Update README.md

README.md

@@ -32,17 +32,17 @@ You can use the model for fine-tuning of semantic segmentation. See the [model h
 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 SegformerFeatureExtractor, SegformerForImageClassification
+from transformers import SegformerImageProcessor, SegformerForImageClassification
 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 = SegformerFeatureExtractor.from_pretrained("nvidia/mit-b0")
+image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
 model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")
 
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = image_processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 logits = outputs.logits
 # model predicts one of the 1000 ImageNet classes