Vit Gopt 16 SigLIP2 256

🚀 ViT - gopt - 16 - SigLIP2 - 256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。

🚀 快速开始

本模型可用于零样本图像分类任务，以下是使用示例：

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-gopt-16-SigLIP2-256')
tokenizer = get_tokenizer('hf-hub:timm/ViT-gopt-16-SigLIP2-256')

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SigLIP 2架构，在WebLI数据集上训练，具有良好的视觉 - 语言理解能力。
• 可用于零样本图像分类任务，无需额外的训练数据。
• 从原始的JAX检查点转换而来，适用于OpenCLIP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: https://arxiv.org/abs/2502.14786
  • Sigmoid loss for language image pre - training: https://arxiv.org/abs/2303.15343

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

本模型使用的许可证为Apache 2.0。