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NonMaxSuppressionV5

public final class NonMaxSuppressionV5

Greedily selects a subset of bounding boxes in descending order of score,

pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than `score_threshold` are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the `tf.gather operation`. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices) This op also supports a Soft-NMS (with Gaussian weighting) mode (c.f. Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be larger than 0.

Nested Classes

class NonMaxSuppressionV5.Options Optional attributes for NonMaxSuppressionV5  

Public Methods

static <T extends Number> NonMaxSuppressionV5<T>
create(Scope scope, Operand<T> boxes, Operand<T> scores, Operand<Integer> maxOutputSize, Operand<T> iouThreshold, Operand<T> scoreThreshold, Operand<T> softNmsSigma, Options... options)
Factory method to create a class wrapping a new NonMaxSuppressionV5 operation.
static NonMaxSuppressionV5.Options
padToMaxOutputSize(Boolean padToMaxOutputSize)
Output<Integer>
selectedIndices()
A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<T>
selectedScores()
A 1-D float tensor of shape `[M]` representing the corresponding scores for each selected box, where `M <= max_output_size`.
Output<Integer>
validOutputs()
A 0-D integer tensor representing the number of valid elements in `selected_indices`, with the valid elements appearing first.

Inherited Methods

Public Methods

public static NonMaxSuppressionV5<T> create (Scope scope, Operand<T> boxes, Operand<T> scores, Operand<Integer> maxOutputSize, Operand<T> iouThreshold, Operand<T> scoreThreshold, Operand<T> softNmsSigma, Options... options)

Factory method to create a class wrapping a new NonMaxSuppressionV5 operation.

Parameters
scope current scope
boxes A 2-D float tensor of shape `[num_boxes, 4]`.
scores A 1-D float tensor of shape `[num_boxes]` representing a single score corresponding to each box (each row of boxes).
maxOutputSize A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
iouThreshold A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
scoreThreshold A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
softNmsSigma A 0-D float tensor representing the sigma parameter for Soft NMS; see Bodla et al (c.f. https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which is default), we fall back to standard (hard) NMS.
options carries optional attributes values
Returns
  • a new instance of NonMaxSuppressionV5

public static NonMaxSuppressionV5.Options padToMaxOutputSize (Boolean padToMaxOutputSize)

Parameters
padToMaxOutputSize If true, the output `selected_indices` is padded to be of length `max_output_size`. Defaults to false.

public Output<Integer> selectedIndices ()

A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.

public Output<T> selectedScores ()

A 1-D float tensor of shape `[M]` representing the corresponding scores for each selected box, where `M <= max_output_size`. Scores only differ from corresponding input scores when using Soft NMS (i.e. when `soft_nms_sigma>0`)

public Output<Integer> validOutputs ()

A 0-D integer tensor representing the number of valid elements in `selected_indices`, with the valid elements appearing first.