I just wanted to summarize developers experience and sharing some tips about tensorflow object detection API on TX2.
At the moment I am just talking about what is actually doable and not, with a focus on inference, rather than training.
Browsing the forum, my experience and other resources, this is what I understood.
Let’s consider only available pretrained frozen graph.
TF obj.det. API can be used for inference with ssd_mobilenet_v1 network architecture at approx ~5-8 fps. Faster Rcnn resnet pretrained models seems to cause OOM errors (in my experience, all of them). Was anybody able to run one of the Faster Rcnn resnet model? If yes, could you share some tips?
Does the conversion to TensorRT have an impact also on memory usage?
What I mean is the following: even if I am not able to run a specific network architecture due to OOM error on TX2 from TF Obj Det API, I could potentially train a model in a different, more powerful machine, export trained graph to UFF format (through python API), then transfer it to TX2 where it can be imported using C++ API for inference. Does it sound correct? performance would certainly benefit from a TF->TensorRT conversion, but I am not sure about memory usage.
I am considering this as an option beacuse I’ve notice in jetson-inference DetectNet a FasterRcnnResnet50 network.
Thanks for your contribution!