readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  datasets:
  - imagenet-1k
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  ---
  
  # Convolutional Vision Transformer (CvT)
  
  CvT-13 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT). 
  
  Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Usage
  
  Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
  
  ```python
  from transformers import AutoFeatureExtractor, CvtForImageClassification
  from PIL import Image
  import requests
  
  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-13-384')
  model = CvtForImageClassification.from_pretrained('microsoft/cvt-13-384')
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  # model predicts one of the 1000 ImageNet classes
  predicted_class_idx = logits.argmax(-1).item()
  print("Predicted class:", model.config.id2label[predicted_class_idx])
  ```
  ```