After configuring the optimizer to achieve fast and stable training, we turned into optimizing the accuracy of the model. There are a few techniques that helped us achieve this. First of all, to avoid overfitting we augmented out data using the AutoAugment algorithm, followed by RandomErasing. Additionally we tuned parameters such as the weight decay using cross validation. We also found beneficial to perform weight averaging across different epoch checkpoints after the end of the training. Finally, though not used in our published training recipe, we found that using Label Smoothing, Stochastic Depth and LR noise injection improve the overall accuracy by over 1.5 points.