Convolutional Neural Networks


Introduced by Radosavovic et al. in Designing Network Design Spaces

RegNetY is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w_{0} > 0$, and slope $w_{a} > 0$, and generates a different block width $u_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):

$$ u_{j} = w_{0} + w_{a}\cdot{j} $$

For RegNetX we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w_{m} \geq 2$ (the width multiplier).

For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks.

Source: Designing Network Design Spaces


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