  While novel computer vision architectures are gaining traction, the impact of
model architectures is often related to changes or exploring in training
methods. Identity mapping-based architectures ResNets and DenseNets have
promised path-breaking results in the image classification task and are go-to
methods for even now if the data given is fairly limited. Considering the ease
of training with limited resources this work revisits the ResNets and improves
the ResNet50 \cite{resnets} by using mixup data-augmentation as regularization
and tuning the hyper-parameters.
