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)
1e-4
or 5e-4
if you wish to use it.