Vit H 14 CLIPA 336 Laion2b

🚀 ViT - H - 14 - CLIPA - laion2B模型卡

這是一個CLIPA - v2模型，可用於零樣本圖像分類等任務，在圖像 - 文本對比學習方面有出色表現。

🚀 快速開始

本模型可用於零樣本圖像分類任務，通過以下代碼示例，你可以快速體驗模型的使用。

✨ 主要特性

• 模型類型：對比式圖像 - 文本、零樣本圖像分類。
• 原始倉庫：https://github.com/UCSC - VLAA/CLIPA
• 訓練數據集：laion/laion2B - en
• 相關論文：
  • 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

💻 使用示例

基礎用法

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

📚 詳細文檔

模型信息表格

📄 許可證

本模型使用的許可證為apache - 2.0。

📚 引用信息

@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},
}