DetectionOutput#
Versioned name: DetectionOutput-1
Category: Object detection
Short description: DetectionOutput performs non-maximum suppression to generate the detection output using information on location and confidence predictions.
Detailed description: Reference . The layer has 3 mandatory inputs: tensor with box logits, tensor with confidence predictions and tensor with box coordinates (proposals). It can have 2 additional inputs with additional confidence predictions and box coordinates described in the article . The output tensor contains information about filtered detections described with 7 element tuples: [batch_id, class_id, confidence, x_1, y_1, x_2, y_2]
. The first tuple with batch_id
equal to -1
means end of output.
At each feature map cell, DetectionOutput predicts the offsets relative to the default box shapes in the cell, as well as the per-class scores that indicate the presence of a class instance in each of those boxes. Specifically, for each box out of k at a given location, DetectionOutput computes class scores and the four offsets relative to the original default box shape. This results in a total of \((c + 4)k\) filters that are applied around each location in the feature map, yielding \((c + 4)kmn\) outputs for a m * n feature map.
Attributes:
num_classes
Description: number of classes to be predicted
Range of values: positive integer number
Type:
int
Required: yes
background_label_id
Description: background label id. If there is no background class, set it to -1.
Range of values: integer values
Type:
int
Default value: 0
Required: no
top_k
Description: maximum number of results to be kept per batch after NMS step. -1 means keeping all bounding boxes.
Range of values: integer values
Type:
int
Default value: -1
Required: no
variance_encoded_in_target
Description: variance_encoded_in_target is a flag that denotes if variance is encoded in target. If flag is false then it is necessary to adjust the predicted offset accordingly.
Range of values: false or true
Type:
boolean
Default value: false
Required: no
keep_top_k
Description: maximum number of bounding boxes per batch to be kept after NMS step. -1 means keeping all bounding boxes after NMS step.
Range of values: integer values
Type:
int[]
Required: yes
code_type
Description: type of coding method for bounding boxes
Range of values: “caffe.PriorBoxParameter.CENTER_SIZE”, “caffe.PriorBoxParameter.CORNER”
Type:
string
Default value: “caffe.PriorBoxParameter.CORNER”
Required: no
share_location
Description: share_location is a flag that denotes if bounding boxes are shared among different classes.
Range of values: false or true
Type:
boolean
Default value: true
Required: no
nms_threshold
Description: threshold to be used in the NMS stage
Range of values: floating-point values
Type:
float
Required: yes
confidence_threshold
Description: only consider detections whose confidences are larger than a threshold. If not provided, consider all boxes.
Range of values: floating-point values
Type:
float
Default value: 0
Required: no
clip_after_nms
Description: clip_after_nms flag that denotes whether to perform clip bounding boxes after non-maximum suppression or not.
Range of values: false or true
Type:
boolean
Default value: false
Required: no
clip_before_nms
Description: clip_before_nms flag that denotes whether to perform clip bounding boxes before non-maximum suppression or not.
Range of values: false or true
Type:
boolean
Default value: false
Required: no
decrease_label_id
Description: decrease_label_id flag that denotes how to perform NMS.
Range of values:
false - perform NMS like in Caffe.
true - perform NMS like in Apache MxNet.
Type:
boolean
Default value: false
Required: no
normalized
Description: normalized flag that denotes whether input tensor with proposal boxes is normalized. If tensor is not normalized then input_height and input_width attributes are used to normalize box coordinates.
Range of values: false or true
Type:
boolean
Default value: false
Required: no
input_height (input_width)
Description: input image height (width). If the normalized is 1 then these attributes are not used.
Range of values: positive integer number
Type:
int
Default value: 1
Required: no
objectness_score
Description: threshold to sort out confidence predictions. Used only when the DetectionOutput layer has 5 inputs.
Range of values: non-negative float number
Type:
float
Default value: 0
Required: no
Inputs
1: 2D input tensor with box logits with shape
[N, num_prior_boxes * num_loc_classes * 4]
and type T.num_loc_classes
is equal tonum_classes
whenshare_location
is 0 or it’s equal to 1 otherwise. Required.2: 2D input tensor with class predictions with shape
[N, num_prior_boxes * num_classes]
and type T. Required.3: 3D input tensor with proposals with shape
[priors_batch_size, 1, num_prior_boxes * prior_box_size]
or[priors_batch_size, 2, num_prior_boxes * prior_box_size]
.priors_batch_size
is either 1 orN
. Size of the second dimension depends onvariance_encoded_in_target
. Ifvariance_encoded_in_target
is equal to 0, the second dimension equals to 2 and variance values are provided for each boxes coordinates. Ifvariance_encoded_in_target
is equal to 1, the second dimension equals to 1 and this tensor contains proposals boxes only.prior_box_size
is equal to 4 whennormalized
is set to 1 or it’s equal to 5 otherwise. Required.4: 2D input tensor with additional class predictions information described in the article . Its shape must be equal to
[N, num_prior_boxes * 2]
. Optional.5: 2D input tensor with additional box predictions information described in the article. Its shape must be equal to first input tensor shape. Optional.
Outputs
1: 4D output tensor with type T. Its shape depends on
keep_top_k
ortop_k
being set. Itkeep_top_k[0]
is greater than zero, then the shape is[1, 1, N * keep_top_k[0], 7]
. Ifkeep_top_k[0]
is set to -1 andtop_k
is greater than zero, then the shape is[1, 1, N * top_k * num_classes, 7]
. Otherwise, the output shape is equal to[1, 1, N * num_classes * num_prior_boxes, 7]
.
Types
T: any supported floating-point type.
Example
<layer ... type="DetectionOutput" ... >
<data background_label_id="1" code_type="caffe.PriorBoxParameter.CENTER_SIZE" confidence_threshold="0.019999999552965164" input_height="1" input_width="1" keep_top_k="200" nms_threshold="0.44999998807907104" normalized="true" num_classes="2" share_location="true" top_k="200" variance_encoded_in_target="false" clip_after_nms="false" clip_before_nms="false" objectness_score="0" decrease_label_id="false"/>
<input>
<port id="0">
<dim>1</dim>
<dim>5376</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>2688</dim>
</port>
<port id="2">
<dim>1</dim>
<dim>2</dim>
<dim>5376</dim>
</port>
</input>
<output>
<port id="3" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>200</dim>
<dim>7</dim>
</port>
</output>
</layer>