The DetectionOutput plugin layer generates the detection output based on location and confidence predictions by doing non maximum suppression. This plugin first decodes the bounding boxes based on the anchors generated. It then performs non_max_suppression on the decoded bounding boxes. DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer. It contains: More...

#include <NvInferPluginUtils.h>

Public Attributes
bool	shareLocation

bool	varianceEncodedInTarget

int32_t	backgroundLabelId

int32_t	numClasses

int32_t	topK

int32_t	keepTopK

float	confidenceThreshold

float	nmsThreshold

CodeTypeSSD	codeType

int32_t	inputOrder [3]

bool	confSigmoid

bool	isNormalized

bool	isBatchAgnostic {true}

Detailed Description

The DetectionOutput plugin layer generates the detection output based on location and confidence predictions by doing non maximum suppression. This plugin first decodes the bounding boxes based on the anchors generated. It then performs non_max_suppression on the decoded bounding boxes. DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer. It contains:

Parameters

shareLocation	If true, bounding box are shared among different classes.
varianceEncodedInTarget	If true, variance is encoded in target. Otherwise we need to adjust the predicted offset accordingly.
backgroundLabelId	Background label ID. If there is no background class, set it as -1.
numClasses	Number of classes to be predicted.
topK	Number of boxes per image with top confidence scores that are fed into the NMS algorithm.
keepTopK	Number of total bounding boxes to be kept per image after NMS step.
confidenceThreshold	Only consider detections whose confidences are larger than a threshold.
nmsThreshold	Threshold to be used in NMS.
codeType	Type of coding method for bbox.
inputOrder	Specifies the order of inputs {loc_data, conf_data, priorbox_data}.
confSigmoid	Set to true to calculate sigmoid of confidence scores.
isNormalized	Set to true if bounding box data is normalized by the network.
isBatchAgnostic	Defaults to true. Set to false if prior boxes are unique per batch

Member Data Documentation

◆ backgroundLabelId

int32_t nvinfer1::plugin::DetectionOutputParameters::backgroundLabelId

◆ codeType

CodeTypeSSD nvinfer1::plugin::DetectionOutputParameters::codeType

◆ confidenceThreshold

float nvinfer1::plugin::DetectionOutputParameters::confidenceThreshold

◆ confSigmoid

bool nvinfer1::plugin::DetectionOutputParameters::confSigmoid

◆ inputOrder

int32_t nvinfer1::plugin::DetectionOutputParameters::inputOrder[3]

◆ isBatchAgnostic

bool nvinfer1::plugin::DetectionOutputParameters::isBatchAgnostic {true}

◆ isNormalized

bool nvinfer1::plugin::DetectionOutputParameters::isNormalized

◆ keepTopK

int32_t nvinfer1::plugin::DetectionOutputParameters::keepTopK

◆ nmsThreshold

float nvinfer1::plugin::DetectionOutputParameters::nmsThreshold

◆ numClasses

int32_t nvinfer1::plugin::DetectionOutputParameters::numClasses

◆ shareLocation

bool nvinfer1::plugin::DetectionOutputParameters::shareLocation

◆ topK

int32_t nvinfer1::plugin::DetectionOutputParameters::topK

◆ varianceEncodedInTarget

bool nvinfer1::plugin::DetectionOutputParameters::varianceEncodedInTarget

The documentation for this struct was generated from the following file:

NvInferPluginUtils.h

Public Attributes