  The tremendous success of CLIP (Radford et al., 2021) has promoted the
research and application of contrastive learning for vision-language
pretraining. In this work, we construct a large-scale dataset of image-text
pairs in Chinese, where most data are retrieved from publicly available
datasets, and we pretrain Chinese CLIP models on the new dataset. We develop 5
Chinese CLIP models of multiple sizes, spanning from 77 to 958 million
parameters. Furthermore, we propose a two-stage pretraining method, where the
model is first trained with the image encoder frozen and then trained with all
parameters being optimized, to achieve enhanced model performance. Our
comprehensive experiments demonstrate that Chinese CLIP can achieve the
state-of-the-art performance on MUGE, Flickr30K-CN, and COCO-CN in the setups
of zero-shot learning and finetuning, and it is able to achieve competitive
performance in zero-shot image classification based on the evaluation on the
ELEVATER benchmark (Li et al., 2022). We have released our codes, models, and
demos in https://github.com/OFA-Sys/Chinese-CLIP
