Description
Hi, I am using this repo (https://github.com/wang-xinyu/tensorrtx/blob/master/) to convert a yolov5 network to .engine model.
I have an issue regarding inference time. If I follow this script: wang-xinyu/tensorrtx/blob/master/yolov5/yolov5_det_trt.py
I can see a slow but gradual increase in inference time. Is the solution in a for loop optimal?
Is it recommended to do another procedure to predict a large number of images?
I am very beginner when it comes to optimising models with respect to inference time.
Thanks in advance
Environment
TensorRT Version: TensorRT: 8.4.0.11
GPU Type: Jetson Orin
Nvidia Driver Version:
CUDA Version: 8.7
CUDNN Version: 8.3.2.49
Operating System + Version: 20.04.5 LTS (Focal Fossa)"
Python Version (if applicable):
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag): nvcr.io/nvidia/l4t-pytorch:r34.1.1-pth1.12-py3
Relevant Files
Steps To Reproduce
Explained in tensorrtx/yolov5 at master · wang-xinyu/tensorrtx · GitHub