Fix/update example

README.md

@@ -47,24 +47,24 @@ from PIL import Image
 import torch
 import numpy
 
-from transformers import DetrFeatureExtractor, DetrForSegmentation
-from transformers.models.detr.feature_extraction_detr import rgb_to_id
+from transformers import DetrForSegmentation, DetrImageProcessor
+from transformers.image_transforms import rgb_to_id
 
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
+image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
 model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
 
 # prepare image for the model
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = image_processor(images=image, return_tensors="pt")
 
 # forward pass
 outputs = model(**inputs)
 
-# use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
+# use the `post_process_panoptic` method of `DetrImageProcessor` to convert to COCO format
 processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
-result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
+result = image_processor.post_process_panoptic(outputs, processed_sizes)[0]
 
 # the segmentation is stored in a special-format png
 panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
@@ -73,7 +73,7 @@ panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
 panoptic_seg_id = rgb_to_id(panoptic_seg)
 ```
 
-Currently, both the feature extractor and model support PyTorch. 
+Currently, both the image processor and model support PyTorch. 
 
 ## Training data