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 this https URL.

提出了一种类似于 Inception 的新型卷积操作 Inception depthwise convolution，将大核卷积分解为四个通道维度上的平方小核、两个正交带状核和一个恒等映射，用于构建高吞吐量且性能表现强劲的网络 InceptionNeXt，取得了与性能相当的同时大幅提升训练效率，并可作为减少碳足迹的未来架构设计基线。