Large-kernel convolutional neural networks (ConvNets) have recently received
extensive research attention, but there are two unresolved and critical issues
that demand further investigation. 1) The architectures of existing
large-kernel ConvNets largely follow the design principles of conventional
ConvNets or transformers, while the architectural design for large-kernel
ConvNets remains under-addressed. 2) As transformers have dominated multiple
modalities, it remains to be investigated whether ConvNets also have a strong
universal perception ability in domains beyond vision. In this paper, we
contribute from two aspects. 1) We propose four architectural guidelines for
designing large-kernel ConvNets, the core of which is to exploit the essential
characteristics of large kernels that distinguish them from small kernels -
they can see wide without going deep. Following such guidelines, our proposed
large-kernel ConvNet shows leading performance in image recognition. For
example, our models achieve an ImageNet accuracy of 88.0%, ADE20K mIoU of
55.6%, and COCO box AP of 56.4%, demonstrating better performance and higher
speed than a number of recently proposed powerful competitors. 2) We discover
that large kernels are the key to unlocking the exceptional performance of
ConvNets in domains where they were originally not proficient. With certain
modality-related preprocessing approaches, the proposed model achieves
state-of-the-art performance on time-series forecasting and audio recognition
tasks even without modality-specific customization to the architecture. Code
and all the models at this https URL

我们提出了四种用于设计大核卷积神经网络的架构指南，并发现大核对于拓展卷积神经网络在非视觉领域具有卓越表现的关键因素。我们的模型在图像识别、时间序列预测和音频识别任务中表现出卓越性能，优于其他竞争模型，并且无需对体系结构进行特定模态的定制化处理。