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

ROI (region of interest) layer is introduced in Fast R-CNN and is a special case of spatial pyramid pooling layer which is introduced in Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. The main function of ROI layer is reshape inputs with arbitrary size into a fixed length output because of size constraint in Fully Connected layers.

How ROI layer works is showed below:

In this image, the input image with arbitrary size is fed into this layer which has 3 different window: 4x4 (blue), 2x2 (green), 1x1 (gray) to produce outputs with fixed size of 16 x F, 4 x F, and 1 x F, respectively, where F is the number of filters. Then, those outputs are concatenated into a vector to be fed to Fully Connected layer.