This is your go-to playground for training Vision Transformers (ViT) and its related models on CIFAR-10, a common benchmark dataset in computer vision.
The whole codebase is implemented in Pytorch, which makes it easier for you to tweak and experiment. Over the months, we've made several notable updates including adding different models like ConvMixer, CaiT, ViT-small, SwinTransformers, and MLP mixer. We've also adapted the default training settings for ViT to fit better with the CIFAR-10 dataset.
Using the repository is straightforward - all you need to do is run the train.py
script with different arguments, depending on the model and training parameters you'd like to use.