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Vision Transformers (ViTs) have transformed the way machines process and understand images, replacing convolutions with self-attention. At the heart of this shift lies the question: how do these models “see”? This article explores the internal workings of ViTs, focusing on attention heads, patch embeddings, and interpretability techniques that help us decode how these models perceive the visual world.
