I created a realtime object detection pipline (https://github.com/GustavZ/realtime_object_detection) for Inference Based on Google’s well-known Object Detection API.
It runs on the Jetson Tx2 with around 5fps using SSD_Mobilenet, which is the smallest and fastest network provided by Googles API. And i am not happy with this performance
Of course i prepare the jetson with:
sudo nvpmodel -m 0 sudo ./jetson_clocks.sh
While the script is running sudo ./tegrastats gives me following output:
RAM 7565/7851MB (lfb 5x4MB) CPU [46%@2025,20%@2035,12%@2034,44%@2029,45%@2031,45%@2028] EMC_FREQ 5%@1866 GR3D_FREQ 6%@1300 APE 150 MTS fg 0% bg 0% BCPU@34.5C MCPU@34.5C GPU@40.5C PLL@34.5C AO@32C Tboard@29C Tdiode@32.25C PMIC@100C firstname.lastname@example.orgC VDD_IN 6342/4735 VDD_CPU 2063/1405 VDD_GPU 1069/368 VDD_SOC 992/934 VDD_WIFI 19/42 VDD_DDR 1514/1316
It seems that the whole RAM is used, which is good.
The CPU Usage is only between around 10 and 50%, which is i would say not optimal? Right?!
But the biggest Problem is that the GPU Usage is only at 6%.
Does Anybody know how i can increase the GPU Usage? This can’t be the end of the story.
I am sure the Jetson Tx2 can go way faster than 5fps.
I am very thankfull for any hints to increase the GPU Usage using Tensorflow for Inference on realtime Object Detection