openvino.runtime.opset7.detection_output#
- openvino.runtime.opset7.detection_output(box_logits: Node, class_preds: Node, proposals: Node, attrs: dict, aux_class_preds: Node | int | float | ndarray = None, aux_box_preds: Node | int | float | ndarray = 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:
- 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.