@@ -15,20 +15,23 @@ to perform scalability study. The network implemented here replicates the networ
Different stages for training MC-CNN are described below.
1. Preprocessing data
Preprocessing data
------------------
The dataloader for training MCCNN assumes the data is already preprocessed. The preprocessing can be done with ``spoof.py`` script from ``bob.pad.face`` package. The preprocessed files are stored in the location ``<PREPROCESSED_FOLDER>``. Each
file in the preprocessed folder contains ``.hdf5`` files which contains a FrameContainer with each frame being a multichannel
image with dimensions ``NUM_CHANNELSxHxW``. Please refer to the section entitled **Multi-channel CNN for face PAD ** in the
documentation of ``bob.pad.face`` package, for an explicit example on how to preprocess the data for training MCCNN.
2. Training MCCNN
Training MCCNN
--------------
All the parameters required to train MCCNN are defined in the configuration file ``config.py`` file.
The ``config.py`` file should contain atleast the network definition and the dataset class to be used for training.
It can also define the transforms, number of channels in mccnn, training parameters such as number of epochs, learning rate and so on.
a. Structure of the config file
Structure of the config file
----------------------------
An example configuration file to train MCCNN with WMCA dataset is shown below
...
...
@@ -136,7 +139,8 @@ For a more detailed documentation of functionality available in the training scr
Please inspect the corresponding configuration file, ``wmca_mccn.py`` for example, for more details on how to define the database, network architecture and training parameters.
3. Running experiments with the trained model
Running experiments with the trained model
------------------------------------------
The trained model file can be used with ``MCCNExtractor`` to run PAD experiments with ``spoof.py`` script. A dummy algorithm is
added to forward the scalar values computed as the final scores.
