application_efficientnet
Instantiates the EfficientNetB0 architecture
Description
Instantiates the EfficientNetB0 architecture
Usage
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",
...
) Arguments
| Arguments | Description |
|---|---|
| include_top | Whether to include the fully-connected layer at the top of the network. Defaults to TRUE. |
| weights | One of NULL (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 layer_input()) to use as image input for the model. |
| input_shape | Optional shape list, 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 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. |
| 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 = 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 |
Details
Reference:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)
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.
Note
Each Keras Application typically expects a specific kind of input preprocessing. For EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus a calling a preprocessing function is not necessary.