@Daniel thanks for the github link.
are you leveraging transfer learning in these demos?
@Tara the demos we are showing are pi 4, nordic nrf52840, and samd 51
@Duncan our speech model that can detect "yes" or "no" is ~20kb, our vision model that can detect the presence or absence of a person is ~250kb. I'm not sure
@Tara we're actually working on a book that has a chapter about setting up TF Lite for new devices. TF Lite Micro should be a lot easier to port than regular TF Lite too - http://shop.oreilly.com/product/0636920254508.do
that looks amazing for when you're far out @Evan Juras thank you
@Andres Manjarres Unfortunately no. 1) we train regular tensor flow models (and factor in micro controller constraints if possible- small model size, low latency) 2) convert it to a tensorflow lite model 3) run it on a microcontroller that we support.
*I'm not sure of the exact latency, but it's pretty low!
@Duncan we'll show the yes no demo on video right now here too ---
Thanks @pt and @Pete Warden !! I'll definitely check it out.
@Tara drop an email to email@example.com and we might be able to send you a draft copy
Thanks @Matteo Borri :D
@Meghna Natraj Okey, thanks
@Dan Maloney yes! we would have to take ~100+ photos of me and pt and train a new model to detect us
Limor and PT any Cortex M7 Boards in the works?
Question: Do you plan to build a Deep Learning demo also?
I use google colab for deep learning/keras/tensorflow , mu for CP...what is/will be the ideal tool chain for this experience?
@Pete Warden I'd be interested in that for a high school class...
we haven't written a guide on that yet - hopefully soon, we're learning a lot every weekend!
@limor Cool, thanks. Thought it would be something like that.
@Daniel Situnayake 20kB is impressive! Nice work.
@jcradford please drop me an email too
Thanks @Daniel Situnayake
@limor do some models made for desktop/server run with tensorflow lite on the SAMD51? Like the more popular image recognition networks like mobilnet or tiny-yolo?
Very cool. Also, Matteo, you misremembered what we generated. It was not a learning capable program, it was a fixed program. We used genetic programming and simulation to develop task specific programs.
I was planning on scraping family photos from Google Photos to train a model for a security system I'm working on
ok! all the demos worked, yay
@Dick Brooks if you want to see the absolute basics of training a deep learning model and deploying it to device, check out this sample: http://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/hello_world
one tip for folks who want to play around, check out https://runwayml.com
@pt Is the demo code you are showing for the pi4, Nordic nrf52840, and samd 51 available?
Everyone, if you miss something, rest assured that there will be a full transcript posted after the chat, as well as links to the demos and livestream.
@Daniel - thank you for the github on learning tensorflow...besides Adafruit learning (most awesome) where else should we go to explore?
@Evan Juras yay i used your awsome...
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