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docs/en/guides/deepstream-nvidia-jetson.md
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---
comments: true
description: Learn how to deploy Ultralytics YOLOv8 on NVIDIA Jetson devices using TensorRT and DeepStream SDK. Explore performance benchmarks and maximize AI capabilities.
keywords: Ultralytics, YOLOv8, NVIDIA Jetson, JetPack, AI deployment, embedded systems, deep learning, TensorRT, DeepStream SDK, computer vision
description: Learn how to deploy Ultralytics YOLO11 on NVIDIA Jetson devices using TensorRT and DeepStream SDK. Explore performance benchmarks and maximize AI capabilities.
keywords: Ultralytics, YOLO11, NVIDIA Jetson, JetPack, AI deployment, embedded systems, deep learning, TensorRT, DeepStream SDK, computer vision
---
# Ultralytics YOLOv8 on NVIDIA Jetson using DeepStream SDK and TensorRT
# Ultralytics YOLO11 on NVIDIA Jetson using DeepStream SDK and TensorRT
<p align="center">
<br>
@ -14,10 +14,10 @@ keywords: Ultralytics, YOLOv8, NVIDIA Jetson, JetPack, AI deployment, embedded s
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Run Multiple Streams with DeepStream SDK on Jetson Nano using Ultralytics YOLOv8
<strong>Watch:</strong> How to Run Multiple Streams with DeepStream SDK on Jetson Nano using Ultralytics YOLO11
</p>
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform.
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO11 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform.
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/deepstream-nvidia-jetson.avif" alt="DeepStream on NVIDIA Jetson">
@ -33,7 +33,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
Before you start to follow this guide:
- Visit our documentation, [Quick Start Guide: NVIDIA Jetson with Ultralytics YOLOv8](nvidia-jetson.md) to set up your NVIDIA Jetson device with Ultralytics YOLOv8
- Visit our documentation, [Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO11](nvidia-jetson.md) to set up your NVIDIA Jetson device with Ultralytics YOLO11
- Install [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) according to the JetPack version
- 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)
@ -43,7 +43,7 @@ Before you start to follow this guide:
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.
## DeepStream Configuration for YOLOv8
## DeepStream Configuration for YOLO11
Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) GitHub repository which includes NVIDIA DeepStream SDK support for YOLO models. We appreciate the efforts of marcoslucianops for his contributions!
@ -61,7 +61,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
cd DeepStream-Yolo
```
3. Download Ultralytics YOLOv8 detection model (.pt) of your choice from [YOLOv8 releases](https://github.com/ultralytics/assets/releases). Here we use [yolov8s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt).
3. Download Ultralytics YOLO11 detection model (.pt) of your choice from [YOLO11 releases](https://github.com/ultralytics/assets/releases). Here we use [yolov8s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt).
```bash
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
@ -69,7 +69,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
!!! note
You can also use a [custom trained YOLOv8 model](https://docs.ultralytics.com/modes/train/).
You can also use a [custom trained YOLO11 model](https://docs.ultralytics.com/modes/train/).
4. Convert model to ONNX
@ -179,7 +179,7 @@ deepstream-app -c deepstream_app_config.txt
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>
<div align=center><img width=1000 src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-deepstream.avif" alt="YOLO11 with deepstream"></div>
!!! tip
@ -317,21 +317,21 @@ This guide was initially created by our friends at Seeed Studio, Lakshantha and
## FAQ
### How do I set up Ultralytics YOLOv8 on an NVIDIA Jetson device?
### How do I set up Ultralytics YOLO11 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.
To set up Ultralytics YOLO11 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 YOLO11 deployment.
### What is the benefit of using TensorRT with YOLOv8 on NVIDIA Jetson?
### What is the benefit of using TensorRT with YOLO11 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](https://www.ultralytics.com/glossary/deep-learning-dl) 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.
Using TensorRT with YOLO11 optimizes the model for inference, significantly reducing latency and improving throughput on NVIDIA Jetson devices. TensorRT provides high-performance, low-latency [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) 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?
### Can I run Ultralytics YOLO11 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.
Yes, the guide for deploying Ultralytics YOLO11 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 YOLO11](#deepstream-configuration-for-yolo11) for detailed steps.
### How can I convert a YOLOv8 model to ONNX for DeepStream?
### How can I convert a YOLO11 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.
To convert a YOLO11 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:
@ -341,12 +341,12 @@ 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?
### What are the performance benchmarks for YOLO 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:
The performance of YOLO11 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.
These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLO11 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.