Since RandAugment is known to enhance the robustness of models to common perturbations and corruptions, we will also evaluate our models on the CIFAR-10-C dataset, proposed in Benchmarking Neural Network Robustness to Common Corruptions and Perturbations by Hendrycks et al. The CIFAR-10-C dataset consists of 19 different image corruptions and perturbations (for example speckle noise, fog, Gaussian blur, etc.) that too at varying severity levels. For this example we will be using the following configuration: cifar10_corrupted/saturate_5. The images from this configuration look like so: