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Integrating YOLOv8 into YOLOv3 Ultralytics #2172

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jeongjin0 opened this issue Jan 8, 2024 · 2 comments
Closed
1 task done

Integrating YOLOv8 into YOLOv3 Ultralytics #2172

jeongjin0 opened this issue Jan 8, 2024 · 2 comments
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@jeongjin0
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Firstly, I'd like to express my gratitude for the impressive work on the YOLOv3 Ultralytics project. It's truly commendable.

I'm interested in integrating the YOLOv8 model within this framework. Could you guide me on the feasibility and steps required for integrating YOLOv8 into the YOLOv3 Ultralytics repository?

Thank you for your time and assistance.

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@jeongjin0 jeongjin0 added the question Further information is requested label Jan 8, 2024
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github-actions bot commented Jan 8, 2024

👋 Hello @jeongjin0, thank you for your interest in YOLOv3 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov3  # clone
cd yolov3
pip install -r requirements.txt  # install

Environments

YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

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YOLOv3 CI

If this badge is green, all YOLOv3 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

@glenn-jocher
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@jeongjin0 hello! Thank you for your kind words and for reaching out with your question. 😊

Integrating YOLOv8 into the YOLOv3 Ultralytics framework would be a significant undertaking, as YOLOv8 is a more advanced version with substantial differences in architecture and functionality. This would require deep modifications to the codebase, including adapting the model architecture, data preprocessing, postprocessing, and potentially the training pipeline.

While we encourage innovation and experimentation, such an integration is beyond the scope of support we can provide through GitHub issues. If you're interested in pursuing this, you would need to have a strong understanding of both codebases and be prepared for a challenging project.

For detailed guidance on YOLOv3, please refer to our documentation at https://docs.ultralytics.com. If you're looking to work with the latest YOLO models, I would recommend using the repositories and documentation specific to those versions.

Best of luck with your project, and thank you for being part of the YOLO and Ultralytics community!

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