4 ms·

Well I wasn't going to spill the beans just yet but if you promise to keep a secret.. working on that for v2 :) Edit: Although so far I've learned that some pe

Well I wasn't going to spill the beans just yet but if you promise to keep a secret.. working on that for v2 :) Edit: Although so far I've learned that some people have VERY bad handwriting. I'm having a hard time sanitizing a dataset because I can't even tell what digit they were trying to write

ianamartin 9y ago
I'm that guy. Sorry :( I have the worst handwriting. People are like, "Dude, wtf?" when I try to whiteboard stuff.
yeldarb 9y ago
All good; validates that I'm at least on the right track! Had to pare back my ambitions quite a bit to get _something_ finished before the iOS11 launch. I didn't expect the reception to be so enthusiastic for v1 tbh; but I guess I chose a good place to call it an "MVP"
- inlined 9y ago
  I'm really interested in the growth of ML on mobile apps. Did you test the ability to call existing ML APIs in the cloud and found they weren't responsive enough, or did it just feel more natural to run the net locally?
- yeldarb 9y ago
  I've played with AWS Rekognition a little bit. It's definitely WAY faster to do it on-device. Plus it works even in Airplane mode. I'm running the iOS11 Vision stuff, my own text recognition CoreML neural network (81 times per loop), plus all the ARKit stuff and I can still get 30-60 fps on my iPhone 7+
IanCal 9y ago
Do the standard datasets not work too well (e.g. MNIST or NIST)? http://yann.lecun.com/exdb/mnist/ http://yann.lecun.com/exdb/mnist/ http://www.cs.toronto.edu/~tijmen/affNIST/ http://www.cs.toronto.edu/~tijmen/affNIST/ https://www.nist.gov/srd/nist-special-database-19 https://www.nist.gov/srd/nist-special-database-19
- yeldarb 9y ago
  Tried those first but no, they didn't work great with the data I was able to extract; those datasets are only handwritten digits which apparently weren't close enough to the computer printed ones. I think the artifacts from the square borders (eg if it's slightly mis-cropped or misaligned) were also tripping up the model before I had my custom dataset loaded in there. Edit: I should say this was my first stab at doing machine learning so I may be missing something; definitely open to ideas or suggestions!
  - Tenobrus 9y ago
    Shouldn't you be able to generate a lot of synthetic "printed-looking" digits very easily? Just get say 20 common fonts and render each digit with random rotations and scaling etc.
  - yeldarb 9y ago
    That's definitely on my list of things to try! Thanks for the push in that direction

ianamartin 9y ago

I'm that guy. Sorry :( I have the worst handwriting. People are like, "Dude, wtf?" when I try to whiteboard stuff.

yeldarb 9y ago

All good; validates that I'm at least on the right track! Had to pare back my ambitions quite a bit to get _something_ finished before the iOS11 launch. I didn't expect the reception to be so enthusiastic for v1 tbh; but I guess I chose a good place to call it an "MVP"

inlined 9y ago
I'm really interested in the growth of ML on mobile apps. Did you test the ability to call existing ML APIs in the cloud and found they weren't responsive enough, or did it just feel more natural to run the net locally?
yeldarb 9y ago
I've played with AWS Rekognition a little bit. It's definitely WAY faster to do it on-device. Plus it works even in Airplane mode. I'm running the iOS11 Vision stuff, my own text recognition CoreML neural network (81 times per loop), plus all the ARKit stuff and I can still get 30-60 fps on my iPhone 7+

IanCal 9y ago

Do the standard datasets not work too well (e.g. MNIST or NIST)? http://yann.lecun.com/exdb/mnist/ http://yann.lecun.com/exdb/mnist/ http://www.cs.toronto.edu/~tijmen/affNIST/ http://www.cs.toronto.edu/~tijmen/affNIST/ https://www.nist.gov/srd/nist-special-database-19 https://www.nist.gov/srd/nist-special-database-19

yeldarb 9y ago
Tried those first but no, they didn't work great with the data I was able to extract; those datasets are only handwritten digits which apparently weren't close enough to the computer printed ones. I think the artifacts from the square borders (eg if it's slightly mis-cropped or misaligned) were also tripping up the model before I had my custom dataset loaded in there. Edit: I should say this was my first stab at doing machine learning so I may be missing something; definitely open to ideas or suggestions!
- Tenobrus 9y ago
  Shouldn't you be able to generate a lot of synthetic "printed-looking" digits very easily? Just get say 20 common fonts and render each digit with random rotations and scaling etc.
- yeldarb 9y ago
  That's definitely on my list of things to try! Thanks for the push in that direction