Update README.md

README.md

@@ -29,17 +29,17 @@ You can use the raw model for semantic segmentation. 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 SegformerFeatureExtractor, SegformerForSemanticSegmentation
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
 from PIL import Image
 import requests
 
-feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b4-finetuned-cityscapes-1024-1024")
+processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b4-finetuned-cityscapes-1024-1024")
 model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b4-finetuned-cityscapes-1024-1024")
 
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
 ```