UniRepLKNet

A ConvNet unifies multiple modalities and outperforms modality-specific models. This paper summarizes architectural guidelines to build large-kernel CNN, which works amazingly well with images and other modalities. This is the latest contribution to both two influential areas - Structural Re-param (since RepVGG, Ding et al. 2021) and very-large-kernel ConvNet (since RepLKNet, Ding et al. 2022). ImageNet accuracy of 88.0%, COCO AP of 56.4, ADE20K mIoU of 55.6 with only ImageNet-22K pretraining. Higher actual speed and performance than recent models like ConvNeXt v2 and InternImage. With a unified architecture and extremely simple modality-specific preprocessing, achieves state-of-the-art performances on audio recognition and, most amazingly, Global Temperature & Wind Speed Forecasting (a challenging huge-scale time-series forecasting task), outperforming the existing global forecasting system.