Efficientnet-V1#
eqxvision.models.EfficientNet
#
A simple port of torchvision.models.efficientnet
.
__init__(self, inverted_residual_setting: Sequence[Union[_MBConvConfig, _FusedMBConvConfig]], dropout: float, stochastic_depth_prob: float = 0.2, num_classes: int = 1000, norm_layer: Optional[eqx.Module] = None, last_channel: Optional[int] = None, *, key: jax.random.PRNGKey = None)
#
Arguments:
inverted_residual_setting
: Network structuredropout
: The dropout probabilitystochastic_depth_prob
: Probability of dropping a sample along channelsnum_classes
: Number of classes in the classification task. Also controls the final output shape(num_classes,)
norm_layer
: Normalisation applied to the intermediate outputslast_channel
: The number of channels on the penultimate layerkey
: Ajax.random.PRNGKey
used to provide randomness for parameter initialisation. (Keyword only argument.)
__call__(self, x: Array, *, key: jax.random.PRNGKey) -> Array
#
Arguments:
x
: The inputJAX
array.key
: Required parameter. Utilised by few layers such asDropout
orDropPath
.
eqxvision.models.efficientnet_b0(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B0 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b1(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B1 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b2(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B2 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b3(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B3 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b4(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B4 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b5(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B5 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b6(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B6 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.efficientnet_b7(torch_weights: str = None, **kwargs: Any) -> EfficientNet
#
EfficientNet B7 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone