Configuration File Explanation

Here we explain what it is expected in a typical configuration.yaml file:

speckle

• nscreens: The number of screens used in the simulation.
• original_res: The original resolution of the images.
• datadirectory: The directory where the data files are located.

preproc

• polarize: A boolean value indicating whether the images should be transformed into polar coordinate.
• polresize: The size to which the polarized images are resized.
• equivariant: A boolean value indicating whether the images should be made pseudo-equivariant, by setting the azimutal angle to the maximum pixel intensity.
• randomrotate: A boolean value indicating whether the images should be randomly rotated.
• centercrop: The size of the central crop of the images. Test this value to guarantee that the empty boundaries are removed.
• resize: The size to which the images are resized.
• speckreps: The number of times that we want to repeat each speckle pattern in order to augment the data. Use only in combination with random rotations.
• multichannel: The number of speckle pattern from the same Cn2 to use as image channels.
• normalization: How to normalize the tags: unif, lin, log or zscore.
• dataname: The name of the file where the preprocessed images are saved.

model

• name: String representing the name of the model. Used to store states and plots. It can be any name.
• type: The type of the model. We have implemented resnet18, resnet50, resnet152 from the ResNet family, and scnnC8, scnnC16, small_scnnC16, which are equivariant CNN.
• save_every: The frequency (in epochs) at which the model is saved.
• pretrained: A boolean value indicating whether a pretrained model should be used. It is available only for the ResNet.

hyppar

• maxepochs: The maximum number of epochs for training the model.
• batch_size: The size of the batches used in training.
• lr: The learning rate for the optimizer.
• loss: The loss function used in training. We have implemented MSELoss, BCELoss and Pearson.
• optimizer: The optimizer used in training.