Cvt 13 384

🚀 Convolutional Vision Transformer (CvT)

The CvT-13 model is pre-trained on ImageNet-1k at a resolution of 384x384. It provides a new approach to image classification by integrating convolutions into vision transformers.

🚀 Quick Start

The CvT-13 model was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.

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 Examples

Basic Usage

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])

📄 License

This model is licensed under the Apache-2.0 license.