Module google/‌imagenet/‌resnet_v1_101/‌feature_vector/1

Feature vectors of images with ResNet V1 101 trained on ImageNet (ILSVRC-2012-CLS).

Module URL:


ResNet (later renamed ResNet V1) is a family of network architectures for image classification with a variable number of layers, originally published by

This TF-Hub module uses the TF-Slim implementation of resnet_v1_101 with 101 layers. 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/resnet_v1_101/classification/1 instead.


The weights for this module were obtained by training on the ILSVRC-2012-CLS dataset for image classification ("Imagenet") with TF-Slim's "Inception-style" preprocessing.


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 = 2048.

For this module, the size of the input image is fixed to height x width = 224 x 224 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.