  The influential Residual Networks designed by He et al. remain the
gold-standard architecture in numerous scientific publications. They typically
serve as the default architecture in studies, or as baselines when new
architectures are proposed. Yet there has been significant progress on best
practices for training neural networks since the inception of the ResNet
architecture in 2015. Novel optimization & data-augmentation have increased the
effectiveness of the training recipes. In this paper, we re-evaluate the
performance of the vanilla ResNet-50 when trained with a procedure that
integrates such advances. We share competitive training settings and
pre-trained models in the timm open-source library, with the hope that they
will serve as better baselines for future work. For instance, with our more
demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at
resolution 224x224 on ImageNet-val without extra data or distillation. We also
report the performance achieved with popular models with our training
procedure.
