# 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:

![](/files/-M5-0WMNj3vkyPeFge_4)

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.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/main-10/convolutional-neural-networks/object-detection/non-max-suppression.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.