Vision Transformer
Also known as: Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).
Key highlights
- Surpasses CNNs on large image datasets by modelling global relationships through self-attention.
- Treats an image as a sequence of patches, applying the proven Transformer architecture to vision.
- Captures long-range dependencies across the whole image, not just local neighbourhoods.
- Benefits strongly from large-scale pre-training and transfer learning to pre-trained checkpoints.
- Does not assume normally distributed data.
Intuition
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How it works
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When to use it
Use ViT for image classification or prediction on continuous (pixel) image data when you have a large dataset — on the order of 1000 or more images, and ideally far more — and access to a GPU. It works best with a large pre-training corpus or transfer learning from a pre-trained model. On small image datasets (below a few hundred images) ViT cannot learn its patch-based attention reliably, and a CNN or classical machine-learning method such as Random Forest or SVM is the safer choice.
Strengths & limitations
- Surpasses CNNs on large image datasets by modelling global relationships through self-attention.
- Treats an image as a sequence of patches, applying the proven Transformer architecture to vision.
- Captures long-range dependencies across the whole image, not just local neighbourhoods.
- Benefits strongly from large-scale pre-training and transfer learning to pre-trained checkpoints.
- Does not assume normally distributed data.
- Requires a large training set (about 1000 images or more); on small data it underperforms CNNs.
- A GPU is required, and training from scratch is data- and compute-hungry.
- On very small datasets (below a few hundred images) patch-based attention fails to learn reliably.
- Strong results typically depend on a large pre-training corpus or transfer learning rather than training from scratch.
Common pitfalls
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Applications
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Frequently asked
How is ViT different from a CNN?
A CNN processes an image through local convolutional filters, while ViT splits the image into fixed-size patches, treats them as a sequence, and uses self-attention so any patch can relate to any other. This lets ViT model global relationships and, on large datasets, surpass CNNs.
How much data does ViT need?
ViT is data-hungry: it works best with around 1000 or more images and a large pre-training corpus. Below a few hundred images its patch-based attention cannot learn reliably, and a CNN or classical method is preferable.
Do I need a GPU?
Yes. ViT requires a GPU, and training it is data- and compute-intensive. Using a pre-trained checkpoint with transfer learning reduces the burden considerably.
What if my image dataset is small?
On small datasets ViT underperforms. Below roughly 500 images consider a method like Random Forest, and below a few hundred a CNN or SVM is the safer choice, or rely on transfer learning from a pre-trained model.
Sources
- 1.Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
- 2.Touvron, H. et al. (2021). Training Data-Efficient Image Transformers. ICML.
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Cite this page
ScholarGate. (2026, June 1). Vision Transformer. ScholarGate. https://scholargate.app/deep-learning/vision-transformer