Performance difference between jetson-inference & object-detection-tensorrt-example on TX2


I am wondering if I am doing this right or now, but I have some Qs about TX2 performance for object detection.

Following this example on jetson-inference (, I saw that TX2 is capable of running/inferencing around 50-70 FPS. (I just followed the example. I didn’t change anything)

On the other hand, if I go through this example - , I got about 1.63 FPS (I used imutil package from pip to measure the FPS).

My main goal for using the above example (SSD_Model) was so that I could use TensorRT model that gets generated from this guide - The model that I am trying to use is FP16 TensorRT using DetectNet_v2.

As 1.63 FPS seems extremely slow, I wanted to check to see if there is normal or if I implemented something incorrectly.

Could it be that this was due to the fact that the SSD example uses SSD model rather than DetectNet? If I wanted to test on DetectNet, what would I have to do test this out? Also, would you recommend using jetson-inference for production? If you could please help, I would appreciate it.

Thanks you


The SSD sample reads the camera via default OpenCV, which is slow since it is a CPU implementation.
It’s recommended to try our Deepstream SDK for a better performance first.




Thanks for sharing the info.

I looked into DeepStream doc, and I looked through dev forum to figure out how to hook up FLIR Machine Vision USB3 camera + FLIR’s PySpin SDK & DeepStream.

It seems like FLIR cameras are not compatible with DeepStream as they lack GStreamer support, and DeepStream needs USB (accessed through /dev/video0) cameras.

Would you have any other suggestion on what can be done to improve FPS on the TX2 board?

Thank you


Is there a way to boost performance without using DeepStream?