Using mixup as regularization and tuning hyper-parameters for ResNets

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 citeresnets by using mixup data-augmentation as regularization and tuning the hyper-parameters.