Please provide the following information when requesting support.
• Hardware: Nano
• Network Type Detectnet_v2
I am currently using peoplenet with deepstream’s test5 app to detect people entering a store and count them. My next step is developing an age/gender classifier, to do this I thought of two approaches:
1-retraining facenet on classes such as:
2-retraining peoplenet on the class person as well as the face class with classes as mentioned in point 1.
I still can’t decide which classifier to go with especially since I am currently using peoplenet and I haven’t noticed any face detections happening in the frames. It is also mentioned that facenet only detects faces that occupy over 10% of the frame which might not be the case here since the camera is put above the entrance door, and peoplenet only detects objects that are over 10x10 pixels.
Can someone help guide me towards the right model training choice, especially since it would need quite a bit of cloud training resources, so it would be wiser to ask for an opinion on this?