# Channel Redundancy Optimization

ReXNet image classification model pretrained on ImageNette, utilizing improved residual block structures to reduce channel redundancy

ReXNet-1.3x is an image classification model based on the ReXNet architecture, pretrained on the ImageNette dataset. The model reduces channel redundancy by improving the Squeeze-Excitation layers in residual blocks.

ReXNet-1.5x is a lightweight image classification model pretrained on the ImageNette dataset, utilizing the ReXNet architecture. It reduces channel redundancy by improving the Squeeze-Excitation layers within residual blocks.