# PriorBoxClustered#

Versioned name: PriorBoxClustered-1

Category: Object detection

Short description: PriorBoxClustered operation generates prior boxes of specified sizes normalized to the input image size.

Detailed description

Let

$W \equiv image\_width, \quad H \equiv image\_height.$

Then calculations of PriorBoxClustered can be written as

$center_x=(w+offset)*step$
$center_y=(h+offset)*step$
$w \subset \left( 0, W \right )$
$h \subset \left( 0, H \right )$

For each $$s = \overline{0, W - 1}$$ calculates the prior boxes coordinates:

$xmin = \frac{center_x - \frac{width_s}{2}}{W}$
$ymin = \frac{center_y - \frac{height_s}{2}}{H}$
$xmax = \frac{center_x - \frac{width_s}{2}}{W}$
$ymax = \frac{center_y - \frac{height_s}{2}}{H}$

If clip is defined, the coordinates of prior boxes are recalculated with the formula: $$coordinate = \min(\max(coordinate,0), 1)$$

Attributes

• width (height)

• Description: width (height) specifies desired boxes widths (heights) in pixels.

• Range of values: floating-point positive numbers

• Type: float[]

• Default value: 1.0

• Required: no

• clip

• Description: clip is a flag that denotes if each value in the output tensor should be clipped within [0,1].

• Range of values:

• false or 0 - clipping is not performed

• true or 1 - each value in the output tensor is within [0,1]

• Type: boolean

• Default value: true

• Required: no

• step (step_w, step_h)

• Description: step (step_w, step_h) is a distance between box centers. For example, step equal 85 means that the distance between neighborhood prior boxes centers is 85. If both step_h and step_w are 0 then they are updated with value of step. If after that they are still 0 then they are calculated as input image width(height) divided with first input width(height).

• Range of values: floating-point positive number

• Type: float

• Default value: 0.0

• Required: no

• offset

• Description: offset is a shift of box respectively to top left corner. For example, offset equal 85 means that the shift of neighborhood prior boxes centers is 85.

• Range of values: floating-point positive number

• Type: float

• Required: yes

• variance

• Description: variance denotes a variance of adjusting bounding boxes. The attribute could be 0, 1 or 4 elements.

• Range of values: floating-point positive numbers

• Type: float[]

• Default value: []

• Required: no

Inputs:

• 1: output_size - 1D tensor of type T_INT with two elements [height, width]. Specifies the spatial size of generated grid with boxes. Required.

• 2: image_size - 1D tensor of type T_INT with two elements [image_height, image_width] that specifies shape of the image for which boxes are generated. Optional.

Outputs:

• 1: 2D tensor of shape [2, 4 * height * width * priors_per_point] and type T_OUT with box coordinates. The priors_per_point is the number of boxes generated per each grid element. The number depends on layer attribute values.

Types

• T_INT: any supported integer type.

• T_OUT: supported floating-point type.

Example

<layer type="PriorBoxClustered" ... >
<data clip="false" height="44.0,10.0,30.0,19.0,94.0,32.0,61.0,53.0,17.0" offset="0.5" step="16.0" variance="0.1,0.1,0.2,0.2" width="86.0,13.0,57.0,39.0,68.0,34.0,142.0,50.0,23.0"/>
<input>
<port id="0">
<dim>2</dim>        <!-- [10, 19] -->
</port>
<port id="1">
<dim>2</dim>        <!-- [180, 320] -->
</port>
</input>
<output>
<port id="2">
<dim>2</dim>
<dim>6840</dim>
</port>
</output>
</layer>