Hi,
I’m working on real time detection in Xavier by using yolov3 network.
I would like to detect in multi sources so I am planning to use “deepstream app” not the “deepstream-yolo-app”.
I followed installation “https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps” and also the patches instructed in “https://devtalk.nvidia.com/default/topic/1047633/deepstream-sdk-on-jetson/yolo-for-deepstream-app/” forum.
Finally running yolo network with deepstream-app by the sample on deepstream_reference_apps but nothing detects…
it works well on tensor-rt and deepstream-yolo-app but just not in deepstream-app.
I don’t know why it is not working, the video just streams and detects nothing.
Do you know how to figure out this problem?
fyi1:deepstream_app_config_yoloV3.txt
[application]
enable-perf-measurement=1
perf-measurement-interval-sec=1
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:///home/nvidia/deepstream_reference_apps/yolo/video/1.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
[yoloplugin]
enable=1
gpu-id=0
unique-id=15
processing-width=640
processing-height=480
full-frame=1
config-file-path=/home/nvidia/deepstream_reference_apps/yolo/config/yolov3.txt
fyi2:config/yolov3.txt
--network_type=yolov3
--config_file_path=data/yolov3-obj.cfg
--wts_file_path=data/yolov3-obj_14000.weights
--labels_file_path=data/labels.txt
#Optional config params
# precision : Inference precision of the network
# calibration_table_path : Path to pre-generated calibration table. If flag is not set, a new calib table <network-type>-<precision>-calibration.table will be generated
# engine_file_path : Path to pre-generated engine(PLAN) file. If flag is not set, a new engine <network-type>-<precision>-<batch-size>.engine will be generated
# input_blob_name : Name of the input layer in the tensorRT engine file. Default value is 'data'
# print_perf_info : Print performance info on the console. Default value is false
# print_detection_info : Print detection info on the console. Default value is false
# calibration_images : Text file containing absolute paths of calibration images. Flag required if precision is kINT8 and there is no pre-generated calibration table
# prob_thresh : Probability threshold for detected objects. Default value is 0.5
# nms_thresh : IOU threshold for bounding box candidates. Default value is 0.5
#Uncomment the lines below to use a specific config param
--precision=kFLOAT
--calibration_table_path=/home/nvidia/deepstream_reference_apps/yolo/data/calibration/yolov3-calibration.table
--engine_file_path=/home/nvidia/deepstream_reference_apps/yolo/data/yolov3-obj_14000-kFLOAT-kGPU-batch1.engine
--print_prediction_info=true
--print_perf_info=true
### Config params trt-yolo-app only
# test_images : [REQUIRED] Text file containing absolute paths of all the images to be used for inference. Default value is data/test_images.txt.
# batch_size : Set batch size for inference engine. Default value is 1.
# view_detections : Flag to view images overlayed with objects detected. Default value is false.
# save_detections : Flag to save images overlayed with objects detected. Default value is true.
# save_detections_path : Path where the images overlayed with bounding boxes are to be saved. Required param if save_detections is set to true.
# decode : Decode the detections. This can be set to false if benchmarking network for throughput only. Default value is true.
# seed : Seed for the random number generator. Default value is std::time(0)
#Uncomment the lines below to use a specific config param
#--test_images=data/test_images.txt
#--batch_size=4
#--do_benchmark=true
#--view_detections=true
#--save_detections=true
#--save_detections_path=data/detections/
#--decode=false
#--seed
#--shuffle_test_set=false