Command Line Interface Usage

The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the yolo command.

Watch: Mastering Ultralytics YOLO: CLI

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.

yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640

yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128

yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg

Where:

• TASK (optional) is one of [detect, segment, classify, pose, obb]. If it is not passed explicitly YOLO11 will try to guess the TASK from the model type.
• 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. For a full list of available ARGS see the Configuration page and defaults.yaml

Train

Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640

yolo detect train resume model=last.pt

Val

Validate trained YOLO11n model accuracy on the COCO8 dataset. No arguments are needed as the model retains its training data and arguments as model attributes.

yolo detect val model=yolo11n.pt

yolo detect val model=path/to/best.pt

Predict

Use a trained YOLO11n model to run predictions on images.

yolo detect predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'

yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'

Export

Export a YOLO11n model to a different format like ONNX, CoreML, etc.

yolo export model=yolo11n.pt format=onnx

yolo export model=path/to/best.pt format=onnx

Available YOLO11 export formats are in the table below. You can export to any format using the format argument, i.e. format='onnx' or format='engine'.

See full export details in the Export page.

Overriding default arguments

Default arguments can be overridden by simply passing them as arguments in the CLI in arg=value pairs.

yolo detect train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

yolo segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640

Overriding default config file

You can override the default.yaml config file entirely by passing a new file with the cfg arguments, i.e. cfg=custom.yaml.

To do this first create a copy of default.yaml in your current working dir with the yolo copy-cfg command.

This will create default_copy.yaml, which you can then pass as cfg=default_copy.yaml along with any additional args, like imgsz=320 in this example:

yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320

FAQ

How do I use the Ultralytics YOLO11 command line interface (CLI) for model training?

To train a YOLO11 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, you would run:

yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

This command uses the train mode with specific arguments. Refer to the full list of available arguments in the Configuration Guide.

What tasks can I perform with the Ultralytics YOLO11 CLI?

The Ultralytics YOLO11 CLI supports a variety of tasks including detection, segmentation, classification, validation, prediction, export, and tracking. For instance:

• Train a Model: Run yolo train data=<data.yaml> model=<model.pt> epochs=<num>.
• Run Predictions: Use yolo predict model=<model.pt> source=<data_source> imgsz=<image_size>.
• Export a Model: Execute yolo export model=<model.pt> format=<export_format>.

Each task can be customized with various arguments. For detailed syntax and examples, see the respective sections like Train, Predict, and Export.

How can I validate the accuracy of a trained YOLO11 model using the CLI?

To validate a YOLO11 model's accuracy, use the val mode. For example, to validate a pretrained detection model with a batch size of 1 and image size of 640, run:

yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640

This command evaluates the model on the specified dataset and provides performance metrics. For more details, refer to the Val section.

What formats can I export my YOLO11 models to using the CLI?

YOLO11 models can be exported to various formats such as ONNX, CoreML, TensorRT, and more. For instance, to export a model to ONNX format, run:

yolo export model=yolo11n.pt format=onnx

For complete details, visit the Export page.

How do I customize YOLO11 CLI commands to override default arguments?

To override default arguments in YOLO11 CLI commands, pass them as arg=value pairs. For example, to train a model with custom arguments, use:

yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

For a full list of available arguments and their descriptions, refer to the Configuration Guide. Ensure arguments are formatted correctly, as shown in the Overriding default arguments section.

📅 Created 1 year ago ✏️ Updated 2 months ago

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