​​InceptionNeXt: When Inception Meets ConvNeXt Large-kernel c | Data Science by ODS.ai 🦜

​​InceptionNeXt: When Inception Meets ConvNeXt

Large-kernel convolutions, such as those employed in ConvNeXt, can improve model performance but often come at the cost of efficiency due to high memory access costs. Although reducing kernel size may increase speed, it often leads to significant performance degradation.

To address this issue, the authors propose InceptionNeXt, which decomposes large-kernel depthwise convolution into four parallel branches along the channel dimension. This new Inception depthwise convolution results in networks with high throughputs and competitive performance. For example, InceptionNeXt-T achieves 1.6x higher training throughputs than ConvNeX-T and a 0.2% top-1 accuracy improvement on ImageNet-1K. InceptionNeXt has the potential to serve as an economical baseline for future architecture design, helping to reduce carbon footprint.

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-inceptionnext

Paper link:https://arxiv.org/abs/2303.16900

Code link: https://github.com/sail-sg/inceptionnext

#cnn #deeplearning #computervision