YOLOv8-16bit/docs/usage/cli.md

comments	description	keywords
true	Learn how to use Ultralytics YOLO through Command Line: train models, run predictions and exports models to different formats easily using terminal commands.	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`   ✅
- `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 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
    ```