nielsr HF staff commited on
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  1. README.md +3 -3
README.md CHANGED
@@ -47,19 +47,19 @@ processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_l
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  model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
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  # Semantic Segmentation
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- semantic_inputs = processor(images=image, ["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, ["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, ["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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  model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
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  # Semantic Segmentation
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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"]