  In the pursuit of achieving ever-increasing accuracy, large and complex
neural networks are usually developed. Such models demand high computational
resources and therefore cannot be deployed on edge devices. It is of great
interest to build resource-efficient general purpose networks due to their
usefulness in several application areas. In this work, we strive to effectively
combine the strengths of both CNN and Transformer models and propose a new
efficient hybrid architecture EdgeNeXt. Specifically in EdgeNeXt, we introduce
split depth-wise transpose attention (STDA) encoder that splits input tensors
into multiple channel groups and utilizes depth-wise convolution along with
self-attention across channel dimensions to implicitly increase the receptive
field and encode multi-scale features. Our extensive experiments on
classification, detection and segmentation tasks, reveal the merits of the
proposed approach, outperforming state-of-the-art methods with comparatively
lower compute requirements. Our EdgeNeXt model with 1.3M parameters achieves
71.2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute
gain of 2.2% with 28% reduction in FLOPs. Further, our EdgeNeXt model with 5.6M
parameters achieves 79.4% top-1 accuracy on ImageNet-1K. The code and models
are available at https://t.ly/_Vu9.

Computer Vision and Pattern Recognition