In the world of technology, AI (Artificial Intelligence) is an innovative way to enhance the business experience—it is a great tool to utilize, and a lot of businesses are using it. Your company may be one of them, so you will need a data labeling system to label a substantial number of images, videos, or text. It is best to find people with experience because even smart technology requires a human operator.
The thing about data labeling is that it is always improving—down the road you and your team will find better ways to improve the quality and model performance. It is a forever evolving process that will help you to meet your business’s objectives as they change.
Once you have found the suitable data labeling tool, there will be tutorials on how to use it. There are many functions, but some get overlooked. In this article, I will introduce the four forgotten functions of data labeling that you might not know.
The bounding box is a way to track a single object and store it as an image and a text file. The bounding box then trains the system to recognize data. It measures the dimensions and pixels and then creates a data recognition file. For example, facial recognition: A box appears over the individual’s face—the AI will check to see if the data matches any algorithms in the database.
The box is easy to use. I am sure you have highlighted shortcuts on your desktop by dragging your mouse—maybe you put multiple files in the trash, or perhaps you were playing around with the mouse. That expanding square is a bounding box.
Image segmentation is the method of separating an image into segments. It simplifies the representation of an image into something more significant and better to analyze. I am sure you have seen image segmentation in videos or pictures of cars, where cars get highlighted with a specific color (one might be bluish while the other is red). To get started: Create a new project and select the “Image Segmentation” labeling interface. If you cannot find the option, be sure to contact support, because it is there somewhere.
Classification for data labeling helps to find data files with similar contextual information. For example: the system will focus on nearby pixel pixels (a neighborhood), search for similar patterns, and then organize them.
Most programs have an Image Classification bar.
The automatic suggestion is a great tool to help organize your data. Smart labeling with automatic suggestions will automatically find a label for your input data for specific tasks. Through active learning, the machine learns how to identify data that is labeled by you and your team.
Next time you are working on labeling data, check out the four tools I have mentioned. User-friendly is the key to making a complex task like labeling data, much simpler.
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