Custom Models

Coming soon!
:::{warning}
Power users only. This requires some setup, such as obtaining your own dataset and installing various tools. It's additionally advised to have a general knowledge of ML before attempting to train your own model. Additionally, this is not officialy supported by Photonvision, and any problems that may arise are not attributable to Photonvision.
:::

Before beginning, it is necessary to install the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2). Then, install the relevant [Ultralytics repository](https://github.com/airockchip?tab=repositories&q=yolo&type=&language=&sort=) from this list. After training your model, export it to ``rknn``. This will give you an ``onnx`` file, formatted for conversion. Copy this file to the relevant folder in [rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo), and use the conversion script located there to convert it. If necessary, modify the script to provide the path to your training database for quantization.

## Uploading Custom Models

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