Summary
Vision Transformers (ViT) are transformers that are specifically designed for vision processing tasks such as image recognition. They are spatial non-sequential signals that are converted to a sequence and trained on datasets with more than 14 million images. The transformer is supervised by a standard encoder block, with the only modification being the removal of the prediction head and the addition of a new D K D times K D K linear layer.
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Summary
A Vision Transformer ( ViT ) is a transformer that is targeted at vision processing tasks such as image recognition .
Vision transformer - Wikipedia
wikipedia.org
Summary
This article explains how the Vision Transformer (ViT) works for image classification problems, which lack the inductive biases of Convolutional Neural Networks (CNNs). It is a spatial non-sequential signal that is converted to a sequence, and is trained on datasets with more than 14M images. The transformer is supervised by a standard encoder block, and the only modification is to discard the prediction head and attach a new D K D times K D K linear layer.
How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words | AI Summer
theaisummer.com
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openreview.net
