利用huggingface调用VIT

使用Hugging Face库调用Vision Transformer (ViT)模型可以分为以下几个步骤：

1. 安装必要的库和依赖项：

!pip install torch
!pip install torchvision
!pip install transformers

2. 导入所需的库：

from PIL import Image
import requests
import torch
from torchvision.transforms import ToTensor
from transformers import ViTFeatureExtractor, ViTForImageClassification

3. 加载预训练的ViT模型和特征提取器：

model_name = 'google/vit-base-patch16-224-in21k'
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
model = ViTForImageClassification.from_pretrained(model_name)

4. 定义图像预处理函数：

def preprocess_image(image):
    image = ToTensor()(image).unsqueeze(0)
    inputs = feature_extractor(images=image, return_tensors='pt')
    return inputs

5. 加载图像并进行预测：

image_url = 'URL_OF_YOUR_IMAGE'
image = Image.open(requests.get(image_url, stream=True).raw)
inputs = preprocess_image(image)
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=1)

请确保将 "URL_OF_YOUR_IMAGE" 替换为您要预测的图像的实际URL。

这样，您就可以使用Hugging Face的transformers库调用Vision Transformer模型。