Update README.md

README.md

@@ -1,4 +1,26 @@
-### How to use
+---
+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:
 
@@ -10,8 +32,8 @@ import requests
 url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
 image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = AutoFeatureExtractor.from_pretrained('anugunj/cvt-13-384-1k')
-model = CvtForImageClassification.from_pretrained('anugunj/cvt-13-384-1k')
+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)
@@ -19,4 +41,5 @@ 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])
+```
 ```