AugMix is a novel data processing technique designed to enhance the robustness and uncertainty estimates of image classifiers. It addresses the common issue of mismatched training and test distributions, which can significantly impact model performance in real-world scenarios. AugMix combines stochastic and diverse augmentations, a Jensen-Shannon Divergence consistency loss, and a method to mix multiple augmented images. This approach helps models better generalize to unforeseen corruptions and improves robustness and uncertainty measures on challenging image classification benchmarks. On datasets like CIFAR-10, CIFAR-100, and ImageNet, AugMix achieves state-of-the-art results in corruption robustness and uncertainty estimation, often outperforming other techniques with minimal computational overhead. The method is simple to implement and maintains or improves accuracy on standard benchmark datasets, making it a valuable tool for improving the reliability of machine learning models in safety-critical applications.AugMix is a novel data processing technique designed to enhance the robustness and uncertainty estimates of image classifiers. It addresses the common issue of mismatched training and test distributions, which can significantly impact model performance in real-world scenarios. AugMix combines stochastic and diverse augmentations, a Jensen-Shannon Divergence consistency loss, and a method to mix multiple augmented images. This approach helps models better generalize to unforeseen corruptions and improves robustness and uncertainty measures on challenging image classification benchmarks. On datasets like CIFAR-10, CIFAR-100, and ImageNet, AugMix achieves state-of-the-art results in corruption robustness and uncertainty estimation, often outperforming other techniques with minimal computational overhead. The method is simple to implement and maintains or improves accuracy on standard benchmark datasets, making it a valuable tool for improving the reliability of machine learning models in safety-critical applications.