metadata

comments: true
description: >-
  Learn how to use Ultralytics YOLO through Command Line: train models, run
  predictions and exports models to different formats easily using terminal
  commands.
keywords: >-
  Ultralytics, YOLO, CLI, train, validation, prediction, command line interface,
  YOLO CLI, YOLO terminal, model training, prediction, exporting

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.

!!! example

=== "Syntax"

    Ultralytics `yolo` commands use the following syntax:
    ```bash
    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.
    ```
    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=coco128.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/Zgi9g1ksQHc' imgsz=320
    ```

=== "Val"

    Val a pretrained detection model at batch-size 1 and image size 640:
    ```bash
    yolo val model=yolov8n.pt data=coco128.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]. 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 GitHub source.

!!! warning "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` &nbsp; ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` &nbsp; ❌
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` &nbsp; ❌

Train

Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

!!! example "Example"

=== "Train"

    Start training YOLOv8n on COCO128 for 100 epochs at image-size 640.
    ```bash
    yolo detect train data=coco128.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 COCO128 dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.

!!! example "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 "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 "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'.

Format	`format` Argument	Model	Metadata	Arguments
PyTorch	-	`yolov8n.pt`	✅	-
TorchScript	`torchscript`	`yolov8n.torchscript`	✅	`imgsz`, `optimize`
ONNX	`onnx`	`yolov8n.onnx`	✅	`imgsz`, `half`, `dynamic`, `simplify`, `opset`
OpenVINO	`openvino`	`yolov8n_openvino_model/`	✅	`imgsz`, `half`
TensorRT	`engine`	`yolov8n.engine`	✅	`imgsz`, `half`, `dynamic`, `simplify`, `workspace`
CoreML	`coreml`	`yolov8n.mlpackage`	✅	`imgsz`, `half`, `int8`, `nms`
TF SavedModel	`saved_model`	`yolov8n_saved_model/`	✅	`imgsz`, `keras`
TF GraphDef	`pb`	`yolov8n.pb`	❌	`imgsz`
TF Lite	`tflite`	`yolov8n.tflite`	✅	`imgsz`, `half`, `int8`
TF Edge TPU	`edgetpu`	`yolov8n_edgetpu.tflite`	✅	`imgsz`
TF.js	`tfjs`	`yolov8n_web_model/`	✅	`imgsz`
PaddlePaddle	`paddle`	`yolov8n_paddle_model/`	✅	`imgsz`
ncnn	`ncnn`	`yolov8n_ncnn_model/`	✅	`imgsz`, `half`

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=coco128.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/Zgi9g1ksQHc' imgsz=320
    ```

=== "Val"
    Validate a pretrained detection model at batch-size 1 and image size 640:
    ```bash
    yolo detect val model=yolov8n.pt data=coco128.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
    ```