0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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 (SDTA) 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 publicly available at https://t.ly/_Vu9.

          Related collections

          Author and article information

          Journal
          21 June 2022
          Article
          2206.10589
          88a4dd1b-bf81-4e45-811e-4f9000e8bff6

          http://creativecommons.org/licenses/by/4.0/

          Custom metadata
          Technical Report
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

          Comments

          Comment on this article