  MobileViT (MobileViTv1) combines convolutional neural networks (CNNs) and
vision transformers (ViTs) to create light-weight models for mobile vision
tasks. Though the main MobileViTv1-block helps to achieve competitive
state-of-the-art results, the fusion block inside MobileViTv1-block, creates
scaling challenges and has a complex learning task. We propose changes to the
fusion block that are simple and effective to create MobileViTv3-block, which
addresses the scaling and simplifies the learning task. Our proposed
MobileViTv3-block used to create MobileViTv3-XXS, XS and S models outperform
MobileViTv1 on ImageNet-1k, ADE20K, COCO and PascalVOC2012 datasets. On
ImageNet-1K, MobileViTv3-XXS and MobileViTv3-XS surpasses MobileViTv1-XXS and
MobileViTv1-XS by 2% and 1.9% respectively. Recently published MobileViTv2
architecture removes fusion block and uses linear complexity transformers to
perform better than MobileViTv1. We add our proposed fusion block to
MobileViTv2 to create MobileViTv3-0.5, 0.75 and 1.0 models. These new models
give better accuracy numbers on ImageNet-1k, ADE20K, COCO and PascalVOC2012
datasets as compared to MobileViTv2. MobileViTv3-0.5 and MobileViTv3-0.75
outperforms MobileViTv2-0.5 and MobileViTv2-0.75 by 2.1% and 1.0% respectively
on ImageNet-1K dataset. For segmentation task, MobileViTv3-1.0 achieves 2.07%
and 1.1% better mIOU compared to MobileViTv2-1.0 on ADE20K dataset and
PascalVOC2012 dataset respectively. Our code and the trained models are
available at: https://github.com/micronDLA/MobileViTv3
