🚀 ViT-SO400M-14-SigLIP2-378模型卡片

本模型是一个基于WebLI数据集训练的SigLIP 2视觉-语言模型，可用于零样本图像分类任务。它从原始的JAX检查点转换而来，适用于OpenCLIP库。

✨ 主要特性

• 基于SigLIP 2架构，具备多语言视觉-语言编码能力，语义理解、定位和特征提取能力更强。
• 支持零样本图像分类，无需针对特定任务进行微调。
• 模型类型为对比图像-文本模型，可有效学习图像和文本之间的关联。

📦 安装指南

文档未提及具体安装步骤，可参考OpenCLIP库的官方文档进行安装：https://github.com/mlfoundations/open_clip

💻 使用示例

基础用法

import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch >= 2.31.0, timm >= 1.0.15

model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP2-378')
tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-14-SigLIP2-378')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

📚 详细文档

模型信息

📄 许可证

本模型采用Apache-2.0许可证。

📚 引用信息

@article{tschannen2025siglip,
  title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features},
  author={Tschannen, Michael and Gritsenko, Alexey and Wang, Xiao and Naeem, Muhammad Ferjad and Alabdulmohsin, Ibrahim and Parthasarathy, Nikhil and Evans, Talfan and Beyer, Lucas and Xia, Ye and Mustafa, Basil and H'enaff, Olivier and Harmsen, Jeremiah and Steiner, Andreas and Zhai, Xiaohua},
  year={2025},
  journal={arXiv preprint arXiv:2502.14786}
}        

@article{zhai2023sigmoid,
  title={Sigmoid loss for language image pre-training},
  author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
  journal={arXiv preprint arXiv:2303.15343},
  year={2023}
}

@misc{big_vision,
  author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
  title = {Big Vision},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/google-research/big_vision}}
}