You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 

5.6 KiB

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 and defaults.yaml GitHub source.

!!! 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 ""

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

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 ""

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

Predict

Use a trained YOLOv8n model to run predictions on images.

!!! example ""

  ```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
  ```

Export

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

!!! example ""

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

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 overridden 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-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
    ```