CIFAR-10 is among the most widely used datasets in machine learning, facilitating thousands of research projects per year.
To accelerate research and reduce the cost of experiments, we introduce training methods for CIFAR-10 which reach 94% accuracy in 3.29 seconds, 95% in 10.4 seconds, and 96% in 46.3 seconds, when run on a single NVIDIA A100 GPU.
As one factor contributing to these training speeds, we propose a derandomized variant of horizontal flipping augmentation, which we show improves over the standard method in every case where flipping is beneficial over no flipping at all.
Our code is released athttps://github.com/KellerJordan/cifar10-airbench.