epochs=600,

This further increases our top-1 Accuracy by 1.8 points on top of the previous step. This is the biggest increase we will observe in this iterative process. It’s worth noting that the effect of this single optimization is overstated and somehow misleading. Just increasing the number of epochs on top of the old baseline won’t yield such significant improvements. Nevertheless the combination of the LR optimizations with strong Augmentation strategies helps the model benefit from longer cycles. It’s also worth mentioning that the reason we introduce the lengthy training cycles so early in the process is because in the next steps we will introduce techniques that require significantly more epochs to provide good results.
Random Erasing