diff --git a/README.md b/README.md
index 7da6006..17267aa 100644
--- a/README.md
+++ b/README.md
@@ -29,24 +29,24 @@ fine-tuned versions on a task that interests you.
 Here is how to use this model:
 
 ```python
->>> from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
->>> from PIL import Image
->>> import requests
+from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
+from PIL import Image
+import requests
 
->>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
->>> image = Image.open(requests.get(url, stream=True).raw)
->>> feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade")
->>> inputs = feature_extractor(images=image, return_tensors="pt")
+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")
 
->>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
->>> outputs = model(**inputs)
->>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
->>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
->>> class_queries_logits = outputs.class_queries_logits
->>> masks_queries_logits = outputs.masks_queries_logits
+model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
+outputs = model(**inputs)
+# model predicts class_queries_logits of shape `(batch_size, num_queries)`
+# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+class_queries_logits = outputs.class_queries_logits
+masks_queries_logits = outputs.masks_queries_logits
 
->>> # you can pass them to feature_extractor for postprocessing
->>> predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs)[0]
+# you can pass them to feature_extractor for postprocessing
+predicted_semantic_map = feature_extractor.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).
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