Hello Classification Sample — OpenVINO™ documentation (original) (raw)

This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API. Before using the sample, refer to the following requirements:

Models with only one input and output are supported.
The sample accepts any file format supported by core.read_model.
To build the sample, use instructions available at Build the Sample Applicationssection in “Get Started with Samples” guide.

How It Works#

At startup, the sample application sets log message capturing callback and reads command-line parameters. Then it prepares input data, loads a specified model and image to the OpenVINO™ Runtime plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.

You can see the explicit description of each sample step atIntegration Stepssection of “Integrate OpenVINO™ Runtime with Your Application” guide.

Running#

Python

python hello_classification.py

C++

hello_classification

hello_classification_c

To run the sample, you need to specify a model and an image:

You can get a model specific for your inference task from one of model repositories, such as TensorFlow Zoo, HuggingFace, or TensorFlow Hub.
You can use images from the media files collection available atthe storage.

Note

By default, OpenVINO™ Toolkit Samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using model conversion API withreverse_input_channels argument specified. For more information about the argument, refer to the Color Conversion section ofPreprocessing API.
Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using the model conversion API.
The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
The sample supports NCHW model layout only.

Example#

Download a pre-trained model.
You can convert it by using:
Python
import openvino as ov
ov_model = ov.convert_model('./models/alexnet')

or, when model is a Python model object

ov_model = ov.convert_model(alexnet)
CLI 3. Perform inference of an image, using a model on a GPU, for example:
Python
python hello_classification.py ./models/alexnet/alexnet.xml ./images/banana.jpg GPU
C++
hello_classification ./models/googlenet-v1.xml ./images/car.bmp GPU
C
hello_classification_c alexnet.xml ./opt/intel/openvino/samples/scripts/car.png GPU

Sample Output#

Python

The sample application logs each step in a standard output stream and outputs top-10 inference results.

[ INFO ] Creating OpenVINO Runtime Core [ INFO ] Reading the model: /models/alexnet/alexnet.xml [ INFO ] Loading the model to the plugin [ INFO ] Starting inference in synchronous mode [ INFO ] Image path: /images/banana.jpg [ INFO ] Top 10 results: [ INFO ] class_id probability [ INFO ] -------------------- [ INFO ] 954 0.9703885 [ INFO ] 666 0.0219518 [ INFO ] 659 0.0033120 [ INFO ] 435 0.0008246 [ INFO ] 809 0.0004433 [ INFO ] 502 0.0003852 [ INFO ] 618 0.0002906 [ INFO ] 910 0.0002848 [ INFO ] 951 0.0002427 [ INFO ] 961 0.0002213 [ INFO ] [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

C++

The application outputs top-10 inference results.

[ INFO ] OpenVINO Runtime version ......... [ INFO ] 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

The application outputs top-10 inference results.

Top 10 results:

Image /opt/intel/openvino/samples/scripts/car.png