Update README.md

README.md

@@ -33,16 +33,21 @@ 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 BeitFeatureExtractor, BeitForImageClassification
+from transformers import BeitImageProcessor, BeitForImageClassification
 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 = BeitFeatureExtractor.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k')
+
+processor = BeitImageProcessor.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k')
 model = BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k')
-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 21,841 ImageNet-22k classes
 predicted_class_idx = logits.argmax(-1).item()
 print("Predicted class:", model.config.id2label[predicted_class_idx])