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Not Hotdog App

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Re: Not Hotdog App

#121

Post by luv2lrn »

timanglade wrote: post #42
alexcnwy wrote: post #36

Earlier quoted context omitted.

AWESOME! Can you please elaborate a bit more on the model architecture and what you tried with respect to transfer learning? Did you use an imagenet architecture e.g. VGG and retrain from scratch or a custom architecture? Did you try chop off the last 1/2/3 layers of a prerrained mode and fine-tune? Bonus points: 1. How much better were your results trained from scratch vs fine-tuned? 2. How long did it take to train…

Hey so I actually tried Vgg, Inception and SqueezeNet, out of the box, chopped and trained from scratch (SqueezeNet only for the latter due to resource constraints). We ended up with a custom architecture trained from scratch due to runtime constraints more so than accuracy reasons (the inference runs on phones, so we have to be efficient with CPU + memory), but that model also ended up being the most accurate model…

I'm confused about the 80 hours to train. Wasn't this episode shown on HBO less than 48 hrs ago?

Edit: Just read your bio. Now it makes sense!

Re: Not Hotdog App

#123

Post by WorldMaker »

iNeal wrote: post #30
avaer wrote: post #25

Hm, is this an officially licensed HBO marketing stunt? Or does SeeFood Technologies Inc. actually exist as a corporation? If not, I'm curious what the legal ramifications are of doing business as a corporation that does not exist.

I would guess HBO registered SeeFood Technologies Inc. as it definitely looks like an official stunt otherwise it might have been taken down by now. It's not uncommon for them to do this - see http://www.piedpiper.com/ and http://raviga.com/

Also: http://hooli.xyz

Re: Not Hotdog App

#124

Post by peterjlee »

timanglade wrote: post #26
pkulak wrote: post #21

Earlier quoted context omitted.

It was written in Objective C?

It's actually written in React Native with a fair bit of C++ (TensorFlow), and some Objective-C++ to glue the two. One cool thing we added on top of React Native was a hack to let us inject new versions of our deep learning model on the fly without going through an App Store review. If you thought injecting JavaScript to change the behavior of your app was cool, you need to try injecting neural nets, it's quite a fee…

So what you're saying is you can update the neural net to do dick or not dick without going through the App Store review?

Re: Not Hotdog App

#125

Post by [deleted] »

ip_addr wrote: post #112
a_c wrote: post #108

Earlier quoted context omitted.

Had not heard MobileNets before. Mind sharing which paper are you referring to?

I believe they are referring to this paper titled "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" https://arxiv.org/pdf/1704.04861.pdf

[deleted]

Re: Not Hotdog App

#126

Post by justifier »

timanglade wrote: post #23
elmigranto wrote: post #5

US only, welcome to the "global system of interconnected computer networks". Hope Richard's P2P-internet will work better :)

Yup sorry about that, the app is available only in the US (& Canada) due to some legal restrictions we couldn't avoid. FWIW I also worked about on Richard's New Internet concept for this season so I definitely hear ya ;)

how does a decentralised mesh network cross oceans?

Re: Not Hotdog App

#127

Post by nsxwolf »

throwanem wrote: post #98
kmonsen wrote: post #97

Earlier quoted context omitted.

I'm pretty sure he means the app is only available in the US store and not commenting on the shape or size of american "hot-dogs".

Hey, the show did a whole scene about taking a dick joke too seriously, I figured a comment doing the same could work. I appear to have been wrong about that! Or, at the very least, I need to work on my delivery.

I don't believe you but you got my upvote anyway.

Re: Not Hotdog App

#128

Post by j_s »

tomduncalf wrote: post #46
timanglade wrote: post #26

Earlier quoted context omitted.

It's actually written in React Native with a fair bit of C++ (TensorFlow), and some Objective-C++ to glue the two. One cool thing we added on top of React Native was a hack to let us inject new versions of our deep learning model on the fly without going through an App Store review. If you thought injecting JavaScript to change the behavior of your app was cool, you need to try injecting neural nets, it's quite a fee…

This looks great, haha! Shame I can't access it from the UK. It would be really interesting to read more about your thoughts on working with RN and C++ and perhaps how you did some of it. I'm currently doing the same (but with a C++ audio engine rather than image processing stuff) and I think it's an incredibly powerful combination - but I do feel like I'm making up some interop patterns as I go and there might be be…

See this sub-discussion re:performance (+serialization/marshalling) in today's React Native discussion:

https://news.ycombinator.com/item?id=14349426

Re: Not Hotdog App

#129

Post by DonHopkins »

luv2lrn wrote: post #121
timanglade wrote: post #42

Earlier quoted context omitted.

Hey so I actually tried Vgg, Inception and SqueezeNet, out of the box, chopped and trained from scratch (SqueezeNet only for the latter due to resource constraints). We ended up with a custom architecture trained from scratch due to runtime constraints more so than accuracy reasons (the inference runs on phones, so we have to be efficient with CPU + memory), but that model also ended up being the most accurate model…

I'm confused about the 80 hours to train. Wasn't this episode shown on HBO less than 48 hrs ago? Edit: Just read your bio. Now it makes sense!

He's a guest lecturer at Stanford, and he had the students help him.

Re: Not Hotdog App

#130

Post by DonHopkins »

skraelingjar wrote: post #101
timanglade wrote: post #13

Ha didn't expect this to end up here. If anyone is interested, I'm working on a blogpost explaining how we built the app in detail… It uses embedded TensorFlow on device (better availability, better speed, better privacy, $0 cost), with a custom neural net inspired by last month's MobileNets paper, built & trained with Keras. It was loads of fun to build :)

I would love to look at the source code for this!

And the training set! Mmmmmm!
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