class_docstr: An trained model that is ready to use for classification, exported to
  Core ML, or for feature extraction.
  
  This model should not be constructed directly.
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docstr: Predict object instances in an SFrame of images.
  
  Parameters
  ----------
  dataset : SFrame | SArray | turicreate.Image
      The images on which to perform object detection.
      If dataset is an SFrame, it must have a column with the same name
      as the feature column during training. Additional columns are
      ignored.
  
  confidence_threshold : float
      Only return predictions above this level of confidence. The
      threshold can range from 0 to 1.
  
  iou_threshold : float
      Threshold value for non-maximum suppression. Non-maximum suppression
      prevents multiple bounding boxes appearing over a single object.
      This threshold, set between 0 and 1, controls how aggressive this
      suppression is. A value of 1 means no maximum suppression will
      occur, while a value of 0 will maximally suppress neighboring
      boxes around a prediction.
  
  verbose : bool
      If True, prints prediction progress.
  
  Returns
  -------
  out : SArray
      An SArray with model predictions. Each element corresponds to
      an image and contains a list of dictionaries. Each dictionary
      describes an object instances that was found in the image. If
      `dataset` is a single image, the return value will be a single
      prediction.
  
  See Also
  --------
  draw_bounding_boxes
  
  Examples
  --------
  .. sourcecode:: python
  
      # Make predictions
      >>> pred = model.predict(data)
      >>> predictions_with_bounding_boxes = tc.one_shot_object_detector.util.draw_bounding_boxes(data['images'], pred)
      >>> predictions_with_bounding_boxes.explore()
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code: def predict(
      self,
      dataset,
      confidence_threshold=0.25,
      iou_threshold=0.45,
      verbose=True,
  ):
      """
      Predict object instances in an SFrame of images.
  
      Parameters
      ----------
      dataset : SFrame | SArray | turicreate.Image
          The images on which to perform object detection.
          If dataset is an SFrame, it must have a column with the same name
          as the feature column during training. Additional columns are
          ignored.
  
      confidence_threshold : float
          Only return predictions above this level of confidence. The
          threshold can range from 0 to 1.
  
      iou_threshold : float
          Threshold value for non-maximum suppression. Non-maximum suppression
          prevents multiple bounding boxes appearing over a single object.
          This threshold, set between 0 and 1, controls how aggressive this
          suppression is. A value of 1 means no maximum suppression will
          occur, while a value of 0 will maximally suppress neighboring
          boxes around a prediction.
  
      verbose : bool
          If True, prints prediction progress.
  
      Returns
      -------
      out : SArray
          An SArray with model predictions. Each element corresponds to
          an image and contains a list of dictionaries. Each dictionary
          describes an object instances that was found in the image. If
          `dataset` is a single image, the return value will be a single
          prediction.
  
      See Also
      --------
      draw_bounding_boxes
  
      Examples
      --------
      .. sourcecode:: python
  
          # Make predictions
          >>> pred = model.predict(data)
          >>> predictions_with_bounding_boxes = tc.one_shot_object_detector.util.draw_bounding_boxes(data['images'], pred)
          >>> predictions_with_bounding_boxes.explore()
  
      """
      return self.__proxy__["detector"].predict(
          dataset=dataset,
          confidence_threshold=confidence_threshold,
          iou_threshold=iou_threshold,
          verbose=verbose,
      )
  
file_path: turicreate/src/python/turicreate/toolkits/one_shot_object_detector/one_shot_object_detector.py
imports: import turicreate
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy, ToolkitError
  from turicreate.toolkits.object_detector.object_detector import ObjectDetector
  from turicreate.toolkits.one_shot_object_detector.util._augmentation import preview_synthetic_training_data
  import turicreate.toolkits._internal_utils
label_desc: Use image node_id 2 for calling the OneShotObjectDetector obj's underlying member method code with example usage: obj.predict(dataset, confidence_threshold, iou_threshold, verbose) and returns: self