Vision Transformers are a type of deep learning model designed for processing images using the transformer architecture, which was originally developed for natural language processing tasks. They operate by dividing images into patches, treating each patch as a token similar to words in a sentence, and then applying self-attention mechanisms to capture the relationships between these patches. This innovative approach has shown significant promise in image classification and other vision tasks, often outperforming traditional convolutional neural networks.
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