  In this work, we present a new network design paradigm. Our goal is to help
advance the understanding of network design and discover design principles that
generalize across settings. Instead of focusing on designing individual network
instances, we design network design spaces that parametrize populations of
networks. The overall process is analogous to classic manual design of
networks, but elevated to the design space level. Using our methodology we
explore the structure aspect of network design and arrive at a low-dimensional
design space consisting of simple, regular networks that we call RegNet. The
core insight of the RegNet parametrization is surprisingly simple: widths and
depths of good networks can be explained by a quantized linear function. We
analyze the RegNet design space and arrive at interesting findings that do not
match the current practice of network design. The RegNet design space provides
simple and fast networks that work well across a wide range of flop regimes.
Under comparable training settings and flops, the RegNet models outperform the
popular EfficientNet models while being up to 5x faster on GPUs.
