Vision Transformer

The Vision Transformer (ViT) is a novel architecture that applies the principles of transformer models, originally designed for natural language processing, to the domain of image analysis. Instead of processing images as a whole, ViT divides an image into fixed-size patches, which are then linearly embedded into a sequence of tokens. This sequence is processed using self-attention mechanisms, allowing the model to capture global dependencies across the image. The architecture typically consists of multiple layers of multi-head self-attention and feed-forward networks, with positional encodings added to retain spatial information. The performance of ViT has been shown to rival or exceed that of traditional convolutional neural networks (CNNs) on various image classification benchmarks, particularly when trained on large datasets.