EfficientNetV2B0
function
keras.applications.EfficientNetV2B0(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2B0 architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A model instance.
EfficientNetV2B1
function
keras.applications.EfficientNetV2B1(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2B1 architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A model instance.
EfficientNetV2B2
function
keras.applications.EfficientNetV2B2(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2B2 architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A model instance.
EfficientNetV2B3
function
keras.applications.EfficientNetV2B3(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2B3 architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A model instance.
EfficientNetV2S
function
keras.applications.EfficientNetV2S(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2S architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A model instance.
EfficientNetV2M
function
keras.applications.EfficientNetV2M(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2M architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A model instance.
EfficientNetV2L
function
keras.applications.EfficientNetV2L(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
)
Instantiates the EfficientNetV2L architecture.
Reference
- EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling
layer), and thus keras.applications.efficientnet_v2.preprocess_input
is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255]
range. At the same time, preprocessing as a part of the model (i.e. Rescaling
layer) can be disabled by setting include_preprocessing
argument to False
. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [-1, 1]
range.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to
True
. - weights: One of
None
(random initialization),"imagenet"
(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to"imagenet"
. - input_tensor: Optional Keras tensor (i.e. output of
layers.Input()
) to use as image input for the model. - input_shape: Optional shape tuple, only to be specified if
include_top
isFalse
. It should have exactly 3 inputs channels. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
. Defaults to None.None
means that the output of the model will be the 4D tensor output of the last convolutional layer."avg"
means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor."max"
means that global max pooling will be applied.
- classes: Optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000 (number of ImageNet classes). - classifier_activation: A string or callable. The activation function to use on the “top” layer. Ignored unless
include_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. Defaults to"softmax"
. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
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