Answers.Body: <p>IOU makes sense for determining accuracy against ground truth, but for non max suppression have you tried intersection over minimum size?</p>
  
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Answers.CreationDate: 2021-06-10T01:55:14.550Z
Answers.Text: Possibly but this isn’t standard practice. Standard COCO object detectors like SSD and FasterRCNN don’t seem to have such issues
Answers.UserDisplayName: A Tyshka
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Answers.CreationDate: 2023-07-01T05:24:27.013Z
Answers.Text: then maybe there's something going on with your training data that you're getting these boxes. That, or should boxes be this small for your use case even? Maybe there's some class confusion going on.
Answers.UserDisplayName: juicedatom
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Answers.CreationDate: 2021-06-09T22:35:59.070Z
Answers.LastActivityDate: 2021-06-09T22:35:59.070Z
Answers.UserDisplayName: user7834
Body: <p>I’m working on an object detection cnn, and having some issues with non max suppression. When I have a small box inside a large box, NMS is not rejecting the smaller, incorrect box, because its IOU is small (large union, small intersection). How is this scenario typically dealt with? When using out of the box pretrained models for object detection I don’t seem to get boxes completely inside other boxes. Example here: <a href="https://i.sstatic.net/BiOB4.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BiOB4.jpg" alt="enter image description here" /></a> green is ground truth, blue is prediction. The center box has a tiny blue box inside that’s not getting rejected by NMS</p>
  
Tags: <convolutional-neural-networks><object-detection><non-max-suppression>
Title: How to reject boxes inside each other with Non Max Suppression
UserDisplayName: A Tyshka
ThreadText: # How to reject boxes inside each other with Non Max Suppression
  
  (Asked by: username_0 on 2021-06-09)
  
  I’m working on an object detection cnn, and having some issues with non max suppression. When I have a small box inside a large box, NMS is not rejecting the smaller, incorrect box, because its IOU is small (large union, small intersection). How is this scenario typically dealt with? When using out of the box pretrained models for object detection I don’t seem to get boxes completely inside other boxes. Example here: [![enter image description here](https://i.sstatic.net/BiOB4.jpg)](https://i.sstatic.net/BiOB4.jpg) green is ground truth, blue is prediction. The center box has a tiny blue box inside that’s not getting rejected by NMS
  
  **Tags:** convolutional-neural-networks, object-detection, non-max-suppression
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-06-09. Score: 0
  
  IOU makes sense for determining accuracy against ground truth, but for non max suppression have you tried intersection over minimum size?
  
  > **Comments:**
  > **username_0**: Possibly but this isn’t standard practice. Standard COCO object detectors like SSD and FasterRCNN don’t seem to have such issues  
  > **username_2**: then maybe there's something going on with your training data that you're getting these boxes. That, or should boxes be this small for your use case even? Maybe there's some class confusion going on.  
  
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