This sample demonstrates how to execute an synchronous inference using model built on the fly which uses weights from LeNet classification model, which is known to work well on digit classification tasks.

You do not need an XML file to create a model. The API of ov::Model allows creating a model on the fly from the source code.

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

Requirements

Options	Values
Validated Models	LeNet
Model Format	model weights file (*.bin)
Validated images	single-channel `MNIST ubyte` images
Supported devices	All
Other language realization	Python

The following C++ API is used in the application:

Feature	API	Description
OpenVINO Runtime Info	`ov::Core::get_versions`	Get device plugins versions
Shape Operations	`ov::Output::get_shape`,	Operate with shape
	`ov::Shape::size`,
	`ov::shape_size`
Tensor Operations	`ov::Tensor::get_byte_size`,	Get tensor byte size and its data
	`ov::Tensor:data`
Model Operations	`ov::set_batch`	Operate with model batch size
Infer Request Operations	`ov::InferRequest::get_input_tensor`	Get a input tensor
Model creation objects	`ov::opset8::Parameter`,	Used to construct an OpenVINO model
	`ov::Node::output`,
	`ov::opset8::Constant`,
	`ov::opset8::Convolution`,
	`ov::opset8::Add`,
	`ov::opset1::MaxPool`,
	`ov::opset8::Reshape`,
	`ov::opset8::MatMul`,
	`ov::opset8::Relu`,
	`ov::opset8::Softmax`,
	`ov::descriptor::Tensor::set_names`,
	`ov::opset8::Result`,
	`ov::Model`,
	`ov::ParameterVector::vector`

Basic OpenVINO™ Runtime API is covered by Hello Classification C++ sample.