Update README.md

update how to use this model

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 AutoImageProcessor, DetrForSegmentation
+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 = AutoImageProcessor.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_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps
 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_segmentation(outputs, processed_sizes)[0]
 
 # the segmentation is stored in a special-format png
 panoptic_seg = Image.open(io.BytesIO(result["png_string"]))