Converting an ONNX Mask R-CNN Model#


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 Mask R-CNN model converted to the ONNX file format from the maskrcnn-benchmark model.

  1. Download the pretrained model file from onnx/models (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).

  2. 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 mask_rcnn_R_50_FPN_1x.onnx \
    --input "0:2" \
    --input_shape [1,3,800,800] \
    --mean_values [102.9801,115.9465,122.7717] \
    --transformations_config front/onnx/mask_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 documentation.

  3. Interpret the outputs of the generated IR file: masks, class indices, probabilities and box coordinates:

    • masks

    • class indices

    • probabilities

    • box coordinates

The first one is a layer with the name 6849/sink_port_0, and rest are outputs from the DetectionOutput layer.