Instantiates the EfficientNetB0 architecture
application_efficientnet_b0(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)application_efficientnet_b1(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
application_efficientnet_b2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
application_efficientnet_b3(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
application_efficientnet_b4(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
application_efficientnet_b5(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
application_efficientnet_b6(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
application_efficientnet_b7(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
Whether to include the fully-connected
layer at the top of the network. Defaults to TRUE
.
One of NULL
(random initialization),
'imagenet'
(pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to 'imagenet'
.
Optional Keras tensor
(i.e. output of layer_input()
)
to use as image input for the model.
Optional shape list, only to be specified
if include_top
is FALSE.
It should have exactly 3 inputs channels.
Optional pooling mode for feature extraction
when include_top
is FALSE
. Defaults to NULL
.
NULL
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.
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).
A string or callable. The activation function to
use on the "top" layer. Ignored unless include_top = TRUE
. Set
classifier_activation = NULL
to return the logits of the "top" layer.
Defaults to 'softmax'
. When loading pretrained weights,
classifier_activation
can only be NULL
or "softmax"
.
For backwards and forwards compatibility
Reference:
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.
EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255]
range.