Vit SO400M 16 SigLIP I18n 256

🚀 ViT - SO400M - 16 - SigLIP - i18n - 256模型卡片

這是一個基於SigLIP（語言 - 圖像預訓練的Sigmoid損失）的模型，它使用多語言分詞器在WebLI數據集上進行多語言（i18n變體）訓練。

該模型已從Big Vision中的原始JAX檢查點轉換為PyTorch格式。這些權重可在OpenCLIP（圖像 + 文本）和timm（僅圖像）中使用。

🚀 快速開始

本模型可用於零樣本圖像分類任務，藉助多語言分詞器，能處理多種語言的輸入。

✨ 主要特性

• 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
• 數據集：WebLI
• 相關論文：
  • Sigmoid loss for language image pre - training: https://arxiv.org/abs/2303.15343

📦 安裝指南

文檔未提及具體安裝步驟，可參考OpenCLIP和timm的官方文檔進行安裝。

💻 使用示例

基礎用法

使用OpenCLIP

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>=2.27, timm>=1.0.10

model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-16-SigLIP-i18n-256')
tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-16-SigLIP-i18n-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)
    text_features = model.encode_text(text)
    image_features = F.normalize(image_features, dim=-1)
    text_features = F.normalize(text_features, dim=-1)

    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

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

高級用法

使用timm（僅用於圖像嵌入）

from urllib.request import urlopen
from PIL import Image
import timm

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

model = timm.create_model(
    'vit_so400m_patch14_siglip_256.webli_i18n',
    pretrained=True,
    num_classes=0,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(image).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

📚 詳細文檔

模型信息表格

📄 許可證

本模型使用Apache 2.0許可證。

📚 引用信息

@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}}
}