How to measure accuracy of a TensorRT engine

Description

Hello,
I am trying to benchmark multiple standard classification and detection models (mobilenet, resnet, squeezenet, ssd, etc) on Jetson AGX Xavier. I was able to build the engines for all the models and run inference with no problem (using either trtexec manually or Jetson-Benchmarks wrapper).
What I am looking for now however is to validate the accuracy of the INT8 and FP16 engines to quantify the quantization loss. Is there any tool or scripts to measure TOP1/TOP5 accuracy for classification using a built TensorRT engine?
I tried to search online and in the documentation for any provided tools with no success. Thanks.

Environment

TensorRT Version: 7.1.3.0
GPU Type: Jetson Xavier
Nvidia Driver Version: from JetPack 4.4.1
CUDA Version: 10.2.89
CUDNN Version: 8.0.0.180
Operating System + Version: Ubuntu 18.04
Python Version (if applicable): Python 3.6
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag): Baremetal

Hi, Request you to share your model and script, so that we can help you better.

Alternatively, you can try running your model with trtexec command.

Thanks!

Did you even read my post? or is this a BOT answer?

Hi @youcef4tak,

Please refer the following doc. For accuracy you need to define logic.
You can use Nvidia Nsight or Nvidia visual profiler for performance analysis.
https://docs.nvidia.com/deeplearning/tensorrt/best-practices/index.html#measure-performance

Thank you.