r/MachineLearning :https://www.reddit.com/r/MachineLearning/comments/b8jdho/p_d...
r/chess: https://www.reddit.com/r/chess/comments/b826h5/created_chess...
If you have any opinions and suggestions I'd love to hear them!
I would pay $10 or $1.99 a month for this.
Food for thought!
Also, a 10% error rate wouldn't be a big deal as long as it was easy to correct the position within a few seconds.
Not to say that this is necessarily malicious. But I personally wouldn't recommend this to anybody as of right now, unless you want to risk leaking potentially sensitive data to a third-party.
It takes a lot of shortcuts, works with just the right lightning, etc., but worked great as a proof-of-concept :)
We got away with not identifying the pieces by just detecting the color, assuming the game started from the initial position, and assuming only legal moves (the whole game is unambiguous using these assumptions).
It's all old-school computer vision with hand-written features, and I'm pretty confident there is tons of low-hanging fruit, but who has the time.
To be honest, I trust you, even as a random stranger on hacker news. But I don't trust that you'll never sell this, and I don't trust whoever you you might sell it to.
Set one up at the park and get some insight on those hustled blitz games!
Are you restricted to to a single game piece set?
This might be hugely easier if you can keep state between turn analyses. IE, if a white rook disappears from A5 and some unidentified white piece appears at C5, then it’s probably the rook.
That lack of constraints led us into running face first into issues of generalisation and variability within datasets. As in, exactly what you allude to with limiting the piece sets.
I think in my undergraduate naivety my aspirations were too high with what could reasonably be accomplished. I've spent a lot of time trying to improve an aspect of the project that really didn't need to be improved, which prevented meaningful progress.
Now finals are coming up and I feel terribly stressed. Having trouble functioning. Brain fog, etc. I feel so sad right now.
EDIT: I keep forgetting my password so apparently I have multiple throwaway now. Sorry.
1) "A First Book of Morphy" by del Rosario -- takes the very well-considered principles of GM Reuben Fine and illustrates them with [mostly] Paul Morphy's games. Of all the books I've ingested on the subject of chess, this one has stuck with me most. Even my kid loves it!
2) "Winning Chess Strategy for Kids" by Coakley -- it's not just for kids! And it's not just strategy! Covers fundamental tactical concerns such as pins, forks, etc in a straightforward way. Similar in some regards to Pandolfini's "Weapons of Chess" but pedagogically superior in my view.
3) "Silman's Complete Endgame Course" by Silman -- the old saw that one should study the endgame first is pretty true -- Silman is the best at teaching it -- man, if I had a dollar for every game I was winning in the middlegame and then lost in the endgame, I'd have... well, a lot of dollars
cheater extra: 3.5) "Bobby Fischer Teaches Chess" -- the old standby -- this is a tactics book first and foremost, and the main value of it is that it gives you puzzles of gradually increasing complexity so that you can really feel your comprehension improving and say to your self, "I'm getting it!" Truth is that getting discouraged is the thing that stalls or scares off many / most novice chessplayers
When you start getting up in the 1100-1300 range, pick up "My System" by Nimzowitsch and "How to Reassess your Chess" by Silman, "Soviet Middlegame Technique" by Romanovsky -- and after then, start learning openings in more detail. The biggest mistake most beginners make, including me, is building up a repertoire of openings before having a solid grasp of the fundamentals. Truth is, below 1300, most players are "off book" by the ~tenth move anyway, so learning the intricacies of the Nimzo-Indian isn't gonna do a person much good at that stage.
Indeed, the checkerboard itself gives valuable information about the pose of the checkerboard, it is even used for calibration in multi-view geometry: https://en.wikipedia.org/wiki/Chessboard_detection
Compared to all the other feats of machine learning that have blown my mind, parsing a photo of a limited set of a handful of different piece variants, in two colors, that located on a grid, doesn't seem too difficult.
Yes but does real world photos ever have that poor contrast? IMO, top down photo is worth exploring.