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Update README.md

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  1. README.md +4 -4
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@@ -34,7 +34,7 @@ Here is how to use this model:
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  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
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  from PIL import Image
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  import requests
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- url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/cityscapes.png"
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  image = Image.open(requests.get(url, stream=True).raw)
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  # Loading a single model for all three tasks
@@ -45,19 +45,19 @@ model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ci
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  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
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  semantic_outputs = model(**semantic_inputs)
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  # pass through image_processor for postprocessing
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- predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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  # Instance Segmentation
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  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
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  instance_outputs = model(**instance_inputs)
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  # pass through image_processor for postprocessing
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- predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  # Panoptic Segmentation
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  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
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  panoptic_outputs = model(**panoptic_inputs)
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  # pass through image_processor for postprocessing
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- predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  ```
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  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
 
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  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
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  from PIL import Image
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  import requests
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+ url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/resolve/main/cityscapes.png"
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  image = Image.open(requests.get(url, stream=True).raw)
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  # Loading a single model for all three tasks
 
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  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
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  semantic_outputs = model(**semantic_inputs)
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  # pass through image_processor for postprocessing
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+ predicted_semantic_map = processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]
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  # Instance Segmentation
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  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
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  instance_outputs = model(**instance_inputs)
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  # pass through image_processor for postprocessing
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+ predicted_instance_map = processor.post_process_instance_segmentation(instance_outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  # Panoptic Segmentation
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  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
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  panoptic_outputs = model(**panoptic_inputs)
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  # pass through image_processor for postprocessing
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+ predicted_semantic_map = processor.post_process_panoptic_segmentation(panoptic_outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  ```
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  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).