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	`ov::runtime::Core::read_model`, `ov::runtime::Core::compile_model`, `ov::runtime::CompiledModel::create_infer_request`, `ov::runtime::InferRequest::get_input_tensor`, `ov::runtime::InferRequest::set_input_tensor`, `ov::runtime::InferRequest::get_output_tensor`	Common API to do inference: configure input and output tensors, reading model, create infer request
Synchronous Infer	`ov::runtime::InferRequest::infer`	Do synchronous inference
Model Operations	`ov::Model::inputs`, `ov::Model::outputs`	Managing of model
Tensor Operations	`ov::runtime::Tensor::get_element_type`, `ov::runtime::Tensor::get_shape`, `ov::runtime::Tensor::data`	Work with storing inputs, outputs of the model, weights and biases of the layers
Input auto-resize	`ov::preprocess::PreProcessSteps::resize`, `ov::preprocess::InputInfo::model::set_layout`	Set image of the original size as input for a model with other input size. Resize and layout conversions will be performed automatically by the corresponding plugin just before inference

Options	Values
Validated Models	[alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1)
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	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 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:

you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.

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 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

Download a pre-trained model using [Model Downloader](@ref omz_tools_downloader):

python <path_to_omz_tools>/downloader.py --name alexnet

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

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

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

README.md