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# tf.image.non_max_suppression

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)
``````

`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.
`score_threshold` A float representing the threshold for deciding when to remove boxes based on score.
`name` A name for the operation (optional).

`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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