how do I run two separate deep learning based model together?
I have read your question and from that, I can infer that your classification network takes the input from the output of your first network(object locator). i.e the located object from your first network is passed to the second network which in turn classifies them into different colors. The entire Pipeline you are using seems to be a sequential one. Your best bet is to first supply input to the first network, get its output, apply some trigger to activate the second network, feed the output of the first net into the second net, and lastly get the output of the second net. You can run both of these networks in separate GPUs.
The Trigger that calls the second function can be something as simple as cropping the located object in local storage and have a function running that checks for any changes in the file structure(adding a new file). If this function returns true you can grab that cropped object and run the network with this image as input.
I trained a deep learning-based detection network to detect and locate some objects. I also trained a deep learning-based classification network to classify the color of the detected objects. Now I want to combine these two networks to detect the object and also classify color. I have some problems with combining these two networks and running them together. How do I call classification while running detection?
They are in two different frameworks: the classifier is based on the Keras and TensorFlow backend, the detection is based on opencv DNN module.