Please provide complete information as applicable to your setup.
• Hardware Platform (Ubuntu / GPU:RTX1080ti)
• DeepStream Version(6.1.0)
• Issue Type( questions)
**• | How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)** |
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deepstream_gang_config_m6new.txt(use deepstream-test5-app to Identify) |
[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
[tiled-display]
enable=1
rows=1
columns=1
width=1280
height=720
gpu-id=1
nvbuf-memory-type=0
[source0]
enable=1
type=3
uri=file:///opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_yolo/gang1_video_2.mp4
num-sources=1
gpu-id=1
cudadec-memtype=0
[source1]
enable=0
type=3
uri=file:///opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_yolo/gang_video_1.mp4
num-sources=1
gpu-id=1
cudadec-memtype=0
[source2]
enable=0
type=3
uri=file:///opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_yolo/gang_video_1.mp4
num-sources=1
gpu-id=1
cudadec-memtype=0
[sink0]
enable=1
type=2
sync=0
gpu-id=1
nvbuf-memory-type=0
[sink1]
enable=0
type=6
msg-conv-config=dstest5_msgconv_sample_config.txt
msg-conv-payload-type=1
msg-broker-proto-lib=/opt/nvidia/deepstream/deepstream-6.1/lib/libnvds_kafka_proto.so
msg-broker-conn-str=10.129.53.100;9092;video
topic=video
iframeinterval=10
[nvds-analytics]
enable=0
config-file=/opt/nvidia/deepstream/deepstream-6.1/sources/apps/sample_apps/deepstream-nvdsanalytics-test/config_nvdsanalytics.txt
[osd]
enable=1
gpu-id=1
border-width=5
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Serif
show-clock=0
clock-x-offset=800
clock-y-offset=820
clock-text-size=12
clock-color=1;0;0;0
nvbuf-memory-type=0
[streammux]
gpu-id=1
live-source=0
batch-size=3
batched-push-timeout=40000
width=1920
height=1080
enable-padding=0
nvbuf-memory-type=0
[primary-gie]
enable=1
gpu-id=1
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_gang_best_m6new.txt
[tests]
file-loop=1
config_infer_gang_best_m6new.txt
[property]
gpu-id=1
net-scale-factor=0.0039215697906911373
model-color-format=0
custom-network-config=yolov5_gang_best_new.cfg
model-file=yolov5_gang_best_new.wts
model-engine-file=model_n2_gpu0_fp32_new.engine
#int8-calib-file=calib.table
labelfile-path=labels_gang_best.txt
batch-size=1
network-mode=2
num-detected-classes=16
interval=0
gie-unique-id=1
process-mode=1
cluster-mode=2
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseYolo
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet
[class-attrs-all]
pre-cluster-threshold=0.50
|•|Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)|
My model file yolov5_ gang_ best_ new. WTS is based on yolov5m Pt is trained using its own dataset. Yolov5 official documents indicate that the recognition rate of this model is about 11ms. I used the trained model to identify on a computer with win11 as my system and 3070ti as my gpu. The recognition speed is 18ms, which is about 56fps per second. However, only 30fps is deployed on the deepstream for single channel source recognition, and only 10fps is used to identify each of the three sources. I want to know what the problem is, Presumably, the effect of deepstream is impossible