Image Models

Vision Transformer

Introduced by Dosovitskiy et al. in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

The Vision Transformer, or ViT, is a model for image classification that employs a Transformer-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard Transformer encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.

Source: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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