A Machine Learning Engineer's Tutorial to Transfer Learning for Multi-class Image Segmentation…

Image semantic segmentation is one of the most significant areas of research and engineering in the computer vision domain. From segmenting pedestrians and cars for autonomous drive [1] to segmentation and localization of pathology in medical images [2], there are several use-cases of image segmentation. With the wide-spread use of deep learning models for end-to-end delivery for machine learning (ML) models, the U-net model has emerged as a scalable solution across autonomous drive and medical imaging use-cases [3–4]. However, most existing papers and methods implement binary classification tasks for detecting objects/regions of interest over the backgrounds [4]. In this hands-on tutorial we will review how to start from a binary semantic segmentation task and transfer the learning to suit multi-class image segmentation tasks.