# tf.raw_ops.NonMaxSuppressionV5

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

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.NonMaxSuppressionV5`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/NonMaxSuppressionV5)

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    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.

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| ## Args ||
|---|---|
| `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). |

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| ## Returns ||
|---|---|
| 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`. |

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