Vit SO400M 16 SigLIP2 384

🚀 ViT - SO400M - 16 - SigLIP2 - 384模型卡片

本模型是一個基於WebLI數據集訓練的SigLIP 2視覺 - 語言模型，可用於零樣本圖像分類任務。它從原始的JAX檢查點轉換而來，適用於OpenCLIP庫。

✨ 主要特性

• 基於SigLIP 2架構，具備多語言視覺 - 語言編碼能力，語義理解、定位和特徵提取能力得到提升。
• 支持零樣本圖像分類，無需針對特定任務進行微調即可對圖像進行分類。

📦 安裝指南

文檔未提及安裝步驟，可參考OpenCLIP庫的官方安裝說明進行安裝。

💻 使用示例

基礎用法

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-16-SigLIP2-384')
tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-16-SigLIP2-384')

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)

📚 詳細文檔

模型詳情

這是一個在WebLI數據集上訓練的SigLIP 2視覺 - 語言模型。該模型已從Big Vision中的原始JAX檢查點轉換為可在OpenCLIP中使用的形式。

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}}
}

📄 許可證

本模型使用Apache 2.0許可證。