mobilenet_v3_small¶

lucid.models.mobilenet_v3_small(num_classes: int = 1000, **kwargs) → MobileNet_V3¶

The mobilenet_v3_small function creates an instance of the MobileNet-v3-Small model, a lightweight variant of the MobileNetV3 architecture optimized for mobile and embedded applications with limited computational resources.

This model prioritizes efficiency and speed, making it suitable for low-power environments.

Total Parameters: 2,537,238

Function Signature¶

@register_model
def mobilenet_v3_small(num_classes: int = 1000, **kwargs) -> MobileNet_V3

Parameters¶

num_classes (int, optional): Number of output classes for the classification task. Default is 1000, commonly used for ImageNet.
kwargs (dict, optional): Additional keyword arguments passed to the MobileNet_V3 constructor, allowing for further customization.

Returns¶

MobileNet_V3: An instance of the MobileNet_V3 model configured with the Small variant’s architecture.

Bottleneck Configuration¶

The mobilenet_v3_small function uses the following bottleneck configurations:

Each entry in the configuration corresponds to a layer with parameters: [kernel_size, mid_channels, out_channels, use_se, use_hs, stride]
where:
- kernel_size: Size of the convolutional kernel.
- mid_channels: Number of channels after expansion in the bottleneck layer.
- out_channels: Number of output channels.
- use_se: Whether to use squeeze-and-excitation.
- use_hs: Whether to use the Hard-Swish activation function.
- stride: Stride of the convolution.

The full configuration:

cfg = [
    [3, 16, 16, True, False, 2],
    [3, 72, 24, False, False, 2],
    [3, 88, 24, False, False, 1],
    [5, 96, 40, True, True, 2],
    [5, 240, 40, True, True, 1],
    [5, 240, 40, True, True, 1],
    [5, 120, 48, True, True, 1],
    [5, 144, 48, True, True, 1],
    [5, 288, 96, True, True, 2],
    [5, 576, 96, True, True, 1],
    [5, 576, 96, True, True, 1],
]

Examples¶

Creating a MobileNet-v3-Small model:

>>> from lucid.models import mobilenet_v3_small
>>> model = mobilenet_v3_small(num_classes=1000)
>>> print(model)

Forward pass with MobileNet-v3-Small:

>>> from lucid.tensor import Tensor
>>> input_tensor = Tensor([[...]])  # Input tensor with appropriate shape
>>> output = model(input_tensor)
>>> print(output)