I have executed a TF-TRT model(FP16) for image classification on Jetson Nano.
For more information see https://github.com/NVIDIA-AI-IOT/tf_trt_models/blob/master/examples/classification/classification.ipynb
I used an inception-ResNet-v2 model for the prediction. The timing I got for the inference was about 140ms. In addition that takes around ~3.5GiB Memory and ~5.2GiB Swap.
Can I get support in order to optimize the timing and efficiency of my system? the details are:
Framework: Tensorflow TensorRT
inference: ~140 msec.
I would like to know if I can get the support to improve it or if I can get literature and examples on how to take advantage of the hardware as much as.