I am benchmarking five different models,
YOLO v2 YOLO v3 Cascade R-CNN Faster R-CNN Retina Net
In my comparison, I would like to know what the sizes of each model, to discuss the potential overfitting (if some are larger than others). It can be either the number of layers, the number of parameters. For Yolo models these are stated in the paper, e.g YOLOv2 has 106 layers with 51,000,657 parameters, whereas YOLOv3 has 349 layers and 65,252,682 parameters.
I looked into the other models, but couldn't find similar numbers in their own papers. Therefore, I appreciate if you help me to figure out whether those three are simpler than YOLOs or where they stand in terms of complexity.