tf.image.non_max_suppression_padded

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 tensor of rank 2 or higher with a shape of [..., num_boxes, 4]. Dimensions except the last two are batch dimensions.
scores a tensor of rank 1 or higher with a shape of