  Image Segmentation has been an active field of research as it has a wide
range of applications, ranging from automated disease detection to self-driving
cars. In recent years, various research papers proposed different loss
functions used in case of biased data, sparse segmentation, and unbalanced
dataset. In this paper, we introduce SemSegLoss, a python package consisting of
some of the well-known loss functions widely used for image segmentation. It is
developed with the intent to help researchers in the development of novel loss
functions and perform an extensive set of experiments on model architectures
for various applications. The ease-of-use and flexibility of the presented
package have allowed reducing the development time and increased evaluation
strategies of machine learning models for semantic segmentation. Furthermore,
different applications that use image segmentation can use SemSegLoss because
of the generality of its functions. This wide range of applications will lead
to the development and growth of AI across all industries.
