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13-Model Conversion on Jetson

This section explains how to convert YOLO models on NVIDIA Jetson devices for optimal inference performance using TensorRT.

The standard conversion pipeline is:

PyTorch (.pt) → ONNX (.onnx) → TensorRT (.engine)

1. Enable Optimal Jetson Performance​

Before model conversion or inference, configure Jetson for maximum performance.

Enable MAX Power Mode​

sudo nvpmodel -m 2

Enable Jetson Clocks​

sudo jetson_clocks

2. Model Conversion Overview​

TensorRT provides the fastest inference on Jetson. Ultralytics YOLO supports direct export to TensorRT, automatically generating an intermediate ONNX model.

3. CLI Model Conversion​

Navigate to the Ultralytics directory:

cd ~/ultralytics/ultralytics

Export YOLO models to TensorRT:

yolo export model=yolo11n.pt format=engine
# yolo export model=yolo11n-seg.pt format=engine
# yolo export model=yolo11n-pose.pt format=engine
# yolo export model=yolo11n-cls.pt format=engine
# yolo export model=yolo11n-obb.pt format=engine

The generated .engine file will be saved alongside the original model.

4. Python Model Conversion​

Navigate to the demo directory:

cd ~/ultralytics/ultralytics/yahboom_demo

Run the conversion script:

python3 model_pt_onnx_engine.py

Example Python Code​

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.export(format="engine")

5. Model Inference​

USB Camera Inference (CLI)​

yolo predict model=yolo11n.engine source=0 show save=False

ONNX Inference​

yolo predict model=yolo11n.onnx source=0 show save=False
note

CLI inference supports USB cameras. For CSI cameras, use Python-based inference.

6. Common Issues​

onnxslim Error​

If you encounter an onnxslim error:

sudo pip3 install onnxslim

Then re-run the export command.

7. Summary​

  • Always enable MAX power mode and Jetson clocks
  • TensorRT delivers the best inference performance
  • Ultralytics simplifies conversion using CLI and Python APIs
  • Use .engine models for production deployment

Maintained by HemiHex for Jetson-based advanced vision workflows.