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], and for each output box contains batch index and box coordinates. 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 \(intersection/union > nms\_thresh\)

  7. Takes top post_nms_topn proposals

  8. Returns top proposals, if there is not enough proposals to fill the whole output tensor, the valid proposals will be terminated with a single -1.

Attributes:

  • 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

    • 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

    • 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

    • 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

    • 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

    • 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

    • Required: yes

  • ratio

    • Description: ratio is the ratios for anchor generation.

    • Range of values: a list of floating-point numbers

    • Type: float[]

    • Required: yes

  • scale

    • Description: scale is the scales for anchor generation.

    • Range of values: a list of floating-point numbers

    • Type: float[]

    • 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 tensor of type T and shape [batch_size, 2*K, H, W] with class prediction scores. Required.

  • 2: 4D tensor of type T and shape [batch_size, 4*K, H, W] with deltas for each bounding box. Required.

  • 3: 1D tensor of type T with 3 or 4 elements: [image_height, image_width, scale_height_and_width] or [image_height, image_width, scale_height, scale_width]. Required.

Outputs:

  • 1: Tensor of type T and shape [batch_size * post_nms_topn, 5].

Types

  • T: floating-point type.

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>