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TensorFlow 1 version View source on GitHub

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

Performs algorithmically equivalent operation to tf.image.non_max_suppression, with the addition of an optional parameter which zero-pads the output to be of size max_output_size. The output of this operation is a tuple containing the set of integers indexing into the input collection of bounding boxes representing the selected boxes and the number of valid indices in the index set. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.slice and tf.gather operations. For example:

selected_indices_padded, num_valid = tf.image.non_max_suppression_padded(
    boxes, scores, max_output_size, iou_threshold,
    score_threshold, pad_to_max_output_size=True)
selected_indices = tf.slice(
    selected_indices_padded, tf.constant([0]), num_valid)
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.
pad_to_max_output_size bool. If True, size of selected_indices output is padded to max_output_size.
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.
valid_outputs A scalar integer Tensor denoting how many elements in selected_indices are valid. Valid elements occur first, then padding.