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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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MatthewNoyce 2024-09-06 16:33:26 +01:00 • committed by GitHub
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16
docs/en/guides/deepstream-nvidia-jetson.md
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@ -21,7 +21,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/deepstream-nvidia-jetson.avif" alt="DeepStream on NVIDIA Jetson">
!!! Note
!!! note
This guide has been tested with both [Seeed Studio reComputer J4012](https://www.seeedstudio.com/reComputer-J4012-p-5586.html) which is based on NVIDIA Jetson Orin NX 16GB running JetPack release of [JP5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and [Seeed Studio reComputer J1020 v2](https://www.seeedstudio.com/reComputer-J1020-v2-p-5498.html) which is based on NVIDIA Jetson Nano 4GB running JetPack release of [JP4.6.4](https://developer.nvidia.com/jetpack-sdk-464). It is expected to work across all the NVIDIA Jetson hardware lineup including latest and legacy.
@ -39,7 +39,7 @@ Before you start to follow this guide:
- For JetPack 4.6.4, install [DeepStream 6.0.1](https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_Quickstart.html)
- For JetPack 5.1.3, install [DeepStream 6.3](https://docs.nvidia.com/metropolis/deepstream/6.3/dev-guide/text/DS_Quickstart.html)
!!! Tip
!!! tip
In this guide we have used the Debian package method of installing DeepStream SDK to the Jetson device. You can also visit the [DeepStream SDK on Jetson (Archived)](https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived) to access legacy versions of DeepStream.
@ -67,7 +67,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
```
!!! Note
!!! note
You can also use a [custom trained YOLOv8 model](https://docs.ultralytics.com/modes/train/).
@ -77,7 +77,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
python3 utils/export_yoloV8.py -w yolov8s.pt
```
!!! Note "Pass the below arguments to the above command"
!!! note "Pass the below arguments to the above command"
For DeepStream 6.0.1, use opset 12 or lower. The default opset is 16.
@ -175,13 +175,13 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
deepstream-app -c deepstream_app_config.txt
```
!!! Note
!!! note
It will take a long time to generate the TensorRT engine file before starting the inference. So please be patient.
<div align=center><img width=1000 src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-deepstream.avif" alt="YOLOv8 with deepstream"></div>
!!! Tip
!!! tip
If you want to convert the model to FP16 precision, simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yoloV8.txt`
@ -217,7 +217,7 @@ If you want to use INT8 precision for inference, you need to follow the steps be
done
```
!!! Note
!!! note
NVIDIA recommends at least 500 images to get a good accuracy. On this example, 1000 images are chosen to get better accuracy (more images = more accuracy). You can set it from **head -1000**. For example, for 2000 images, **head -2000**. This process can take a long time.
@ -234,7 +234,7 @@ If you want to use INT8 precision for inference, you need to follow the steps be
export INT8_CALIB_BATCH_SIZE=1
```
!!! Note
!!! note
Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to you GPU memory.