Update README.md

#3
by merve HF staff - opened
Files changed (1) hide show
  1. README.md +4 -4
README.md CHANGED
@@ -38,7 +38,7 @@ Here is how to use this model:
38
  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
39
  from PIL import Image
40
  import requests
41
- url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.jpeg"
42
  image = Image.open(requests.get(url, stream=True).raw)
43
 
44
  # Loading a single model for all three tasks
@@ -49,19 +49,19 @@ model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_co
49
  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
50
  semantic_outputs = model(**semantic_inputs)
51
  # pass through image_processor for postprocessing
52
- predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
53
 
54
  # Instance Segmentation
55
  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
56
  instance_outputs = model(**instance_inputs)
57
  # pass through image_processor for postprocessing
58
- predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
59
 
60
  # Panoptic Segmentation
61
  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
62
  panoptic_outputs = model(**panoptic_inputs)
63
  # pass through image_processor for postprocessing
64
- predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
65
  ```
66
 
67
  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
 
38
  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
39
  from PIL import Image
40
  import requests
41
+ url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/resolve/main/coco.jpeg"
42
  image = Image.open(requests.get(url, stream=True).raw)
43
 
44
  # Loading a single model for all three tasks
 
49
  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
50
  semantic_outputs = model(**semantic_inputs)
51
  # pass through image_processor for postprocessing
52
+ predicted_semantic_map = processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]
53
 
54
  # Instance Segmentation
55
  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
56
  instance_outputs = model(**instance_inputs)
57
  # pass through image_processor for postprocessing
58
+ predicted_instance_map = processor.post_process_instance_segmentation(instance_outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
59
 
60
  # Panoptic Segmentation
61
  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
62
  panoptic_outputs = model(**panoptic_inputs)
63
  # pass through image_processor for postprocessing
64
+ predicted_semantic_map = processor.post_process_panoptic_segmentation(panoptic_outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
65
  ```
66
 
67
  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).