tfma.metrics.ObjectDetectionThresholdAtRecall

Computes maximum threshold where recall is >= specified value.

Inherits From: Metric

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

recall A scalar or a list of scalar values in range [0, 1].
thresholds (Optional) Thresholds to use for calculating the matrices. Use one of either thresholds or num_thresholds.
num_thresholds (Optional) Defaults to 1000. The number of thresholds to use for matching the given recall.
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.