yolov8_test/docs/quickstart.md

Install

Install YOLOv8 via the ultralytics pip package for the latest stable release or by cloning the https://github.com/ultralytics/ultralytics repository for the most up-to-date version.

!!! example "Pip install method (recommended)"

```bash
pip install ultralytics
```

!!! example "Git clone method (for development)"

```bash
git clone https://github.com/ultralytics/ultralytics
cd ultralytics
pip install -e '.[dev]'
```
See contributing section to know more about contributing to the project

Use with CLI

The YOLO command line interface (CLI) lets you simply train, validate or infer models on various tasks and versions. CLI requires no customization or code. You can simply run all tasks from the terminal with the yolo command.

!!! example

=== "Syntax"
    ```bash
    yolo task=detect    mode=train    model=yolov8n.yaml      args...
              classify       predict        yolov8n-cls.yaml  args...
              segment        val            yolov8n-seg.yaml  args...
                             export         yolov8n.pt        format=onnx  args...
    ```

=== "Example training"
    ```bash
    yolo detect train model=yolov8n.pt data=coco128.yaml device=0
    ```
=== "Example Multi-GPU training"
    ```bash
    yolo detect train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
    ```

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Use with Python

Python usage allows users to easily use YOLOv8 inside their Python projects. It provides functions for loading and running the model, as well as for processing the model's output. The interface is designed to be easy to use, so that users can quickly implement object detection in their projects.

Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or classification into their Python projects using YOLOv8.

!!! example

```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)

# Use the model
results = model.train(data="coco128.yaml", epochs=3)  # train the model
results = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
success = model.export(format="onnx")  # export the model to ONNX format
```

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