  The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant
of the Residual Networks (ResNets) by stacking a small number of residual
blocks with large channel sizes, have demonstrated outstanding performance on
multiple dense prediction tasks. However, since proposed, the Wide-ResNet
architecture has barely evolved over the years. In this work, we revisit its
architecture design for the recent challenging panoptic segmentation task,
which aims to unify semantic segmentation and instance segmentation. A baseline
model is obtained by incorporating the simple and effective
Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets.
Its network capacity is further scaled up or down by adjusting the width (i.e.,
channel size) and depth (i.e., number of layers), resulting in a family of
SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such
a simple scaling scheme, coupled with grid search, identifies several
SWideRNets that significantly advance state-of-the-art performance on panoptic
segmentation datasets in both the fast model regime and strong model regime.
