Update README.md

README.md

@@ -39,17 +39,17 @@ fine-tuned versions on a task that interests you.
 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 ViTFeatureExtractor, ViTForImageClassification
+from transformers import ViTImageProcessor, ViTForImageClassification
 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 = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
 model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
 
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 logits = outputs.logits
 # model predicts one of the 1000 ImageNet classes