RegNetX 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).
Source: Designing Network Design SpacesPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Ensemble Learning | 1 | 25.00% |
Medical Object Detection | 1 | 25.00% |
Object Detection | 1 | 25.00% |
Image Classification | 1 | 25.00% |