A collection of weights and logs for image classification experiments with modern Transformer architectures on CIFAR-100. These benchmarks are proposed for the convenience of conducting research in Mixup augmentations with Transformers since the most published benchmarks of Mixup variants with ViTs are based on ImageNet-1K. Please refer to our tech report for more details.

• Since the original resolutions of CIFAR-100 are too small for ViTs, we resize the input images to $224\times 224$ (training and testing) while not modifying the ViT architectures. This benchmark uses the DeiT setup and trains the model for 200 or 600 epochs with a batch size of 100 on CIFAR-100. The basic learning rates of DeiT and Swin are $1e-3$ and $5e-4$, which is the optimal setup in our experiments. We search and report $\alpha$ in $Beta(\alpha, \alpha)$ for all compared methods. View config files in mixups/vits.
• The best of top-1 accuracy in the last 10 training epochs is reported for ViT architectures. We released the trained models and logs in vits-mix-cifar100-weights.

ViTs' Mixup Benchmark on CIFAR-100