» 케라스 API / 케라스 애플리케이션 / EfficientNet B0~B7

EfficientNet B0~B7

EfficientNetB0 function

tf.keras.applications.EfficientNetB0(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB0 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB1 function

tf.keras.applications.EfficientNetB1(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB1 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB2 function

tf.keras.applications.EfficientNetB2(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB2 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB3 function

tf.keras.applications.EfficientNetB3(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB3 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB4 function

tf.keras.applications.EfficientNetB4(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB4 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB5 function

tf.keras.applications.EfficientNetB5(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB5 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB6 function

tf.keras.applications.EfficientNetB6(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB6 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.


EfficientNetB7 function

tf.keras.applications.EfficientNetB7(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs
)

Instantiates the EfficientNetB7 architecture.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. If you have never configured it, it defaults to "channels_last".

Arguments

  • include_top: 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 str 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'.

Returns

A keras.Model instance.