Auto annotation for specific labels about ultralytics

[github-actions]

👋 Hello @toni-santos, 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.

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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.

[glenn-jocher]

Hello!

Thanks for reaching out with your question. The auto_annotate function indeed utilizes pre-trained models which are capable of detecting and segmenting multiple classes. If you're seeing unwanted classes like "car" or "table," it's because the pre-trained model includes these classes by default.

To generate segmentations only for your specific class, you would need to modify the detection model to detect only your class of interest before running auto_annotate. This involves re-training the detection model on your dataset with only the relevant class. Once you have a model trained specifically for your class, you can then use auto_annotate with this model to generate the desired segmentation annotations.

If you need further guidance on re-training your model or any other questions, feel free to ask. Happy to help!

[toni-santos]

That's it! :) Thank you very much!

[glenn-jocher]

You're welcome! 😊 If you have any more questions in the future, feel free to reach out. Happy coding!