  Light-weight convolutional neural networks (CNNs) are the de-facto for mobile
vision tasks. Their spatial inductive biases allow them to learn
representations with fewer parameters across different vision tasks. However,
these networks are spatially local. To learn global representations,
self-attention-based vision trans-formers (ViTs) have been adopted. Unlike
CNNs, ViTs are heavy-weight. In this paper, we ask the following question: is
it possible to combine the strengths of CNNs and ViTs to build a light-weight
and low latency network for mobile vision tasks? Towards this end, we introduce
MobileViT, a light-weight and general-purpose vision transformer for mobile
devices. MobileViT presents a different perspective for the global processing
of information with transformers, i.e., transformers as convolutions. Our
results show that MobileViT significantly outperforms CNN- and ViT-based
networks across different tasks and datasets. On the ImageNet-1k dataset,
MobileViT achieves top-1 accuracy of 78.4% with about 6 million parameters,
which is 3.2% and 6.2% more accurate than MobileNetv3 (CNN-based) and DeIT
(ViT-based) for a similar number of parameters. On the MS-COCO object detection
task, MobileViT is 5.7% more accurate than MobileNetv3 for a similar number of
parameters.
  Our source code is open-source and available at:
https://github.com/apple/ml-cvnets
