  Inspired by the long-range modeling ability of ViTs, large-kernel
convolutions are widely studied and adopted recently to enlarge the receptive
field and improve model performance, like the remarkable work ConvNeXt which
employs 7x7 depthwise convolution. Although such depthwise operator only
consumes a few FLOPs, it largely harms the model efficiency on powerful
computing devices due to the high memory access costs. For example, ConvNeXt-T
has similar FLOPs with ResNet-50 but only achieves 60% throughputs when trained
on A100 GPUs with full precision. Although reducing the kernel size of ConvNeXt
can improve speed, it results in significant performance degradation. It is
still unclear how to speed up large-kernel-based CNN models while preserving
their performance. To tackle this issue, inspired by Inceptions, we propose to
decompose large-kernel depthwise convolution into four parallel branches along
channel dimension, i.e. small square kernel, two orthogonal band kernels, and
an identity mapping. With this new Inception depthwise convolution, we build a
series of networks, namely IncepitonNeXt, which not only enjoy high throughputs
but also maintain competitive performance. For instance, InceptionNeXt-T
achieves 1.6x higher training throughputs than ConvNeX-T, as well as attains
0.2% top-1 accuracy improvement on ImageNet-1K. We anticipate InceptionNeXt can
serve as an economical baseline for future architecture design to reduce carbon
footprint. Code is available at https://github.com/sail-sg/inceptionnext.
