Instantiates the MobileNetV3Large architecture
application_mobilenet_v3_large(
input_shape = NULL,
alpha = 1,
minimalistic = FALSE,
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
classes = 1000L,
pooling = NULL,
dropout_rate = 0.2,
classifier_activation = "softmax",
include_preprocessing = TRUE
)application_mobilenet_v3_small(
input_shape = NULL,
alpha = 1,
minimalistic = FALSE,
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
classes = 1000L,
pooling = NULL,
dropout_rate = 0.2,
classifier_activation = "softmax",
include_preprocessing = TRUE
)
Optional shape vector, to be specified if you would
like to use a model with an input image resolution that is not
c(224, 224, 3).
It should have exactly 3 inputs channels c(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. c(160, 160, 3) would be one valid value.
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.
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 TRUE.
String, one of NULL (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
Optional Keras tensor (i.e. output of
layer_input())
to use as image input for the model.
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.
String, optional pooling mode for feature extraction
when include_top is FALSE.
NULL 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.
fraction of the input units to drop on the last layer.
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.
When loading pretrained weights, classifier_activation can only
be NULL or "softmax".
Boolean, whether to include the preprocessing
layer (Rescaling) at the bottom of the network. Defaults to TRUE.
A keras Model instance
Reference:
Searching for MobileNetV3 (ICCV 2019)
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 |
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.