  In this paper I investigate the effect of random seed selection on the
accuracy when using popular deep learning architectures for computer vision. I
scan a large amount of seeds (up to $10^4$) on CIFAR 10 and I also scan fewer
seeds on Imagenet using pre-trained models to investigate large scale datasets.
The conclusions are that even if the variance is not very large, it is
surprisingly easy to find an outlier that performs much better or much worse
than the average.
