Giving class weight to Yolov8 about ultralytics

[github-actions]

👋 Hello @jaiydv, 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 about using class weights in YOLOv8 training. Currently, YOLOv8 does not natively support direct class weighting within the training process. However, you can manage class imbalance by focusing on data augmentation and oversampling strategies, as you've mentioned.

If you're specifically looking to integrate class weights, you might consider modifying the loss computation part of the source code to manually incorporate these weights. This would involve adjusting the loss functions in the model's training script to account for the imbalance based on your class frequencies.

For a more straightforward approach, ensuring your dataset is as balanced as possible or using techniques like image augmentation to artificially boost underrepresented classes remains a practical solution.

If you need further guidance on how to modify the loss functions or any other assistance, feel free to ask. Happy training! 🚀

[jaiydv]

@glenn-jocher I am curious why there is no support to pass on class weights, we can have a parameter of class weights that we can pass while training, that will be easier and more feasible, and having this functionality will be good for our YOLOv8 training process.

[glenn-jocher]

@jaiydv hello! Thanks for your suggestion about adding class weights directly in the training parameters for YOLOv8. Currently, YOLOv8 does not support class weights natively, primarily to keep the training process streamlined and consistent across various use cases.

However, we understand the importance of handling class imbalances effectively. For now, you might consider using data augmentation or modifying the loss computation in the source code to manually incorporate class weights. We appreciate your input and will consider this feature for future updates. Keep the feedback coming! 🚀

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

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• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

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