Key point callouts
In data annotation, the most basic is the labeling of key points, that is, the picture frame. Since machine learning relies on accurate data, if it is not framed accurately, the machine may learn the wrong information, which can have a bad impact.
For example, in human gesture recognition, 18 key points need to be marked. These key points include shoulders, elbows, wrists, hips, knees, and ankles, among others. The labeling of these key points requires a trained labeller to master. They need to accurately identify these key points and accurately mark them with a picture frame on the image.
In order to ensure that the labeled data meets the standards of machine learning, annotators also need to pay attention to the following points: first, to ensure the accuracy of the frame, to ensure that the machine can accurately identify these key points; Secondly, it is necessary to maintain the uniformity of annotation to ensure that the annotation method in different images is consistent and avoid differences; Finally, avoid mislabeling or missing bids and ensure that all key points are accurately marked.
Key point labeling for data labeling is the foundation of machine learning. Only trained labelers can accurately grasp the labeling techniques for these key points to provide high-quality data that meets machine learning standards.