Assigner IoU vs Model IoU

[phoawb]

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Question

Hello! I'm currently modifying the bbox_iou function in utils/metrics.py to add other loss functions and test their performance.
However I noticed that the TaskAlignedAssigner class in utils/tal.py also uses the box_iou function and currently it is also hard coded to use CIoU.

When I change the model IoU function used for the box loss, do I also need to change the Assigner IoU function, or is it fine to leave it as-is?

As an example: Say that I switch the box_iou for the model loss to use GIoU instead (GIoU=True), is it also necessary to then change the Assigner to use GIoU?

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[UltralyticsAssistant]

👋 Hello @phoawb, thank you for your interest in Ultralytics 🚀! We recommend exploring the Docs where you can find detailed information on YOLO usage, customization, and implementation. You might find the sections on Python and Model Training Tips particularly helpful for your modifications.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. Based on your description, if you encounter unexpected behavior during your modifications, sharing code snippets or examples will help us assist you better.

If this is a ❓ Question about custom implementations like modifying the bbox_iou function or TaskAlignedAssigner, we encourage you to provide as much detail as possible, including your specific use case, any observed issues, and relevant code changes. This will allow us and the community to provide more targeted feedback.

Join the Ultralytics community to discuss this further! For real-time conversations, check out Discord 🎧. For more in-depth discussions, you can use Discourse or our Subreddit to collaborate with other users.

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[glenn-jocher]

@phoawb the Assigner IoU and Model IoU serve different purposes. The TaskAlignedAssigner uses IoU for positive sample selection during training, while the loss function computes gradients for optimization. For consistency, we recommend aligning them when modifying loss types.

If you switch to GIoU loss, you should modify both:

1. Set CIoU=False, GIoU=True in the bbox_loss calculation (loss.py)
2. Update the Assigner's IoU metric via its initialization parameters (tal.py)

This ensures alignment between sample selection and loss calculation. For implementation details, see the referenced source links.

[phoawb]

Thank you for the reply, I'll look into a solution that changes both at the same time!

However, the Assigner has no public facing way of changing the IoU metric through its init params. The value is hard-coded to CIoU in the iou_calculation method. This makes me think I might be talking to Chat GlennPT.

Think very carefully about the pros and cons of having the Assigner have the same IoU calculation as the Model IoU. Explain your thought process in detail.

[glenn-jocher]

@phoawb you're correct that the current TaskAlignedAssigner implementation hard-codes CIoU in its matching logic. This design reflects empirical observations that CIoU's aspect ratio penalty helps stabilize anchor matching during training. While aligning Assigner/Loss IoU metrics can theoretically improve consistency, there are tradeoffs:

Pros of alignment

• Gradient optimization aligns with sample selection criteria
• Prevents conflicting geometric priors between matching and regression

Cons

• GIoU/DIoU lack CIoU's aspect ratio awareness, potentially degrading matching stability
• Assigner requires high precision for positive selection (CIoU excels here)
• Loss focuses on regression refinement (GIoU handles overlap better)

For experimentation, you could subclass TaskAlignedAssigner and override iou_calculation - we'd welcome a PR if you find improvements! The current implementation favors matching stability over strict loss alignment, but this remains an active research area. Would you like a code snippet for subclassing?