Update README.md

README.md

@@ -30,15 +30,15 @@ You can use the raw model for image classification. See the [model hub](https://
 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 MobileViTv2FeatureExtractor, MobileViTv2ForImageClassification
+from transformers import MobileViTImageProcessor, MobileViTV2ForImageClassification
 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 = MobileViTv2FeatureExtractor.from_pretrained("shehan97/mobilevitv2-1.0-imagenet1k-256")
-model = MobileViTv2ForImageClassification.from_pretrained("shehan97/mobilevitv2-1.0-imagenet1k-256")
+feature_extractor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-imagenet1k-256")
+model = MobileViTV2ForImageClassification.from_pretrained("shehan97/mobilevitv2-1.0-imagenet1k-256")
 
 inputs = feature_extractor(images=image, return_tensors="pt")