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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