Update README.md

README.md

@@ -34,14 +34,15 @@ fine-tuned versions on a task that interests you.
 Here is how to use this model:
 
 ```python
-from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
+from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation
 from PIL import Image
 import requests
 
 url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
-feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-large-ade")
-inputs = feature_extractor(images=image, return_tensors="pt")
+
+processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-ade")
+inputs = processor(images=image, return_tensors="pt")
 
 model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
 outputs = model(**inputs)
@@ -50,9 +51,9 @@ outputs = model(**inputs)
 class_queries_logits = outputs.class_queries_logits
 masks_queries_logits = outputs.masks_queries_logits
 
-# you can pass them to feature_extractor for postprocessing
+# you can pass them to processor for postprocessing
 # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
-predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
 ```
 
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).