ANSLibs/OpenVINO/samples/cpp/hello_classification

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.

For more detailed information on how this sample works, check the dedicated article

Requirements

Options	Values
Model Format	OpenVINO™ toolkit Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)
Supported devices	All
Other language realization	Python, C,

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

Feature	API	Description
OpenVINO Runtime Version	ov::get_openvino_version	Get Openvino API version
Basic Infer Flow	ov::Core::read_model,	Common API to do inference: read and compile a model, create an infer request,
	ov::Core::compile_model,	configure input and output tensors
	ov::CompiledModel::create_infer_request,
	ov::InferRequest::set_input_tensor,
	ov::InferRequest::get_output_tensor
Synchronous Infer	ov::InferRequest::infer	Do synchronous inference
Model Operations	ov::Model::inputs,	Get inputs and outputs of a model
	ov::Model::outputs
Tensor Operations	ov::Tensor::get_shape	Get a tensor shape
Preprocessing	ov::preprocess::InputTensorInfo::set_element_type,	Set image of the original size as input for a model with other input size. Resize
	ov::preprocess::InputTensorInfo::set_layout,	and layout conversions are performed automatically by the corresponding plugin
	ov::preprocess::InputTensorInfo::set_spatial_static_shape,	just before inference.
	ov::preprocess::PreProcessSteps::resize,
	ov::preprocess::InputModelInfo::set_layout,
	ov::preprocess::OutputTensorInfo::set_element_type,
	ov::preprocess::PrePostProcessor::build