tfm.vision.backbones.EfficientNet

Creates an EfficientNet family model.

tfm.vision.backbones.EfficientNet(
    model_id: str,
    input_specs: tf_keras.layers.InputSpec = layers.InputSpec(shape=[None, None, None, 3]),
    se_ratio: float = 0.0,
    stochastic_depth_drop_rate: float = 0.0,
    kernel_initializer: str = 'VarianceScaling',
    kernel_regularizer: tf_keras.regularizers.Regularizer = None,
    bias_regularizer: tf_keras.regularizers.Regularizer = None,
    activation: str = 'relu',
    se_inner_activation: str = 'relu',
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    **kwargs
)

This implements the EfficientNet model from: Mingxing Tan, Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. (https://arxiv.org/pdf/1905.11946)

model_id A str of model ID of EfficientNet.
input_specs A tf_keras.layers.InputSpec of the input tensor.
se_ratio A float of squeeze and excitation ratio for inverted bottleneck blocks.
stochastic_depth_drop_rate A float of drop rate for drop connect layer.
kernel_initializer A str for kernel initializer of convolutional layers.
kernel_regularizer A tf_keras.regularizers.Regularizer object for Conv2D. Default to None.
bias_regularizer A tf_keras.regularizers.Regularizer object for Conv2D. Default to None.
activation A str of name of the activation function.
se_inner_activation A str of name of the activation function used in Sequeeze and Excitation layer.
use_sync_bn If True, use synchronized batch normalization.
norm_momentum A float of normalization momentum for the moving average.
norm_epsilon A float added to variance to avoid dividing by zero.
**kwargs Additional keyword arguments to be passed.

output_specs A dict of {level: TensorShape} pairs for the model output.

Methods

call

call(
    inputs, training=None, mask=None
)

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.