Tensorflow Object Detection Api With Gpu On Windows And Real-time Detection

I am testing the new Tensorflow Object Detection API in Python, and I succeeded in installing it on Windows using docker. However, my trained model (Faster RCNN resnet101 COCO) tak

Solution 1:

Considering the latency, where is the problem ? I heard Faster RCNN was a good model for low latency visual detection, is it because of the CPU-only execution ?

Of course, it's because you are using CPU.

With such latency, is it possible to make efficient realtime video processing by using tensorflow GPU, or should I use a more popular model like YOLO ?

Yolo is fast, but I once used it for face and accuracy was not that great. But a good alternative.

The popular mean to use tensorflow GPU in docker is nvidia-docker but is not supported on windows. Should I continue to look for a docker (or conda) solution for local prediction, or should I deploy my model directly to a virtual instance with GPU (I am comfortable with Google Cloud Platform) ?

I think you can still use your local GPU in windows, as Tensorflow supports GPU on python.

And here is an example, simply to do that. It has a client which can read webcam or IP cam stream. The server is using Tensorflow python GPU version and ready to use pre-trained model for predictions.

Unfortunately, Tensoflow does not support tensorflow-serving on windows. Also as you said Nvidia-Docker is not supported on windows. Bash on windows has no support for GPU either. So I think this is the only easy way to go for now.