Image recognition service architecture A question for developers of projects for pattern recognition. How best to organize the architecture of such a service?

At what stage do you conduct logic? (for example, for the recognition of a photo of a male blue jacket, a cascade of queries is performed: "recognizing men" -> "recognizing the jacket" -> "recognizing the color of the jacket.")

Does it make sense to implement all search options within a single neural network or is it better to create a set of individual neuronets that are confined to fairly simple tasks?
That is one of the good example for research. Personally, I prefer to segment out all the desired outputs at once. Then, check the success rate. If you cannot hit the success rate that you desire, you can go for more specific solutions for the specific problem that you face.

However, in general, the localization, segmentation, recognition are implemented in same network and are obtained all-at-once.