Scaling Vision Transformers

Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby et al.

2022 · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 778 citations

Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we…

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