MobileNetV3Config¶

class lucid.models.MobileNetV3Config(bottleneck_cfg: tuple[tuple[object, ...], ...] | list[list[object]], last_channels: int, num_classes: int = 1000, in_channels: int = 3, stem_channels: int = 16, dropout: float = 0.2)¶

MobileNetV3Config stores the bottleneck sequence used by lucid.models.MobileNet_V3. It defines the MobileNet-v3 bottleneck layout, stem width, classifier width, and classifier settings.

Class Signature¶

@dataclass
class MobileNetV3Config:
    bottleneck_cfg: tuple[tuple[object, ...], ...] | list[list[object]]
    last_channels: int
    num_classes: int = 1000
    in_channels: int = 3
    stem_channels: int = 16
    dropout: float = 0.2

Parameters¶

bottleneck_cfg: Sequence of bottleneck specs in the form (kernel_size, mid_channels, out_channels, use_se, use_hswish, stride, se_reduction).
last_channels (int): Output width of the penultimate classifier layer.
num_classes (int): Number of output classes.
in_channels (int): Number of channels in the input image tensor.
stem_channels (int): Output width of the initial stem convolution.
dropout (float): Dropout probability applied before the final classifier.

Validation¶

bottleneck_cfg must contain at least one seven-value bottleneck spec.
Kernel, channel, stride, and reduction values inside each bottleneck spec must be positive integers.
use_se and use_hswish must be booleans.
last_channels, num_classes, in_channels, and stem_channels must be greater than 0.
dropout must be in the range [0, 1).

Usage¶

import lucid.models as models

config = models.MobileNetV3Config(
    bottleneck_cfg=[
        (3, 16, 16, True, False, 2, 2),
        (3, 72, 24, False, False, 2, 4),
    ],
    last_channels=1024,
    num_classes=10,
    in_channels=1,
)
model = models.MobileNet_V3(config)