  Neural architecture search has become an indispensable part of the deep
learning field. Modern methods allow to find out the best performing
architectures for a task, or to build a network from scratch, but they usually
require a tremendous amount of training. In this paper we present a simple
method, allowing to discover a suitable architecture for a task based on its
untrained performance. We introduce the metric score as the relative standard
deviation of the untrained accuracy, which is the standard deviation divided by
the mean. Statistics for each neural architecture are calculated over multiple
initialisations with different seeds on a single batch of data. An architecture
with the lowest metric score value has on average an accuracy of $91.90 \pm
2.27$, $64.08 \pm 5.63$ and $38.76 \pm 6.62$ for CIFAR-10, CIFAR-100 and a
downscaled version of ImageNet, respectively. The results show that a good
architecture should be stable against initialisations before training. The
procedure takes about $190$ s for CIFAR and $133.9$ s for ImageNet, on a batch
of $256$ images and $100$ initialisations.
