tf.keras.applications.MobileNetV3Large

Instantiates the MobileNetV3Large architecture.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.applications.MobileNetV3Large

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'
)

Reference:

• Searching for MobileNetV3 (ICCV 2019)

The following table describes the performance of MobileNets:

MACs stands for Multiply Adds

Classification Checkpoint	MACs(M)	Parameters(M)	Top1 Accuracy	Pixel1 CPU(ms)
mobilenet_v3_large_1.0_224	217	5.4	75.6	51.2
mobilenet_v3_large_0.75_224	155	4.0	73.3	39.8
mobilenet_v3_large_minimalistic_1.0_224	209	3.9	72.3	44.1
mobilenet_v3_small_1.0_224	66	2.9	68.1	15.8
mobilenet_v3_small_0.75_224	44	2.4	65.4	12.8
mobilenet_v3_small_minimalistic_1.0_224	65	2.0	61.9	12.2

The weights for all 6 models are obtained and translated from the Tensorflow checkpoints from TensorFlow checkpoints found here.

Optionally loads weights pre-trained on ImageNet.

Note: each Keras Application expects a specific kind of input preprocessing. For MobileNetV3, call tf.keras.applications.mobilenet_v3.preprocess_input on your inputs before passing them to the model.

Arguments
input_shape	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. E.g. (160, 160, 3) would be one valid value.
alpha	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. If alpha < 1.0, proportionally decreases the number of filters in each layer. If alpha > 1.0, proportionally increases the number of filters in each layer. If alpha = 1, default number of filters from the paper are used at each layer.
minimalistic	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.
include_top	Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
weights	String, one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
input_tensor	Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
pooling	String, optional pooling mode for feature extraction when include_top is False. None means that the output of the model will be the 4D tensor output of the last convolutional block. avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. max means that global max pooling will be applied.
classes	Integer, optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.
dropout_rate	fraction of the input units to drop on the last layer.
classifier_activation	A str 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.

Returns
A keras.Model instance.

Raises
ValueError	in case of invalid argument for weights, or invalid input shape or invalid alpha, rows when weights='imagenet'
ValueError	if classifier_activation is not softmax or None when using a pretrained top layer.