Prolip ViT B 16 DC 1B 12 8B

🚀 基于DataComp 1B的预训练ViT - B/16 ProLIP官方实现

本项目是基于DataComp 1B数据集，对ViT - B/16模型进行概率语言 - 图像预训练（ProLIP）的官方实现。该预训练权重在图像分类、检索等零样本任务中表现出色，为相关领域的研究和应用提供了强大的支持。

🚀 快速开始

环境准备

确保你已经安装了必要的库：

import requests
from PIL import Image

import torch
from prolip.model import ProLIPHF
from transformers import CLIPProcessor
from prolip.tokenizer import HFTokenizer

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

模型加载

processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
model = ProLIPHF.from_pretrained("SanghyukChun/ProLIP-ViT-B-16-DC-1B-12_8M")
tokenizer = HFTokenizer("timm/ViT-B-16-SigLIP", context_length=64, clean="canonicalize")

示例代码

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt", padding=True)
texts = ["A couple of cats laying on top of a pink blanket.", "A man walks through a flooded road during a rainstorm", "photo"]
texts = tokenizer(texts)

outputs = model(image=inputs["pixel_values"], text=texts)

l2_logit = outputs["image_features"]["mean"] @ outputs["text_features"]["mean"].T
i_unc = torch.exp(outputs["image_features"]["std"]).sum(dim=-1)
t_unc = torch.exp(outputs["text_features"]["std"]).sum(dim=-1)
csd_logit = l2_logit - 0.5 * t_unc
csd_logit2 = l2_logit.T - 0.5 * i_unc
print("Mean-only image-to-text logits (by L2 distance):", l2_logit)
print("Uncertainty-aware image-to-text logits (by CSD):", csd_logit)
print("Uncertainty-aware text-to-image logits (by CSD):", csd_logit2.T)
print("Image uncertainty: ", i_unc)
print("Text uncertainty: ", t_unc)

✨ 主要特性

• 预训练模型：本权重是通过概率语言 - 图像预训练（ProLIP）得到的预训练ViT - B/16模型。
• 预训练数据集：使用DataComp 1B数据集，可见样本达128亿。
• 多任务表现出色：在零样本图像分类、零样本图像分布偏移、零样本VTAB任务、零样本检索等任务上均有良好表现。

📚 详细文档

项目概述

• 论文链接：Probabilistic Language - Image Pre - Training
• GitHub仓库：https://github.com/naver - ai/prolip
• 更多模型：可在Hugging Face查看更多相关模型。

性能概述

📄 许可证

本项目采用MIT许可证。

📚 引用

如果你使用了本项目的代码或模型，请引用以下论文：

@inproceedings{chun2025prolip,
    title={Probabilistic Language-Image Pre-Training},
    author={Chun, Sanghyuk and Kim, Wonjae and Park, Song and Yun, Sangdoo},
    year={2025},
    booktitle={International Conference on Learning Representations (ICLR)},
}

@inproceedings{chun2025longprolip,
    title={LongProLIP: A Probabilistic Vision-Language Model with Long Context Text},
    author={Chun, Sanghyuk and Yun, Sangdoo},
    year={2025},
    booktitle={ICLR Workshop on Quantify Uncertainty and Hallucination in Foundation Models},
}