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.

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

Requirements

Options	Values
Validated Models	alexnet, 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

Hello Classification C sample application demonstrates how to use the C API from OpenVINO in applications.

Feature	API	Description
OpenVINO Runtime Version	`ov_get_openvino_version`	Get Openvino API version.
Basic Infer Flow	`ov_core_create`,	Common API to do inference: read and compile a model, create an infer request, configure input and output tensors
	`ov_core_read_model`,
	`ov_core_compile_model`,
	`ov_compiled_model_create_infer_request`,
	`ov_infer_request_set_input_tensor_by_index`,
	`ov_infer_request_get_output_tensor_by_index`
Synchronous Infer	`ov_infer_request_infer`	Do synchronous inference
Model Operations	`ov_model_const_input`,	Get inputs and outputs of a model
	`ov_model_const_output`
Tensor Operations	`ov_tensor_create_from_host_ptr`	Create a tensor shape
Preprocessing	`ov_preprocess_prepostprocessor_create`,	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.
	`ov_preprocess_prepostprocessor_get_input_info_by_index`,
	`ov_preprocess_input_info_get_tensor_info`,
	`ov_preprocess_input_tensor_info_set_from`,
	`ov_preprocess_input_tensor_info_set_layout`,
	`ov_preprocess_input_info_get_preprocess_steps`,
	`ov_preprocess_preprocess_steps_resize`,
	`ov_preprocess_input_model_info_set_layout`,
	`ov_preprocess_output_set_element_type`,
	`ov_preprocess_prepostprocessor_build`