opencv/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

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Load Caffe framework models {#tutorial_dnn_googlenet}
===========================
Introduction
------------
In this tutorial you will learn how to use opencv_dnn module for image classification by using
GoogLeNet trained network from [Caffe model zoo](http://caffe.berkeleyvision.org/model_zoo.html).
We will demonstrate results of this example on the following picture.
![Buran space shuttle](images/space_shuttle.jpg)
Source Code
-----------
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We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/caffe_googlenet.cpp).
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@include dnn/caffe_googlenet.cpp
Explanation
-----------
-# Firstly, download GoogLeNet model files:
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[bvlc_googlenet.prototxt ](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/bvlc_googlenet.prototxt) and
[bvlc_googlenet.caffemodel](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel)
Also you need file with names of [ILSVRC2012](http://image-net.org/challenges/LSVRC/2012/browse-synsets) classes:
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[synset_words.txt](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/synset_words.txt).
Put these files into working dir of this program example.
-# Read and initialize network using path to .prototxt and .caffemodel files
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@snippet dnn/caffe_googlenet.cpp Read and initialize network
-# Check that network was read successfully
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@snippet dnn/caffe_googlenet.cpp Check that network was read successfully
-# Read input image and convert to the blob, acceptable by GoogleNet
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@snippet dnn/caffe_googlenet.cpp Prepare blob
We convert the image to a 4-dimensional blob (so-called batch) with 1x3x224x224 shape after applying necessary pre-processing like resizing and mean subtraction using cv::dnn::blobFromImage constructor.
-# Pass the blob to the network
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@snippet dnn/caffe_googlenet.cpp Set input blob
In bvlc_googlenet.prototxt the network input blob named as "data", therefore this blob labeled as ".data" in opencv_dnn API.
Other blobs labeled as "name_of_layer.name_of_layer_output".
-# Make forward pass
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@snippet dnn/caffe_googlenet.cpp Make forward pass
During the forward pass output of each network layer is computed, but in this example we need output from "prob" layer only.
-# Determine the best class
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@snippet dnn/caffe_googlenet.cpp Gather output
We put the output of "prob" layer, which contain probabilities for each of 1000 ILSVRC2012 image classes, to the `prob` blob.
And find the index of element with maximal value in this one. This index correspond to the class of the image.
-# Print results
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@snippet dnn/caffe_googlenet.cpp Print results
For our image we get:
> Best class: #812 'space shuttle'
>
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> Probability: 99.6378%