train_config.yaml

Dataset:
    name: "my_data"
    basepath: "/path/to/data/folders"
    shape: [160, 480, 480]
    dim_resolution: [2., .65, .65]
    cell_radius: [3, 9, 9] # in pixels, for border exclusion

Experiment:
    name: "my_exp"
    basepath: "/path/to/experiment/outputs"

UNet:
    model: "unet" # can be one of ['unet', 'res-unet', 'se-unet', 'eca-unet', 'attention-unet', 'moe-unet']
    input_shape: [80, 240, 240]
    epochs: 3000
    batch_size: 4
    learning_rate: 0.04
    n_blocks: 4
    n_filters: 16
    k_size: !!python/tuple [3, 5, 5]
    k_stride: !!python/tuple [2, 2, 2]
    dropout: null
    regularizer: null # { "l2": 0.0001 }

    squeeze_factor: 2 # only used if model == 'se-unet'
    moe_n_experts: 5 # only used if model == 'moe-unet'
    moe_top_k_experts: null # only used if model == 'moe-unet'
    moe_noise: true # only used if model == 'moe-unet'
    moe_balance_loss: "load" # only used if model == 'moe-unet'; can be 'load' or 'importance'

DoG:
    iterations: 100
    max_match_dist: 10 # same scale as given by dim_resolution
    n_cpu: 5

PreProcessing:
    clip: "bit" # can be one of ['constant', 'bit', 'quantile', 'auto', null]
    clip_value: 14
    center: null # can be one of ['constant', 'min', 'mean', null]
    center_value: null
    scale: "bit" # can be one of ['constant', 'bit', 'max', 'std', null]
    scale_value: 14

DataAugmentation:
    augment: true

    gamma:
        param_range: [0.9, 1.1]
        p: 0.3
    # contrast:
    #     param_range     : [1., 3.]
    brightness:
        param_range: [-0.06, 0.06]
        p: 0.3
    # zoom:
    #     param_range     : [1.0, 1.1]
    #     order           : 1
    #     p               : 0.3
    blur:
        param_range: [0., 0.3]
        p: 0.3
    noise:
        param_range: [0., 0.03]
        p: 0.3
    # rotation:
    #     param_range     : [0., 270.]
    #     axes            : [-2, -1]
    #     p               : 0.3
    flip:
        axes: [-2]
        p: 0.3