Convert ONNX* Mask R-CNN Model to the Intermediate Representation¶
Step 1. Download the pre-trained model file.
Step 2. To generate the Intermediate Representation (IR) of the model, change your current working directory to the Model Optimizer installation directory and run the Model Optimizer with the following parameters:
python3 ./mo_onnx.py --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 ./extensions/front/onnx/mask_rcnn.json
Note that the height and width specified with the
input_shape command line parameter could be different. Refer to the documentation for more information about supported input image dimensions and required pre- and post-processing steps.
Step 3. Interpret the outputs. The generated IR file has several outputs: masks, class indices, probabilities and box coordinates. The first one is a layer with the name “6849/sink_port_0”. The rest three are outputs from the “DetectionOutput” layer.