Update README.md
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merve
HF staff
- opened
README.md
CHANGED
@@ -38,7 +38,7 @@ 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/
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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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@@ -49,19 +49,19 @@ model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_co
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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(
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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(
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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(
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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/coco.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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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).
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