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Instantiates the MobileNetV3Large architecture.
Compat aliases for migration
See Migration guide for more details.
tf.keras.applications.MobileNetV3Large( input_shape=None, alpha=1.0, minimalistic=False, include_top=True, weights='imagenet', input_tensor=None, classes=1000, pooling=None, dropout_rate=0.2, classifier_activation='softmax' )
- Searching for MobileNetV3 (ICCV 2019)
The following table describes the performance of MobileNets v3:
MACs stands for Multiply Adds
|Classification Checkpoint||MACs(M)||Parameters(M)||Top1 Accuracy||Pixel1 CPU(ms)|
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.
Optional shape tuple, to be specified if you would
like to use a model with an input image resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
to infer input_shape from an input_tensor.
If you choose to include both input_tensor and input_shape then
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
controls the width of the network. This is known as the
depth multiplier in the MobileNetV3 paper, but the name is kept for
consistency with MobileNetV1 in Keras.
||In addition to large and small models this module also contains so-called minimalistic models, these models have the same per-layer dimensions characteristic as MobilenetV3 however, they don't utilize any of the advanced blocks (squeeze-and-excite units, hard-swish, and 5x5 convolutions). While these models are less efficient on CPU, they are much more performant on GPU/DSP.|
Boolean, whether to include the fully-connected
layer at the top of the network. Defaults to
String, one of
Optional Keras tensor (i.e. output of
String, optional pooling mode for feature extraction