YOLOv8-16bit/docs/cli.md

The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting YOLOv8 models.

The yolo command is used for all actions:

!!! example ""

=== "CLI"

    ```bash
    yolo TASK MODE ARGS
    ```

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]
• 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.

!!! note ""

<b>Note:</b> Arguments MUST be passed as `arg=val` with an equals sign and a space between `arg=val` pairs

- `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 ""

=== "CLI"

    ```bash
    yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
    ```

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO("yolov8n.yaml")  # build a new model from scratch
    model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
    
    # Train the model
    results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
    ```

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 ""

=== "CLI"

    ```bash
    yolo detect val model=yolov8n.pt  # val official model
    yolo detect val model=path/to/best.pt  # val custom model
    ```

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO("yolov8n.pt")  # load an official model
    model = YOLO("path/to/best.pt")  # load a custom model
    
    # Validate the model
    results = model.val()  # no arguments needed, dataset and settings remembered
    ```

Predict

Use a trained YOLOv8n model to run predictions on images.

!!! example ""

=== "CLI"

    ```bash
    yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"  # predict with official model
    yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
    ```

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO("yolov8n.pt")  # load an official model
    model = YOLO("path/to/best.pt")  # load a custom model
    
    # Predict with the model
    results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
    ```

Export

Export a YOLOv8n model to a different format like ONNX, CoreML, etc.

!!! example ""

=== "CLI"

    ```bash
    yolo export model=yolov8n.pt format=onnx  # export official model
    yolo export model=path/to/best.pt format=onnx  # export custom trained model
    ```

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO("yolov8n.pt")  # load an official model
    model = YOLO("path/to/best.pt")  # load a custom trained
    
    # Export the model
    model.export(format="onnx")
    ```

Available YOLOv8 export formats include:

| Format                                                                     | `format=`          | Model                     |
|----------------------------------------------------------------------------|--------------------|---------------------------|
| [PyTorch](https://pytorch.org/)                                            | -                  | `yolov8n.pt`              |
| [TorchScript](https://pytorch.org/docs/stable/jit.html)                    | `torchscript`      | `yolov8n.torchscript`     |
| [ONNX](https://onnx.ai/)                                                   | `onnx`             | `yolov8n.onnx`            |
| [OpenVINO](https://docs.openvino.ai/latest/index.html)                     | `openvino`         | `yolov8n_openvino_model/` |
| [TensorRT](https://developer.nvidia.com/tensorrt)                          | `engine`           | `yolov8n.engine`          |
| [CoreML](https://github.com/apple/coremltools)                             | `coreml`           | `yolov8n.mlmodel`         |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`      | `yolov8n_saved_model/`    |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`               | `yolov8n.pb`              |
| [TensorFlow Lite](https://www.tensorflow.org/lite)                         | `tflite`           | `yolov8n.tflite`          |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`          | `yolov8n_edgetpu.tflite`  |
| [TensorFlow.js](https://www.tensorflow.org/js)                             | `tfjs`             | `yolov8n_web_model/`      |
| [PaddlePaddle](https://github.com/PaddlePaddle)                            | `paddle`           | `yolov8n_paddle_model/`   |

---

Overriding default arguments

Default arguments can be overriden by simply passing them as arguments in the CLI in arg=value pairs.

!!! tip ""

=== "Example 1"
    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
    ```

=== "Example 2"
    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
    ```

=== "Example 3"
    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-config 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-config
    yolo cfg=default_copy.yaml imgsz=320
    ```