Object Detection models and FPS

Hi ALL,

Device: Jetson TX2 (JP3.3).
Tensorflow: v1.9.

I trained a fasterRCNN model using object detection API on my computer and got the inference graph but when I ran it using Jetson TX2 it gave me only 1 FPS , so I tried YOLOv3 it gave me 1.7 FPS

Q1: can I enhance the above FPS?
Q2: If I train my data on SSD-mobilenet-v1 can I guarantee a descent number of FPS?

please recommend anything
thanks in advance.

Hi,

You can run jetson_clocks to improve performance, also nvpmodel can improve performance:
https://developer.ridgerun.com/wiki/index.php?title=Nvidia_TX2_NVP_model

We have a Gstreamer plugin that performs inference on different networks, here you can check some benchmarks results from some supported networks:
https://developer.ridgerun.com/wiki/index.php?title=GstInference/Benchmarks

Regards.

Hi,

It’s recommended to reflash your device with our latest JetPack first.
There is around 1.5x speed up when upgrading software from rel-28 into rel-32.

You can find some benchmark result for Jetson here: (tested on the Nano)
https://developer.nvidia.com/embedded/jetson-nano-dl-inference-benchmarks
We get 39fps on SSD Mobilenet-V2 + Nano.

Thanks.

Hi AastaLLL,

so my concern is that I want to work on ROS kinetic later which is supported by ubuntu 16.04 thats why I am using Jetpack3.3

For Now I am going to train my data on ssd-mobilenet and will get back to you when I have the results.