Hello Classification C++ Sample

This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API.

Models with only one input and output are supported.



Validated Models

alexnet, googlenet-v1

Model Format

OpenVINO™ toolkit Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)

Supported devices


Other language realization

C, Python

The following C++ API is used in the application:




OpenVINO Runtime Version


Get Openvino API version

Basic Infer Flow

ov::Core::read_model, ov::Core::compile_model, ov::CompiledModel::create_infer_request, ov::InferRequest::set_input_tensor, ov::InferRequest::get_output_tensor

Common API to do inference: read and compile a model, create an infer request, configure input and output tensors

Synchronous Infer


Do synchronous inference

Model Operations

ov::Model::inputs, ov::Model::outputs

Get inputs and outputs of a model

Tensor Operations


Get a tensor shape


ov::preprocess::InputTensorInfo::set_element_type, ov::preprocess::InputTensorInfo::set_layout, ov::preprocess::InputTensorInfo::set_spatial_static_shape, ov::preprocess::PreProcessSteps::resize, ov::preprocess::InputModelInfo::set_layout, ov::preprocess::OutputTensorInfo::set_element_type, ov::preprocess::PrePostProcessor::build

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

// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0

#include <iterator>
#include <memory>
#include <sstream>
#include <string>
#include <vector>

// clang-format off
#include "openvino/openvino.hpp"

#include "samples/args_helper.hpp"
#include "samples/common.hpp"
#include "samples/classification_results.h"
#include "samples/slog.hpp"
#include "format_reader_ptr.h"
// clang-format on

 * @brief Main with support Unicode paths, wide strings
int tmain(int argc, tchar* argv[]) {
    try {
        // -------- Get OpenVINO runtime version --------
        slog::info << ov::get_openvino_version() << slog::endl;

        // -------- Parsing and validation of input arguments --------
        if (argc != 4) {
            slog::info << "Usage : " << argv[0] << " <path_to_model> <path_to_image> <device_name>" << slog::endl;
            return EXIT_FAILURE;

        const std::string args = TSTRING2STRING(argv[0]);
        const std::string model_path = TSTRING2STRING(argv[1]);
        const std::string image_path = TSTRING2STRING(argv[2]);
        const std::string device_name = TSTRING2STRING(argv[3]);

        // -------- Step 1. Initialize OpenVINO Runtime Core --------
        ov::Core core;

        // -------- Step 2. Read a model --------
        slog::info << "Loading model files: " << model_path << slog::endl;
        std::shared_ptr<ov::Model> model = core.read_model(model_path);

        OPENVINO_ASSERT(model->inputs().size() == 1, "Sample supports models with 1 input only");
        OPENVINO_ASSERT(model->outputs().size() == 1, "Sample supports models with 1 output only");

        // -------- Step 3. Set up input

        // Read input image to a tensor and set it to an infer request
        // without resize and layout conversions
        FormatReader::ReaderPtr reader(image_path.c_str());
        if (reader.get() == nullptr) {
            std::stringstream ss;
            ss << "Image " + image_path + " cannot be read!";
            throw std::logic_error(ss.str());

        ov::element::Type input_type = ov::element::u8;
        ov::Shape input_shape = {1, reader->height(), reader->width(), 3};
        std::shared_ptr<unsigned char> input_data = reader->getData();

        // just wrap image data by ov::Tensor without allocating of new memory
        ov::Tensor input_tensor = ov::Tensor(input_type, input_shape, input_data.get());

        const ov::Layout tensor_layout{"NHWC"};

        // -------- Step 4. Configure preprocessing --------

        ov::preprocess::PrePostProcessor ppp(model);

        // 1) Set input tensor information:
        // - input() provides information about a single model input
        // - reuse precision and shape from already available `input_tensor`
        // - layout of data is 'NHWC'
        // 2) Adding explicit preprocessing steps:
        // - convert layout to 'NCHW' (from 'NHWC' specified above at tensor layout)
        // - apply linear resize from tensor spatial dims to model spatial dims
        // 4) Here we suppose model has 'NCHW' layout for input
        // 5) Set output tensor information:
        // - precision of tensor is supposed to be 'f32'

        // 6) Apply preprocessing modifying the original 'model'
        model = ppp.build();

        // -------- Step 5. Loading a model to the device --------
        ov::CompiledModel compiled_model = core.compile_model(model, device_name);

        // -------- Step 6. Create an infer request --------
        ov::InferRequest infer_request = compiled_model.create_infer_request();
        // -----------------------------------------------------------------------------------------------------

        // -------- Step 7. Prepare input --------

        // -------- Step 8. Do inference synchronously --------

        // -------- Step 9. Process output
        const ov::Tensor& output_tensor = infer_request.get_output_tensor();

        // Print classification results
        ClassificationResult classification_result(output_tensor, {image_path});
        // -----------------------------------------------------------------------------------------------------
    } catch (const std::exception& ex) {
        std::cerr << ex.what() << std::endl;
        return EXIT_FAILURE;

    return EXIT_SUCCESS;

How It Works

At startup, the sample application reads command line parameters, prepares input data, loads a specified model and image to the OpenVINO™ Runtime plugin and performs synchronous inference. Then processes output data and write it to a standard output stream.

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


To build the sample, please use instructions available at Build the Sample Applications section in OpenVINO™ Toolkit Samples guide.


hello_classification <path_to_model> <path_to_image> <device_name>

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

  • You can use public or Intel’s pre-trained models from the Open Model Zoo. The models can be downloaded using the Model Downloader.

  • You can use images from the media files collection available at the storage.


  • By default, OpenVINO™ Toolkit 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 mo with reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Embedding Preprocessing Computation.

  • Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using the model conversion API.

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


  1. Install the openvino-dev Python package to use Open Model Zoo Tools:

    python -m pip install openvino-dev[caffe]
  2. Download a pre-trained model using:

    omz_downloader --name googlenet-v1
  3. If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:

    omz_converter --name googlenet-v1
  4. Perform inference of car.bmp using the googlenet-v1 model on a GPU, for example:

    hello_classification googlenet-v1.xml car.bmp GPU

Sample Output

The application outputs top-10 inference results.

[ INFO ] OpenVINO Runtime version ......... <version>
[ INFO ] Build ........... <build>
[ INFO ]
[ INFO ] Loading model files: /models/googlenet-v1.xml
[ INFO ] model name: GoogleNet
[ INFO ]     inputs
[ INFO ]         input name: data
[ INFO ]         input type: f32
[ INFO ]         input shape: {1, 3, 224, 224}
[ INFO ]     outputs
[ INFO ]         output name: prob
[ INFO ]         output type: f32
[ INFO ]         output shape: {1, 1000}

Top 10 results:

Image /images/car.bmp

classid probability
------- -----------
656     0.8139648
654     0.0550537
468     0.0178375
436     0.0165405
705     0.0111694
817     0.0105820
581     0.0086823
575     0.0077515
734     0.0064468
785     0.0043983