openvino/samples/c/hello_classification

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