Understanding Machine Learning Data Labeling Tools

Posted by Hailey on June 11th, 2021

Today, it is almost inconceivable to imagine a company that doesn't collect and analyze data in an effort to improve efficiency and operational flow. For many companies, massive amounts of data are being collected and stored in databases every second, each one piece to a greater puzzle. But, without the proper tools to sort and organize these data, analyses can be incredibly difficult, time-consuming, and inaccurate. That's why many companies have invested in machine learning data labeling tools to aid them in swift analyses. 

Machine learning data labeling tools are great pieces of software that are particularly useful for image labeling, although they can be used for a host of other data analysis tasks. In this article, we will look specifically at how machine learning data labeling tools can aid those whose work involves image and image recognition.


What is Data Labeling?

Before we get into the ins and outs of machine learning data labeling tools, we should first look at what data labeling is in the context of image recognition. 

Let's say you work for a newspaper or digital media company and have a substantial archive of photos ranging back to the early 1990s. If you estimate that, on average, ten photos were added to your archive each day, you're looking at over 100,000 images in your archive.

As your company went digital, these photos were scanned and made available in a digital archive but were not labeled properly. They may have some labels attached to them, such as the name of the person, place, or event the picture is of, but that's only a few elements of a photo. 

Those few labels attached to each photo are a good representation of manual data labeling, but as we'll come to learn, those labels are insufficient. 

How Can Machine Learning Data Labeling Tools Help?

Continuing our example, let's say you dig into the archive and find a photo of a Memorial Day parade. Your manual labels show a date, location, and maybe one or two other bits of data. But as we all know, there is much more to be seen in a photo than just the focal point. 

Perhaps there is a Beagle in the background, or an airplane in the sky, or a patriotic motorcycle driver off to the side. Those small details can be detected by the human eye, but because they aren't the main focus, they likely won't be labeled.

What machine learning data labeling tools do is use advanced image recognition software to identify every element of an image, cross-reference it with other properly labeled images, and choose whether to add a label or not. 

In practice, that Beagle may not have been labeled by humans, but by machines, it will be. Now, when an article calls for a picture of a Beagle, that photo will come up in the archives when someone searches for "Beagles".

This is just one example of how helpful these tools are, and they can be expanded to cover a wide range of other data sets. 


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