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Tutorial - Ultralytics YOLOv8

YOLO Vision

Let's run Ultralytics YOLOv8 on Jetson with NVIDIA TensorRT .

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

Ultralytics YOLO supported tasks

What you need

  1. One of the following Jetson devices:

    Jetson AGX Orin (64GB) Jetson AGX Orin (32GB) Jetson Orin NX (16GB) Jetson Orin Nano (8GB) Jetson Nano (4GB)

  2. Running one of the following versions of JetPack :

    JetPack 4 (L4T r32.x) JetPack 5 (L4T r35.x) JetPack 6 (L4T r36.x)

How to start

Execute the below commands according to the JetPack version to pull the corresponding Docker container and run on Jetson. 

t=ultralytics/ultralytics:latest-jetson-jetpack4
sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

t=ultralytics/ultralytics:latest-jetson-jetpack5
sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

t=ultralytics/ultralytics:latest-jetson-jetpack6
sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

Convert model to TensorRT and run inference

The YOLOv8n model in PyTorch format is converted to TensorRT to run inference with the exported model.

Example 

from ultralytics import YOLO

# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")

# Export the model
model.export(format="engine")  # creates 'yolov8n.engine'

# Load the exported TensorRT model
trt_model = YOLO("yolov8n.engine")

# Run inference
results = trt_model("https://ultralytics.com/images/bus.jpg")

# Export a YOLOv8n PyTorch model to TensorRT format
yolo export model=yolov8n.pt format=engine  # creates 'yolov8n.engine'

# Run inference with the exported model
yolo predict model=yolov8n.engine source='https://ultralytics.com/images/bus.jpg'
Manufacturing Sports Wildlife
Vehicle Spare Parts Detection Football Player Detection Tiger-pose
Vehicle Spare Parts Detection Football Player Detection Tiger pose Detection

Note

Visit the Export page to access additional arguments when exporting models to different model formats. Note that the default arguments require inference using fixed image dimensions when dynamic=False . To change the input source for inference, please refer to Model Prediction page.

Benchmarks

Benchmarks of the YOLOv8 variants with TensorRT were run by Seeed Studio on their reComputer systems:

Model PyTorch FP32 FP16 INT8
YOLOv8n 32 63 120 167
YOLOv8s 25 26 69 112
YOLOv8m 11 11 33 56
YOLOv8l 6 6 20 38
Model PyTorch FP32 FP16 INT8
YOLOv8n 56 115 204 256
YOLOv8s 53 67 128 196
YOLOv8m 26 31 63 93
YOLOv8l 16 20 42 69
Model PyTorch FP32 FP16 INT8
YOLOv8n 77 192 323 385
YOLOv8s 67 119 213 303
YOLOv8m 40 56 105 145
YOLOv8l 27 38 73.5 114
  • FP32/FP16/INT8 with TensorRT (frames per second)
  • Original post with the benchmarks are found here

Further reading

To learn more, visit our comprehensive guide on running Ultralytics YOLOv8 on NVIDIA Jetson including benchmarks!

Note

Ultralytics YOLOv8 models are offered under AGPL-3.0 License which is an OSI-approved open-source license and is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the LICENSE file for more details.

One-Click Run Ultralytics YOLO on Jetson Orin - by Seeed Studio jetson-examples

Quickstart âš¡

  1. Install the package: 

    pip install jetson-examples

  2. Restart your reComputer: 

    sudo reboot

  3. Run Ultralytics YOLO on Jetson with one command: 

    reComputer run ultralytics-yolo
  4. Enter http://127.0.0.1:5001 or http://device_ip:5001 in your browser to access the Web UI.

    ultralytics_yolo_webui_by_seeedstudio.gif

For more details, please read: Jetson-Example: Run Ultralytics YOLO Platform Service on NVIDIA Jetson Orin .

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