EfficientNetV2 models
EfficientNetV2Backbone class
keras_cv.models.EfficientNetV2Backbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2 architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(1/255.0)layer. - width_coefficient: float, scaling coefficient for network width.
- depth_coefficient: float, scaling coefficient for network depth.
- 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_squeeze_and_excite_ratios: list of ints, the squeeze and excite ratios passed to the squeeze and excitation blocks.
- stackwise_strides: list of ints, stackwise_strides for each conv block.
- stackwise_conv_types: list of strings. Each value is either 'unfused' or 'fused' depending on the desired blocks. FusedMBConvBlock is similar to MBConvBlock, but instead of using a depthwise convolution and a 1x1 output convolution blocks fused blocks use a single 3x3 convolution block.
- skip_connection_dropout: float, dropout rate at skip connections.
- depth_divisor: integer, a unit of network width.
- min_depth: integer, minimum number of filters.
- activation: activation function to use between each convolutional layer.
- input_shape: optional shape tuple, defaults to (None, None, 3).
- input_tensor: optional Keras tensor (i.e. output of
keras.layers.Input()) to use as image input for the model.
Example
# Construct an EfficientNetV2 from a preset:
efficientnet = keras_cv.models.EfficientNetV2Backbone.from_preset(
"efficientnetv2_s"
)
images = tf.ones((1, 256, 256, 3))
outputs = efficientnet.predict(images)
# Alternatively, you can also customize the EfficientNetV2 architecture:
model = EfficientNetV2Backbone(
stackwise_kernel_sizes=[3, 3, 3, 3, 3, 3],
stackwise_num_repeats=[2, 4, 4, 6, 9, 15],
stackwise_input_filters=[24, 24, 48, 64, 128, 160],
stackwise_output_filters=[24, 48, 64, 128, 160, 256],
stackwise_expansion_ratios=[1, 4, 4, 4, 6, 6],
stackwise_squeeze_and_excite_ratios=[0.0, 0.0, 0, 0.25, 0.25, 0.25],
stackwise_strides=[1, 2, 2, 2, 1, 2],
stackwise_conv_types=[
"fused",
"fused",
"fused",
"unfused",
"unfused",
"unfused",
],
width_coefficient=1.0,
depth_coefficient=1.0,
include_rescaling=False,
)
images = tf.ones((1, 256, 256, 3))
outputs = efficientnet.predict(images)
from_preset method
EfficientNetV2Backbone.from_preset()
Instantiate EfficientNetV2Backbone model from preset config and weights.
Arguments
- preset: string. Must be one of "efficientnetv2_s", "efficientnetv2_m", "efficientnetv2_l", "efficientnetv2_b0", "efficientnetv2_b1", "efficientnetv2_b2", "efficientnetv2_b3", "efficientnetv2_s_imagenet", "efficientnetv2_b0_imagenet", "efficientnetv2_b1_imagenet", "efficientnetv2_b2_imagenet". If looking for a preset with pretrained weights, choose one of "efficientnetv2_s_imagenet", "efficientnetv2_b0_imagenet", "efficientnetv2_b1_imagenet", "efficientnetv2_b2_imagenet".
- 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.EfficientNetV2Backbone.from_preset(
"efficientnetv2_s_imagenet",
)
# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.EfficientNetV2Backbone.from_preset(
"efficientnetv2_s_imagenet",
load_weights=False,
| Preset name | Parameters | Description |
|---|---|---|
| efficientnetv2_s | 20.33M | EfficientNet architecture with 6 convolutional blocks. |
| efficientnetv2_m | 53.15M | EfficientNet architecture with 7 convolutional blocks. |
| efficientnetv2_l | 117.75M | EfficientNet architecture with 7 convolutional blocks, but more filters the in efficientnetv2_m. |
| efficientnetv2_b0 | 5.92M | EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has width_coefficient=1.0 and depth_coefficient=1.0. |
| efficientnetv2_b1 | 6.93M | EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has width_coefficient=1.0 and depth_coefficient=1.1. |
| efficientnetv2_b2 | 8.77M | EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has width_coefficient=1.1 and depth_coefficient=1.2. |
| efficientnetv2_b3 | 12.93M | EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has width_coefficient=1.2 and depth_coefficient=1.4. |
| efficientnetv2_s_imagenet | 20.33M | EfficientNet architecture with 6 convolutional blocks. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 83.9%top 1 accuracy and 96.7% top 5 accuracy on imagenet. |
| efficientnetv2_b0_imagenet | 5.92M | EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has width_coefficient=1.0 and depth_coefficient=1.0. Weights are initialized to pretrained imagenet classification weights. Published weights are capable of scoring 77.1% top 1 accuracy and 93.3% top 5 accuracy on imagenet. |
| efficientnetv2_b1_imagenet | 6.93M | EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has width_coefficient=1.0 and depth_coefficient=1.1. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 79.1% top 1 accuracy and 94.4% top 5 accuracy on imagenet. |
| efficientnetv2_b2_imagenet | 8.77M | EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has width_coefficient=1.1 and depth_coefficient=1.2. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 80.1% top 1 accuracy and 94.9% top 5 accuracy on imagenet. |
EfficientNetV2B0Backbone class
keras_cv.models.EfficientNetV2B0Backbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2B0 architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.
EfficientNetV2B1Backbone class
keras_cv.models.EfficientNetV2B1Backbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2B1 architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.
EfficientNetV2B2Backbone class
keras_cv.models.EfficientNetV2B2Backbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2B2 architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.
EfficientNetV2B3Backbone class
keras_cv.models.EfficientNetV2B3Backbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2B3 architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.
EfficientNetV2SBackbone class
keras_cv.models.EfficientNetV2SBackbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2S architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.
EfficientNetV2MBackbone class
keras_cv.models.EfficientNetV2MBackbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2M architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.
EfficientNetV2LBackbone class
keras_cv.models.EfficientNetV2LBackbone(
include_rescaling,
width_coefficient,
depth_coefficient,
stackwise_kernel_sizes,
stackwise_num_repeats,
stackwise_input_filters,
stackwise_output_filters,
stackwise_expansion_ratios,
stackwise_squeeze_and_excite_ratios,
stackwise_strides,
stackwise_conv_types,
skip_connection_dropout=0.2,
depth_divisor=8,
min_depth=8,
activation="swish",
input_shape=(None, None, 3),
input_tensor=None,
**kwargs
)
Instantiates the EfficientNetV2L architecture.
Reference
Arguments
- include_rescaling: bool, whether to rescale the inputs. If set
to
True, inputs will be passed through aRescaling(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.