Vit H 14 CLIPA 336 Datacomp1b

🚀 ViT - H - 14 - CLIPA - 336 - datacomp1B模型卡

這是一個CLIPA - v2模型，可用於對比圖像文本和零樣本圖像分類任務。

🚀 快速開始

本模型可用於零樣本圖像分類任務。下面為你展示如何使用OpenCLIP庫來加載和使用該模型。

✨ 主要特性

• 模型類型：支持對比圖像文本和零樣本圖像分類。
• 原始倉庫：https://github.com/UCSC-VLAA/CLIPA
• 訓練數據集：mlfoundations/datacomp_1b
• 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

📦 安裝指南

文檔中未提及安裝步驟，若需使用open_clip庫，可通過pip install open_clip進行安裝（此為常見安裝方式，非原文內容）。

💻 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-336')
tokenizer = get_tokenizer('hf-hub:ViT-H-14-CLIPA-336')

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Apache - 2.0許可證。

📚 引用信息

如果你在研究中使用了該模型，請引用以下論文：

@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},
}