...
By using semantic segmentation, the exact areas, locations, and boundaries of an object or a group of objects can be determined. It allows you to find out which regions of the image belong to which label. The result is, for each label, a pixel-wise map of the original image corresponding to the output labelsshowing which pixels belong to each output label. Different labels can overlap.
Semantic segmentation is one of the main tasks performed in autonomous driving, e.g. the car needs to be aware of where the road is and where its exact boundaries to other objects are. In medical settings, semantic segmentation is often used to find the boundaries of organs in CT scans or to reconstruct the 3D structure of the observed organ.
...
Combining object detection and semantic segmentation capabilities, instance segmentation is one of the more versatile training types. Just like semantic segmentation, an instance segmentation app training yields for each label a pixel-wise map assigning each pixel in the image to one of the detected labels. At the same time it provides a bounding box location for all individual instances of this object class shown in the image.
...
This allows for further analysis on the instance-level such as counting instances, measuring distances between instances or examining the area covered with (or even surrounding the) instances.
Different labels can overlap, however, we do not support instances to overlap on pixel-level.
Now you know everything about the various application training types.
...