Yolov11/Training

[memoriesbytalha]

Is there any argument while training that model does not override previous classes, class use 80 previous classes and train on that new on I want to add.

[UltralyticsAssistant]

👋 Hello @memoriesbytalha, thank you for reaching out with your question about YOLOv11 training 🚀! If you're looking to expand your model with additional classes without overriding existing ones, you might want to delve into our Docs where comprehensive tutorials on Python and CLI usages are available. These resources might already address your query or guide you toward a solution.

If this is a 🐛 Bug Report, please share a minimum reproducible example, as it will greatly assist us in identifying and addressing the issue.

If your question pertains to custom training ❓, kindly include detailed information such as your dataset's specific configurations and any relevant training logs. Ensure you're aligning with our Tips for Best Training Results for optimal performance.

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Upgrade

Make sure you're using the latest version of the ultralytics package along with all necessary requirements in a Python>=3.8 environment with PyTorch>=1.8. Execute the following command to verify you're up-to-date:

pip install -U ultralytics

Environments

Effortlessly run YOLO in verified environments equipped with all essential dependencies, including CUDA/CUDNN, Python, and PyTorch:

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

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Please remember this is an automated response 🤖, and an Ultralytics engineer will assist you soon. Thank you for your patience!

[glenn-jocher]

@memoriesbytalha to add new classes without overriding existing ones, you can modify your dataset's YAML file to include both the existing and new classes, ensuring the model is aware of all classes during training.

[Y-T-G]

You can't "add new classes" to an existing model without having the images for those classes in your dataset. Classes in deep learning models aren't like blocks that you can add or remove.