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    YOLOv8 is a state-of-the-art object detection model developed by Ultralytics. It builds upon the success of previous YOLO versions and introduces new features and improvements to enhance performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it suitable for a wide range of tasks such as object detection, instance segmentation, image classification, and pose estimation.

    Installation and Usage

    To install YOLOv8, you can use the following pip command in a Python environment with version 3.8 or higher and PyTorch version 1.8 or higher:

    pip install ultralytics

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

    YOLOv8 can be used directly in the Command Line Interface (CLI) with the yolo command. For example, to perform object detection on an image, you can use:

    yolo predict model=yolo8n.pt source='https://ultralytics.com/images/bus.jpg'

    In a Python environment, you can use YOLOv8 as follows:

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo8n.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
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    Includes AI-generated content
    Where can I download yolov8 models?
    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.
    huggingface.co
    Github
    https://github.com/ultralytics/ultralytics/blob/main/docs/en/models/yol…
    ultralytics/docs/en/models/yolov8.md at main - GitHub

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