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 5-input version of the layer is supported with Myriad plugin only. 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 filters that are applied around each location in the feature map, yielding 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
  • Default value: None
  • 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[]
  • Default value: None
  • 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: 0 or 1
  • Type: int
  • Default value: 1
  • Required: no
• nms_threshold
  • Description: threshold to be used in the NMS stage
  • Range of values: floating point values
  • Type: float
  • Default value: None
  • 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: 0 or 1
  • Type: int
  • Default value: 0
  • 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: 0 or 1
  • Type: int
  • Default value: 0
  • Required: no
• decrease_label_id
  • Description: decrease_label_id flag that denotes how to perform NMS.
  • Range of values:
    • 0 - perform NMS like in Caffe*.
    • 1 - perform NMS like in MxNet*.
  • Type: int
  • Default value: 0
  • Required: no
• normalized
  • Description: normalized flag that denotes whether input tensors with boxes are normalized. If tensors are not normalized then input_height and input_width attributes are used to normalize box coordinates.
  • Range of values: 0 or 1
  • Type: int
  • Default value: 0
  • 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. Required.
• 2: 2D input tensor with class predictions. Required.
• 3: 3D input tensor with proposals. Required.
• 4: 2D input tensor with additional class predictions information described in the article. Optional.
• 5: 2D input tensor with additional box predictions information described in the article. Optional.

Example

<layer ... type="DetectionOutput" ... >
    <data num_classes="21" share_location="1" background_label_id="0" nms_threshold="0.450000" top_k="400" input_height="1" input_width="1" code_type="caffe.PriorBoxParameter.CENTER_SIZE" variance_encoded_in_target="0" keep_top_k="200" confidence_threshold="0.010000"/>
    <input> ... </input>
    <output> ... </output>
</layer>