Scaling Vision Transformers
This work addresses the scaling challenge for Vision Transformers, providing insights for designing future models in computer vision, though it is incremental as it builds on existing ViT frameworks.
The authors investigated the scaling properties of Vision Transformers (ViT) by scaling models and data, refining architecture and training to reduce memory and increase accuracy, resulting in a two-billion-parameter model achieving 90.45% top-1 accuracy on ImageNet and 84.86% with few-shot transfer.
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 refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class.