对比语言图像预训练（Contrastive language-image pre-training，简称CLIP）已经在各种场景中引起了越来越多的关注。在本文中，作者提出了EVA-CLIP，这是一系列显著提高CLIP训练效率和效果的模型。作者的方法结合了新的表示学习、优化和增强技术，使得EVA-CLIP相比于具有相同参数数量但训练成本显著较小的之前的CLIP模型具有更优异的性能。值得注意的是，作者最大的5.0B参数EVA-02-CLIP-E/14+，仅使用了90亿数据样本，就在ImageNet-1K的val数据集上达到了82.0的零样例一级准确率。较小的EVA-02-CLIP-L/14+仅使用了4.3亿参数和60亿数据样本，在ImageNet-1K val数据集上实现了80.4的零样例一级准确率。
标题：EVA-CLIP: Improved Training Techniques for CLIP at Scale
作者：Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, Yue Cao
论文：http://arxiv.org/abs/2303.15389
代码：https://github.com/baaivision/EVA/tree/master/EVA-CLIP
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