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 YOLOv8: CLI

yolo TASK MODE ARGS

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

yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01

yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640

yolo export model=yolov8n-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]. If it is not passed explicitly YOLOv8 will try to guess the TASK from the model type.
• MODE (required) is one of [train, val, predict, export, track]
• 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 YOLOv8n 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=yolov8n.pt epochs=100 imgsz=640

yolo detect train resume model=last.pt

Val

Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the model retains its training data and arguments as model attributes.

yolo detect val model=yolov8n.pt

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

Predict

Use a trained YOLOv8n model to run predictions on images.

yolo detect predict model=yolov8n.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 YOLOv8n model to a different format like ONNX, CoreML, etc.

yolo export model=yolov8n.pt format=onnx

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

Available YOLOv8 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=yolov8n.pt epochs=10 lr0=0.01

yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

yolo detect val model=yolov8n.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 YOLOv8 command line interface (CLI) for model training?

To train a YOLOv8 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=yolov8n.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 YOLOv8 CLI?

The Ultralytics YOLOv8 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 YOLOv8 model using the CLI?

To validate a YOLOv8 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=yolov8n.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 YOLOv8 models to using the CLI?

YOLOv8 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=yolov8n.pt format=onnx

For complete details, visit the Export page.

How do I customize YOLOv8 CLI commands to override default arguments?

To override default arguments in YOLOv8 CLI commands, pass them as arg=value pairs. For example, to train a model with custom arguments, use:

yolo train data=coco8.yaml model=yolov8n.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.

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