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README.md

Sync Benchmark C++ Sample

This sample demonstrates how to estimate performance of a model using Synchronous Inference Request API. It makes sense to use synchronous inference only in latency oriented scenarios. Models with static input shapes are supported. Unlike demos this sample doesn't have other configurable command line arguments. Feel free to modify sample's source code to try out different options.

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

Requirements

Options	Values
Validated Models	alexnet,
	googlenet-v1,
	yolo-v3-tf,
	face-detection-0200
Model Format	OpenVINO™ toolkit Intermediate Representation
	(.xml + .bin), ONNX (*.onnx)
Supported devices	All
Other language realization	Python

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`, `ov::Core::compile_model`,	Common API to do inference: compile a model,
	`ov::CompiledModel::create_infer_request`,	create an infer request,
	`ov::InferRequest::get_tensor`	configure input tensors.
Synchronous Infer	`ov::InferRequest::infer`,	Do synchronous inference.
Model Operations	`ov::CompiledModel::inputs`	Get inputs of a model.
Tensor Operations	`ov::Tensor::get_shape`,	Get a tensor shape and its data.
	`ov::Tensor::data`