Hi,
I have tried deepstream-app and deepstream-yolo-app and they are running successfully. To get a better understanding of plugins I tried objectDetector_YoloV3 samples from ‘https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/master/yolo/samples/objectDetector_YoloV3’. I used the engine from deepstream-yolo-app and it generates random bounding boxes. Cannot figure out how to fix this. Hope I can get help from Forum.
System: Ubuntu 16.04
Cuda 10.0 TensorRT 5.0.2 Opencv3.4.0
DeepStream3.0 for Tesla
RTX2080 with i9-9900k
config_infer_primary_YoloV3.txt:
[property]
gpu-id=0
net-scale-factor=1
0=RGB, 1=BGR
model-color-format=0
model-engine-file=…/…/data/yolov3-kFLOAT-kGPU-batch1.engine
batch-size=1
0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=80
gie-unique-id=1
parse-func=0
is-classifier=0
parse-bbox-func-name=NvDsInferParseCustomYoloV3
custom-lib-path=nvdsinfer_custom_impl_YoloV3/build/libnvdsinfer_custom_impl_YoloV3.so
deepstream_app_config_yoloV3.txt:
[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
gie-kitti-output-dir=streamscl
[tiled-display]
enable=1
rows=1
columns=1
width=1280
height=720
gpu-id=0
[source0]
enable=1
Type - 1=CameraV4L2 2=URI 3=MultiURI.
type=3
num-sources=1
uri=file://black.mp4
gpu-id=0
[streammux]
gpu-id=0
batch-size=1
batched-push-timeout=-1
Set muxer output width and height.
width=1280
height=720
cuda-memory-type=1
[sink0]
enable=1
Type - 1=FakeSink 2=EglSink 3=File.
type=2
sync=1
source-id=0
gpu-id=0
[osd]
enable=1
gpu-id=0
border-width=3
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Arial
show-clock=0
clock-x-offset=800
clock-y-offset=820
clock-text-size=12
clock-color=1;0;0;0
[primary-gie]
enable=1
gpu-id=0
batch-size=1
gie-unique-id=1
interval=0
labelfile-path=labels.txt
model-engine-file=…/…/data/yolov3-kFLOAT-kGPU-batch1.engine
config-file=config_infer_primary_YoloV3.txt
