  Recently, MBConv blocks, initially designed for efficiency in
resource-limited settings and later adapted for cutting-edge image
classification performances, have demonstrated significant potential in image
classification tasks. Despite their success, their application in semantic
segmentation has remained relatively unexplored. This paper introduces a novel
adaptation of MBConv blocks specifically tailored for semantic segmentation.
Our modification stems from the insight that semantic segmentation requires the
extraction of more detailed spatial information than image classification. We
argue that to effectively perform multi-scale semantic segmentation, each
branch of a U-Net architecture, regardless of its resolution, should possess
equivalent segmentation capabilities. By implementing these changes, our
approach achieves impressive mean Intersection over Union (IoU) scores of 84.5%
and 84.0% on the Cityscapes test and validation datasets, respectively,
demonstrating the efficacy of our proposed modifications in enhancing semantic
segmentation performance.
