What is the architecture of ViT (Vision Transformer)?

The architecture of ViT (Vision Transformer) is an approach to image processing using transformers, originally developed for sequence processing in NLP.

The main idea of ViT is to divide the image into small patches (e.g., 16x16 pixels), each of which is considered as a token (analogous to a word in text). These patches are converted into feature vectors and fed into the transformer.

Key features:

• Use of the attention mechanism (self-attention) to capture global dependencies between patches.
• Abandonment of traditional convolutional layers, which allows for better modeling of long-term relationships in the image.
• Requires large amounts of data for training but demonstrates high efficiency in classification tasks and other computer vision tasks.

Thus, ViT transfers the successes of transformers from NLP to the field of computer vision, offering an alternative to convolutional neural networks.