The most common type of data annotation. Bounding boxes are mainly intended to detect objects, delineating their borders with transparent rectangles. Bounding boxes are applied in almost all industries of the data labelling market.
This method is used to determine the presence of an object in the image. A specific label is assigned to each image. Image tagging helps train AI to recognize objects' classes.
This type of annotation uses dots across an image to identify an object or its details/parts. Keypoints are applied mostly with facial recognition, body parts, and postures.
Polygons are used to determine an object's shape and location with maximum precision while avoiding additional noise. Polygon annotation is applied in almost all industries.
Lines annotation is used for detecting and recognizing lanes. This type of annotation is applied mostly in the automotive industry to make self-driving cars safe.
A data labelling method where objects are delineated in the image at the pixel level, depending on class. It's called semantic segmentation because each pixel has semantic meaning. Segmentation is usually applied in the automotive industry.