Vit Bigg 14 CLIPA Datacomp1b

🚀 ViT-bigG-14-CLIPA-datacomp1B模型卡片

ViT-bigG-14-CLIPA-datacomp1B是一个CLIPA-v2模型，可用于零样本图像分类任务，在图像和文本的对比学习方面表现出色。

🚀 快速开始

本模型可用于零样本图像分类任务，通过对比图像和文本特征，实现对图像的分类预测。

✨ 主要特性

• 模型类型：对比图像 - 文本、零样本图像分类。
• 原始代码库：https://github.com/UCSC-VLAA/CLIPA
• 训练数据集：mlfoundations/datacomp_1b
• 相关论文：
  • CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy: https://arxiv.org/abs/2306.15658
  • An Inverse Scaling Law for CLIP Training: https://arxiv.org/abs/2305.07017

📦 安装指南

文档未提供具体安装步骤，可参考原代码库https://github.com/UCSC-VLAA/CLIPA 进行安装。

💻 使用示例

基础用法

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:ViT-bigG-14-CLIPA')
tokenizer = get_tokenizer('hf-hub:ViT-bigG-14-CLIPA')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

text = tokenizer(["a diagram", "a dog", "a cat", "a beignet"], 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 = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)  # prints: [[0., 0., 0., 1.0]]

📚 详细文档

模型信息表格

📄 许可证

本模型使用Apache-2.0许可证。

📚 引用

@article{li2023clipav2,
      title={CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy}, 
      author={Xianhang Li and Zeyu Wang and Cihang Xie},
      journal={arXiv preprint arXiv:2306.15658},
      year={2023},
}

@inproceedings{li2023clipa,
      title={An Inverse Scaling Law for CLIP Training}, 
      author={Xianhang Li and Zeyu Wang and Cihang Xie},
      booktitle={NeurIPS},
      year={2023},
}