As discussed in the Vision Transformers (ViT) paper, a Transformer-based architecture for vision typically requires a larger dataset than usual, as well as a longer pre-training schedule. ImageNet-1k (which has about a million images) is considered to fall under the medium-sized data regime with respect to ViTs. This is primarily because, unlike CNNs, ViTs (or a typical Transformer-based architecture) do not have well-informed inductive biases (such as convolutions for processing images). This begs the question: can't we combine the benefits of convolution and the benefits of Transformers in a single network architecture? These benefits include parameter-efficiency, and self-attention to process long-range and global dependencies (interactions between different regions in an image).