# Build a model
model = BuildRCNN(nbChannels, shape1, shape2, nbClasses, nbRCL, nbFilters, filtersize)
_where_
...nbChannels -> number of channels
...shape1, shape2 -> dimensions of image
...nbClasses -> number of classes
...nbRCL -> number of RCL block (default = 5). Defines the depth of recurrence
...nbFilters -> number of filters
...filtersize -> size of the filter

#Compile it
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

#fit
model.fit(X_train, y_train, batch_size=64, nb_epoch=100, validation_data = (X_valid, y_valid))