OFA-Sys / chinese-clip-rn50

Chinese-CLIP-RN50

Introduction

This is the smallest model of the Chinese CLIP series, with ResNet-50 as the image encoder and RBT3 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

Use with the official API

We provide a simple code snippet to show how to use the API for Chinese-CLIP. For starters, please install cn_clip:

# to install the latest stable release
pip install cn_clip

# or install from source code
cd Chinese-CLIP
pip install -e .

After installation, use Chinese CLIP as shown below:

import torch
from PIL import Image

import cn_clip.clip as clip
from cn_clip.clip import load_from_name, available_models
print("Available models:", available_models())  
# Available models: ['ViT-B-16', 'ViT-L-14', 'ViT-L-14-336', 'ViT-H-14', 'RN50']

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = load_from_name("RN50", device=device, download_root='./')
model.eval()
image = preprocess(Image.open("examples/pokemon.jpeg")).unsqueeze(0).to(device)
text = clip.tokenize(["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]).to(device)

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    # Normalize the features. Please use the normalized features for downstream tasks.
    image_features /= image_features.norm(dim=-1, keepdim=True) 
    text_features /= text_features.norm(dim=-1, keepdim=True)      

    logits_per_image, logits_per_text = model.get_similarity(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs)  # [[1.268734e-03 5.436878e-02 6.795761e-04 9.436829e-01]]

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 :

Flickr30K-CN Retrieval :

COCO-CN Retrieval :

Zero-shot Image Classification :

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}
}