Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, questions, or discussions, become a member of the Ultralytics Discord , Reddit and Forums !

To request an Enterprise License please complete the form at Ultralytics Licensing .

See below for a quickstart installation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment.

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8 .

pip install ultralytics

For alternative installation methods including Conda , Docker , and Git, please refer to the Quickstart Guide .

YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:

yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

yolo can be used for a variety of tasks and modes and accepts additional arguments, i.e. imgsz=640 . See the YOLOv8 CLI Docs for examples.

YOLOv8 may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt")
# Train the model
train_results = model.train(
    data="coco8.yaml",  # path to dataset YAML
    epochs=100,  # number of training epochs
    imgsz=640,  # training image size
    device="cpu",  # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu
# Evaluate model performance on the validation set
metrics = model.val()
# Perform object detection on an image
results = model("path/to/image.jpg")
results[0].show()
# Export the model to ONNX format
path = model.export(format="onnx")  # return path to exported model

See YOLOv8 Python Docs for more examples.

Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a YouTube tutorial, making it easy to learn and implement advanced YOLOv8 features.

YOLOv8 Detect , Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLOv8 Classify models pretrained on the ImageNet dataset. Track mode is available for all Detect, Segment and Pose models.

All Models download automatically from the latest Ultralytics release on first use.