openvino.runtime.opset3.proposal

openvino.runtime.opset3.proposal(class_probs: Node, bbox_deltas: Node, image_shape: Node | int | float | ndarray, attrs: dict, name: str | None = None) → Node

Filter bounding boxes and outputs only those with the highest prediction confidence.

Parameters:

• class_probs – 4D input floating point tensor with class prediction scores.

• bbox_deltas – 4D input floating point tensor with box logits.

• image_shape – The 1D input tensor with 3 or 4 elements describing image shape.

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

• name – Optional name for the output node.

Returns:

Node representing Proposal operation.

• base_size The size of the anchor to which scale and ratio attributes are applied.

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

• pre_nms_topn The number of bounding boxes before the NMS operation.

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

• post_nms_topn The number of bounding boxes after the NMS operation.

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

• nms_thresh The minimum value of the proposal to be taken into consideration.

  Range of values: a positive floating-point number Default value: None Required: yes

• feat_stride The step size to slide over boxes (in pixels).

  Range of values: a positive unsigned integer Default value: None Required: yes

• min_size The minimum size of box to be taken into consideration.

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

• ratio The ratios for anchor generation.

  Range of values: a list of floating-point numbers Default value: None Required: yes

• scale The scales for anchor generation.

  Range of values: a list of floating-point numbers Default value: None Required: yes

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

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

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

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

• normalize The flag that specifies whether to perform normalization of output boxes to

  [0,1] interval or not. Range of values: True or False Default value: False Required: no

• box_size_scale Specifies the scale factor applied to logits of box sizes before decoding.

  Range of values: a positive floating-point number Default value: 1.0 Required: no

• box_coordinate_scale Specifies the scale factor applied to logits of box coordinates

  before decoding. Range of values: a positive floating-point number Default value: 1.0 Required: no

• framework Specifies how the box coordinates are calculated.

  Range of values: “” (empty string) - calculate box coordinates like in Caffe*

  tensorflow - calculate box coordinates like in the TensorFlow*

  Object Detection API models

  Default value: “” (empty string) Required: no

Example of attribute dictionary:

# just required ones
attrs = {
    'base_size': 85,
    'pre_nms_topn': 10,
    'post_nms_topn': 20,
    'nms_thresh': 0.34,
    'feat_stride': 16,
    'min_size': 32,
    'ratio': [0.1, 1.5, 2.0, 2.5],
    'scale': [2, 3, 3, 4],
}

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