# The R-CNN family
## Traditional Approach
* Region Proposal Generator: Selective Search , Edge Boxes
* Image descriptors: histogram of oriented gradients (HOG)
* Classifier: Support Vector Machine
## R-CNN
1. Extract features using a CNN network
## Fast R-CNN
Faster R-CNN builds a network that has only a single stage:
1. Input image is fed into a pretrained CNN network to get a feature map
2. Input image is also used to propose $$n$$ region proposals
3. Project the region proposals on the feature map yields $$n$$ region of interest (ROI)
4. ROI pooling layers are used to extract a fixed-length feature vector from those ROIs
Fast R-CNN is faster than R-CNN because:
1. Faster R-CNN shares computations (i.e. convolutional layer calculations) across all proposals (i.e. ROIs) rather than doing the calculations for each proposal independently.
2. Fast R-CNN does not cache the extracted features and thus does not need so much disk storage compared to R-CNN, which needs hundreds of gigabytes.
## Faster R-CNN
1. Changes the selective search portion in Fast R-CNN into a Regional Proposal Network.
## Mask R-CNN
1. Replaces the region of interest pooling layer with the *region of interest (RoI) alignment* layer
2. More suitable for pixel-level prediction (semantic segmentation)
## Resources
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