openvino.runtime.opset15.detection_output

openvino.runtime.opset15.detection_output(box_logits: Node | int | float | ndarray, class_preds: Node | int | float | ndarray, proposals: Node | int | float | ndarray, attrs: dict, aux_class_preds: Node | int | float | ndarray | None = None, aux_box_preds: Node | int | float | ndarray | None = None, name: str | None = None) → Node

Generate the detection output using information on location and confidence predictions.

Parameters:

• box_logits – The 2D input tensor with box logits.

• class_preds – The 2D input tensor with class predictions.

• proposals – The 3D input tensor with proposals.

• attrs – The dictionary containing key, value pairs for attributes.

• aux_class_preds – The 2D input tensor with additional class predictions information.

• aux_box_preds – The 2D input tensor with additional box predictions information.

• name – Optional name for the output node.

Returns:

Node representing DetectionOutput operation. Available attributes are:

• background_label_id The background label id.

  Range of values: integer value Default value: 0 Required: no

• top_k Maximum number of results to be kept per batch after NMS step.

  Range of values: integer value Default value: -1 Required: no

• variance_encoded_in_target The flag that denotes if variance is encoded in target.

  Range of values: {False, True} Default value: False Required: no

• keep_top_k Maximum number of bounding boxes per batch to be kept after NMS step.

  Range of values: integer values Default value: None Required: yes

• code_type The type of coding method for bounding boxes.

  Range of values: {‘caffe.PriorBoxParameter.CENTER_SIZE’,

  ‘caffe.PriorBoxParameter.CORNER’}

  Default value: ‘caffe.PriorBoxParameter.CORNER’ Required: no

• share_location The flag that denotes if bounding boxes are shared among different

  classes. Range of values: {True, False} Default value: True Required: no

• nms_threshold The threshold to be used in the NMS stage.

  Range of values: floating point value Default value: None Required: yes

• confidence_threshold Specifies the minimum confidence threshold for detection boxes to be

  considered. Range of values: floating point value Default value: 0 Required: no

• clip_after_nms The flag that denotes whether to perform clip bounding boxes after

  non-maximum suppression or not. Range of values: {True, False} Default value: False Required: no

• clip_before_nms The flag that denotes whether to perform clip bounding boxes before

  non-maximum suppression or not. Range of values: {True, False} Default value: False Required: no

• decrease_label_id The flag that denotes how to perform NMS.

  Range of values: False - perform NMS like in Caffe*.

  True - perform NMS like in MxNet*.

  Default value: False Required: no

• normalized The flag that denotes whether input tensors with boxes are normalized.

  Range of values: {True, False} Default value: False Required: no

• input_height The input image height.

  Range of values: positive integer number Default value: 1 Required: no

• input_width The input image width.

  Range of values: positive integer number Default value: 1 Required: no

• objectness_score The threshold to sort out confidence predictions.

  Range of values: non-negative float number Default value: 0 Required: no

Example of attribute dictionary:

# just required ones
attrs = {
    'keep_top_k': [1, 2, 3],
    'nms_threshold': 0.645,
}
attrs = {
    'keep_top_k': [1, 2, 3],
    'nms_threshold': 0.645,
    'normalized': True,
    'clip_before_nms': True,
    'input_height': [32],
    'input_width': [32],
}

Optional attributes which are absent from dictionary will be set with corresponding default.