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Did you try quantizing the parameters to shrink the model size some more? If so, how did it affect the results? It also runs slightly faster on mobile from my experience.
Earlier quoted context omitted.
My takeaway is that local development has a huge developer experience advantage when you are going through your initial network design / data wrangling phase. You can iterate quickly on labeling images, develop using all your favorite tools/IDEs, and dealing with the lack of official eGPU support is bearable. Efficiency-wise it’s not bad. As far as I could tell the bottleneck ended up being on the GPU, even on a 2016…
Awesome, thanks!
Mike Judge, Alec Berg, Clay Tarver, and all the awesome writers that actually came up with the concept: Meghan Pleticha (who wrote the episode), Adam Countee, Carrie Kemper, Dan O’Keefe (of Festivus fame), Chris Provenzano (who wrote the amazing “Hooli-con” episode this season), Graham Wagner, Shawn Boxee, Rachele Lynn & Andrew Law…
Todd Silverstein, Jonathan Dotan, Amy Solomon, Jim Klever-Weis and our awesome Transmedia Producer Lisa Schomas for shepherding it through and making it real!
Our kick-ass production designers Dorothy Street & Rich Toyon.
Meaghan, Dana, David, Jay, Jonathan and the entire crew at HBO that worked hard to get the app published (yay! we did it!)
Finally for Android! Cool to see a cross-platform implementation of this, and how much can be done by one person and some reasonable gear.
Yes, I was very excited we were able to release it for Android… And even though we used React Native, there were so many native (and C++) bits, it ended up being quite complex! As for the gear, I think it’s really damaging that so many people think Deep Learning is only for people with large datasets, cloud farms (and PhDs) — as the app proves, you can do a lot with just data you curate by hand, a laptop (and a lowly…
While we’re here and chatting about this, I should say most of the credit for this app should really go towards the following people: Mike Judge, Alec Berg, Clay Tarver, and all the awesome writers that actually came up with the concept: Meghan Pleticha (who wrote the episode), Adam Countee, Carrie Kemper, Dan O’Keefe (of Festivus fame), Chris Provenzano (who wrote the amazing “Hooli-con” episode this season), Graham…
This is amazing - impressed by your persistence to source the training data yourself, that must have been tedious! Did you try quantizing the parameters to shrink the model size some more? If so, how did it affect the results? It also runs slightly faster on mobile from my experience.
It’s also my understanding at the moment that quantization does not help with inference speed or memory usage, which were my chief concerns. I was comfortable with the binary size (Finally, it now seems that quantization may be best applied at training time rather than at shipping time, according to a recent paper by the University of Iowa & Snapchat [0], so I would probably want to bake that earlier into my design phase next time around.
Earlier quoted context omitted.
Yes, I was very excited we were able to release it for Android… And even though we used React Native, there were so many native (and C++) bits, it ended up being quite complex! As for the gear, I think it’s really damaging that so many people think Deep Learning is only for people with large datasets, cloud farms (and PhDs) — as the app proves, you can do a lot with just data you curate by hand, a laptop (and a lowly…
Do you think it's possible to generalize the way you handled the cross-platform complexity into a shared component?
Just wanted to say thanks for the warm welcome from HN when the app was released last month — I hope this blogpost answers the questions that were raised back then. I’d be happy to answer anything else you’d like to know! Original thread: https://news.ycombinator.com/item?id=14347211 Demo of the app (in the show): https://www.youtube.com/watch?v=ACmydtFDTGs App for iOS: https://itunes.apple.com/app/not-hotdog/id12124…
I am making an app that takes pictures and tries to tell you if the food in the picture has allergens. I didn't know if I should feel humble or just laugh. (I decided it was hilarious in the end) But it made me aim higher in a hackathon last weekend. I also use your app in my elevator pitch for people to understand.
Why not be able to search over a list, and once on an item, show frequently related (e.g. garlic if you search onion). It may not involve any “AI,” but it’ll be far more accurate and easier to implement.