Inception V3 is a neural network architecture for image classification, originally published by
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna: "Rethinking the Inception Architecture for Computer Vision", 2015.
This TF-Hub module uses the TF-Slim implementation 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
TF-Slim's 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/inception_v3/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 = 2048.
For this module, the size of the input image is fixed to
width = 299 x 299 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.