The OpenVINO™ samples are simple console applications that show how to utilize specific OpenVINO API capabilities within an application. They can assist you in executing specific tasks such as loading a model, running inference, querying specific device capabilities, etc.
The applications include:
All C++ samples support input paths containing only ASCII characters, except for the Hello Classification Sample, which supports Unicode.
Hello Classification Sample - Inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API. Input of any size and layout can be set to an infer request which will be pre-processed automatically during inference. The sample supports only images as input and supports input paths containing only Unicode characters.
Hello NV12 Input Classification Sample - Input of any size and layout can be provided to an infer request. The sample transforms the input to the NV12 color format and pre-process it automatically during inference. The sample supports only images as input.
Hello Query Device Sample - Query of available OpenVINO devices and their metrics, configuration values.
Hello Reshape SSD Sample - Inference of SSD networks resized by ShapeInfer API according to an input size.
Image Classification Async Sample - Inference of image classification networks like AlexNet and GoogLeNet using Asynchronous Inference Request API. The sample supports only images as inputs.
OpenVINO Model Creation Sample - Construction of the LeNet model using the OpenVINO model creation sample.
Benchmark Samples - Simple estimation of a model inference performance
Benchmark Application - Estimates deep learning inference performance on supported devices for synchronous and asynchronous modes.
Python version of the benchmark tool is a core component of the OpenVINO installation package and may be executed with the following command:
benchmark_app -m <model> -i <input> -d <device>
[DEPRECATED]Automatic Speech Recognition Sample - Acoustic model inference based on Kaldi neural networks and speech feature vectors.