  Over the past years, computer vision community has contributed to enormous
progress in semantic image segmentation, a per-pixel classification task,
crucial for dense scene understanding and rapidly becoming vital in lots of
real-world applications, including driverless cars and medical imaging. Most
recent models are now reaching previously unthinkable numbers (e.g., 89% mean
iou on PASCAL VOC, 83% on CityScapes), and, while intersection-over-union and a
range of other metrics provide the general picture of model performance, in
this paper we aim to extend them into other meaningful and important for
applications characteristics, answering such questions as 'how accurate the
model segmentation is on small objects in the general scene?', or 'what are the
sources of uncertainty that cause the model to make an erroneous prediction?'.
Besides establishing a methodology that covers the performance of a single
model from different perspectives, we also showcase several extensions that can
be worth pursuing in order to further improve current results in semantic
segmentation.
