Number of Features

Base number of convolutional filters in the U-Net model.

This controls the initial number of feature maps (filters) in the first layer of the U-Net. The number of features doubles at each encoding stage and halves at each decoding stage.

More features allow the model to learn more complex patterns but require more GPU memory and training time. Fewer features create a lighter model that trains faster but may miss subtle details.