application_efficientnet_v2b1 | R Documentation |
Instantiates the EfficientNetV2B1 architecture.
application_efficientnet_v2b1(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
include_preprocessing = TRUE
)
include_top |
Boolean, whether to include the fully-connected
layer at the top of the network. Defaults to |
weights |
One of |
input_tensor |
Optional Keras tensor
(i.e. output of |
input_shape |
Optional shape tuple, only to be specified
if |
pooling |
Optional pooling mode for feature extraction
when
|
classes |
Optional number of classes to classify images
into, only to be specified if |
classifier_activation |
A string or callable. The activation function to use
on the "top" layer. Ignored unless |
include_preprocessing |
Boolean, whether to include the preprocessing layer at the bottom of the network. |
A model instance.
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.
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
application_preprocess_inputs()
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.
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