nielsr HF staff commited on
Commit
3a26301
1 Parent(s): 0f87c66
Files changed (1) hide show
  1. README.md +3 -3
README.md CHANGED
@@ -46,19 +46,19 @@ processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_coco_swin_lar
46
  model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_swin_large")
47
 
48
  # Semantic Segmentation
49
- semantic_inputs = processor(images=image, ["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, ["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, ["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"]
 
46
  model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_swin_large")
47
 
48
  # Semantic Segmentation
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"]