As proposed by Alexey Dosovitskiy in their paper, “An Image is Worth 16×16 Words: Transformers for Image Recognition” (2020), a Vision Transformer model is a type of neural network architecture designed for computer vision tasks. It is based on the Transformer architecture, originally introduced for natural language processing tasks, but adapted to work with image data.
The Vision Transformer model represents an image as a sequence of non-overlapping fixed-size patches, which are then linearly embedded into 1D vectors. These vectors are then treated as input tokens for the Transformer architecture. The key idea is to apply the self-attention mechanism, which allows the model to weigh the importance of different tokens in the sequence when processing the input data. The self-attention mechanism allows the model to capture global contextual information, enabling it to learn long-range dependencies and relationships between image patches.
The Vision Transformer model consists of an encoder, which contains multiple layers of self-attention and feed-forward neural networks, and a decoder, which produces the final output, such as image classification or object detection predictions. During training, the model is optimized using a suitable loss function, such as cross-entropy, to minimize the difference between predicted and ground-truth labels.
To read more – https://www.leewayhertz.com/vision-transformer-model/
Matthew Haws 