In the 'Why is object detection much more challenging than image classification?' section, we used a non-CNN method to draw region proposals and CNN for classification, and we realized that this is not going to work well because the regions generated and fed into CNN were not optimal. R-CNN or regions with CNN features, as the name suggests, flips that example completely and use CNN to generate features that are classified using a (non-CNN) technique called SVM (Support Vector Machines)
R-CNN uses the sliding window method (much like we discussed earlier, taking some L x W and stride) to generate around 2,000 regions of interest, and then it converts them into features for classification using CNN. Remember what we discussed in the transfer learning chapter—the last flattened layer (before the classification or softmax layer) can be extracted to transfer learning from models trained on generalistic data, and further train them (often requiring much less data...
