tfma.metrics.ObjectDetectionMaxRecall

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Computes the max recall of the predictions with respect to the labels.

Inherits From: MaxRecall, Recall, Metric

The metric uses true positives and false negatives to compute recall by dividing the true positives by the sum of true positives and false negatives.

Effectively the recall at threshold = epsilon(1.0e-12). It is equilvalent to the recall defined in COCO metrics.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

name (Optional) string name of the metric instance.
iou_threshold (Optional) Thresholds for a detection and ground truth pair with specific iou to be considered as a match. Default to 0.5
class_id (Optional) The class id for calculating metrics.
class_weight (Optional) The weight associated with the object class id.
area_range (Optional) A tuple (inclusive) representing the area-range for objects to be considered for metrics. Default to (0, inf).
max_num_detections (Optional) The maximum number of detections for a single image. Default to None.
labels_to_stack (Optional) Keys for columns to be stacked as a single numpy array as the labels. It is searched under the key labels, features and transformed features. The desired format is [left bounadary, top boudnary, right boundary, bottom boundary, class id]. e.g. ['xmin', 'ymin', 'xmax', 'ymax', 'class_id']
predictions_to_stack (Optional) Output names for columns to be stacked as a single numpy array as the prediction. It should be the model's output names. The desired format is [left bounadary, top boudnary, right boundary, bottom boundary, class id, confidence score]. e.g. ['xmin', 'ymin', 'xmax', 'ymax', 'class_id', 'scores']
num_detections_key (Optional) An output name in which to find the number of detections to use for evaluation for a given example. It does nothing if predictions_to_stack is not set. The value for this output should be a scalar value or a single-value tensor. The stacked predicitions will be truncated with the specified number of detections.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

Methods

computations

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Creates computations associated with metric.

get_config

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Returns serializable config.

result

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Function for computing metric value from TP, TN, FP, FN values.