size of deep learning object detection pre-train models
If you are researching different architectures, I would recommend you to look at https://www.modelzoo.co/. It contains plenty of links to implementations in different architectures. This can help you get an intuitive understanding of how the models are built via code in case the papers aren't intuitive.

I use PyTorch more so I focused on finding PyTorch implementations. Here are links to Github repositories for the models you listed:


YOLO v2
YOLO v3
Cascade R-CNN
Faster R-CNN
Retina Net


With PyTorch networks, you can count the number of weights/parameters in multiple ways, this StackOverflow question has multiple answers all showing different ways to do it. The simplest one being:

pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)


In the post, there are also ways which detail the size of each layer in the network.