Converting an ONNX Faster R-CNN Model#

Danger

The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Python tutorials.

The instructions below are applicable only to the Faster R-CNN model converted to the ONNX file format from the maskrcnn-benchmark model:

Download the pretrained model file from onnx/models (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
Generate the Intermediate Representation of the model, by changing your current working directory to the model conversion API installation directory, and running model conversion with the following parameters:
```
mo \
--input_model FasterRCNN-10.onnx \
--input_shape [1,3,800,800] \
--input 0:2 \
--mean_values [102.9801,115.9465,122.7717] \
--transformations_config front/onnx/faster_rcnn.json
```
Be aware that the height and width specified with the input_shape command line parameter could be different. For more information about supported input image dimensions and required pre- and post-processing steps, refer to the Faster R-CNN article.
Interpret the outputs of the generated IR: class indices, probabilities and box coordinates. Below are the outputs from the DetectionOutput layer:
- class indices
- probabilities
- box coordinates