Label Bounding Boxes with the Universal Data Tool

Labeling your data with bounding boxes is an essential first step before training a machine learning algorithm. The Universal Data Tool is a free, open-source application that makes this process simple.

Before you start, you should have either…

• A directory of images
• A file where each line is an image url (this is what I’ll use)

Configure the Job

To begin navigate to universaldatatool.com or download the latest release from github. I’ll use the web version for now.

This is the landing page for the Universal Data Tool

Click “Create from Template” and select Computer Vision, then configure it to only do bounding boxes. If you also want to label each bounding box with a classification, you can also configure that here.

Configure a Computer Vision task

Import the Data

Navigate to “Samples”, then to the “Import” page. Click “Paste Image URLs”, paste the image urls and click “Add Samples”.

We can view all of our Samples from the SAMPLES tab

Label the Data

You can now begin labeling. Click the “Label” button at the top, then click on any sample to begin. Samples will turn blue as they’re labeled.

Samples turn blue as you annotate them

Wrapping Up

When you’re done, navigate to the “Settings” tab and click Download JSON. The resulting file can be loaded into the Universal Data Tool again, or parsed for usage with a machine learning algorithm.

That’s it! You now have everything you need to add bounding boxes to a image dataset.