However, traditional computer vision techniques like Convolutional Neural Networks (CNNs) have some limitations when analyzing images.
CNNs work by passing images through a series of convolutional and pooling layers to extract relevant features. However, as images become larger and more complex, CNNs become less effective. This is where Vision Transformers come in.
Vision Transformers, or ViTs, are a type of deep learning model that uses self-attention mechanisms to process visual data. They were introduced in a paper by Dosovitskiy in 2020 and have since gained popularity in computer vision research.