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# Following properties are mandatory when engine files are not specified:
#   int8-calib-file(Only in INT8)
#   Caffemodel mandatory properties: model-file, proto-file, output-blob-names
#   UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names
#   ONNX: onnx-file
#
# Mandatory properties for detectors:
#   num-detected-classes
#
# Optional properties for detectors:
#   cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)
#   custom-lib-path,
#   parse-bbox-func-name
#
# Mandatory properties for classifiers:
#   classifier-threshold, is-classifier
#
# Optional properties for classifiers:
#   classifier-async-mode(Secondary mode only, Default=false)
#
# Optional properties in secondary mode:
#   operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),
#   input-object-min-width, input-object-min-height, input-object-max-width,
#   input-object-max-height
#
# Following properties are always recommended:
#   batch-size(Default=1)
#
# Other optional properties:
#   net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
#   model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,
#   mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),
#   custom-lib-path, network-mode(Default=0 i.e FP32)
#
# The values in the config file are overridden by values set through GObject
# properties.

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-file=/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector_Nano/resnet10.caffemodel
proto-file=/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector_Nano/resnet10.prototxt
model-engine-file=resnet10.caffemodel_b8_gpu0_fp16.engine
labelfile-path=/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector_Nano/labels.txt
batch-size=8
process-mode=1
model-color-format=0
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid
force-implicit-batch-dim=1
#parse-bbox-func-name=NvDsInferParseCustomResnet
#custom-lib-path=/path/to/libnvdsparsebbox.so
## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
#cluster-mode=1
#scaling-filter=0
#scaling-compute-hw=0

#Use these config params for group rectangles clustering mode
[class-attrs-all]
pre-cluster-threshold=0.2
group-threshold=1
eps=0.2
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

#Use the config params below for dbscan clustering mode
#[class-attrs-all]
#detected-min-w=4
#detected-min-h=4
#minBoxes=3

## Per class configurations
#[class-attrs-0]
#pre-cluster-threshold=0.05
#eps=0.7
#dbscan-min-score=0.95

#[class-attrs-1]
#pre-cluster-threshold=0.05
#eps=0.7
#dbscan-min-score=0.5

#[class-attrs-2]
#pre-cluster-threshold=0.1
#eps=0.6
#dbscan-min-score=0.95

#[class-attrs-3]
#pre-cluster-threshold=0.05
#eps=0.7
#dbscan-min-score=0.5