Emily Clarke

3 Benefits of Automated Image Labeling

When dealing with a mountain of data, making sense of it all requires careful annotation. Machines need appropriate metadata and labels to recognize and process objects accurately. Annotation is the foundation of machine learning.

Unfortunately, the processes that many companies use today are inefficient. Manual annotation is time and resource-heavy, requiring strict labeling rules and human processing. Humans can quickly identify objects and apply labels in seconds, but what if you have millions of images needing multiple annotations? Suddenly, the task is an uphill battle.

AI image annotation transforms how you label images. AI technology can process hundreds of thousands of images in only a fraction of the time. Here are just a few benefits automated labeling could bring to your business.

Streamline Your Workflow

Automated labeling applies AI technology to label datasets quickly, significantly decreasing the need for manual work. The technology annotates promptly and efficiently, eliminating lengthy wait times and allowing you to utilize images much sooner.

You also can pre-annotate data, speeding up the process even more and improving accuracy. Instead of having your workforce handle annotation from start to finish, all they have to do is review, correct, or complete labels. It streamlines your entire processes, saving you time and money.

Cost-Effective Operations

Manual approaches can be cost-prohibitive as your needs change. As your company continues to collect and generate more data, you will have substantially more manual annotations to do. That results in your expenses skyrocketing. Even if you use multiple teams, shipping that data back and forth is costly. It limits your growth potential.

That's not the case with AI image annotation. The technology is scalable and addresses human bottlenecks. The best part? Economies of scale come into play. Automated labeling can get cheaper as your needs expand!

Better Accuracy and Quality Control

Finally, automated labeling improves accuracy. Manual labeling is rife with inconsistencies. Pair that with disagreements on set rules, which can lead to many quality issues. AI technology has a single source of truth. With pre-annotation, you also free up your resources, turning labelers into supervisors and reviewers.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at DICOM annotation blog.