Integrating YOLOv8 into YOLOv3 Ultralytics about yolov3

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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):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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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]

@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!