Nvidia Jetson NX extremely slow even with TensorRT inference for yolov3



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


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

Please attach or include links to any models, data, files, or scripts necessary to reproduce your issue. (Github repo, Google Drive, Dropbox, etc.)

Steps To Reproduce

Please include:

  • Exact steps/commands to build your repro
  • Exact steps/commands to run your repro
  • Full traceback of errors encountered

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



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


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