SemSegLoss: A python package of loss functions for semantic segmentation

TL;DR

SemSegLoss is a versatile Python package that provides a collection of loss functions for semantic segmentation, aiming to facilitate research, experimentation, and application development across various domains.

Contribution

It introduces a flexible, easy-to-use package of well-known loss functions for semantic segmentation, supporting research and development of novel loss functions and models.

Findings

Reduces development time for segmentation models

Enables extensive experimentation with loss functions

Applicable across diverse image segmentation applications

Abstract

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.