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This article examines how standard axis-aligned bounding box representations can limit performance in computer vision tasks, proposing that representation choices should be treated as explicit design decisions rather than fixed defaults. By providing reproducible code and logging strategies, the study offers practical insights for improving object detection models in traffic scenarios without relying on hype or marketing fluff.
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When axis-aligned boxes break: Lessons from a CVPR-published traffic AI
Weights & Biases1 min de cititArticolul și perspectiva sunt în engleză.
A case study in treating representation as a design decision, not a given — with the code and the logging to prove it.
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