Command Line Interface

The Ultralytics command line interface (CLI) provides a straightforward way to use Ultralytics YOLO models without needing a Python environment. The CLI supports running various tasks directly from the terminal using the yolo command, requiring no customization or Python code.

yolo TASK MODE ARGS

Where:

• TASK (optional) is one of [detect, segment, classify, pose, obb]. If not explicitly passed, YOLO will attempt to infer 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 default.yaml.

Train

Train YOLO 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=yolo26n.pt epochs=100 imgsz=640

Val

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

yolo detect val model=yolo26n.pt

Predict

Use a trained model to run predictions on images.

yolo detect predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'

Export

Export a model to a different format like ONNX or CoreML.

yolo export model=yolo26n.pt format=onnx

Available Ultralytics 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 on the Export page.

Overriding Default Arguments

Override default arguments by passing them in the CLI as arg=value pairs.

yolo detect train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01

Overriding Default Config File

Override the default.yaml configuration file entirely by passing a new file with the cfg argument, such as cfg=custom.yaml.

To do this, first create a copy of default.yaml in your current working directory with the yolo copy-cfg command, which creates a default_copy.yaml file.

You can then pass this file as cfg=default_copy.yaml along with any additional arguments, like imgsz=320 in this example:

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

Solutions Commands

Ultralytics provides ready-to-use solutions for common computer vision applications through the CLI. The yolo solutions command exposes object counting, cropping, blurring, workout monitoring, heatmaps, instance segmentation, VisionEye, speed estimation, queue management, analytics, Streamlit inference, and zone-based tracking — see the Solutions page for the full catalog. Run yolo solutions help to list every supported solution and its arguments.

yolo solutions count show=True
yolo solutions count source="path/to/video.mp4" # specify video file path

For more information on Ultralytics solutions, visit the Solutions page.

FAQ

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

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

yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01

This command uses the train mode with specific arguments. For a full list of available arguments, refer to the Configuration Guide.

What tasks can I perform with the Ultralytics YOLO CLI?

The Ultralytics YOLO CLI supports various tasks, including detection, segmentation, classification, pose estimation, and oriented bounding box detection. You can also perform operations like:

• 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>.
• Use Solutions: Run yolo solutions <solution_name> for ready-made applications.

Customize each task 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 YOLO model using the CLI?

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

yolo val model=yolo26n.pt data=coco8.yaml batch=1 imgsz=640

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

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

You can export YOLO models to various formats including ONNX, TensorRT, CoreML, TensorFlow, and more. For instance, to export a model to ONNX format, run:

yolo export model=yolo26n.pt format=onnx

The export command supports numerous options to optimize your model for specific deployment environments. For complete details on all available export formats and their specific parameters, visit the Export page.

How do I use the pre-built solutions in the Ultralytics CLI?

Ultralytics provides ready-to-use solutions through the solutions command. For example, to count objects in a video:

yolo solutions count source="path/to/video.mp4"

These solutions require minimal configuration and provide immediate functionality for common computer vision tasks. To see all available solutions, run yolo solutions help. Each solution has specific parameters that can be customized to fit your needs.