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: Evaluate the model by making predictions and comparing these to ground
  truth bounding box annotations.
  
  Parameters
  ----------
  dataset : SFrame
      Dataset of new observations. Must include columns with the same
      names as the annotations and feature used for model training.
      Additional columns are ignored.
  
  metric : str or list, optional
      Name of the evaluation metric or list of several names. The primary
      metric is average precision, which is the area under the
      precision/recall curve and reported as a value between 0 and 1 (1
      being perfect). Possible values are:
  
      - 'auto'                      : Returns all primary metrics.
      - 'all'                       : Returns all available metrics.
      - 'average_precision_50'      : Average precision per class with
                                      intersection-over-union threshold at
                                      50% (PASCAL VOC metric).
      - 'average_precision'         : Average precision per class calculated over multiple
                                      intersection-over-union thresholds
                                      (at 50%, 55%, ..., 95%) and averaged.
      - 'mean_average_precision_50' : Mean over all classes (for ``'average_precision_50'``).
                                      This is the primary single-value metric.
      - 'mean_average_precision'    : Mean over all classes (for ``'average_precision'``)
  
  Returns
  -------
  out : dict / SFrame
      Output type depends on the option `output_type`.
  
  See Also
  --------
  create, predict
  
  Examples
  --------
  >>> results = model.evaluate(data)
  >>> print('mAP: {:.1%}'.format(results['mean_average_precision']))
  mAP: 43.2%
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code: def evaluate(
      self,
      dataset,
      metric="auto",
      output_type="dict",
      confidence_threshold=0.001,
      iou_threshold=0.45,
  ):
      """
      Evaluate the model by making predictions and comparing these to ground
      truth bounding box annotations.
  
      Parameters
      ----------
      dataset : SFrame
          Dataset of new observations. Must include columns with the same
          names as the annotations and feature used for model training.
          Additional columns are ignored.
  
      metric : str or list, optional
          Name of the evaluation metric or list of several names. The primary
          metric is average precision, which is the area under the
          precision/recall curve and reported as a value between 0 and 1 (1
          being perfect). Possible values are:
  
          - 'auto'                      : Returns all primary metrics.
          - 'all'                       : Returns all available metrics.
          - 'average_precision_50'      : Average precision per class with
                                          intersection-over-union threshold at
                                          50% (PASCAL VOC metric).
          - 'average_precision'         : Average precision per class calculated over multiple
                                          intersection-over-union thresholds
                                          (at 50%, 55%, ..., 95%) and averaged.
          - 'mean_average_precision_50' : Mean over all classes (for ``'average_precision_50'``).
                                          This is the primary single-value metric.
          - 'mean_average_precision'    : Mean over all classes (for ``'average_precision'``)
  
      Returns
      -------
      out : dict / SFrame
          Output type depends on the option `output_type`.
  
      See Also
      --------
      create, predict
  
      Examples
      --------
      >>> results = model.evaluate(data)
      >>> print('mAP: {:.1%}'.format(results['mean_average_precision']))
      mAP: 43.2%
      """
      _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
      return self.__proxy__.evaluate(
          dataset, metric, output_type, 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 8 for calling the ObjectDetector obj's underlying member method code with example usage: obj.evaluate(dataset, metric, output_type, confidence_threshold, iou_threshold) and returns: self