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Update README.md

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  ---
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- license: apache-2.0
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  tags:
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  - vision
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  - image-segmentation
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  datasets:
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- - ade-20k
 
 
 
 
 
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  ---
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  # MaskFormer
@@ -29,24 +34,24 @@ fine-tuned versions on a task that interests you.
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  Here is how to use this model:
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  ```python
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- >>> from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
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- >>> from PIL import Image
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- >>> import requests
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-
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- >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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- >>> image = Image.open(requests.get(url, stream=True).raw)
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- >>> feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade")
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- >>> inputs = feature_extractor(images=image, return_tensors="pt")
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-
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- >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
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- >>> outputs = model(**inputs)
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- >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
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- >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
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- >>> class_queries_logits = outputs.class_queries_logits
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- >>> masks_queries_logits = outputs.masks_queries_logits
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-
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- >>> # you can pass them to feature_extractor for postprocessing
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- >>> predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs)[0]
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  ```
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  For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).
 
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  ---
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+ license: other
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  tags:
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  - vision
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  - image-segmentation
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  datasets:
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+ - scene_parse_150
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+ widget:
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+ - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
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+ example_title: House
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+ - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
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+ example_title: Castle
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  ---
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  # MaskFormer
 
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  Here is how to use this model:
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  ```python
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+ from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
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+ from PIL import Image
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+ import requests
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+
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+ url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-small-ade")
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+ inputs = feature_extractor(images=image, return_tensors="pt")
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+
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+ model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-ade")
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+ outputs = model(**inputs)
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+ # model predicts class_queries_logits of shape `(batch_size, num_queries)`
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+ # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
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+ class_queries_logits = outputs.class_queries_logits
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+ masks_queries_logits = outputs.masks_queries_logits
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+
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+ # you can pass them to feature_extractor for postprocessing
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+ predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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  ```
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  For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).