Vit Gopt 16 SigLIP2 256

🚀 ViT - gopt - 16 - SigLIP2 - 256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。

🚀 快速開始

本模型可用於零樣本圖像分類任務，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

• 基於SigLIP 2架構，在WebLI數據集上訓練，具有良好的視覺 - 語言理解能力。
• 可用於零樣本圖像分類任務，無需額外的訓練數據。
• 從原始的JAX檢查點轉換而來，適用於OpenCLIP庫。

📚 詳細文檔

模型詳情

• 模型類型：對比圖像 - 文本、零樣本圖像分類。
• 原始倉庫：https://github.com/google - research/big_vision
• 訓練數據集：WebLI
• 相關論文：
  • 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}}
}

📄 許可證

本模型使用的許可證為Apache 2.0。