In this blog will talk in detail about how to create a celebrity-look-alike demo, and how to prepare it for the use on an embedded NPU. If you are only interested how to make a model run on the i.mx8M Plus NPU, please visit this article to save yourselves some time and get more details on post training quantization with different TensorFlow versions.
To watch the associated video, click this youtube link or watch below
When you want to create a data set to compare your face to the face of celebrities and run it for example on a phyBoard Pollux neural processing unit, like we did here, or any other aim where you would use images of e.g., celebrities, the good images are mostly not under a creative common license. We used a Bing image crawler to look for celebrity faces and had troubles when using the filter set to: commercial and reuse. …
In this article, we will explain in this article which steps you have to take to transform and quantize your model with different TensorFlow versions. We are only looking into post training quantization.
This tutorial explains every step in detail. I know the struggle of pre-assumed knowledge to well.
For the more knowledgeable ones, here is the fast version:
sudo apt-get install blobfuse
sudo blobfuse ~/mycontainer --tmp-path=/mnt/resource/blobfusetmp --config-file=./fuse_connection.cfg -o attr_timeout=240 -o entry_timeout=240 -o negative_timeout=120 -o allow_other
flow_from_directory()with the path pointing to the mounted blob
In this tutorial I want to show how to connect your Azure Blob storage to your Azure DSVM to use it with your Jupyter notebook and e.g., Keras2.
I struggled to set up this connection to be able to use my uploaded images with the Keras ImageDataGenerator() function. Therefore, I want to show step by step how I achieved this. There might be better and more elegant ways. If you have one, please let me know. …