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
- ✓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