NOTE: This topic describes usage of C++ implementation of the Object Detection Sample SSD. For the Python* implementation, refer to Object Detection Python* Sample SSD.
Upon the start-up the sample application reads command line parameters and loads a network and an image to the Inference Engine device. When inference is done, the application creates an output image and outputs data to the standard output stream.
NOTE: By default, Inference Engine 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 the Model Optimizer tool with
--reverse_input_channelsargument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.
Running the application with the
-h option yields the following usage message:
Running the application with the empty list of options yields the usage message given above and an error message.
NOTE: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.
The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
For example, to do inference on a CPU with the OpenVINO™ toolkit person detection SSD models, run one of the following commands:
The application outputs an image (
out_0.bmp) with detected objects enclosed in rectangles. It outputs the list of classes of the detected objects along with the respective confidence values and the coordinates of the rectangles to the standard output stream.