Here is my personal opinion, I’m not an expert either.

Caffe2 is superior in deploying because it can “CODE ONCE, RUN ANYWHERE”, It can be deployed in mobile, which is really appealing and it’s said to be much faster than other implementation.
PyTorch is super elegant and flexible, it can be used like tensorfow (low level), it can also be used like keras(which reference a lot from the torch), and it could do what they can’t because it’s dynamic.
In research, we need to experiment a lot, debug a lot, adjust parameter, try latest wired model architecture, build our own special network. PyTorch is super qualified  and flexible for these tasks.
when deploying, we care more about a robust universalizable scalable system. Amazon, Intel, Qualcomm, Nvidia all claims to support caffe2.
the line gets blurred sometimes, caffe2 can be used for research, PyTorch could also be used for deploy.
caffe2 are planning to share a lot of backends with Torch and PyTorch,  Caffe2 Integration is one work in PyTorch(medium priority), we can export PyTorch nn.Module to caffe2 model in future.
if you are a beginner want to learn deeplearning/framework, use PyTorch. You’ll enjoy it.