Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been successfully scaled to nearly the same degree; the largest dense ViT contains 4B parameters (Chen et al., 2022). We present a recipe for highly efficient and stable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety of experiments on the resulting model. When evaluated on downstream tasks (often with a lightweight linear model on frozen features), ViT-22B demonstrates increasing performance with scale. We further observe other interesting benefits of scale, including an improved tradeoff between fairness and performance, state-of-the-art alignment to human visual perception in terms of shape/texture bias, and improved robustness. ViT-22B demonstrates the potential for "LLM-like" scaling in vision, and provides key steps towards getting there.
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ImageNet
CelebA
Kinetics
Places
ADE20K
Kinetics 400
ImageNet-R
Perceptual Similarity
ImageNet-A
ObjectNet
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | ViT-B/16 | Top 1 Accuracy | 88.6% | # 44 | ||
| Number of params | 86M | # 814 | |||||
| Image Classification | ImageNet | ViT-L/16 (384res, distilled from ViT-22B) | Top 1 Accuracy | 89.6% | # 24 | ||
| Number of params | 307M | # 915 | |||||
| Zero-Shot Transfer Image Classification | ImageNet | LiT-22B | Accuracy (Private) | 85.9 | # 4 | ||
| Zero-Shot Transfer Image Classification | ImageNet-A | LiT-22B | Accuracy (Private) | 90.1 | # 2 | ||
| Zero-Shot Transfer Image Classification | ImageNet-R | LiT-22B | Accuracy | 96.0 | # 4 | ||
| Zero-Shot Transfer Image Classification | ImageNet V2 | LiT-22B | Accuracy (Private) | 80.9 | # 2 | ||
| Action Classification | Kinetics-400 | ViT-22B | Acc@1 | 88.0 | # 22 | ||
| Zero-Shot Transfer Image Classification | ObjectNet | LiT-22B | Accuracy (Private) | 87.6 | # 1 |
