MobileNet-V3#
eqxvision.models.MobileNetV3
#
A simple port of torchvision.models.mobilenetv3
__init__(self, inverted_residual_setting: List[_InvertedResidualConfig], last_channel: int, num_classes: int = 1000, block: Optional[eqx.Module] = None, norm_layer: Optional[eqx.Module] = None, dropout: float = 0.2, *, key: Optional[jax.random.PRNGKey] = None)
#
Arguments:
inverted_residual_setting
: Network structurelast_channel
: The number of channels on the penultimate layernum_classes
: Number of classes in the classification task. Also controls the final output shape(num_classes,)
. Defaults to1000
block
: Module specifying inverted residual building block for mobilenetnorm_layer
: Module specifying the normalization layer to usedropout
: The dropout probabilitykey
: Ajax.random.PRNGKey
used to provide randomness for parameter initialisation. (Keyword only argument.)
__call__(self, x, *, key: jax.random.PRNGKey) -> Array
#
Arguments:
x
: The inputJAX
arraykey
: Required parameter. Utilised by few layers such asDropout
orDropPath
eqxvision.models.mobilenet_v3_small(torch_weights: str = None, **kwargs: Any) -> MobileNetV3
#
Constructs a small MobileNetV3 architecture from Searching for MobileNetV3.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.mobilenet_v3_large(torch_weights: str = None, **kwargs: Any) -> MobileNetV3
#
Constructs a large MobileNetV3 architecture from Searching for MobileNetV3.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone