This is the large-version of the Chinese CLIP, with ViT-L/14@336px as the image encoder and RoBERTa-wwm-base as the text encoder. Chinese CLIP is a simple implementation of CLIP on a large-scale dataset of around 200 million Chinese image-text pairs. For more details, please refer to our technical report https://arxiv.org/abs/2211.01335 and our official github repo https://github.com/OFA-Sys/Chinese-CLIP (Welcome to star! 🔥🔥)
Use with the official API
We provide a simple code snippet to show how to use the API of Chinese-CLIP to compute the image & text embeddings and similarities.
However, if you are not satisfied with only using the API, feel free to check our github repo https://github.com/OFA-Sys/Chinese-CLIP for more details about training and inference.
Results
MUGE Text-to-Image Retrieval:
Setup
Zero-shot
Finetune
Metric
R@1
R@5
R@10
MR
R@1
R@5
R@10
MR
Wukong
42.7
69.0
78.0
63.2
52.7
77.9
85.6
72.1
R2D2
49.5
75.7
83.2
69.5
60.1
82.9
89.4
77.5
CN-CLIP
63.0
84.1
89.2
78.8
68.9
88.7
93.1
83.6
Flickr30K-CN Retrieval:
Task
Text-to-Image
Image-to-Text
Setup
Zero-shot
Finetune
Zero-shot
Finetune
Metric
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
Wukong
51.7
78.9
86.3
77.4
94.5
97.0
76.1
94.8
97.5
92.7
99.1
99.6
R2D2
60.9
86.8
92.7
84.4
96.7
98.4
77.6
96.7
98.9
95.6
99.8
100.0
CN-CLIP
71.2
91.4
95.5
83.8
96.9
98.6
81.6
97.5
98.8
95.3
99.7
100.0
COCO-CN Retrieval:
Task
Text-to-Image
Image-to-Text
Setup
Zero-shot
Finetune
Zero-shot
Finetune
Metric
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
Wukong
53.4
80.2
90.1
74.0
94.4
98.1
55.2
81.0
90.6
73.3
94.0
98.0
R2D2
56.4
85.0
93.1
79.1
96.5
98.9
63.3
89.3
95.7
79.3
97.1
98.7
CN-CLIP
69.2
89.9
96.1
81.5
96.9
99.1
63.0
86.6
92.9
83.5
97.3
99.2
Zero-shot Image Classification:
Task
CIFAR10
CIFAR100
DTD
EuroSAT
FER
FGVC
KITTI
MNIST
PC
VOC
GIT
88.5
61.1
42.9
43.4
41.4
6.7
22.1
68.9
50.0
80.2
ALIGN
94.9
76.8
66.1
52.1
50.8
25.0
41.2
74.0
55.2
83.0
CLIP
94.9
77.0
56.0
63.0
48.3
33.3
11.5
79.0
62.3
84.0
Wukong
95.4
77.1
40.9
50.3
-
-
-
-
-
-
CN-CLIP
96.0
79.7
51.2
52.0
55.1
26.2
49.9
79.4
63.5
84.9
Citation
If you find Chinese CLIP helpful, feel free to cite our paper. Thanks for your support!
@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}
}
