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Scaling Vision Transformers
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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.
Forward citations
Cited by 4 Pith papers
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Under fixed-distribution neural scaling laws, the capability gap between state-of-the-art and low-compute AI models shrinks over time toward zero.
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A benchmark shows lightweight CNNs with transfer learning reach 92-99% accuracy on Arabic handwritten character datasets, but the reported best-model rankings are undermined by best-fold selection and internal contradictions.
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