Update README.md

README.md

@@ -33,16 +33,16 @@ fine-tuned versions on a task that interests you.
 Here is how to use this model in PyTorch:
 
 ```python
-from transformers import ViTFeatureExtractor, ViTModel
+from transformers import ViTImageProcessor, ViTModel
 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-in21k')
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
 model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = processor(images=image, return_tensors="pt")
 
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
@@ -51,17 +51,17 @@ last_hidden_states = outputs.last_hidden_state
 Here is how to use this model in JAX/Flax:
 
 ```python
-from transformers import ViTFeatureExtractor, FlaxViTModel
+from transformers import ViTImageProcessor, FlaxViTModel
 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-in21k')
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
 model = FlaxViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
 
-inputs = feature_extractor(images=image, return_tensors="np")
+inputs = processor(images=image, return_tensors="np")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 ```