IPBI
Instances-Pixels Balance Index
In a given dataset for semantic image segmentation, the number of samples per class should be the same, so that no classifier would be biased towards the majority class (here included the background). It is very difficult, if not impossible, to achieve a perfect balance between the several classes of objects of a dataset. Considering that the segmentation of the objects  is accomplished at the pixel level, the number of pixels for each class must be taken into account. As a matter of fact, in image semantic segmentation, 
different classes and the background may have quite different
sizes. Therefore, the image segmentation problem is naturally unbalanced. The IPBI is based on the concept of entropy, a common measure used in many fields of science. In a general sense, it measures the amount of disorder of a system. For the sake of semantic image segmentation, the ideal dataset should have the same number of instances per class, as well as the same number of pixels in all classes. Similar reasoning can be done considering the number of pixels of all samples in a class, so that we can obtain the
pixels balance measure for the dataset. Overall, IPBI evaluates the balance of pixels and number of instances of an image semantic segmentation dataset and, so, it is usefull to compare different datasets.
EPYNET: Efficient Pyramidal Network for Clothing Segmentation
https://paperswithcode.com/paper/epynet-efficient-pyramidal-network-for
EPYNET: Efficient Pyramidal Network for Clothing Segmentation