WE LAUNCHED YOLOv8! 🚀

[taliabender]

It's been a long time coming of research and development, and we're so excited to finally bring you the latest installment of the YOLO family of architectures. YOLOv8 builds on the successes of countless experiments and previous architectures, we've created models that are the best in the world at what they do: real-time object detection, classification, and segmentation.

So, what actually sets YOLOv8 apart?

⭐️ Well-documented workflows, prioritizing clarity and thoroughness.
⭐️ Spotless code, written from the ground up.
⭐️ Simple usage of the easiest YOLO models ever to train and deploy.
⭐️ Flexible solutions via support for all YOLO versions.

...And did we mention that it's SOTA?

Get started with YOLOv8!

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Ultralytics YOLOv8, developed by Ultralytics, 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, image segmentation and image classification tasks.

Documentation

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

Install

Pip install the ultralytics package including all requirements.txt in a 3.10>=Python>=3.7 environment, including PyTorch>=1.7.

pip install ultralytics

Usage

CLI

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.

Python

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.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model
model.train(data="coco128.yaml", epochs=3)  # train the model
metrics = 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

Models download automatically from the latest Ultralytics release. See YOLOv8 Python Docs for more examples.