YOLOv8-16bit/docs/quickstart.md

Installation

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.

!!! note "pip install (recommended)" pip install ultralytics !!! note "git clone" git clone https://github.com/ultralytics/ultralytics cd ultralytics pip install -e '.[dev]' See contributing section to know more about contributing to the project

CLI

The command line YOLO interface 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. !!! note === "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 task=detect mode=train model=yolov8n.pt data=coco128.yaml device=0
    ```
=== "Example Multi-GPU training"
    ```bash
    yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
    ```

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Python API

The Python API allows users to easily use YOLOv8 in 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.

!!! note ```python from ultralytics import YOLO

model = YOLO('yolov8n.yaml')                # build a new model from scratch
model = YOLO('yolov8n.pt')                  # load a pretrained model (recommended for best training results)
results = model.train(data='coco128.yaml')  # train the model
results = model.val()                       # evaluate model performance on the validation set
results = model.predict(source='bus.jpg')   # predict on an image
success = model.export(format='onnx')       # export the model to ONNX format
```

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