Scaling Vision Transformers

Attention-based neural networks such as the Vision Transformer (ViT) have recently attained advanced results on many computer vision benchmarks. As a result, we successfully train a ViT model with two billion parameters, which attains a new advanced on ImageNet of 90.45% top-1 accuracy.

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• 1 Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively.
• 2 While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale.
• 3 To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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