Vit B 16 SigLIP I18n 256

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

本模型是基於WebLI數據集訓練的SigLIP（用於語言 - 圖像預訓練的Sigmoid損失）模型。它從Big Vision中的原始JAX檢查點轉換為PyTorch模型。這些權重可用於OpenCLIP（圖像 + 文本）和timm（僅圖像）。

🚀 快速開始

本模型可用於零樣本圖像分類任務，通過對比圖像和文本的特徵來實現分類。

✨ 主要特性

• 基於SigLIP架構，在WebLI數據集上進行訓練。
• 可從原始JAX檢查點轉換為PyTorch模型。
• 權重可同時用於OpenCLIP和timm。

📦 安裝指南

文檔未提及安裝步驟，跳過此章節。

💻 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>=2.23.0, timm>=0.9.8

model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-i18n-256')
tokenizer = get_tokenizer('hf-hub:timm/ViT-B-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)

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_base_patch16_siglip_256',
    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

模型詳情

🔧 技術細節

文檔未提供足夠的技術實現細節，跳過此章節。

📄 許可證

本模型使用Apache 2.0許可證。

📚 引用

@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}}
}