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Fix mkdocs.yml raw image URLs (#14213)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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docs/en/guides/deepstream-nvidia-jetson.md
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@ -303,3 +303,39 @@ The following table summarizes how YOLOv8s models perform at different TensorRT
### Acknowledgements
This guide was initially created by our friends at Seeed Studio, Lakshantha and Elaine.
## FAQ
### How do I set up Ultralytics YOLOv8 on an NVIDIA Jetson device?
To set up Ultralytics YOLOv8 on an [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) device, you first need to install the [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) compatible with your JetPack version. Follow the step-by-step guide in our [Quick Start Guide](nvidia-jetson.md) to configure your NVIDIA Jetson for YOLOv8 deployment.
### What is the benefit of using TensorRT with YOLOv8 on NVIDIA Jetson?
Using TensorRT with YOLOv8 optimizes the model for inference, significantly reducing latency and improving throughput on NVIDIA Jetson devices. TensorRT provides high-performance, low-latency deep learning inference through layer fusion, precision calibration, and kernel auto-tuning. This leads to faster and more efficient execution, particularly useful for real-time applications like video analytics and autonomous machines.
### Can I run Ultralytics YOLOv8 with DeepStream SDK across different NVIDIA Jetson hardware?
Yes, the guide for deploying Ultralytics YOLOv8 with the DeepStream SDK and TensorRT is compatible across the entire NVIDIA Jetson lineup. This includes devices like the Jetson Orin NX 16GB with [JetPack 5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and the Jetson Nano 4GB with [JetPack 4.6.4](https://developer.nvidia.com/jetpack-sdk-464). Refer to the section [DeepStream Configuration for YOLOv8](#deepstream-configuration-for-yolov8) for detailed steps.
### How can I convert a YOLOv8 model to ONNX for DeepStream?
To convert a YOLOv8 model to ONNX format for deployment with DeepStream, use the `utils/export_yoloV8.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
Here's an example command:
```bash
python3 utils/export_yoloV8.py -w yolov8s.pt --opset 12 --simplify
```
For more details on model conversion, check out our [model export section](../modes/export.md).
### What are the performance benchmarks for YOLOv8 on NVIDIA Jetson Orin NX?
The performance of YOLOv8 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLOv8s models achieve:
- **FP32 Precision**: 15.63 ms/im, 64 FPS
- **FP16 Precision**: 7.94 ms/im, 126 FPS
- **INT8 Precision**: 5.53 ms/im, 181 FPS
These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLOv8 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.