Yolo docs

docs/source/docs/objectDetection/about-object-detection.md

@@ -39,7 +39,8 @@ Photonvision will letterbox your camera frame to 640x640. This means that if you
 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 officially 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.
+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