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Co-authored-by: Maciej Smyk <maciejx.smyk@intel.com>
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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