Developing new networks

NiftyNet allows users create new network, and share the network via the model zoo. To fully utilise this feature, a customised network should be prepared in the following steps:

New network and module

Create a new network file, e.g. and place this inside a python module directory, e.g. my_network_collection/ together with a new file.

Make the module loadable

Make sure the new network module can be discovered by NiftyNet by doing either of the following:

  • Place my_network_collection/ inside $NIFTYNET_HOME/extensions/, with $NIFTYNET_HOME defined by home in [global] setting.
  • Append the directory of my_network_collection/ (i.e. the directory where this folder is located) to your $PYTHONPATH.

Extend BaseNet

Create a new Python class, e.g. NewNet in by inheriting the BaseNet class from, a minimal working example of a fully convolutional network, could be a starting point for NewNet.

class ToyNet(BaseNet):
 def __init__(self, num_classes, name='ToyNet'):

     super(ToyNet, self).__init__(
         num_classes=num_classes, acti_func=acti_func, name=name)

     # network specific property
     self.hidden_features = 10

 def layer_op(self, images, is_training):
     # create layer instances
     conv_1 = ConvolutionalLayer(self.hidden_features,

     conv_2 = ConvolutionalLayer(self.num_classes,

     # apply layer instances
     flow = conv_1(images, is_training)
     flow = conv_2(flow, is_training)

     return flow

Implement operations

In NewNet, implement __init__() function for network property initialisations, and implement layer_op() for network connections.

The network properties can be used to specify the number of channels, kernel dilation factors, as well as sub-network components of the network.

An example of sub-networks composition is presented in Simulator GAN.

The layer operation function layer_op() should specify how the input tensors are connected to network layers. For basic building blocks, using the ones in niftynet/layer/ are recommended. as the layers are implemented in a modular design (convenient for parameter sharing) and can handle 2D, 2.5D and 3D cases in a unified manner whenever possible.

Call NewNet from application

Finally training the network could be done by specifying the newly implemented network in the command line argument

--name my_network_collection.new_net.NewNet

(my_network_collection.new_net refer to the file, and NewNet is the class name to be imported from

Command to load NewNet with segmentation application using pip installed NiftyNet is:

net_segment train -c /path/to/customised_config \
                  --name my_network_collection.new_net.NewNet

alternatively, using NiftyNet cloned from source code repository:

python train -c /path/to/customised_config \
                            --name my_network_collection.new_net.NewNet

See also the configuration doc for name parameter.

Share the network and trained weights

Please consider submitting the design to our model zoo (contact if interested).