► Keras 3 API documentation / KerasCV / Models / Backbones / EfficientNetV1 models

EfficientNetV1 models

[source]

EfficientNetV1Backbone class

keras_cv.models.EfficientNetV1Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • width_coefficient: float, scaling coefficient for network width.
  • depth_coefficient: float, scaling coefficient for network depth.
  • dropout_rate: float, dropout rate before final classifier layer.
  • drop_connect_rate: float, dropout rate at skip connections. The default value is set to 0.2.
  • depth_divisor: integer, a unit of network width. The default value is set to 8.
  • activation: activation function to use between each convolutional layer.
  • input_shape: optional shape tuple, it should have exactly 3 input channels.
  • input_tensor: optional Keras tensor (i.e. output of keras.keras.layers.Input()) to use as image input for the model.
  • stackwise_kernel_sizes: list of ints, the kernel sizes used for each conv block.
  • stackwise_num_repeats: list of ints, number of times to repeat each conv block.
  • stackwise_input_filters: list of ints, number of input filters for each conv block.
  • stackwise_output_filters: list of ints, number of output filters for each stack in the conv blocks model.
  • stackwise_expansion_ratios: list of floats, expand ratio passed to the squeeze and excitation blocks.
  • stackwise_strides: list of ints, stackwise_strides for each conv block.
  • stackwise_squeeze_and_excite_ratios: list of ints, the squeeze and excite ratios passed to the squeeze and excitation blocks.

Example

# Construct an EfficientNetV1 from a preset:
efficientnet = keras_cv.models.EfficientNetV1Backbone.from_preset(
    "efficientnetv1_b0"
)
images = np.ones((1, 256, 256, 3))
outputs = efficientnet.predict(images)

# Alternatively, you can also customize the EfficientNetV1 architecture:
model = EfficientNetV1Backbone(
    stackwise_kernel_sizes=[3, 3, 5, 3, 5, 5, 3],
    stackwise_num_repeats=[1, 2, 2, 3, 3, 4, 1],
    stackwise_input_filters=[32, 16, 24, 40, 80, 112, 192],
    stackwise_output_filters=[16, 24, 40, 80, 112, 192, 320],
    stackwise_expansion_ratios=[1, 6, 6, 6, 6, 6, 6],
    stackwise_strides=[1, 2, 2, 2, 1, 2, 1],
    stackwise_squeeze_and_excite_ratios=[
        0.25,
        0.25,
        0.25,
        0.25,
        0.25,
        0.25,
        0.25,
    ],
    width_coefficient=1.0,
    depth_coefficient=1.0,
    include_rescaling=False,
)
images = np.ones((1, 256, 256, 3))
outputs = efficientnet.predict(images)

[source]

from_preset method

EfficientNetV1Backbone.from_preset()

Instantiate EfficientNetV1Backbone model from preset config and weights.

Arguments

  • preset: string. Must be one of "efficientnetv1_b0", "efficientnetv1_b1", "efficientnetv1_b2", "efficientnetv1_b3", "efficientnetv1_b4", "efficientnetv1_b5", "efficientnetv1_b6", "efficientnetv1_b7". If looking for a preset with pretrained weights, choose one of "".
  • load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.

Examples

# Load architecture and weights from preset
model = keras_cv.models.EfficientNetV1Backbone.from_preset(
    "",
)

# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.EfficientNetV1Backbone.from_preset(
    "",
    load_weights=False,
Preset name Parameters Description
efficientnetv1_b0 4.05M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.0 and depth_coefficient=1.0.
efficientnetv1_b1 6.58M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.0 and depth_coefficient=1.1.
efficientnetv1_b2 7.77M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.1 and depth_coefficient=1.2.
efficientnetv1_b3 10.79M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.2 and depth_coefficient=1.4.
efficientnetv1_b4 17.68M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.4 and depth_coefficient=1.8.
efficientnetv1_b5 28.52M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.6 and depth_coefficient=2.2.
efficientnetv1_b6 40.97M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.8 and depth_coefficient=2.6.
efficientnetv1_b7 64.11M EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=2.0 and depth_coefficient=3.1.

[source]

EfficientNetV1B0Backbone class

keras_cv.models.EfficientNetV1B0Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B0 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B1Backbone class

keras_cv.models.EfficientNetV1B1Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B1 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B2Backbone class

keras_cv.models.EfficientNetV1B2Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B2 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B3Backbone class

keras_cv.models.EfficientNetV1B3Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B3 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B4Backbone class

keras_cv.models.EfficientNetV1B4Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B4 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B5Backbone class

keras_cv.models.EfficientNetV1B5Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B5 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B6Backbone class

keras_cv.models.EfficientNetV1B6Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B6 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

[source]

EfficientNetV1B7Backbone class

keras_cv.models.EfficientNetV1B7Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_strides,
    stackwise_squeeze_and_excite_ratios,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    input_shape=(None, None, 3),
    input_tensor=None,
    activation="swish",
    **kwargs
)

Instantiates the EfficientNetV1B7 architecture.

Reference

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • input_shape: optional shape tuple, defaults to (None, None, 3).
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
EfficientNetV1 models
EfficientNetV1Backbone class
from_preset method
EfficientNetV1B0Backbone class
EfficientNetV1B1Backbone class
EfficientNetV1B2Backbone class
EfficientNetV1B3Backbone class
EfficientNetV1B4Backbone class
EfficientNetV1B5Backbone class
EfficientNetV1B6Backbone class
EfficientNetV1B7Backbone class