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.

# clone repo
git clone https://github.com/akanametov/ultralytics

# pip install required packages
pip install ultralytics

# go to code folder
cd ultralytics

On image:

yolo task=detect \
mode=predict \
model=yolov8m-football.pt \
conf=0.25 \
source=examples/football.jpg

PR curve:

Losses and mAP:

Confusion matrix:

Data preparation

• Download dataset:

bash scripts/get_dataset.sh

and pretrained yolov8m.pt model.

Single GPU training

# train model
yolo task=detect \
mode=train \
model=yolov8m.pt \
data=datasets/data.yaml \
epochs=100 \
imgsz=640

yolov8m.pt

YOLOv8 is available under two different licenses:

• GPL-3.0 License: See LICENSE file for details.
• Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at Ultralytics Licensing.

For YOLOv8 bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.