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

EVA-CLIP: Improved Training Techniques for CLIP at Scale