tf.raw_ops.NonMaxSuppressionV5

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

tf.raw_ops.NonMaxSuppressionV5(
    boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma,
    pad_to_max_output_size=False, name=None
)

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.

boxes A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].
scores A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
max_output_size A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
iou_threshold A Tensor. Must have the same type as boxes. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
score_threshold A Tensor. Must have the same type as boxes. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
soft_nms_sigma A Tensor. Must have the same type as boxes. 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.
pad_to_max_output_size An optional bool. Defaults to False. If true, the output selected_indices is padded to be of length max_output_size. Defaults to false.
name A name for the operation (optional).

A tuple of Tensor objects (selected_indices, selected_scores, valid_outputs).
selected_indices A Tensor of type int32.
selected_scores A Tensor. Has the same type as boxes.
valid_outputs A Tensor of type int32.