class_docstr: A trained model using C++ implementation that is ready to use for classification
  or export to CoreML.
  
  This model should not be constructed directly.
class_docstr_tok: ["A","trained","model","using","C++","implementation","that","is","ready","to","use","for","classification","or","export","to","CoreML",".","This","model","should","not","be","constructed","directly","."]
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.
  
  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
  --------
  evaluate
  
  Examples
  --------
  .. sourcecode:: python
  
      # Make predictions
      >>> pred = model.predict(data)
  
      # Stack predictions, for a better overview
      >>> turicreate.object_detector.util.stack_annotations(pred)
      Data:
      +--------+------------+-------+-------+-------+-------+--------+
      | row_id | confidence | label |   x   |   y   | width | height |
      +--------+------------+-------+-------+-------+-------+--------+
      |   0    |    0.98    |  dog  | 123.0 | 128.0 |  80.0 | 182.0  |
      |   0    |    0.67    |  cat  | 150.0 | 183.0 | 129.0 | 101.0  |
      |   1    |    0.8     |  dog  |  50.0 | 432.0 |  65.0 |  98.0  |
      +--------+------------+-------+-------+-------+-------+--------+
      [3 rows x 7 columns]
  
      # Visualize predictions by generating a new column of marked up images
      >>> data['image_pred'] = turicreate.object_detector.util.draw_bounding_boxes(data['image'], data['predictions'])
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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.
  
      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
      --------
      evaluate
  
      Examples
      --------
      .. sourcecode:: python
  
          # Make predictions
          >>> pred = model.predict(data)
  
          # Stack predictions, for a better overview
          >>> turicreate.object_detector.util.stack_annotations(pred)
          Data:
          +--------+------------+-------+-------+-------+-------+--------+
          | row_id | confidence | label |   x   |   y   | width | height |
          +--------+------------+-------+-------+-------+-------+--------+
          |   0    |    0.98    |  dog  | 123.0 | 128.0 |  80.0 | 182.0  |
          |   0    |    0.67    |  cat  | 150.0 | 183.0 | 129.0 | 101.0  |
          |   1    |    0.8     |  dog  |  50.0 | 432.0 |  65.0 |  98.0  |
          +--------+------------+-------+-------+-------+-------+--------+
          [3 rows x 7 columns]
  
          # Visualize predictions by generating a new column of marked up images
          >>> data['image_pred'] = turicreate.object_detector.util.draw_bounding_boxes(data['image'], data['predictions'])
      """
      _numeric_param_check_range(
          "confidence_threshold", confidence_threshold, 0.0, 1.0
      )
      _numeric_param_check_range(
          "iou_threshold", iou_threshold, 0.0, 1.0
      )
      options = {}
      options["confidence_threshold"] = confidence_threshold
      options["iou_threshold"] = iou_threshold
      options["verbose"] = verbose
      return self.__proxy__.predict(dataset, options)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import time
  from datetime import datetime
  import six
  import turicreate
  from turicreate.toolkits._model import Model
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits import _coreml_utils
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe, _numeric_param_check_range, _raise_error_if_not_iterable
  from turicreate.toolkits._main import ToolkitError
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from ..None import _pre_trained_models
  from .._mps_utils import MpsGraphAPI, MpsGraphNetworkType
label_desc: Use image node_id 7 for calling the ObjectDetector obj's underlying member method code with example usage: obj.predict(dataset, confidence_threshold, iou_threshold, verbose) and returns: self