Hi, I followed the guide in this project to setup caffe on nano.
I trained my own model and tried to detect objects using USB camera.
The speed can only reach 4.7FPS which is far away from the mentioned speed in the following link. https://devblogs.nvidia.com/jetson-nano-ai-computing/
Can you help verify the performance of nano? Thanks.
Hi bobzeng, the inferencing was performed using TensorRT. You can try using the trt-exec program to benchmark your model. Note that the model from the article is SSD-Mobilenet-V2.
where sample_unpruned_mobilenet_v2.uff comes from? i use ssd_inception_v2_coco_2017_11_17,and transfer the model into .uff file. not the same as yours.
dusty_nv Thank you for the answer! I can see on the github profile there is three different mobilenet models! Where non of them is labeled with anything about unpruned. Can you be kind too clarify which of these models you have used for this sample?
mads,
I think that there are 14 models from the link that dusty provided. You need to do some conversion, which isn’t that big of a deal, and only needs to be done once.
cudanexus,
I can get 4.7 FPS from the CPU alone(via a secondary stream on one of my RTSP security cameras). And can get 27+FPS using tensorflow in python.
Unfortunately I don’t have a lot of time to play with this as my life is pretty busy at the moment, or I would post some code. Google or Bing is your friend. I just happened to see this post.
A cheap rtsp stream can be had for 25.99 for a WYZE cam2 on amazon. They just released a rtsp firmware.
I can also see that these mobilenet models are of version 1, but the example uses a mobilenet model of version 2? Do you have a folder with that model, which is possible too train on my own usecase?
Also I can see on the github that you use JetPack 3.2 for the TX2, but this is supposed to be used on the Jetson NANO, but the nano can’t run JetPack 3.2 and can’t therefore get Cuda9.0 which is needed for making this work?
Could anyone please point me to samples_labels.txt which is required to make sense of the detections?
I tried running the model using the sample shared by dusty on images in the data folder of tensorrt. However, I am not able to get detections with good confidence score. For “dog.ppm”, the maximum confidence score is 0.22 (22%) and for “bus.ppm”, the maximum confidence score is 0.03 (3%).
Is there something I am missing out. Please help me out.