The Playment survey on task difficulty suggests that Semantic segmentation is one of the difficult tasks for our annotators.
The Semantic Segmentation or Pixel-level labeling is used to label each and every pixel in the image.
Choosing an appropriate loss function is important as it affects the ability of the algorithm to produce optimum results as fast as possible.
The most important thing that the computer vision expert does is to decide which image annotation type is needed to build the most accurate model(s) for object detection.
State-of-the-Art Semantic Segmentation models need to be tuned in terms of memory consumption and fps output to be used in time-sensitive applications like autonomous vehicles. Here we study models like FCN, SegNets, ENets, and ICNets.