Load ONNX framework models  {#tutorial_dnn_googlenet}
===========================

@tableofcontents

@next_tutorial{tutorial_dnn_openvino}

|    |    |
| -: | :- |
| Original author | Vitaliy Lyudvichenko |
| Compatibility | OpenCV >= 4.5.4 |

Introduction
------------

In this tutorial you will learn how to use opencv_dnn module for image classification by using
GoogLeNet trained network from [ONNX model zoo](https://github.com/onnx/models/).

We will demonstrate results of this example on the following picture.
![Buran space shuttle](dnn/images/space_shuttle.jpg)

Source Code
-----------

We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/5.x/samples/dnn/classification.cpp).

@include dnn/classification.cpp

Explanation
-----------

-# Firstly, download GoogLeNet model files:
   @code
   python download_models.py googlenet
   @endcode

   Also you need file with names of [ILSVRC2012](http://image-net.org/challenges/LSVRC/2012/browse-synsets) classes:
   [classification_classes_ILSVRC2012.txt](https://github.com/opencv/opencv/blob/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt).

   Put these files into working dir of this program example.

-# Read and initialize network using path to .onnx file
   @snippet dnn/classification.cpp Read and initialize network

-# Read input image and convert to the blob, acceptable by GoogleNet
   @snippet dnn/classification.cpp Open a video file or an image file or a camera stream

   cv::VideoCapture can load both images and videos.

   @snippet dnn/classification.cpp Create a 4D blob from a frame
   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
   for each blue, green and red channels correspondingly using cv::dnn::blobFromImage function.

-# Pass the blob to the network
   @snippet dnn/classification.cpp Set input blob

-# Make forward pass
   @snippet dnn/classification.cpp Make forward pass
   During the forward pass output of each network layer is computed, but in this example we need output from the last layer only.

-# Determine the best class
   @snippet dnn/classification.cpp Get a class with a highest score
   We put the output of network, 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 corresponds to the class of the image.

-# Run an example from command line
   @code
   ./example_dnn_classification googlenet
   @endcode
   For our image we get prediction of class `space shuttle` with more than 99% sureness.