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op {
graph_op_name: "NonMaxSuppressionWithOverlaps"
in_arg {
name: "overlaps"
description: <<END
A 2-D float tensor of shape `[num_boxes, num_boxes]` representing
the n-by-n box overlap values.
END
}
in_arg {
name: "scores"
description: <<END
A 1-D float tensor of shape `[num_boxes]` representing a single
score corresponding to each box (each row of boxes).
END
}
in_arg {
name: "max_output_size"
description: <<END
A scalar integer tensor representing the maximum number of
boxes to be selected by non max suppression.
END
}
in_arg {
name: "overlap_threshold"
description: <<END
A 0-D float tensor representing the threshold for deciding whether
boxes overlap too.
END
}
in_arg {
name: "score_threshold"
description: <<END
A 0-D float tensor representing the threshold for deciding when to remove
boxes based on score.
END
}
out_arg {
name: "selected_indices"
description: <<END
A 1-D integer tensor of shape `[M]` representing the selected
indices from the boxes tensor, where `M <= max_output_size`.
END
}
summary: "Greedily selects a subset of bounding boxes in descending order of score,"
description: <<END
pruning away boxes that have high overlaps
with previously selected boxes. Bounding boxes with score less than
`score_threshold` are removed. N-by-n overlap values are supplied as square matrix,
which allows for defining a custom overlap criterium (eg. intersection over union,
intersection over area, etc.).
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_with_overlaps(
overlaps, scores, max_output_size, overlap_threshold, score_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
END
}
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