Convolutional Vision Transformer (CvT)

This model was released on 2021-03-29 and added to Hugging Face Transformers on 2022-05-18.

Convolutional Vision Transformer (CvT)

Convolutional Vision Transformer (CvT) is a model that combines the strengths of convolutional neural networks (CNNs) and Vision transformers for the computer vision tasks. It introduces convolutional layers into the vision transformer architecture, allowing it to capture local patterns in images while maintaining the global context provided by self-attention mechanisms.

You can find all the CvT checkpoints under the Microsoft organization.

Tip

This model was contributed by anujunj.

Click on the CvT models in the right sidebar for more examples of how to apply CvT to different computer vision tasks.

The example below demonstrates how to classify an image with Pipeline or the AutoModel class.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="image-classification",
    model="microsoft/cvt-13",
    dtype=torch.float16,
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")

import torch
import requests
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor

image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
model = AutoModelForImageClassification.from_pretrained(
    "microsoft/cvt-13",
    dtype=torch.float16,
    device_map="auto"
)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(image, return_tensors="pt").to(model.device)

with torch.no_grad():
  logits = model(**inputs).logits
predicted_class_id = logits.argmax(dim=-1).item()

class_labels = model.config.id2label
predicted_class_label = class_labels[predicted_class_id]
print(f"The predicted class label is: {predicted_class_label}")

Resources

Refer to this set of ViT notebooks for examples of inference and fine-tuning on custom datasets. Replace ViTFeatureExtractor and ViTForImageClassification in these notebooks with AutoImageProcessor and CvtForImageClassification.

CvtConfig

[[autodoc]] CvtConfig

CvtModel

[[autodoc]] CvtModel - forward

CvtForImageClassification

[[autodoc]] CvtForImageClassification - forward