The order of optimizations presented above is accurate, though a bit idealized in some cases. For example, though different schedulers were tested during the Hyperparameter tuning phase, none of them provided significant improvements and thus we maintained the MultiStepLR which was used in the baseline. Nevertheless while later experimenting with different LR Schemes, we found it beneficial to switch to CosineAnnealingLR, as it required less configuration. Consequently, we believe that the main takeaway from the above summary should be that even by starting with a correct implementation and a set of optimal hyperparams from a model of the same family, there is always accuracy points to be found by optimizing the training recipe and tuning the implementation. Admittedly the above is a rather extreme case where the accuracy doubled, but still in many cases there is a large number of optimizations that can help us push the accuracy significantly. 
Benchmarks