# Crop Visualization

### About this block

The Crop Visualization block crops detected regions from an image, makes them bigger, then adds the regions back above the detected region.

This block is useful if you want to highlight detections in an image.

This block works with:

* Object detection models
* Segmentation models

<figure><img src="/files/ahOcZG1PEFOYtLl5sV91" alt=""><figcaption><p>The Crop Visualization block.</p></figcaption></figure>

This block does not add class labels from your detection or segmentation model by default. If you want to see the classes that correspond with each crop, you will need to add a [Label Visualization](/workflows/workflow-blocks/visualize-predictions/label-visualization.md).

### What you can send into this block

To use this block, you need:

1. An input image or video frame, and;
2. Predictions from an Object Detection or Segmentation model.

### What this block returns

The Crop Visualization block returns an image with a circle drawn around detected regions in an image.

Here is an example output from this block:

<figure><img src="/files/OtqTGm5lh0gWSzOZvTlj" alt=""><figcaption><p>The defect in the image is magnified.</p></figcaption></figure>

For reference, here is the input image:

<figure><img src="/files/yXDkFembyzdowye7MyED" alt=""><figcaption></figcaption></figure>

In the result from the block, the detected region is made bigger.

### Use cases

This block is useful if you want to see the results from a model on an image. This is common during testing.

Because visualizing predictions adds a small amount of overhead to running a Workflow, we only recommend adding a Visualization in production if you need to see the location of results from your model.


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