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.
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 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_shapecommand line parameter could be different. For more information about supported input image dimensions and required pre- and post-processing steps, refer to the documentation.
Interpret the outputs of the generated IR file: masks, class indices, probabilities and box coordinates:
The first one is a layer with the name
6849/sink_port_0, and rest are outputs from the