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 and input auto-resize feature.

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




Basic Infer Flow

ie_core_create , ie_core_read_network , ie_core_load_network , ie_exec_network_create_infer_request , ie_infer_request_set_blob , ie_infer_request_get_blob

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

Synchronous Infer


Do synchronous inference

Network Operations

ie_network_get_input_name , ie_network_get_inputs_number , ie_network_get_outputs_number , ie_network_set_input_precision , ie_network_get_output_name , ie_network_get_output_precision

Managing of network

Blob Operations

ie_blob_make_memory_from_preallocated , ie_blob_get_dims , ie_blob_get_cbuffer

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

Input auto-resize

ie_network_set_input_resize_algorithm , ie_network_set_input_layout

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



Validated Models

alexnet , googlenet-v1

Model Format

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

Validated images

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

Supported devices


Other language realization

C++ , Python

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 a synchronous inference request object. When inference is done, the application outputs data to the standard output stream.

For more information, refer to the explicit description of Integration Steps in the Integrate OpenVINO Runtime with Your Application guide.


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


Before running the sample, you need to specify a model and image:


  • By default, samples and demos in OpenVINO Toolkit expect input with BGR order of channels. If you trained your model to work with RGB order, you need to manually rearrange the default order of channels in the sample or demo application, or reconvert your model, using Model Optimizer with the --reverse_input_channels argument specified. For more information about the argument, refer to the When to Reverse Input Channels section of Embedding Preprocessing Computation.

  • Before running the sample with a trained model, make sure that the model is converted to the OpenVINO Intermediate Representation (OpenVINO IR) format (*.xml + *.bin) by using Model Optimizer.

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


  1. Download a pre-trained model, using Model Downloader :

    python <path_to_omz_tools>/downloader.py --name alexnet
  2. If a model is not in the OpenVINO IR or ONNX format. You can do this using the model converter script:

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

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

Sample Output

The application outputs top-10 inference results.

Top 10 results:

Image /opt/intel/openvino/samples/scripts/car.png

classid probability
------- -----------
656       0.666479
654       0.112940
581       0.068487
874       0.033385
436       0.026132
817       0.016731
675       0.010980
511       0.010592
569       0.008178
717       0.006336

This sample is an API example. Use the dedicated `benchmark_app` tool for any performance measurements.