Jetson Xavier NX benchmark score

HI
Can we get bechmark score on Jetson NX, such as ResNet-50 ,Inception V4 , VGG-19?
It supposed to be a table for these data.
Like this…

It’s better if we also get the procedure , like this…

Hi @lisoulin, please see here for preliminary benchmarks of Jetson Xavier NX: https://devblogs.nvidia.com/jetson-xavier-nx-the-worlds-smallest-ai-supercomputer/

Thanks for the reply.
Actually, I have seen this link which you post.

From the chart, the score for ResNet-50 of Jetson NX is over 1250 FPS,
And I also tried to run on my Jetson NX to find out best score , but unfortuantely my fine tune is about 850 FPS.
It has real gap with official data.

And I also find an interesting thing, my Jetson is 15W. And from the data https://developer.nvidia.com/embedded/jetson-agx-xavier-dl-inference-benchmarks

If the AGX is in 15W mode, the score of ResNet-50 is near to 850, it simliar with my result on NX 15W mode.
So could you help provide the test procedure that NX can reach over 1250 FPS on ResNet-50?
My command is “./trtexec --output=prob --deploy=…/data/googlenet/ResNet50_224x224.prototxt --int8 --batch=128”

That is only using GPU, whereas the benchmark results are for GPU + 2xDLA. You can run it with this GitHub repo: https://github.com/NVIDIA-AI-IOT/jetson_benchmarks

Thanks for the guideline
I follow the website procedure to run individual benchmark
But I still got the same level score about 800~850.

Interesting , the sameple output on this web sidte https://github.com/NVIDIA-AI-IOT/jetson_benchmarks
ResNet50 score is also locate at 800~850.
Any idea?

Hi @lisoulin, yes that is close to expected performance on JetPack 4.4 DP.

Hi Dusty:
Do you mean Resnet-50 score at 800-850 is expected result?
My question is the chart from the link, Resnet-50 of Jetson NX is over 1250 FPS.
Is it any mistake ? Because it’s chart ,instead of number, and looks like over 1250FPS, is it something wrong?

You can find the latest results from today’s blog:

The preliminary results that were posted previously have been updated with these. There was a change in the benchmarking methodology in the GPU + 2xDLA case that allowed more time for the DLA’s to load the networks and begin running, so we have updated the performance here. The scripts on the jetson_benchmarks repo on GitHub also reflect these updates going forward.

Thank you very much!
My question is resolved.