• Hardware Platform (Jetson / GPU) Jetson Xavier NX • DeepStream Version 6.0 • JetPack Version (valid for Jetson only) 4.6 • TensorRT Version 8.0.1-1 Hello everyone I am trying to use yoloV3 model in deepstream_python_apps, but getting bad performance. As Deepstream 6.0 had an issue for using Yolo, I applied the patch attached on this topic: https://forums.developer.nvidia.com/t/deepstream-6-yolo-performance-issue/194238 After applying the patch, I got about 55 fps by running this example /opt/nvidia/deepstream/deepstream-6.0/sources/objectDetector_Yolo/deepstream_app_config_yoloV3.txt I also tested deepstream_app_config_yoloV3 by using two IP cameras as a input and got about 30~40 fps. But after I used rebuilt libnvdsinfer_custom_impl_Yolo.so on python, the frame dropped to 3 fps. I used the example code: /opt/nvidia/deepstream/deepstream-6.0/sourcesdeepstream_python_apps/apps/deepstream-test3 and changed the files to use yoloV3 by following this step: # cd /opt/nvidia/deepstream/deepstream-5.0/sources/python/apps/deepstream-test3/ # cp ../../../objectDetector_Yolo/config_infer_primary_yoloV3.txt dstest3_pgie_config.txt # cp ../../../objectDetector_Yolo/yolov3.cfg ./ # cp ../../../objectDetector_Yolo/yolov3.weights ./ # mkdir nvdsinfer_custom_impl_Yolo # cp ../../../objectDetector_Yolo/nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so ./nvdsinfer_custom_impl_Yolo/ # cp ../../../objectDetector_Yolo/labels.txt ./ # python deepstream_test_3.py rtsp://127.0.0.1/video1 rtsp://127.0.0.1/video2 The result was like this: Warning: gst-stream-error-quark: No decoder available for type 'application/x-rtp, media=(string)application, payload=(int)107, clock-rate=(int)90000, encoding-name=(string)VND.ONVIF.METADATA, a-recvonly=(string)"", clock-base=(uint)2215878722, seqnum-base=(uint)26690, npt-start=(guint64)0, play-speed=(double)1, play-scale=(double)1, ssrc=(uint)224613528'. (6): gsturidecodebin.c(921): unknown_type_cb (): /GstPipeline:pipeline0/GstBin:source-bin-00/GstURIDecodeBin:uri-decode-bin Decodebin child added: nvv4l2decoder0 Decodebin child added: nvv4l2decoder1 Opening in BLOCKING MODE Opening in BLOCKING MODE NvMMLiteOpen : Block : BlockType = 261 NvMMLiteOpen : Block : BlockType = 261 NVMEDIA: Reading vendor.tegra.display-size : status: 6 NVMEDIA: Reading vendor.tegra.display-size : status: 6 NvMMLiteBlockCreate : Block : BlockType = 261 NvMMLiteBlockCreate : Block : BlockType = 261 In cb_newpad gstname= video/x-raw features= In cb_newpad gstname= video/x-raw features= **********************FPS***************************************** Fps of stream 1 is 3.2 **********************FPS***************************************** Fps of stream 0 is 3.2 **********************FPS***************************************** Fps of stream 1 is 3.0 **********************FPS***************************************** Fps of stream 0 is 3.0 Please help me to fix it. Thank you so much!

Hi @tiyeesa Thanks for the trtexec log! From the log, the pipeline should support ~40fps (1000 ms / ~25ms). Sorry! Yes, “export NVDS_ENABLE_LATENCY_MEASUREMENT=1” does not work for python DS app, Please refer to How to get the latency from deepstream python apps - #13 by Fiona.Chen to capture the latency plugin. Thanks!

Hi sorry but I already applied that patch. the problem is that the patch does not work on deepstream_python_apps

please try capture the deepstream latency log with “export NVDS_ENABLE_LATENCY_MEASUREMENT=1” refer to Troubleshooting — DeepStream 6.1.1 Release documentation

I am not really sure if this is the one you asked but this is the result of running deepstream python after “export NVDS_ENABLE_LATENCY_MEASUREMENT=1” [image]

No, it should be log like the screenshot in topic Deepstream frame latency: frame number still stuck at zero (DS6, JP4.6) or, can you try to use trtexec to run the tensorrt engine generated by DeepStream?

Hi @mchi sorry but do “export NVDS_ENABLE_LATENCY_MEASUREMENT=1” work on deepstream python app? I checked that NVDS_ENABLE_LATENCY_MEASUREMENT is set as 1, but cannot get any log as the screenshot from the link you attached. Also, I used trtexec to run the tecnsorrt engine but I am not sure if I run it right. This is the code I used: ./trtexec --loadEngine=/opt/nvidia/deepstream/deepstream-6.0/sources/objectDetector_Yolo/model_b2_gpu0_int8.engine --plugins=/opt/nvidia/deepstream/deepstream-6.0/sources/objectDetector_Yolo/nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so And this is the result of running trtexec: [image]

Deepstream 6.0 Python Yolo bad performance

Accelerated Computing Intelligent Video Analytics DeepStream SDK

mchi December 2, 2021, 7:20am 3

check Deepstream 6 YOLO performance issue - #22 by mchi

Thx!

Topic		Replies	Views
Deepstream 6 YOLO performance issue DeepStream SDK	26	5906	December 13, 2021
Yolo V3 slow DeepStream SDK yolo	22	3340	December 27, 2021
Develop a Python AI app using YOLOv3 and deepstream DeepStream SDK	9	791	August 6, 2020
PERF issues with DeepStream6.2 + YOLOv8 in Jetson Xavier DeepStream SDK jetson-inference , performance , yolo , fps , deepstream	7	1344	September 26, 2023
Not working custom YOLOv3 object detection with DeepStream 6.0.1 DeepStream SDK yolo , deepstream	3	765	June 23, 2022
PERF issues with DeepStream 6.3, YOLOv8l in Jetson Orin Nano DeepStream SDK jetson	6	553	September 23, 2024
deepstream-yolo-app performance vs Tensor-Core optimized yolo-darknet DeepStream SDK	8	3931	September 21, 2020
Deepstream 6 python app performance degradation DeepStream SDK python , deepstream	8	1873	September 20, 2022
Deepstream 4.0 on YoloV3(Webcam vs. Video) DeepStream SDK	3	1231	October 16, 2019
Improved DeepStream for YOLO models DeepStream SDK	8	2604	March 11, 2022

Deepstream 6.0 Python Yolo bad performance

Related topics