Imagenet (ILSVRC-2012-CLS) classification with Inception V2.
Inception V2 is a neural network architecture for image classification. It builds on the Inception architecture originally published by
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich: "Going deeper with convolutions", 2014.
Inception V2 uses are more powerful architecture made possible by the use of batch normalization. Both were introduced by
- Sergey Ioffe and Christian Szegedy: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015.
This TF-Hub module uses the TF-Slim implementation of
The module contains a trained instance of the network, packaged to do the
that the network was trained on. If you merely want to transform images into
feature vectors, use module
instead, and save the space occupied by the classification layer.
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 image classification. It can be used like
module = hub.Module("https://tfhub.dev/google/imagenet/inception_v2/classification/1") height, width = hub.get_expected_image_size(module) images = ... # A batch of images with shape [batch_size, height, width, 3]. logits = module(images) # Logits with shape [batch_size, num_classes].
...or using the signature name
image_classification. The indices into logits
num_classes = 1001 classes of the classification from
the original training (see above).
This module can also be used to compute image feature
using the signature name
For this module, the size of the input image is fixed to
width = 224 x 224 pixels.
images are expected to have color values in the range [0,1],
common image input
In principle, consumers of this module can fine-tune it. However, fine-tuning through a large classification might be prone to overfit.
Fine-tuning requires importing the graph version with tag set
in order to operate batch normalization in training mode.