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Update to lowercase MkDocs admonitions (#15990)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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26
docs/en/guides/raspberry-pi.md
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@ -19,7 +19,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
<strong>Watch:</strong> Raspberry Pi 5 updates and improvements.
</p>
!!! Note
!!! note
This guide has been tested with Raspberry Pi 4 and Raspberry Pi 5 running the latest [Raspberry Pi OS Bookworm (Debian 12)](https://www.raspberrypi.com/software/operating-systems/). Using this guide for older Raspberry Pi devices such as the Raspberry Pi 3 is expected to work as long as the same Raspberry Pi OS Bookworm is installed.
@ -100,7 +100,7 @@ Out of all the model export formats supported by Ultralytics, [NCNN](https://doc
The YOLOv8n model in PyTorch format is converted to NCNN to run inference with the exported model.
!!! Example
!!! example
=== "Python"
@ -130,7 +130,7 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
yolo predict model='yolov8n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
```
!!! Tip
!!! tip
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options).
@ -138,7 +138,7 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
YOLOv8 benchmarks were run by the Ultralytics team on nine different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN. Benchmarks were run on both Raspberry Pi 5 and Raspberry Pi 4 at FP32 precision with default input image size of 640.
!!! Note
!!! note
We have only included benchmarks for YOLOv8n and YOLOv8s models because other models sizes are too big to run on the Raspberry Pis and does not offer decent performance.
@ -224,7 +224,7 @@ The below table represents the benchmark results for two different models (YOLOv
To reproduce the above Ultralytics benchmarks on all [export formats](../modes/export.md), run this code:
!!! Example
!!! example
=== "Python"
@ -251,11 +251,11 @@ To reproduce the above Ultralytics benchmarks on all [export formats](../modes/e
When using Raspberry Pi for Computer Vision projects, it can be essentially to grab real-time video feeds to perform inference. The onboard MIPI CSI connector on the Raspberry Pi allows you to connect official Raspberry PI camera modules. In this guide, we have used a [Raspberry Pi Camera Module 3](https://www.raspberrypi.com/products/camera-module-3) to grab the video feeds and perform inference using YOLOv8 models.
!!! Tip
!!! tip
Learn more about the [different camera modules offered by Raspberry Pi](https://www.raspberrypi.com/documentation/accessories/camera.html) and also [how to get started with the Raspberry Pi camera modules](https://www.raspberrypi.com/documentation/computers/camera_software.html#introducing-the-raspberry-pi-cameras).
!!! Note
!!! note
Raspberry Pi 5 uses smaller CSI connectors than the Raspberry Pi 4 (15-pin vs 22-pin), so you will need a [15-pin to 22pin adapter cable](https://www.raspberrypi.com/products/camera-cable) to connect to a Raspberry Pi Camera.
@ -267,7 +267,7 @@ Execute the following command after connecting the camera to the Raspberry Pi. Y
rpicam-hello
```
!!! Tip
!!! tip
Learn more about [`rpicam-hello` usage on official Raspberry Pi documentation](https://www.raspberrypi.com/documentation/computers/camera_software.html#rpicam-hello)
@ -275,13 +275,13 @@ rpicam-hello
There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
!!! Usage
!!! usage
=== "Method 1"
We can use `picamera2`which comes pre-installed with Raspberry Pi OS to access the camera and inference YOLOv8 models.
!!! Example
!!! example
=== "Python"
@ -333,7 +333,7 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
Learn more about [`rpicam-vid` usage on official Raspberry Pi documentation](https://www.raspberrypi.com/documentation/computers/camera_software.html#rpicam-vid)
!!! Example
!!! example
=== "Python"
@ -353,7 +353,7 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
yolo predict model=yolov8n.pt source="tcp://127.0.0.1:8888"
```
!!! Tip
!!! tip
Check our document on [Inference Sources](https://docs.ultralytics.com/modes/predict/#inference-sources) if you want to change the image/ video input type
@ -410,7 +410,7 @@ Ultralytics YOLOv8's NCNN format is highly optimized for mobile and embedded pla
You can convert a PyTorch YOLOv8 model to NCNN format using either Python or CLI commands:
!!! Example
!!! example
=== "Python"