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Instantiates the EfficientNetV2M architecture.
tf.keras.applications.efficientnet_v2.EfficientNetV2M(
include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation='softmax',
include_preprocessing=True
)
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.
Args | |
---|---|
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.
|
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" .
|
Returns | |
---|---|
A keras.Model instance.
|