# tf.image.non_max_suppression

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/image_ops_impl.py#L3768-L3818) |

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.image.non_max_suppression`](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression)

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    tf.image.non_max_suppression(
        boxes,
        scores,
        max_output_size,
        iou_threshold=0.5,
        score_threshold=float('-inf'),
        name=None
    )

Prunes away boxes that have high intersection-over-union (IOU) overlap
with previously selected boxes. 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. Note that this
algorithm 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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/gather) operation. For example:

      selected_indices = tf.image.non_max_suppression(
          boxes, scores, max_output_size, iou_threshold)
      selected_boxes = tf.gather(boxes, selected_indices)

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| ## Args ||
|---|---|
| `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). |
| `max_output_size` | A scalar integer `Tensor` representing the maximum number of boxes to be selected by non-max suppression. |
| `iou_threshold` | A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU. |
| `score_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). |

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| ## Returns ||
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| `selected_indices` | A 1-D integer `Tensor` of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`. |

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