Module google/‌imagenet/‌mobilenet_v1_025_160/‌quantops/‌feature_vector/1

Feature vectors of images with MobileNet V1 (depth multiplier 0.25) trained on ImageNet (ILSVRC-2012-CLS).

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MobileNet V1 is a family of neural network architectures for efficient on-device image classification, 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_v1_v1_025, instrumented for quantization, with a depth multiplier of 0.25 and an input size of 160x160 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_v1_025_160/quantops/classification/1 instead.


This module is meant for use in models whose weights will be quantized to uint8 by TensorFlow Lite for deployment to mobile devices.

The trained weights of this module are shipped as float32 numbers, but its graph has been augmented by tf.contrib.quantize with extra ops that simulate the effect of quantization already during training, so that the model can adjust to it.


The checkpoint exported into this module was mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160_quant/mobilenet_v1_0.25_160_quant.ckpt downloaded from MobileNet pre-trained models. Its weights were originally obtained by training on the ILSVRC-2012-CLS dataset for image classification ("Imagenet"), with simulated quantization.


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 = 256.

For this module, the size of the input image is fixed to height x width = 160 x 160 pixels. The input images are expected to have color values in the range [0,1], following the common image input conventions.


The current version of this module only provides an inference graph and cannot be fine-tuned.