  Contrastive language-image pre-training, CLIP for short, has gained
increasing attention for its potential in various scenarios. In this paper, we
propose EVA-CLIP, a series of models that significantly improve the efficiency
and effectiveness of CLIP training. Our approach incorporates new techniques
for representation learning, optimization, and augmentation, enabling EVA-CLIP
to achieve superior performance compared to previous CLIP models with the same
number of parameters but significantly smaller training costs. Notably, our
largest 5.0B-parameter EVA-02-CLIP-E/14+ with only 9 billion seen samples
achieves 82.0 zero-shot top-1 accuracy on ImageNet-1K val. A smaller
EVA-02-CLIP-L/14+ with only 430 million parameters and 6 billion seen samples
achieves 80.4 zero-shot top-1 accuracy on ImageNet-1K val. To facilitate open
access and open research, we release the complete suite of EVA-CLIP to the
community at https://github.com/baaivision/EVA/tree/master/EVA-CLIP.
