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> | |
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.