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May 7, 20266 min read

Why Vision Transformers Matter (and where they don't)

A pragmatic look at when ViTs beat ConvNets, when they don't, and how to choose between them in production.

Why Vision Transformers Matter

Vision Transformers (ViTs) have moved from research curiosity to production default in many computer-vision pipelines. Here is how I think about them.

TL;DR

  • ViTs scale better with data and compute than ConvNets.
  • ConvNets still win on small datasets and tight latency budgets.
  • The interesting space is hybrid (ConvNeXt, Swin, MaxViT).

Where ViTs win

  1. Long-range dependencies
  2. Multimodal tasks
  3. Self-supervised pretraining

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