ultralytics/docs/en/usage/cli.md

comments	description	keywords
true	Explore the YOLOv8 command line interface (CLI) for easy execution of detection tasks without needing a Python environment.	YOLOv8 CLI, command line interface, YOLOv8 commands, detection tasks, Ultralytics, model training, model prediction

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

!!! example

=== "Syntax"

    Ultralytics `yolo` commands use the following syntax:
    ```bash
    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.
    ```
    See all ARGS in the full [Configuration Guide](cfg.md) or with `yolo cfg`

=== "Train"

    Train a detection model for 10 epochs with an initial learning_rate of 0.01
    ```bash
    yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
    ```

=== "Predict"

    Predict a YouTube video using a pretrained segmentation model at image size 320:
    ```bash
    yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
    ```

=== "Val"

    Val a pretrained detection model at batch-size 1 and image size 640:
    ```bash
    yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
    ```

=== "Export"

    Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
    ```bash
    yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
    ```

=== "Special"

    Run special commands to see version, view settings, run checks and more:
    ```bash
    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 YOLOv8 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

!!! warning

Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` between arguments.

- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25`   ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25`   ❌
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25`   ❌

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.

!!! example

=== "Train"

    Start training YOLOv8n on COCO8 for 100 epochs at image-size 640.
    ```bash
    yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
    ```

=== "Resume"

    Resume an interrupted training.
    ```bash
    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.

!!! example

=== "Official"

    Validate an official YOLOv8n model.
    ```bash
    yolo detect val model=yolov8n.pt
    ```

=== "Custom"

    Validate a custom-trained model.
    ```bash
    yolo detect val model=path/to/best.pt
    ```

Predict

Use a trained YOLOv8n model to run predictions on images.

!!! example

=== "Official"

    Predict with an official YOLOv8n model.
    ```bash
    yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
    ```

=== "Custom"

    Predict with a custom model.
    ```bash
    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.

!!! example

=== "Official"

    Export an official YOLOv8n model to ONNX format.
    ```bash
    yolo export model=yolov8n.pt format=onnx
    ```

=== "Custom"

    Export a custom-trained model to ONNX format.
    ```bash
    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'.

{% include "macros/export-table.md" %}

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.

!!! tip

=== "Train"

    Train a detection model for `10 epochs` with `learning_rate` of `0.01`
    ```bash
    yolo detect train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
    ```

=== "Predict"

    Predict a YouTube video using a pretrained segmentation model at image size 320:
    ```bash
    yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
    ```

=== "Val"

    Validate a pretrained detection model at batch-size 1 and image size 640:
    ```bash
    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:

!!! example

=== "CLI"

    ```bash
    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.