Pieces of the picture puzzle
Vision Transformer (ViT) applies the Transformer architecture, originally for language, directly to image patches for classification.
Imagine you have a big puzzle of a cat. Instead of looking at the whole picture at once, you break it into small square pieces, line them up, and read them like a sentence. Then, just like understanding a story from words, the model figures out what the picture shows by learning how these pieces relate to each other.
Before ViT, most image models used convolutions to scan small areas, which was powerful but complex. ViT shows that a simpler, unified approach can match or beat those methods, especially when you have lots of data, making it a key step toward general AI that handles images like text.
Many think ViT doesn't use any spatial information, but it actually adds special position numbers to each patch so it knows where each piece goes. Another mistake is believing ViT needs huge datasets to work, but with modern tricks like data augmentation, it performs well even on smaller sets.
The Vision Transformer (ViT) is a neural network that applies the Transformer encoder architecture to image classification by dividing an image into fixed-size patches, linearly embedding each patch, and adding positional encodings before processing the sequence through standard multi-head self-attention and MLP layers. Unlike convolutional neural networks (CNNs), ViT has no built-in translation equivariance, relying instead on the self-attention mechanism to capture global dependencies across patches. It is trained on large-scale image datasets (e.g., ImageNet) and achieves state-of-the-art results when sufficient data is available.