Update README.md

README.md

@@ -1,10 +1,15 @@
 ---
-license: apache-2.0
+license: other
 tags:
 - vision
 - image-segmentation
 datasets:
-- ade-20k
+- scene_parse_150
+widget:
+- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
+  example_title: House
+- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
+  example_title: Castle
 ---
 
 # MaskFormer
@@ -29,24 +34,24 @@ 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
-
->>> 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")
-
->>> 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
-
->>> # you can pass them to feature_extractor for postprocessing
->>> predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs)[0]
+from transformers import MaskFormerFeatureExtractor, 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-base-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
+
+# 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).