ultralytics/examples/README.md

Ultralytics YOLOv8 Example Applications

This repository features a collection of real-world applications and walkthroughs, provided as either Python files or notebooks. Explore the examples below to see how YOLOv8 can be integrated into various applications.

Ultralytics YOLO Example Applications

Title	Format	Contributor
YOLO ONNX Detection Inference with C++	C++/ONNX	Justas Bartnykas
YOLO OpenCV ONNX Detection Python	OpenCV/Python/ONNX	Farid Inawan
YOLOv8 .NET ONNX ImageSharp	C#/ONNX/ImageSharp	Compunet
YOLO .Net ONNX Detection C#	C# .Net	Samuel Stainback
YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)	Python	Lakshantha
YOLOv8 ONNXRuntime Python	Python/ONNXRuntime	Semih Demirel
YOLOv8 ONNXRuntime CPP	C++/ONNXRuntime	DennisJcy, Onuralp Sezer
RTDETR ONNXRuntime C#	C#/ONNX	Kayzwer
YOLOv8 SAHI Video Inference	Python	Muhammad Rizwan Munawar
YOLOv8 Region Counter	Python	Muhammad Rizwan Munawar
YOLOv8 Segmentation ONNXRuntime Python	Python/ONNXRuntime	jamjamjon
YOLOv8 LibTorch CPP	C++/LibTorch	Myyura

How to Contribute

We greatly appreciate contributions from the community, including examples, applications, and guides. If you'd like to contribute, please follow these guidelines:

1. Create a pull request (PR) with the title prefix [Example], adding your new example folder to the examples/ directory within the repository.
2. Make sure your project adheres to the following standards:
   • Makes use of the ultralytics package.
   • Includes a README.md with clear instructions for setting up and running the example.
   • Refrains from adding large files or dependencies unless they are absolutely necessary for the example.
   • Contributors should be willing to provide support for their examples and address related issues.

For more detailed information and guidance on contributing, please visit our contribution documentation.

If you encounter any questions or concerns regarding these guidelines, feel free to open a PR or an issue in the repository, and we will assist you in the contribution process.