  We present a combined scaling method - named BASIC - that achieves 85.7%
top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from
any labeled ImageNet example. This accuracy surpasses best published similar
models - CLIP and ALIGN - by 9.3%. Our BASIC model also shows significant
improvements in robustness benchmarks. For instance, on 5 test sets with
natural distribution shifts such as ImageNet-{A,R,V2,Sketch} and ObjectNet, our
model achieves 84.3% top-1 average accuracy, only a small drop from its
original ImageNet accuracy. To achieve these results, we scale up the
contrastive learning framework of CLIP and ALIGN in three dimensions: data
size, model size, and batch size. Our dataset has 6.6B noisy image-text pairs,
which is 4x larger than ALIGN, and 16x larger than CLIP. Our largest model has
3B weights, which is 3.75x larger in parameters and 8x larger in FLOPs than
ALIGN and CLIP. Finally, our batch size is 65536 which is 2x more than CLIP and
4x more than ALIGN. We encountered two main challenges with the scaling rules
of BASIC. First, the main challenge with implementing the combined scaling
rules of BASIC is the limited memory of accelerators, such as GPUs and TPUs. To
overcome the memory limit, we propose two simple methods which make use of
gradient checkpointing and model parallelism. Second, while increasing the
dataset size and the model size has been the defacto method to improve the
performance of deep learning models like BASIC, the effect of a large
contrastive batch size on such contrastive-trained image-text models is not
well-understood. To shed light on the benefits of large contrastive batch
sizes, we develop a theoretical framework which shows that larger contrastive
batch sizes lead to smaller generalization gaps for image-text models such as
BASIC.
