openvino/docs/ops/detection/Proposal_1.md

Proposal

Versioned name: Proposal-1

Category: Object detection

Short description: Proposal operation filters bounding boxes and outputs only those with the highest prediction confidence.

Detailed description

Proposal has three inputs: a tensor with probabilities whether particular bounding box corresponds to background and foreground, a tensor with bbox_deltas for each of the bounding boxes, a tensor with input image size in the [image_height, image_width, scale_height_and_width] or [image_height, image_width, scale_height, scale_width] format. The produced tensor has two dimensions [batch_size * post_nms_topn, 5]. Proposal layer does the following with the input tensor:

1. Generates initial anchor boxes. Left top corner of all boxes is at (0, 0). Width and height of boxes are calculated from base_size with scale and ratio attributes.
2. For each point in the first input tensor:
   • pins anchor boxes to the image according to the second input tensor that contains four deltas for each box: for x and y of center, for width and for height
   • finds out score in the first input tensor
3. Filters out boxes with size less than min_size
4. Sorts all proposals (box, score) by score from highest to lowest
5. Takes top pre_nms_topn proposals
6. Calculates intersections for boxes and filter out all boxes with \f$intersection/union > nms_thresh\f$
7. Takes top post_nms_topn proposals
8. Returns top proposals

• base_size

  • Description: base_size is the size of the anchor to which scale and ratio attributes are applied.
  • Range of values: a positive integer number
  • Type: int
  • Default value: None
  • Required: yes

• pre_nms_topn

  • Description: pre_nms_topn is the number of bounding boxes before the NMS operation. For example, pre_nms_topn equal to 15 means to take top 15 boxes with the highest scores.
  • Range of values: a positive integer number
  • Type: int
  • Default value: None
  • Required: yes

• post_nms_topn

  • Description: post_nms_topn is the number of bounding boxes after the NMS operation. For example, post_nms_topn equal to 15 means to take after NMS top 15 boxes with the highest scores.
  • Range of values: a positive integer number
  • Type: int
  • Default value: None
  • Required: yes

• nms_thresh

  • Description: nms_thresh is the minimum value of the proposal to be taken into consideration. For example, nms_thresh equal to 0.5 means that all boxes with prediction probability less than 0.5 are filtered out.
  • Range of values: a positive floating-point number
  • Type: float
  • Default value: None
  • Required: yes

• feat_stride

  • Description: feat_stride is the step size to slide over boxes (in pixels). For example, feat_stride equal to 16 means that all boxes are analyzed with the slide 16.
  • Range of values: a positive integer
  • Type: int
  • Default value: None
  • Required: yes

• min_size

  • Description: min_size is the minimum size of box to be taken into consideration. For example, min_size equal 35 means that all boxes with box size less than 35 are filtered out.
  • Range of values: a positive integer number
  • Type: int
  • Default value: None
  • Required: yes

• ratio

  • Description: ratio is the ratios for anchor generation.
  • Range of values: a list of floating-point numbers
  • Type: float[]
  • Default value: None
  • Required: yes

• scale

  • Description: scale is the scales for anchor generation.
  • Range of values: a list of floating-point numbers
  • Type: float[]
  • Default value: None
  • Required: yes

• clip_before_nms

  • Description: clip_before_nms flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not.
  • Range of values: True or False
  • Type: boolean
  • Default value: True
  • Required: no

• clip_after_nms

  • Description: clip_after_nms is a flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not.
  • Range of values: True or False
  • Type: boolean
  • Default value: False
  • Required: no

• normalize

  • Description: normalize is a flag that specifies whether to perform normalization of output boxes to [0,1] interval or not.
  • Range of values: True or False
  • Type: boolean
  • Default value: False
  • Required: no

• box_size_scale

  • Description: box_size_scale specifies the scale factor applied to bbox_deltas of box sizes before decoding.
  • Range of values: a positive floating-point number
  • Type: float
  • Default value: 1.0
  • Required: no

• box_coordinate_scale

  • Description: box_coordinate_scale specifies the scale factor applied to bbox_deltas of box coordinates before decoding.
  • Range of values: a positive floating-point number
  • Type: float
  • Default value: 1.0
  • Required: no

• framework

  • Description: 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
  • Type: string
  • Default value: "" (empty string)
  • Required: no

Inputs:

• 1: 4D input floating point tensor with class prediction scores. Required.

• 2: 4D input floating point tensor with box bbox_deltas. Required.

• 3: 1D input floating tensor 3 or 4 elements: [image_height, image_width, scale_height_and_width] or [image_height, image_width, scale_height, scale_width]. Required.

Outputs:

• 1: Floating point tensor of shape [batch_size * post_nms_topn, 5].

Example

<layer ... type="Proposal" ... >
    <data base_size="16" feat_stride="16" min_size="16" nms_thresh="0.6" post_nms_topn="200" pre_nms_topn="6000"
     ratio="2.67" scale="4.0,6.0,9.0,16.0,24.0,32.0"/>
    <input> ... </input>
    <output> ... </output>
</layer>