v8.1.0 release - YOLOv8 Oriented Bounding Boxes (OBB) #7472
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Hi, I've been working for 11 months on a project with playing cards, it would make a lot of sense to have an OBB model instead, but all my training images are labeled as squares with labelimg. Is there a new tool for training with oriented boxes now?. I think my model is far too deep into squared recognition to turn into OBB, and it scares me a lot to move now after all this work invested. |
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Thank you, I already did some data augmentation algorithm based on
contours, so I guess I could, still pretty scary to fix something that is
working.
My results so far are good enough, no need to get better precision, my main
problem at the moment is performance, are there any tests regarding if OBB
is faster or slower than 8.0?
…On Tue, Apr 2, 2024 at 3:43 AM Glenn Jocher ***@***.***> wrote:
@fluffytid <https://github.com/fluffytid> hi there!
Great to hear about your dedication to your project! Yes, transitioning to
an Oriented Bounding Box (OBB) model could indeed provide better results
for objects like playing cards that have a specific orientation. For
labeling images with oriented boxes, many users find success with CVAT,
which supports OBB annotations.
Transitioning from square to OBB annotations is a significant step but it
might not be as daunting as it seems. Your already labeled dataset still
holds value, especially when transitioning. While direct conversion tools
are scarce, you could potentially automate the conversion of square labels
to a rough OBB format and refine as needed.
Here's a pseudo-code to get you started on converting square bounding
boxes to OBB:
def square_to_obb(x_center, y_center, width, height, angle=0):
# Your method to convert square bbox to obb might vary
return (x_center, y_center, width, height, angle)
Remember, transitioning to a new model type is a step-by-step journey.
Testing with a small subset of your data can help you slowly move towards
full OBB implementation without risking the solid foundation you've built
over the months. 🚀
Don't hesitate to share your progress or ask more questions. We're here to
support you at every step of this exciting journey!
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is yolov8-obb now yolov11-obb after yolo v11 release?? |
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Ultralytics v8.1.0 Release Notes
Introduction
Ultralytics proudly announces the v8.1.0 release of YOLOv8, celebrating a year of remarkable achievements and advancements. This version continues our commitment to making AI technology accessible and powerful, reflected in our latest breakthroughs and improvements.
2023 in Review
Ultralytics v8.1.0 Key Highlights
Community Engagement and Support
Summary
Ultralytics v8.1.0 is a testament to a year of innovation, with the integration of Oriented Object Detection, enhanced classification models, and a strong focus on user experience and community engagement. We thank our users and contributors for their invaluable support and look forward to another year of groundbreaking advancements in the field of AI and computer vision in 2024! 🌟🚀🎉
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