CenterNet keypoint detector not giving good FPS in jetson xavier NX

[AastaLLL]

Hi,

XavierNX is an embedded device.
It’s expected that 1650 will have a better performance compared to Jetson.
You can try to inference it with fp16 or int8 for extra acceleration.

The unsupported data type error is a known issue.
It is caused by the different default input data type between ONNX and TensorRT.
Please check below comment for the solution:

Exporting Tensorflow models to Jetson Nano

Hi, Elviron The root cause is onnx expects input image to be INT8 but TensorRT use Float32. To solve this issue, you can modify the input data format of ONNX with our graphsurgeon API directly. 1. Install ONNX Graphsurgeon API $ sudo apt-get install python3-pip libprotobuf-dev protobuf-compiler $ git clone https://github.com/NVIDIA/TensorRT.git $ cd TensorRT/tools/onnx-graphsurgeon/ $ make install 2. Modify your model import onnx_graphsurgeon as gs import onnx import numpy as np graph = g…

Thanks.