Update README.md

README.md

@@ -34,17 +34,17 @@ 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
 
 # load MaskFormer fine-tuned on COCO panoptic segmentation
-feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-large-coco")
+processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-coco")
 model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-coco")
 
 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)
 # model predicts class_queries_logits of shape `(batch_size, num_queries)`
@@ -52,8 +52,8 @@ 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
-result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+# you can pass them to processor for postprocessing
+result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
 # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
 predicted_panoptic_map = result["segmentation"]
 ```