[psuter]

Congrats! We tried something similar a few years ago (also at the TC Disrupt Hackathon [1]), but had to take a lot of shortcuts to get to something working. I'm impressed you had the time to train a proper model (we went with old school CV hacks).

Looking forward to seeing what BoardBoss could become. These days I've been wanting a CV app to track backgammon games. Those dice can be pretty tiny though :)

[1] https://devpost.com/software/chesseye

[yeldarb]

Thanks!

ML tools have definitely come a long way in the past few years! We used CreateML for our first pass which is great for prototyping; you just give it your training data and hit go.

Unfortunately it’s not particularly transparent or tweakable. If it doesn’t do the job well enough you you’re out of luck and have to switch to another tool completely.

Edit: cool project!

[rocauc]

Great minds! Love that you deployed to a Pi – I’ve thought about the same to complement or replace smartphones.

Can you shed some insight into your ML process? One thing we did to simplify the vision problem is capture images from the same perspective (hence our tripod). We labeled 2894 objects across 292 images. We had 12 objects to detect: each piece for black and white. We struggled with occlusion, especially if a pawn is behind a queen.

[psuter]

I described some of the process in a previous HN thread: https://news.ycombinator.com/item?id=19567549

There is also a presentation we had prepared for an informal talk: https://github.com/chesseye/chesseye/blob/master/presentatio...

Hope this helps! I always enjoy talking about these things, so feel free to reach out if you want to discuss it more.