  We revisit large kernel design in modern convolutional neural networks
(CNNs). Inspired by recent advances in vision transformers (ViTs), in this
paper, we demonstrate that using a few large convolutional kernels instead of a
stack of small kernels could be a more powerful paradigm. We suggested five
guidelines, e.g., applying re-parameterized large depth-wise convolutions, to
design efficient high-performance large-kernel CNNs. Following the guidelines,
we propose RepLKNet, a pure CNN architecture whose kernel size is as large as
31x31, in contrast to commonly used 3x3. RepLKNet greatly closes the
performance gap between CNNs and ViTs, e.g., achieving comparable or superior
results than Swin Transformer on ImageNet and a few typical downstream tasks,
with lower latency. RepLKNet also shows nice scalability to big data and large
models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K,
which is very competitive among the state-of-the-arts with similar model sizes.
Our study further reveals that, in contrast to small-kernel CNNs, large-kernel
CNNs have much larger effective receptive fields and higher shape bias rather
than texture bias. Code & models at
https://github.com/megvii-research/RepLKNet.
