Vit B 16 SigLIP2 512

🚀 ViT-B-16-SigLIP2-512模型卡片

本项目是一个基于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。该模型从原始的JAX检查点转换而来，适用于OpenCLIP库。

🚀 快速开始

环境准备

确保你的环境满足以下依赖要求：

• open-clip-torch >= 2.31.0
• timm >= 1.0.15

代码示例

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-B-16-SigLIP2-512')
tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP2-512')

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)

✨ 主要特性

• 基于WebLI数据集训练的SigLIP 2视觉语言模型。
• 支持零样本图像分类任务。
• 从原始的JAX检查点转换而来，可在OpenCLIP中使用。

📦 安装指南

文档未提及具体安装步骤，可参考依赖库的官方文档进行安装：

• open-clip-torch：open-clip-torch官方仓库
• timm：timm官方仓库

📚 详细文档

模型详情

• 模型类型：对比图像文本、零样本图像分类。
• 原始仓库：https://github.com/google-research/big_vision
• 训练数据集：WebLI
• 相关论文：
  • SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
  • Sigmoid loss for language image pre-training

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}}
}

📄 许可证

本项目采用Apache-2.0许可证。