openvino.runtime.opset6.detection_output

openvino.runtime.opset6.detection_output(box_logits: openvino._pyopenvino.Node, class_preds: openvino._pyopenvino.Node, proposals: openvino._pyopenvino.Node, attrs: dict, aux_class_preds: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray] = None, aux_box_preds: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray] = None, name: Optional[str] = None) → openvino._pyopenvino.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:

• num_classes The number of classes to be predicted.

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

• 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: .. code-block:: python

# just required ones attrs = {

‘num_classes’: 85, ‘keep_top_k’: [1, 2, 3], ‘nms_threshold’: 0.645,

}

attrs = {

‘num_classes’: 85, ‘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.