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26 lines
2.2 KiB
Markdown
26 lines
2.2 KiB
Markdown
## Ultralytics YOLOv8 Example Applications
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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.
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### Ultralytics YOLO Example Applications
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| Title | Format | Contributor |
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| -------------------------------------------------------------------------------------------------------------- | ------------------ | --------------------------------------------------- |
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| [YOLO ONNX Detection Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX | [Justas Bartnykas](https://github.com/JustasBart) |
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| [YOLO OpenCV ONNX Detection Python](./YOLOv8-OpenCV-ONNX-Python) | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
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| [YOLO .Net ONNX Detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net | [Samuel Stainback](https://github.com/sstainba) |
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| [YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)](https://wiki.seeedstudio.com/YOLOv8-DeepStream-TRT-Jetson/) | Python | [Lakshantha](https://github.com/lakshanthad) |
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### How to Contribute
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We welcome contributions from the community in the form of examples, applications, and guides. To contribute, please follow these steps:
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1. Create a pull request (PR) with the `[Example]` prefix in the title, adding your project folder to the `examples/` directory in the repository.
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1. Ensure that your project meets the following criteria:
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- Utilizes the `ultralytics` package.
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- Includes a `README.md` file with instructions on how to run the project.
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- Avoids adding large assets or dependencies unless absolutely necessary.
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- The contributor is expected to provide support for issues related to their examples.
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If you have any questions or concerns about these requirements, please submit a PR, and we will be more than happy to guide you.
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