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<meta property="og:title" content=" Vision Transformer (ViT)"/>
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<title> Vision Transformer (ViT)</title>
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<h1><a href="https://nn.labml.ai/transformer/vit/index.html">Vision Transformer (ViT)</a></h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
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<a href="https://papers.labml.ai/paper/2010.11929">An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale</a>.</p>
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<p>Vision transformer applies a pure transformer to images
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without any convolution layers.
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They split the image into patches and apply a transformer on patch embeddings.
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<a href="https://nn.labml.ai/transformer/vit/index.html#PathEmbeddings">Patch embeddings</a> are generated by applying a simple linear transformation
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to the flattened pixel values of the patch.
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Then a standard transformer encoder is fed with the patch embeddings, along with a
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classification token <code>[CLS]</code>.
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The encoding on the <code>[CLS]</code> token is used to classify the image with an MLP.</p>
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<p>When feeding the transformer with the patches, learned positional embeddings are
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added to the patch embeddings, because the patch embeddings do not have any information
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about where that patch is from.
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The positional embeddings are a set of vectors for each patch location that get trained
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with gradient descent along with other parameters.</p>
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<p>ViTs perform well when they are pre-trained on large datasets.
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The paper suggests pre-training them with an MLP classification head and
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then using a single linear layer when fine-tuning.
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The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
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They also use higher resolution images during inference while keeping the
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patch size the same.
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The positional embeddings for new patch locations are calculated by interpolating
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learning positional embeddings.</p>
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<p>Here’s <a href="https://nn.labml.ai/transformer/vit/experiment.html">an experiment</a> that trains ViT on CIFAR-10.
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This doesn’t do very well because it’s trained on a small dataset.
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It’s a simple experiment that anyone can run and play with ViTs.</p>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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<a href="https://labml.ai">labml.ai</a>
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