Hands-On Convolutional Neural Networks with TensorFlow

Fast R-CNN

In 2015, Fast R-CNN was proposed to remedy the speed problems of R-CNN. In this method, the main change is where we get proposal regions in the pipeline. Instead of getting them directly from the input image, we first run the entire input image through a CNN and extract the generated feature map close to the end of the network. Next, again using a region-proposal method, candidate regions are extracted from this feature map in a similar manner to R-CNN.

Getting proposals in this way helps reuse and share expensive convolutional computations. The fully connected layers further down in the network that will classify, and additionally localize, only accept fixed-size input. For this reason, the proposed regions from the feature map ...