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/TensorFlow Python

# tf.image.non_max_suppression

```tf.image.non_max_suppression(
boxes,
scores,
max_output_size,
iou_threshold=0.5,
name=None
)
```

See the guide: Images > Working with Bounding Boxes

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

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 operation`. For example: selected_indices = tf.image.non_max_suppression( boxes, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices)

#### 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 float representing the threshold for deciding whether boxes overlap too much with respect to IOU.
• `name`: A name for the operation (optional).

#### Returns:

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