What is the purpose of the ROI layer in a Fast R-CNN?

Question

In this tutorial about object detection, the fast R-CNN is mentioned. The ROI (region of interest) layer is also mentioned.

What is happening, mathematically, when regio

Answer 1

Region-of-Interest(RoI) Pooling:

It is a type of pooling layer which performs max pooling on inputs (here, convnet feature maps) of non-uniform sizes and produces a small feature map of fixed size (say 7x7). The choice of this fixed size is a network hyper-parameter and is predefined.

The main purpose of doing such a pooling is to speed up the training and test time and also to train the whole system from end-to-end (in a joint manner).

It's because of the usage of this pooling layer the training & test time is faster compared to original(vanilla?) R-CNN architecture and hence the name Fast R-CNN.

Simple example (from Region of interest pooling explained by deepsense.io):