CLI Usage

1. Download Source Code

git clone https://github.com/ultralytics/ultralytics.git

2. Enable Maximum Board Performance

2.1. Enable MAX Power Mode

Enabling MAX Power Mode on Jetson will ensure that all CPU and GPU cores are turned on:

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#Orin Nano
sudo nvpmodel -m 2
#Orin Nx
sudo nvpmodel -m 0

2.2. Enable Jetson Clocks

Enabling Jetson Clocks will ensure that all CPU and GPU cores run at maximum frequency:

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sudo jetson_clocks

3. Use CLI for Prediction

CLI (Command-Line Interface) is a way for users to interact with computers or software programs. Users run programs or execute tasks by typing text commands, rather than clicking icons or buttons through a graphical user interface (GUI).

3.1. CLI Usage Syntax

​x
yolo TASK MODE ARGS
​
Where   TASK (optional) is one of [detect, segment, classify, pose, obb]
        MODE (required) is one of [train, val, predict, export, track, benchmark]
        ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.

3.2. Image Prediction

Use yolo11n.pt to predict the built-in images in the ultralytics project: If the system cannot find the corresponding model file in the directory where the command is running, it will be automatically downloaded (if download fails, you can copy the model in yourself)

Enter the project folder:

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cd /home/jetson/ultralytics/ultralytics/

Use yolo11n.pt to detect images in the target folder and output results:

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yolo predict model=yolo11n source='/home/jetson/ultralytics/ultralytics/assets'

Effect Preview

yolo recognition output video location: /home/jetson/ultralytics/ultralytics/runs/detect;

Each time it runs, predict under this folder automatically increments by 1

3.3. Video Prediction

Use yolo11n.pt to predict videos in the ultralytics project (not videos that come with ultralytics): If the system cannot find the corresponding model file in the directory where the command is running, it will be automatically downloaded (if download fails, you can copy the model in yourself)

Enter the project folder:

 YOLO Environment Setup
Object Detection