Benefits of Manual Labeling

Typically, manual data labeling involves a single annotator pointing out particular elements inside still or moving picture frames. To gather detailed, superior training data for AI, these commentators go through tens of thousands of images. The raw data is labeled using a variety of techniques according to the product packaging needs. These techniques include:

To aid an AI in identifying and avoiding an object in an image, a rectangle is painted around it. A bounding box annotation is what it is. This approach is more widely used because of its relative simplicity and higher cost-effectiveness.

Manual data labeling could involve a significant amount of work. Even while it just takes a few seconds to tag a single picture, hundreds of pictures could slow down a project