An Inverse Scaling Law for CLIP Training
Authors: name: Xianhang Li, name: Zeyu Wang, name: Cihang Xie

CLIP, the first foundation model that connects images and text, has enabled
many recent breakthroughs in computer vision. However, its associated training
cost is prohibitively high, imposing a significant barrier to its widespread
exploration. In this paper, we present a surprising finding that there exists
an inverse scaling law for CLIP training, whereby the larger the image/text
encoders used, the shorter the sequence length of image/text tokens that can be
applied in training. Moreover, we showcase that the strategy for reducing
image/text token length plays a crucial role in determining the quality of this
scaling law.
  As a result of this finding, we are able to successfully train CLIP even by
using academic resources. For example, on an A100 eight-GPU server, our CLIP
models achieve zero-shot top-1 ImageNet accuracies of 63.2% in ~2 days, 67.8%
in ~3 days, and 69.3% in ~4 days. By reducing the computation barrier
associated with CLIP, we hope to inspire more research in this field,
particularly from academics. Our code is available at
https://github.com/UCSC-VLAA/CLIPA.