openvino/samples/cpp/hello_classification

Hello Classification C++ Sample

This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API.
Models with only 1 input and output are supported.

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::read_model, ov::Core::compile_model, ov::CompiledModel::create_infer_request, ov::InferRequest::set_input_tensor, ov::InferRequest::get_output_tensor	Common API to do inference: read and compile a model, create an infer request, configure input and output tensors
Synchronous Infer	ov::InferRequest::infer	Do synchronous inference
Model Operations	ov::Model::inputs, ov::Model::outputs	Get inputs and outputs of a model
Tensor Operations	ov::Tensor::get_shape	Get a tensor shape
Preprocessing	ov::preprocess::InputTensorInfo::set_element_type, ov::preprocess::InputTensorInfo::set_layout, ov::preprocess::InputTensorInfo::set_spatial_static_shape, ov::preprocess::PreProcessSteps::resize, ov::preprocess::InputModelInfo::set_layout, ov::preprocess::OutputTensorInfo::set_element_type, ov::preprocess::PrePostProcessor::build	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.

Options	Values
Validated Models	[alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1)
Model Format	OpenVINO Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)
Supported devices	All
Other language realization	C, Python

How It Works

At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image to the OpenVINO Runtime plugin and performs synchronous inference. Then processes output data and writes it to a standard output stream.

For more information, refer to the explicit description of each sample Integration Steps in the Integrate OpenVINO Runtime with Your Application.

Building

To build the sample, use the instructions available at Build the Sample Applications section in OpenVINO™ Toolkit Samples.

Running

Before running the sample, specify a model and an image:

• you may use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
• you may use images from the media files collection, available online in the test data storage.

To run the sample, use the following script:

hello_classification <path_to_model> <path_to_image> <device_name>

NOTES:

• By default, samples and demos in OpenVINO Toolkit expect input with BGR order of channels. If you trained your model to work with RGB order, you need to manually rearrange the default order of channels in the sample or demo application, or reconvert your model, using Model Optimizer with --reverse_input_channels argument specified. For more information about the argument, refer to the When to Reverse Input Channels section of Embedding Preprocessing Computation.

• Before running the sample with a trained model, make sure that the model is converted to the OpenVINO Intermediate Representation (OpenVINO IR) format (*.xml + *.bin) using Model Optimizer.

• The sample accepts models in the ONNX format (.onnx) that do not require preprocessing.

Example

1. Install the openvino-dev Python package to use Open Model Zoo Tools:

   python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]
   

2. Download a pre-trained model, using:

   omz_downloader --name googlenet-v1
   

3. If a model is not in the OpenVINO IR or ONNX format, it must be converted. You can do this using the model converter:

   omz_converter --name googlenet-v1
   

4. Perform inference of the car.bmp image, using the googlenet-v1 model on a GPU, for example:

   hello_classification googlenet-v1.xml car.bmp GPU
   

Sample Output

The application outputs top-10 inference results.

[ INFO ] OpenVINO Runtime version ......... <version>
[ INFO ] Build ........... <build>
[ INFO ]
[ INFO ] Loading model files: /models/googlenet-v1.xml
[ INFO ] model name: GoogleNet
[ INFO ]     inputs
[ INFO ]         input name: data
[ INFO ]         input type: f32
[ INFO ]         input shape: {1, 3, 224, 224}
[ INFO ]     outputs
[ INFO ]         output name: prob
[ INFO ]         output type: f32
[ INFO ]         output shape: {1, 1000}

Top 10 results:

Image /images/car.bmp

classid probability
------- -----------
656     0.8139648
654     0.0550537
468     0.0178375
436     0.0165405
705     0.0111694
817     0.0105820
581     0.0086823
575     0.0077515
734     0.0064468
785     0.0043983

Additional Resources

• Integrate the OpenVINO Runtime with Your Application
• Using OpenVINO Toolkit Samples
• [Model Downloader](@ref omz_tools_downloader)
• Model Optimizer
• OpenVINO Toolkit Test Data Storage