turicreate.object_detector.ObjectDetector.evaluate¶

ObjectDetector.
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
intersectionoverunion threshold at 50% (PASCAL VOC metric).
 ‘average_precision’ : Average precision per class calculated over multiple
intersectionoverunion thresholds (at 50%, 55%, …, 95%) and averaged.
 ‘mean_average_precision_50’ : Mean over all classes (for
'average_precision_50'
). This is the primary singlevalue metric.
 ‘mean_average_precision_50’ : Mean over all classes (for
‘mean_average_precision’ : Mean over all classes (for
'average_precision'
)
Returns:  out : dict / SFrame
Output type depends on the option output_type.
Examples
>>> results = model.evaluate(data) >>> print('mAP: {:.1%}'.format(results['mean_average_precision'])) mAP: 43.2%