Image Pre-Processing
The following pre-processing steps are applied to an image before it is sent through the network. These steps must be identical for both training and inference. The mean vector (
, one number corresponding to each color channel) is not the mean of the pixel values in the current image but a configuration value that is identical across all training and test images.
The default values for 
and 
parameters are 600 and 1200 respectively.
Network Organization
A R-CNN uses neural networks to solve two main problems:
- Identify promising regions (Region of Interest – ROI) in an input image that are likely to contain foreground objects
- Compute the object class probability distribution of each ROI – i.e., compute the probability that the ROI contains an object of a certain class. The user can then select the object class with the highest probability as the classification result.
R-CNNs consist of three main types of networks:
- Head
- Region Proposal Network (RPN)
- Classification Network