> For the complete documentation index, see [llms.txt](https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/main-10/convolutional-neural-networks/object-detection/non-max-suppression.md).

# Non-Max Suppression

A common problem of object detection algorithm is that they tend to detect an object multiple times even if it is only present once in the image.

Non-Max Suppression is a way to fix this.

Consider the following image with multiple bounding boxes generated per object:

![](https://3732905555-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RGo4dZhdwCIHrC1%2F-M5-0TEuN77zAmTpQjIr%2F-M5-0WMNj3vkyPeFge_4%2FNon-Max%20Suppression.JPG?generation=1586990829149087\&alt=media)

As shown, each bounding box is associated with a probability. Non-Max Suppression is the process of eliminating the bounding boxes that have non-max probability for each object, thereby only retaining one bounding box per object.