Tuesday, July 25, 2023

New Tutorial ✍🏼 Weighted Boxes Fusion in Object Detection: A Comparison with Non-Maximum Suppression

WBF - Alternative to Non-Maximum Suppression?  β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ β€Œ

Hello trend,

This is Satya Mallick from LearnOpenCV.com.

Object Detection Models conventionally use Non-Maximum Suppression (NMS) as the default post-processing step to filter out redundant bounding boxes. However, this approach fails to efficiently give unified, averaged predictions across multiple models since they tend to remove less confident boxes having a significant overlap.

To mitigate this problem, we shall discuss an efficient pre-processing step called Weighted Boxes Fusion (WBF) that helps achieve a unified localized prediction across multiple detections.

We will discuss the working along with the PyTorch code for the WBF method in today's article.

The accompanying code for the blog post can be found here:

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Cheers,

Satya

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