Scaling Up Your Kernels: Large Kernel Design in ConvNets towards
  Universal Representations
Authors: name: Yiyuan Zhang, name: Xiaohan Ding, name: Xiangyu Yue

This paper proposes the paradigm of large convolutional kernels in designing
modern Convolutional Neural Networks (ConvNets). We establish that employing a
few large kernels, instead of stacking multiple smaller ones, can be a superior
design strategy. Our work introduces a set of architecture design guidelines
for large-kernel ConvNets that optimize their efficiency and performance. We
propose the UniRepLKNet architecture, which offers systematical architecture
design principles specifically crafted for large-kernel ConvNets, emphasizing
their unique ability to capture extensive spatial information without deep
layer stacking. This results in a model that not only surpasses its
predecessors with an ImageNet accuracy of 88.0%, an ADE20K mIoU of 55.6%, and a
COCO box AP of 56.4% but also demonstrates impressive scalability and
performance on various modalities such as time-series forecasting, audio, point
cloud, and video recognition. These results indicate the universal modeling
abilities of large-kernel ConvNets with faster inference speed compared with
vision transformers. Our findings reveal that large-kernel ConvNets possess
larger effective receptive fields and a higher shape bias, moving away from the
texture bias typical of smaller-kernel CNNs. All codes and models are publicly
available at https://github.com/AILab-CVC/UniRepLKNet promoting further
research and development in the community.