Nvidia Jetson NX extremely slow even with TensorRT inference for yolov3

Description

Hello

I’m using a Nvidia Jetson NX for object detection. I’m using yolov3 as algorithm for the detection. What I’m noticing is that without tensorrt, the device freezes while doing detection. I need to abort the program. With tensorRT the performance is better, but lets put this, it has a huge delay on inference. For example, a frame that happened 10 minutes ago is still not processed and it will take a lot more to be processed. The time accumulates exponentially. I’m using yolov3 608 and a webcam that is connected by IP address. To refer that while doing detections I’m writing the frame on a postgres database. I don’t think this is normal, taking into account the power mode is maximum. Is there any trick to improve the performance? I already maximized the clocks.

Thank you in advance

Environment

TensorRT Version: 8.0
GPU Type: Nvidia Jetson NX
Nvidia Driver Version:
CUDA Version: 10.2
CUDNN Version: 8.2.1
Operating System + Version: Ubuntu 18.04
Python Version (if applicable):
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag):

Relevant Files

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Steps To Reproduce

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  • Full traceback of errors encountered

Hi,
This looks like a Jetson issue. Please refer to the below samlples in case useful.

For any further assistance, we recommend you to raise it to the respective platform from the below link

Thanks!

Hi,

I also noticed a red Exclamation point next to the power mode. What can cause this, and how to solve it?

Thanks

Hi @p.carvalho ,
I think the Jetson team should be able to help you better here.

Thanks!