

CvT models are regular Vision Transformers, but trained with convolutions. They outperform the original model (ViT) when fine-tuned on ImageNet-1K and CIFAR-100.
You can check out demo notebooks regarding inference as well as fine-tuning on custom data here (you can just replace [ViTFeatureExtractor] by [AutoImageProcessor] and [ViTForImageClassification] by [CvtForImageClassification]).
The available checkpoints are either (1) pre-trained on ImageNet-22k (a collection of 14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million
  images and 1,000 classes).