Feature vectors of images with MobileNet V1 (depth multiplier 0.75) trained on ImageNet (ILSVRC-2012-CLS).
MobileNet V1 is a family of neural network architectures for efficient on-device image classification, originally published by
- Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam: "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", 2017.
Mobilenets come in various sizes controlled by a multiplier for the
depth (number of features) in the convolutional layers. They can also be
trained for various sizes of input images to control inference speed.
This TF-Hub module uses the TF-Slim implementation of
with a depth multiplier of 0.75 and an input size of
The module contains a trained instance of the network, packaged to get
feature vectors from images.
If you want the full model including the classification it was originally
trained for, use module
The checkpoint exported into this module was
MobileNet pre-trained models.
Its weights were originally obtained by training on the ILSVRC-2012-CLS
dataset for image classification ("Imagenet").
This module implements the common signature for computing image feature vectors. It can be used like
module = hub.Module("https://tfhub.dev/google/imagenet/mobilenet_v1_075_192/feature_vector/1") height, width = hub.get_expected_image_size(module) images = ... # A batch of images with shape [batch_size, height, width, 3]. features = module(images) # Features with shape [batch_size, num_features].
...or using the signature name
image_feature_vector. The output for each image
in the batch is a feature vector of size
num_features = 768.
For this module, the size of the input image is fixed to
width = 192 x 192 pixels.
images are expected to have color values in the range [0,1],
common image input
Consumers of this module can fine-tune it.
This requires importing the graph version with tag set
in order to operate batch normalization in training mode.