A generalized YOLOv8 model with DET, OBB, SEG and POSE tasks. about ultralytics HOT 2 OPEN

👋 Hello @zhouzq-thu, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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.

Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.

Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 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

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

from ultralytics.

@zhouzq-thu hello there! Thank you for your detailed suggestion regarding a generalized YOLOv8 model that can handle multiple tasks (DET, OBB, SEG, and POSE) in one framework. This is indeed a very interesting idea and could significantly enhance YOLOv8's versatility.

Your proposed approach to integrating a generalized head that can dynamically configure to handle combinations of different tasks is compelling. It would allow the user customization based on specific requirements, and using flags like is_obb, nm, and kpt_shape as parameters offers a straightforward method to toggle functionalities.

I encourage you to proceed with submitting a PR since you are already considering it. The community, including the development team, would greatly benefit from this capability and can provide feedback directly on your implementation. Looking forward to seeing your contribution! 😊🚀

Best of luck!

from ultralytics.