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:
- 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.
- 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
- Filters out boxes with size less than min_size
- Sorts all proposals (box, score) by score from highest to lowest
- Takes top pre_nms_topn proposals
- Calculates intersections for boxes and filter out all boxes with \(intersection/union > nms\_thresh\)
- Takes top post_nms_topn proposals
- 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>