EfficientNetV2 B0 to B3 and S, M, L

[source]

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,
    name="efficientnetv2-b0",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.

[source]

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,
    name="efficientnetv2-b1",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.

[source]

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,
    name="efficientnetv2-b2",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.

[source]

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,
    name="efficientnetv2-b3",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.

[source]

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,
    name="efficientnetv2-s",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.

[source]

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,
    name="efficientnetv2-m",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.

[source]

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,
    name="efficientnetv2-l",
)

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 is False. It should have exactly 3 inputs channels.
• pooling: Optional pooling mode for feature extraction when include_top is False. 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 is True, and if no weights 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. Set classifier_activation=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.