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Add OneFormerProcessor
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README.md
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@@ -35,33 +35,33 @@ You can use this particular checkpoint for semantic, instance and panoptic segme
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Here is how to use this model:
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```python
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-
from transformers import
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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/ade20k.jpeg"
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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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-
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model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
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# Semantic Segmentation
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semantic_inputs =
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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 =
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# Instance Segmentation
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instance_inputs =
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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 =
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# Panoptic Segmentation
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panoptic_inputs =
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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 =
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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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Here is how to use this model:
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```python
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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/ade20k.jpeg"
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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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processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
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model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_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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```
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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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