CNTK Examples: Image/Classification/GoogLeNet/BN-Inception

Overview

Data:	The ILSVRC2012 dataset (http://www.image-net.org/challenges/LSVRC/2012/) for image classification.
Purpose	This folder contains examples that demonstrate how to use CNTK to define BN-Inception (https://arxiv.org/abs/1502.03167) for image classification.
Network	Deep convolutional neural networks codenamed "Inception" (GoogLeNet) with Batch Normalization.
Training	Stochastic gradient descent with momentum.
Comments	See below.

Running the example

Getting the data

We use the ILSVRC2012 datasets to demonstrate how to train a BN-Inception network. BN-Inception was initially published by Researchers at Google Inc., and it is firstly described in the Batch Normalization paper (https://arxiv.org/abs/1502.03167) to demonstrate the power of Batch Normalization with minor changes on the original GoogLeNet. It has been proved that it could increase the training speed and achieve better accuracy, compared with the GoogLeNet v1 which have been well known for winning first place in the ILSVRC 2014 detection challenge.

ILSVRC2012 datasets are not included in the CNTK distribution. You may obtain it through http://image-net.org.

Details

We currently offer the BN-Inception model (https://arxiv.org/abs/1502.03167). Both Python and BrainScript examples are available.