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.
TensorRT Version: 22.214.171.124
GPU Type: Jetson Xavier
Nvidia Driver Version: from JetPack 4.4.1
CUDA Version: 10.2.89
CUDNN Version: 126.96.36.199
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