Scaling Up Your Kernels: Large Kernel Design in ConvNets towards
  Universal Representations
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.
67089917e0c87dc0e549d3b4
Yiyuan Zhang
admin_assigned
2024-10-11T11:05:59.964Z
63176933b58b0184630d2c74
https://cdn-avatars.huggingface.co/v1/production/uploads/63176933b58b0184630d2c74/53b5EASwW76zeyyqeJA3O.jpeg
Yiyuan Zhang
user
Yiyuan
67089917e0c87dc0e549d3b5
Xiaohan Ding
admin_assigned
2024-10-11T11:06:06.209Z
6564f049957ac688429a6754
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/nE6q5TlyLKlJpbxCKoDN8.png
xiaohanding
user
DingXiaoH
67089917e0c87dc0e549d3b6
Xiangyu Yue
admin_assigned
2024-10-11T11:06:13.181Z
666a8f24e2990b0cb16b7bf9
/avatars/fcbaf8f1e3e53a2a4a819b7cb2c53aa4.svg
Xiangyu Yue
user
xyyue
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards
  Universal Representations
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.
Large-kernel ConvNets using fewer large kernels outperform smaller-kernel CNNs across various modalities, offering higher accuracy and faster inference speed.
convolutional kernels ConvNets architecture design guidelines UniRepLKNet effective receptive fields shape bias texture bias ImageNet ADE20K COCO time-series forecasting audio recognition point cloud recognition video recognition