class_docstr: The dataset for the PASCAL Visual Object Classes Challenge 2012
  (VOC2012) for the Classification and Detection competitions.
  
  A total of 11,540 images are included in this dataset, where each image
  contains a set of objects, out of 20 different classes, making a total of
  27,450 annotated objects. In the classification competition, the goal is to
  predict the set of labels contained in the image, while in the detection
  competition the goal is to predict the bounding box and label of each
  individual object.
  
  Example usage::
  
      import fiftyone as fo
      import fiftyone.zoo as foz
  
      dataset = foz.load_zoo_dataset("voc-2012", split="validation")
  
      session = fo.launch_app(dataset)
  
  Dataset size
      3.59 GB
  
  Source
      http://host.robots.ox.ac.uk/pascal/VOC/voc2012
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code: def supported_splits(self):
      return ("train", "validation")
  
imports: import eta.core.utils
  import fiftyone.core.labels
  import fiftyone.core.utils
  import fiftyone.types
  import fiftyone.utils.coco
  import fiftyone.utils.data
  import fiftyone.utils.imagenet
  import fiftyone.utils.voc
  import fiftyone.zoo.datasets
label_desc: Use image node_id 3 for calling the VOC2012Dataset obj's underlying member method code with example usage: obj.supported_splits() and returns: str,  str