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tf.raw_ops.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.

`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`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]