CNN vs. Vision Transformer Architecture

Comparing the traditional CNN approach with the modern Vision Transformer architecture for computer vision

CNN vs. Vision Transformer

CNN

  • Local receptive field - only sees nearby pixels
  • Fixed filters - same pattern applied everywhere
  • Hierarchical - builds features level by level
  • Efficient - fewer parameters for small images
  • Less data hungry - works well with smaller datasets

Vision Transformer

Vision Transformer (ViT) Global AttentionIllustration of the global attention mechanism in Vision Transformers, where any patch can influence any other patch regardless of position.
  • Global context - can look anywhere in the image
  • Dynamic attention - adapts based on content
  • Parallel processing - all patches at once
  • Data hungry - needs large datasets
  • Computationally intensive - requires more resources

Key Architectural Differences

CNN Architecture

CNNs use convolutional filters that slide across the image to extract features. They have a natural inductive bias toward spatial locality. Features are built hierarchically through multiple layers, with each layer seeing a larger portion of the image.

Vision Transformer Architecture

ViTs split images into patches that are treated as tokens. These patches are processed by self-attention mechanisms that allow any patch to influence any other patch regardless of position. This gives ViTs a global receptive field from the first layer.

When to Use CNNs

  • Smaller datasets (thousands of images)
  • Limited computational resources
  • Tasks that benefit from local features
  • When interpretability is important
  • Real-time applications with latency constraints

When to Use ViTs

  • Large datasets (millions of images)
  • Access to substantial compute resources
  • When global context is critical
  • Tasks requiring understanding relationships between distant parts
  • When leveraging pre-trained models and transfer learning
Mastodon