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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 your model and on what hardware?
:)
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 we could build in the time we had. (With more time/resources I have no doubt I could have achieved better accuracy with a heavier model!)
Training the final model took about 80 hours on a single Nvidia GTX 980 Ti (the best thing I could hook to my MacBook Pro at the time). That's for 240 epochs (150k images in an epoch) ran in 3 rate annealing phases, each phase being a handful of CLR (cyclical learning rate) phases.
I'll answer in more detail in the full blogpost, it's a bit complicated to explain in a comment. I'll have charts & figures for y'all :)
Thanks for sharing all the tech details too, it's been great to read. I'm even more amazed to see it as a real app, that I didn't expect!
Edit: Just read your bio. Now it makes sense!
Edit: I guess it's official: The time of XKCD 1425 has passed. https://xkcd.com/1425/ September 2014; Randall undershot that by a bit :)
EDIT: ah, it's in your profile ;)
Would you write a blog post about that journey?
search for "hot dog" or click on the hot dog icon:
In the early 90's, I had the honor of corresponding with "Uncle Frank" Webster [1], the curator of the Hot Dog Hall of Fame [2]! It boasts of more than 2,500 frankfurter items, including the Lamborweenie, a dog on wheels that "Uncle Frank" hopes some day to race against the Oscar Meyer Weinermobile [3]. Unfortunately the hot dog museum is currently closed and in (hopefully refrigerated) storage [4], otherwise the museum, gallery and gift shop would be a great place to train your app.
He asked for permission to publish in his newsletter a gif [5] of a photo I'd taken and put on my web site of the Doggie Diner head [6] on Sloat Boulevard in San Francisco [7]. (This was years before John Law acquired all the Doggie Diner heads he could find for his restoration project [8], so there weren't so many photos on them on the internet at the time.)
Of course I gave him permission because he asked so politely, and although at first he seemed a little creepy, I could tell he was authentic and sincere since he signed his correspondence: "With Relish, Uncle Frank." [9] And he even delivered on his promise to send me copies of his newsletter!
He enthusiastically informed me that the highest quality hot dogs he's ever found are from Top Dog in Berkeley [10]. He admitted that he went through their dumpster to find out where they sourced them from because they wouldn't tell him, and he vouches that they are made from the finest possible ingredients. I agree with Uncle Frank that Top Dog has really excellent hot dogs (ask for them cooked butterfly style), and I take him at his word that they come from a reputable source. You can now actually order them online [11] if you would prefer not to go through their dumpster.
[1] http://www.smithsonianmag.com/arts-culture/hot-dogs-are-us-6...
[2] http://thehotdoghalloffame.blogspot.nl/
[3] https://www.questia.com/magazine/1G1-19007874/the-almost-all...
[4] http://www.roadsideamerica.com/tip/20747
[5] http://donhopkins.com/home/catalog/images/DoggieDiner.gif
[6] https://en.wikipedia.org/wiki/Doggie_Diner
[7] http://www.roadsideamerica.com/story/14441
[8] https://www.kickstarter.com/projects/2118888480/doggie-diner...
[9] http://www.downtownmakeover.com/comments.asp?CZID=17
The effort you put into the show is much appreciated.
I was REALLY depressed after watching these last two episodes. HungryBot was easily the coolest thing I've done in my unfunded nonprofit. But it was too buggy to fly. Kicked it out of the nest too early, but to the best of my knowledge it was the first deep learning food recognition app.
Looking for philanthropists if anyone wants to help me study diabetes with apps like this.
It's sad that this isn't more common outside of hot dog detectors.
Then there are some issues with just the tooling. TensorFlow is great, and works great on mobile, but it was a bit hard to get it working. The whole team there is doing an awesome job documenting and improving everything though! It's just early and not enough users have built apps with it to get the kind of production-readiness you get out of non-Machine Learning components.
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 better ways, so would love to hear how other people use it!
Broadly, I've created a "repository" singleton that stores a reference to both the React Native module instance (which gets set when I call its setup method from JS) and the C++ main class instance (which gets set when it starts up), so they can get a handle on each other (I bet there are better ways to do this, but I'm new to C++/ObjC and couldn't work out a better way to get a reference to the RN module).
I'm then using RCT_EXPORT_METHOD to provide hooks for JS to call into C++ via an ObjC bridge (in an RCT_EXPORT_MODULE class), and using the event emitter to communicate back to JS (so the C++ can get the RN module instance from the singleton and call methods which emit events).
I've not done anything that really pushes the bridge performance to a point where I've seen any noticeable latency/slow down caused by the interop - have you had any issues here?
Like I say, I'm finding a really cool way to build apps that need the power of native code but still with the ease of RN as the GUI and some logic, and I actually quite like the separation it enforces with the communication boundary.
See https://news.ycombinator.com/item?id=13817557 for some more detail/discussion.
[edit: Apart from in Apple-approved manners]
> 3.3.2 […] The only exception to the foregoing is scripts and code downloaded and run by Apple’s built-in WebKit framework or JavascriptCore […]
If the newspaper apps get to load fresh front page images, why should a poor neural net be discriminated in its quest for fresh coefficients?
(Raw oppression and injustice there, get yer indignation reservoir topped up.)