2aec8a610b
* Initialized hello_reshape_ssd Python sample * * removed multiple input images functionality * added couple of checks whether input topology is supported in sample * Added readme * Switched to single-quotes strings style * Switched to f-strings * Removed redundant code * Simplified image original resolution handling * Simplified some checks and assertions * Simplified reading inference results and drawing bounding boxes
Hello Reshape SSD C++ Sample
This topic demonstrates how to run the Hello Reshape SSD application, which does inference using object detection networks like SSD-VGG. The sample shows how to use Shape Inference feature.
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
To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to https://download.01.org/opencv/.
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.
You can use the following command to do inference on CPU of an image using a trained SSD network:
python3 ./hello_reshape_ssd.py -m <path_to_model>/ssd_300.xml -i <path_to_image>/500x500.bmp -d CPU
Sample Output
The application renders an image 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.
See Also
- Using Inference Engine Samples
- [Model Downloader](@ref omz_tools_downloader_README)
- Model Optimizer