Why tf object detection api needs so few pictures? I am wondering why tf object detection api needs so few picture samples for training while regular cnns needs many more?

What I read in tutorials is that tf object detection api needs around 100-500 pictures per class for training (is it true?) while regular CNNs need many many more samples, like tens of thousands or more. Why is it so?
I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning (using those pre-trained models as starting point).