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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
#
# Mandatory properties for classifiers:
#   classifier-threshold, is-classifier
#
# Optional properties for classifiers:
#   classifier-async-mode(Secondary mode only, Default=false)
#
# Following properties are always recommended:
#   batch-size(Default=1)
#
# Other optional properties:
#   net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
#   mean-file, gie-unique-id(Default=0), offsets, gie-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]
# Model files
tlt-encoded-model=/opt/models/cd_v2.1.0.etlt
model-engine-file=/opt/models/cd_v2.1.0.etlt_b1_gpu0_fp16.engine
labelfile-path=/opt/models/cd/retinanet_labels.txt

# Connections
gpu-id=0
gie-unique-id=1

# 0: Detector, 1: Classifier, 2: Segmentation, 3: Instance Segmentation
network-type=0
# 0: FP32 1: INT8 2: FP16
network-mode=2
# 1: Primary, 2: Secondary
process-mode=1

batch-size=1
interval=0
uff-input-blob-name=Input
output-blob-names=NMS
# 0: Platform default - GPU (dGPU), VIC (Jetson) 1: GPU 2: VIC (Jetson only)
#scaling-compute-hw=2

# Model format and dimensions
# 0: RGB 1: BGR 2: GRAY
model-color-format=1
# 0: NCHW 1: NHWC 2: NC
uff-input-order=0
infer-dims=3;288;352
net-scale-factor=1.0
offsets=128.0;128.0;128.0
maintain-aspect-ratio=1

# Post-processing
num-detected-classes=6
filter-out-class-ids=0
# 0: OpenCV groupRectangles() 1: DBSCAN 2: Non Maximum Suppression 3: DBSCAN + NMS Hybrid 4: No clustering
cluster-mode=2
custom-lib-path=/opt/models/deepstream_tlt_apps/post_processor/libnvds_infercustomparser_tlt.so
parse-bbox-func-name=NvDsInferParseCustomNMSTLT

[class-attrs-all]
pre-cluster-threshold=0.2
threshold=0.5
#post-cluster-threshold=0.2
# Float, for cv::grouprectangles() and DBSCAN
#eps=
# Int, for cv::grouprectangles()
#group-threshold=
# Int, for DBSCAN
#minBoxes=
# Minimum sum of confidence of all the neighbors in a cluster for it to be considered a valid cluster.
#dbscan-min-score=
# Maximum IOU score between two proposals after which the proposal with the lower confidence will be rejected.
#nms-iou-threshold=
# Keep only top K objects with highest detection scores.
#topk=
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0