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# tf.raw_ops.NonMaxSuppressionV3

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)

`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 be one of the following types: `half`, `float32`. 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 `iou_threshold`. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
`name` A name for the operation (optional).

A `Tensor` of type `int32`.

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