Module google/‌imagenet/‌mobilenet_v2_100_128/‌feature_vector/2

Feature vectors of images with MobileNet V2 (depth multiplier 1.00) trained on ImageNet (ILSVRC-2012-CLS).

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MobileNet V2 is a family of neural network architectures for efficient on-device image classification and related tasks, originally published by

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 mobilenet_v2 with a depth multiplier of 1.0 and an input size of 128x128 pixels.

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 google/imagenet/mobilenet_v2_100_128/classification/2 instead.


The checkpoint exported into this module was mobilenet_v2_1.0_128/mobilenet_v2_1.0_128.ckpt downloaded from MobileNet V2 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("")
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 = 1280.

For this module, the size of the input image is fixed to height x width = 128 x 128 pixels. The input images are expected to have color values in the range [0,1], following the common image input conventions.


Consumers of this module can fine-tune it. This requires importing the graph version with tag set {"train"} in order to operate batch normalization in training mode.


Version 1

  • Initial release.

Version 2