CNNs vs Transformers for Vision
Convolutions bake in locality and translation invariance; transformers learn global relationships from data — at a cost.
CNNvsVision Transformer
Verdict: CNNs win on small data and efficiency; ViTs win at scale with enough data and compute.
The core difference
A CNN slides learned filters over an image, hard-coding locality and translation invariance. A Vision Transformer (ViT) splits the image into patches and lets self-attention relate any patch to any other — no built-in spatial prior.
Side by side
| Dimension | CNN | Vision Transformer |
|---|---|---|
| Inductive bias | Strong (locality) | Weak (learned) |
| Data hunger | Lower | Higher |
| Global context | Via depth | Immediate |
| Compute | Efficient | Attention is quadratic in patches |
| Sweet spot | Small/medium datasets | Large-scale pretraining |
When to choose which
- Choose a CNN with limited data, tight latency budgets, or edge deployment.
- Choose a ViT when you can pretrain on huge datasets and need to model long-range structure.
- Hybrids (convolutional stems, windowed attention) capture much of both.
Bottom line
Inductive bias is a data substitute. With little data, the CNN's built-in priors win; with lots of data, the transformer learns better priors than we can hand-design.