# Hello Classification C++ Sample¶

This sample demonstrates how to execute an inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API, input auto-resize feature and support of UNICODE paths.

Hello Classification C++ sample application demonstrates how to use the following Inference Engine C++ API in applications:

Feature

API

Description

Basic Infer Flow

InferenceEngine::Core::ReadNetwork , InferenceEngine::Core::LoadNetwork , InferenceEngine::ExecutableNetwork::CreateInferRequest , InferenceEngine::InferRequest::SetBlob , InferenceEngine::InferRequest::GetBlob

Common API to do inference: configure input and output blobs, loading model, create infer request

Synchronous Infer

InferenceEngine::InferRequest::Infer

Do synchronous inference

Network Operations

ICNNNetwork::getInputsInfo , InferenceEngine::CNNNetwork::getOutputsInfo , InferenceEngine::InputInfo::setPrecision

Managing of network

Blob Operations

InferenceEngine::Blob::getTensorDesc , InferenceEngine::TensorDesc::getDims , , InferenceEngine::TensorDesc::getPrecision , InferenceEngine::as , InferenceEngine::MemoryBlob::wmap , InferenceEngine::MemoryBlob::rmap , InferenceEngine::Blob::size

Work with memory container for storing inputs, outputs of the network, weights and biases of the layers

Input auto-resize

InferenceEngine::PreProcessInfo::setResizeAlgorithm , InferenceEngine::InputInfo::setLayout

Set image of the original size as input for a network with other input size. Resize and layout conversions will be performed automatically by the corresponding plugin just before inference

Options

Values

Validated Models

Model Format

Inference Engine Intermediate Representation (.xml + *.bin), ONNX (.onnx)

Validated images

The sample uses OpenCV* to read input image (*.bmp, *.png)

Supported devices

All

Other language realization

## How It Works¶

Upon the start-up, the sample application reads command line parameters, loads specified network and an image to the Inference Engine plugin. Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream.

You can see the explicit description of each sample step at Integration Steps section of “Integrate the Inference Engine with Your Application” guide.

## Building¶

To build the sample, please use instructions available at Build the Sample Applications section in Inference Engine Samples guide.

## Running¶

To run the sample, you need specify a model and image:

NOTES :

• By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of ../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md “Converting a Model Using General Conversion Parameters”.

• Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

• The sample accepts models in ONNX format (.onnx) that do not require preprocessing.

### Example¶

python <path_to_omz_tools>/downloader.py --name alexnet
2. If a model is not in the Inference Engine IR or ONNX format, it must be converted. You can do this using the model converter script:

python <path_to_omz_tools>/converter.py --name alexnet
1. Perform inference of car.bmp using alexnet model on a GPU, for example:

<path_to_sample>/hello_classification <path_to_model>/alexnet.xml <path_to_image>/car.bmp GPU

## Sample Output¶

The application outputs top-10 inference results.

Top 10 results:

Image C:\images\car.bmp

classid probability
------- -----------
656     0.6664789
654     0.1129405
581     0.0684867
874     0.0333845
436     0.0261321
817     0.0167310
675     0.0109796
511     0.0105919
569     0.0081782
717     0.0063356

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool