I am currently trying to perform cloud segmentation on 28x28 patches, by using "segmentation_demo.py" (open_model_zoo 2022.1.0).
Right before, I converted my ONNX model using "mo.py" (openvino 2022.1.0). I tested many paramaters at the conversion and inference level, but I can not get correct prediction masks as results (Outputs.zip attached). Any idea ? I get similar results whatever the device (CPU, Myriad). I also attached:
- the ONNX model (model.LeNet_FCN.0.202205100731.28x28 ONNX.zip)
- the H5 model (model.LeNet_FCN.0.202205100731.28x28 H5.zip) converted to ONNX via "tf2onnx.convert"
- some input images (Inputs.zip) ...
- ... with ground truth masks (GroundTruth.zip).
Thanks in Advance++
For your information, LeNet_FCN.0.202205100731.28x28 is not validated on Image Segmentation Python Demo.
The supported models for Image Segmentation Python Demo are as follows:
1. architecture_type = segmentation
2. architecture_type = salient_object_detection
Thanks for your question.
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