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 \((c + 4)k\) 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
- 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>