Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations

• URL: http://arxiv.org/abs/2410.08049v1
• Date: Thu, 10 Oct 2024 15:43:55 GMT
• Title: Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations
• Authors: Yiyuan Zhang, Xiaohan Ding, Xiangyu Yue,
• Abstract summary: 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. We propose the UniRepLKNet architecture, which offers systematical architecture design principles specifically crafted for large- Kernel ConvNets.
• Score: 17.41381592056492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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