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Gouveia__César
New Contributor I
697 Views

Converting MXNet RetinaFace-MobileNet0.25 model to IR produces different output results on OpenVINO

Hi,

I wanted to convert MXNet RetinaFace-MobileNet0.25 model (https://github.com/deepinsight/insightface/tree/master/RetinaFace#third-party-models) to OpenVINO IR and I got that by using the following command:
python deployment_tools\model_optimizer\mo_mxnet.py --input_model mxnet_models\RetinaFace\mnet.25-0000.params --input_shape (1,3,640,640) --output_dir openvino_models
and it was successfully converted.

After that I tested inference on both mxnet model and openvino model to check if the outputs were the same. However the output sizes for MXNet are:
* 88 for width and height;
* 44 for width and height;
* 22 for width and height.
And the OpenVINO output sizes are:
* 80 for width and height;
* 40 for width and height;
* 20 for width and height.

Thanks,
César.

0 Kudos
4 Replies
samontab
Valued Contributor II
691 Views

The first thing I would check is to make sure all the layers of the model are supported by OpenVINO.

Here you can see the details about the MXNet layers

Then it would be a good idea to check layer by layer if the input/outputs are correct or at which point there's something wrong.

Good luck!

Gouveia__César
New Contributor I
677 Views

Thanks for your response @samontab .

All the layers are apparently supported otherwise the conversion of the model would not have been possible. I can provide both the json and params from mxnet and bin and xml from openvino if needed.

Thanks,
César.

IntelSupport
Community Manager
657 Views

Hi Cesar,


MobileNet0.25 topology is not officially supported by OpenVINO and RetinaFace-MobileNet0.25 model has not been validated for inferencing performance as well.


More information is available at the following page:

https://docs.openvinotoolkit.org/2020.4/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Mode...


Also, I would encourage you to try out Intel® Distribution of OpenVINO™ Toolkit version 2020.4, which is a vastly improved version with latest features and leading performance.


Regards,

Munesh


qiaoq
Beginner
376 Views

why the input_shape is (1,3,640,640) 

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