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ChineseCLIP

Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese

Abstract

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

How to use it?

Use the model for zero-shot classification

from mmpretrain import ImageClassificationInferencer

inferencer = ImageClassificationInferencer(
    'cn-clip_resnet50_zeroshot-cls_cifar100',
    pretrained=True,
    classes=['鸟', '狗', '猫', '蛇'],
    text_prototype=['鸟', '狗', '猫', '蛇'],
)

prediction = inferencer('./demo/bird.JPEG')[0]
print('Results:', prediction['pred_class'])

Train/Test Command

Prepare your dataset according to the docs.

Test:

python tools/test.py configs/chinese_clip/cn-clip_resnet50_zeroshot-cls_cifar100.py https://download.openmmlab.com/mmpretrain/v1.0/chinese_clip/cn-clip_resnet50_3rdparty_20230519-6a2b3eb2.pth

Models and results

Image Classification on CIFAR100

Model Params (M) Top-1 (%) Config Download
cn-clip_resnet50_zeroshot-cls_cifar100* 77.00 40.70 config model
cn-clip_vit-base-p16_zeroshot-cls_cifar100* 188.00 64.50 config model
cn-clip_vit-large-p14_zeroshot-cls_cifar100* 406.00 74.80 config model
cn-clip_vit-huge-p14_zeroshot-cls_cifar100* 958.00 79.10 config model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven't reproduce the training results.

Citation

@article{chinese-clip,
  title={Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese},
  author={Yang, An and Pan, Junshu and Lin, Junyang and Men, Rui and Zhang, Yichang and Zhou, Jingren and Zhou, Chang},
  journal={arXiv preprint arXiv:2211.01335},
  year={2022}
}