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docs/quickstart.md
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## Installation
## Install
Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most up-to-date version.
Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning
the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
up-to-date version.
!!! note "pip install (recommended)"
```
!!! example "Pip install method (recommended)"
```bash
pip install ultralytics
```
!!! note "git clone"
```
!!! 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
## CLI
The command line YOLO interface lets you simply train, validate or infer models on various tasks and versions.
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.
!!! note
!!! example
=== "Syntax"
```bash
yolo task=detect mode=train model=yolov8n.yaml args...
@ -35,22 +42,32 @@ CLI requires no customization or code. You can simply run all tasks from the ter
```bash
yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
```
[CLI Guide](cli.md){ .md-button .md-button--primary}
## 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.
## Use with Python
Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or classification into their Python projects using YOLOv8.
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
!!! 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
# 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
```
[API Guide](sdk.md){ .md-button .md-button--primary}
[Python Guide](python.md){.md-button .md-button--primary}