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Docs improvements and redirect fixes (#16287)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-09-15 00:27:46 +02:00 • committed by GitHub
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docs/en/guides/raspberry-pi.md
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@ -94,7 +94,7 @@ Here we will install Ultralytics package on the Raspberry Pi with optional depen
## Use NCNN on Raspberry Pi
Out of all the model export formats supported by Ultralytics, [NCNN](https://docs.ultralytics.com/integrations/ncnn) delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture). Therefor our recommendation is to use NCNN with Raspberry Pi.
Out of all the model export formats supported by Ultralytics, [NCNN](https://docs.ultralytics.com/integrations/ncnn/) delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture). Therefor our recommendation is to use NCNN with Raspberry Pi.
## Convert Model to NCNN and Run Inference
@ -132,7 +132,7 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
!!! tip
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options).
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).
## Raspberry Pi 5 vs Raspberry Pi 4 YOLOv8 Benchmarks