LearningAlgorithmBeginner-friendly
Vision Transformers (ViT) — Transformers for Images
Learn how ViT uses patches + attention for vision tasks and how it differs from CNNs.
What you'll learn
- How images become sequences of patch tokens.
- Why attention can model global relationships.
- When ViT vs CNN is a better fit.
Patches → tokens
A Vision Transformer splits an image into fixed-size patches and embeds each patch like a token.
These tokens are processed by standard Transformer blocks with attention.
Global context with attention
Attention lets every patch interact with every other patch, which can capture long-range dependencies.
This is different from CNNs where local convolutions dominate unless the network is deep.
Practical trade-offs
ViTs often benefit from large pretraining datasets and strong regularization/augmentation.
CNNs can be more sample-efficient in smaller-data settings.
Key takeaways
- ViT treats images like token sequences.
- Attention models global relationships naturally.
- Pretraining and data scale matter a lot for ViTs.
- CNNs remain strong and efficient baselines.
Want more ML topics added here (SVM, Naive Bayes, CNN, PCA, Decision Trees)?
Browse Machine Learning ->