🚀 ViT - L - 16 - SigLIP - 256模型卡片
這是一個基於WebLI數據集訓練的SigLIP(語言 - 圖像預訓練的Sigmoid損失函數)模型。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
模型詳情
屬性 |
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 |
WebLI |
相關論文 |
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 |
模型使用
💻 使用示例
基礎用法
使用OpenCLIP庫時的示例代碼如下:
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
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP-256')
tokenizer = get_tokenizer('hf-hub:timm/ViT-L-16-SigLIP-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
庫獲取圖像嵌入的示例代碼如下:
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_large_patch16_siglip_256',
pretrained=True,
num_classes=0,
)
model = model.eval()
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))
📄 許可證
本項目採用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}}
}