A study of YOLO v8

This repository contains a study on the YOLO v8 network introduced by Ultralytics.

For more information, check out the GitHub repository and the documentation.

Article

Find a survey on the YOLO architectures at arXive2304.00501.

Quickstart

pip install ultralytics
yolo --help

WARNING '--help' does not require leading dashes '--', updating to 'help'.

    Arguments received: ['yolo', '--help']. Ultralytics 'yolo' commands use the following syntax:

        yolo TASK MODE ARGS

        Where   TASK (optional) is one of ('detect', 'segment', 'classify', 'pose')
                MODE (required) is one of ('train', 'val', 'predict', 'export', 'track', 'benchmark')
                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
                    See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'

    1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
        yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01

    2. Predict a YouTube video using a pretrained segmentation model at image size 320:
        yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

    3. Val a pretrained detection model at batch-size 1 and image size 640:
        yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640

    4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
        yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128

    5. Run special commands:
        yolo help
        yolo checks
        yolo version
        yolo settings
        yolo copy-cfg
        yolo cfg

    Docs: https://docs.ultralytics.com
    Community: https://community.ultralytics.com
    GitHub: https://github.com/ultralytics/ultralytics
    

Install

GPU support

cd gpu
docker compose up

CPU support

cd cpu
docker compose up