MobileNetV2Config¶

class lucid.models.MobileNetV2Config(stage_configs: tuple[tuple[int, int, int, int, int], ...] | list[tuple[int, int, int, int, int]], num_classes: int = 1000, in_channels: int = 3, stem_channels: int = 32, last_channels: int = 1280, dropout: float = 0.2)¶

MobileNetV2Config stores the inverted-residual stage settings used by lucid.models.MobileNet_V2. It defines the per-stage expansion ratios, repeat counts, stem width, final projection width, and classifier settings.

Class Signature¶

@dataclass
class MobileNetV2Config:
    stage_configs: tuple[tuple[int, int, int, int, int], ...] | list[tuple[int, int, int, int, int]]
    num_classes: int = 1000
    in_channels: int = 3
    stem_channels: int = 32
    last_channels: int = 1280
    dropout: float = 0.2

Parameters¶

stage_configs: Sequence of stage specs in the form (in_channels, out_channels, expansion, repeats, stride).
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.
last_channels (int): Output width of the final 1x1 convolution.
dropout (float): Dropout probability applied before the classifier.

Validation¶

stage_configs must contain at least one stage and each stage must contain five positive integers.
num_classes, in_channels, stem_channels, and last_channels must be greater than 0.
dropout must be in the range [0, 1).

Usage¶

import lucid.models as models

config = models.MobileNetV2Config(
    stage_configs=[
        (32, 16, 1, 1, 1),
        (16, 24, 6, 2, 2),
    ],
    num_classes=10,
    in_channels=1,
)
model = models.MobileNet_V2(config)