|Data Labeling at UNISEM|
Until now, UNISEM IoT division has been using manual data labeling based on human annotators in the vision AI training. For accurate object recognition, many images must be prepared, requiring a lot of manpower and a lot of cost and time. The role of the human annotator cannot be overlooked but collaborating with people has a human factor that causes discord. In the process of work, one annotator might perform labeling on two hundred images a day, while another completes only one hundred images a day, moreover, there is often a difference in the quality of the specified labels which brings inequality among annotators and complicates the assessment and rewarding estimation.
Automated labeling AI can manage most of the easily identified labels. This brings the advantage of greatly accelerating the initial labeling step. Human annotators only need to make minor corrections or inspections of automatically labeled images, which improves productivity.