Dual Path Networks are highly efficient networks which combine the strength of both ResNeXt Aggregated Residual Transformations for Deep Neural Networks and DenseNets Densely Connected Convolutional Networks.
Note: Weights have not been ported over yet.
Several of the standard Dual Path Network models have been included. They can be initialized as :
from dual_path_network import DPN92, DPN98, DPN107, DPN137 model = DPN92(input_shape=(224, 224, 3)) # same for the others
To create a custom DualPathNetwork, use the DualPathNetwork builder method :
from dual_path_network import DualPathNetwork model = DualPathNetwork(input_shape=(224, 224, 3), initial_conv_filters=64, depth=[3, 4, 20, 3], filter_increment=[16, 32, 24, 128], cardinality=32, width=3, weight_decay=0, include_top=True, weights=None, input_tensor=None, pooling=None, classes=1000)
5e-4if you wish to use it.