Google I/O is a wrap! Catch up on TensorFlow sessions

# tf.image.non_max_suppression_with_scores

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, selected_scores = tf.image.non_max_suppression_v2(
boxes, scores, max_output_size, iou_threshold=1.0, score_threshold=0.1,
soft_nms_sigma=0.5)
selected_boxes = tf.gather(boxes, selected_indices)
``````

This function generalizes the `tf.image.non_max_suppression` op by also supporting a Soft-NMS (with Gaussian weighting) mode (c.f. Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. Consequently, in contrast to `tf.image.non_max_suppression`, `tf.image.non_max_suppression_v2` returns the new scores of each input box in the second output, `selected_scores`.

To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be larger than 0. When `soft_nms_sigma` equals 0, the behavior of `tf.image.non_max_suppression_v2` is identical to that of `tf.image.non_max_suppression` (except for the extra output) both in function and in running time.

`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.
`soft_nms_sigma` A scalar float representing the Soft NMS sigma parameter; See Bodla et al, https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which is default), we fall back to standard (hard) NMS.
`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`.
`selected_scores` A 1-D float tensor of shape `[M]` representing the corresponding scores for each selected box, where `M <= max_output_size`. Scores only differ from corresponding input scores when using Soft NMS (i.e. when `soft_nms_sigma>0`)

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]