I'm working on running blazeface and posenet models with inference engine. First, I used the following command to convert them:
- For Posenet:
python3 mo_tf.py --input_model posenet.pb --output_dir /users/thibault/Documents/posenet --data_type FP32 --disable_nhwc_to_nchw
- For Blazeface :
python3 mo_tf.py --input_model blazeface.pb --output_dir /users/thibault/Documents/posenet --data_type FP32
After running this model I get a bad display of landmarks for both two models. The code is good but I guess I missed something during tensorflowLite model conversion.
If someone has an idea how to convert correctly this two models.
If possible could you provide the link to the resources of blazenet and posenet so I could try to convert them myself?
Fyi, this is the official documentation on how to convert Tensorflow model:
Thanks for your reply,
I found the frozen models from here with script download:
I just noticed that the posenet model has been converted for openvino (FP32 and FP16) from this link 4 days ago. I'm going to try with this one too:
You can actually get the readily converted model in IR form by running the download script inside openvino folder (in Blazenet/Posenet folder).
Since this came with the Blazenet and Posenet model's package and clearly stated this is for openvino, this should already been correctly converted.
for Blazenet you will get 2 folders (face detection back and front)
Same thing applies for Posenet
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