Have statistics functions return a meaningful, non-none result even if only one value is available

[unfug-at-github]

Have statistics functions return a meaningful, non-none result even if only one value is available

Breaking change

Proposed change

Currently many of the statistics function return none when only one value is available, however, a meaningful value exists (e.g. the average of { 17.5 } is 17.5). This change will return a value for all of those cases. None will still be returned in cases where no value is available.

Type of change

• [ ] Dependency upgrade
• [x] Bugfix (non-breaking change which fixes an issue)
• [ ] New integration (thank you!)
• [ ] New feature (which adds functionality to an existing integration)
• [ ] Deprecation (breaking change to happen in the future)
• [ ] Breaking change (fix/feature causing existing functionality to break)
• [ ] Code quality improvements to existing code or addition of tests

Additional information

• This PR fixes or closes issue: fixes #
• This PR is related to issue:
• Link to documentation pull request:

Checklist

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• [x] Tests have been added to verify that the new code works.

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[UltralyticsAssistant]

👋 Hello @panp4n, thank you for your interest in Ultralytics 🚀!

We recommend checking out our Docs where you can find many Python and CLI examples. They might have answers to common questions.

For your 🐛 Bug Report, please provide a minimum reproducible example to help us debug the issue you're experiencing with YOLO11-seg.

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[Y-T-G]

I can't reproduce this in the latest version. Please update your ultralytics.

[panp4n]

I can't reproduce this in the latest version. Please update your ultralytics.

Thanks for your reply. The latest version is fine.

[glenn-jocher]

Great to hear it's working now! If you have any more questions, feel free to ask.

[panp4n]

Another question: There are extra parts at the edge of the mask in YOLO11-seg predict.

In my own inference code, it is fine.

In YOLOv8-seg predict, it is also fine. I think YOLO11-seg predict script has some difference for mask processing.

[Y-T-G]

You can try passing retina_masks=True

[panp4n]

You can try passing retina_masks=True

It works, Thank you so much!

I need optimize my inference code, How can I implement retina_masks in my code.

[glenn-jocher]

@panp4n you're welcome! To implement retina_masks in your code, you can set the parameter when calling the model's predict function, like this: model.predict(source, retina_masks=True).

[panp4n]

@panp4n you're welcome! To implement retina_masks in your code, you can set the parameter when calling the model's predict function, like this: model.predict(source, retina_masks=True).

I knew that, I want to know how to implement mask post-process like retina_masks in the onnxruntime or openvino inference. As you can see, the edge of this mask is rough.

[Y-T-G]

The code used by retina_masks is here

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/utils/ops.py#L689

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/models/yolo/segment/predict.py#L49-L50

[panp4n]

The code used by retina_masks is here

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/utils/ops.py#L689

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/models/yolo/segment/predict.py#L49-L50

Thank you! This helps me a lot.

[glenn-jocher]

You're welcome! If you have any more questions, feel free to ask.

[panp4n]

The code used by retina_masks is here

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/utils/ops.py#L689

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/models/yolo/segment/predict.py#L49-L50

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/ultralytics/utils/ops.py#L729 I can't reproduce this align_corners=False by using OpenCV C++ api . The example also uses cv2.resize(), My code is similar to this. There is still a gap in effectiveness. https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py#L256-L258

[panp4n]

The same model, why the mask of official predict script is better than deployment code? Do you have any recommendations?

[glenn-jocher]

The official script uses retina_masks=True for smoother edges. Ensure your deployment code applies similar post-processing techniques.

[Y-T-G]

Use the mask for drawing, instead of segments. And don't apply a large confidence threshold like in the example. Use 0.0.

https://github.com/ultralytics/ultralytics/blob/8154a27e9b332dd2dac7a107003462f0e20f6179/examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py#L227

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