The fundamental design principles of the RegNet space—such as shared bottleneck ratios, shared group widths, and linear channel scaling—are robust enough to apply beyond standard convolutional blocks. They remain highly effective when applied to Squeeze-and-Excitation (SE) network designs, which utilize a global channel activation mechanism. This specific adaptation of the RegNet principles with SE blocks is known as the RegNetY architecture.