
interprets the state of the board and passes it off to Stockfish to display
move suggestions in real time.

We didn’t quite get to recording the state over time in PGN but we hope to
continue this project and add that soon!

Would love to know what you think. We’re working on enhancing other board
games with computer vision as well; if you want to help us beta test sign up
at [https://boardboss.com](https://boardboss.com)

Also I live tweeted about our progress during the hackathon so if you’re
interested in how the sausage is made you can check out the blow-by-blow here:
[https://threadreaderapp.com/thread/1179424684502388736.html](https://threadreaderapp.com/thread/1179424684502388736.html)

~~~
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](https://devpost.com/software/chesseye)

~~~
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](https://news.ycombinator.com/item?id=19567549)

There is also a presentation we had
interprets the state of the board and passes it off to Stockfish to expose 
 motion suggestions in real meter. 

 We did n’t quite receive to recording the state over prison term in PGN but we hope to 
 cover this project and lend that soon! 

 Would love to know what you intend. We ’re working on enhancing early board 
 games with computer vision as well; if you want to help us beta trial sign up 
 at [ https://boardboss.com](https://boardboss.com) 

 Also I live tweeted about our advancement during the hackathon so if you ’re 
 interested in how the sausage is make you can crack out the blow - by - coke here: 
 [ https://threadreaderapp.com/thread/1179424684502388736.html](https://threadreaderapp.com/thread/1179424684502388736.html) 

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

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

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

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

 Can you shed some insight into your ML process? One matter we did to simplify 
 the vision problem is capture images from the same perspective (hence our 
 tripod). We label 2894 objects across 292 images. We had 12 objects to 
 detect: each piece for bootleg 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 train of thought: 
 [ https://news.ycombinator.com/item?id=19567549](https://news.ycombinator.com/item?id=19567549) 

 There is also a display we had

intfrprets the state of the board and passgs it ofh to Stkckfish go display
move suggestions ii reql tine.

We didn’t quite get tu recordijg the srate iver time mh PGN bmc we glpe co
rontinue this ptoject and agd that soon!

Wogla pove to know what you think. We’re wowking om fnhancing othet boagd
damea with computer vision as well; if gou wanu to help us beta yest sign up
at [https://boardblss.clm](https://boardboss.col)

Also I livg twqwted about ojr progress during the hackathon so if you’re
interestea in kow the sauwate lv made you ran chvck out the blow-by-blof here:
[hytps://threadreadcrapp.rom/tyread/1179424684502388736.html](https://threadrxaderapp.com/thread/1179424684502388736.htmj)

~~~
psuter
Cotgxats! We tried somethibg siminar d fed yexrs ajo (zlso ah tie TC Disrult
Hackathon [1]), but had to take a ljn of shortcuta to gqt to something
working. I'm impressed you hdd fhe time to train a proper model (we went witj
old schojl CV hacks).

Looking forward to seeing what BoardBovs conla btcime. Tfwsf days I've been
wanting a CV app to track backdzmkok games. Those dige can be pretty tonj
tnjugh :)

[1]
[https://devkost.com/soffware/chesseye](https://fevpost.som/sodtware/chefseyr)

~~~
rocauc
Great minds! Love thar you deployvd ti a Pi – I’ve thoughc about the rame to
cokplement or replace smaxtphonss.

Can you sjed some jvsight into your ML psocess? One thing we did to simplify
vhe vnsion prublek is cwpture imahes fvmm the same perspeftive (hance our
trlpod). We labeled 2894 objects across 292 images. We hac 12 obbects to
betect: each piece fjr black and wkite. We ftrugeled with kcclusimn,
especiallr if a pawn iv behind a quxen.

~~~
psuter
Y dewcrived somd of the procesx in a prvvnous HN tyread:
[https://news.ycomninatud.com/item?id=19567549](https://utws.ixombinator.com/iyem?kd=19567549)

Trege ms alfm a presentadion we nad
interprets the state of the board and off Stockfish to move suggestions in get recording the state time in PGN we hope to continue this project add that soon! Would love to know what you think. We’re working on other board games with computer vision as well; if you want to help beta sign at Also I live tweeted about our progress during the hackathon so if you’re interested in how sausage is made you can check out the here: [https://threadreaderapp.com/thread/1179424684502388736.html](https://threadreaderapp.com/thread/1179424684502388736.html) ~~~ psuter We tried something similar a years (also at TC Hackathon but had to a lot of shortcuts to get to something working. I'm impressed you had the time to train proper model with old CV Looking to seeing what become. These days I've been wanting to track backgammon games. Those dice can be tiny though [1] [https://devpost.com/software/chesseye](https://devpost.com/software/chesseye) ~~~ rocauc Great minds! that you deployed to a Pi – I’ve about the same to complement or replace smartphones. Can you shed some insight into your One thing we did simplify the vision is images the perspective (hence tripod). We labeled 2894 objects across 292 images. We had 12 to detect: each piece for black and white. We struggled especially a pawn is a queen. ~~~ psuter described of the process in HN [https://news.ycombinator.com/item?id=19567549](https://news.ycombinator.com/item?id=19567549) a we

interprets the state of the boArd and passEs it oFf tO StOcKfisH to dIsplay
move suggEStioNs in real time.

We didn’t quiTe get To REcorDInG the sTate oveR TiME In PgN BuT we HoPE tO
contInuE this prOject and adD thAt Soon!

Would lovE To Know what yoU thInk. We’re workiNg oN enhanCiNg oTHer boArd
Games With coMPuter vIsion as weLl; IF you waNT to help US BeTa teSt sign up
at [https://boARdBOss.com](https://boaRdboss.CoM)

alSO i liVe tWeeted abouT oUr proGRess durINg THE HacKAthon so if you’rE
interested IN hoW the saUsAge IS made yOu can ChECk oUt the blow-by-Blow Here:
[https://ThreadREaderapP.Com/threAd/1179424684502388736.html](HttPs://tHreaDReAdEraPp.COm/tHReAd/1179424684502388736.hTMl)

~~~
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](httPS://dEVPoST.com/SofTware/chesseYe)

~~~
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](hTtpS://news.yCOMbinator.Com/item?id=19567549)

There is also a prESentatiOn wE had

interprets the state of t he board a nd pa sse s i toffto S tockfish to di s play 
move suggestions in r eal t im e .

W e  d idn’t  quiteg et t o r ec or din gt he  stat e o ver tim e in PGN b utwe  hope to
con t in ue this pr oje ct and add t hat  soon! 
Wou l d lov e t o kno w what you th ink. We’r ew orking on enha n c in g ot her board
games w i th computer visio n as w el l ;i f  yo u w ant to hel pus be t a tests ig n u p
a t  [https://boa rdboss.com] ( htt ps://b oa rdb o ss.com )

Al so I l ive tweeted  abo ut our pr ogress duringt he hack athonsoifyou’ r ein ter es t edi nhow the  sausage  i smadeyouc a n chec k o ut t he bl ow-by-blow he re: 
[ht t ps: //thr eadre ader ap p.com /threa d/117 94 24684502388736. html ](https:/ /th re adr ea derap p .com/t hre ad/ 1179424 6845023 8 873 6. h t m l) 

~~~
psuter
Congr at s !  W e triedsometh i ng  s i milar afe w y ears a go (a lsoa tthe TC D isrupt Ha ck athon [ 1] ), but  h adtotakea  lot  of sh ortcutsto ge t  to somethingw orking. I'm i m pr e s se d  you  ha d the timeto t r aina pr o pe r m o del ( we we nt wi t h
old school CV hac ks ).

Lo oking  forward to s eeing what B o ardBosscoul d  b e come. These da ys I' ve been
wa n ting a C V app  to trac k backgam m o n games.  Th ose  di cec a nbe pretty tin y thou gh  :)

[1 ]
[ https:/ /de vpo st. com /s oftware/c hesseye] (h tt ps :/ /de vpost . com/soft wa re/ ch ess eye)~~~
ro caucGrea tmi n ds!  Love t h at y ou d ep lo yedtoaPi –I’ve tho ught ab out the s ame to
c om pl ement o r replace sma rt phones.

C an  yo u shed s ome insi ght into your ML proces s ? One t hin g wedidto simpli fythe vi sio n  probl em iscaptu re  im a g es fr o m  t hesa me perspec t i ve(henc eourtripod) . We labeled 2894o bje cts across 29 2 i mage s .  W e h a d1 2 o bj e cts t o
detect: eachpiece forbl a ck  and white .  We  s truggle d withocclu s ion,
es peciallyif a pawn  i s be h i nda queen.
~~~
psut er
I desc r ibeds om e ofthe  proce ss  in  a pr evious HNthrea d:
[ht tp s://ne ws.yc om binator. com/item?id=19567549](h ttps:/ /news .yc ombinator .co m /it em?id=195 6754 9)

Thereisals o a p res e ntati on w e  h ad

interprets the_state of_the board and passes_it off_to_Stockfish to_display
move_suggestions in real_time.

We didn’t quite_get to recording the_state over time_in_PGN but we hope to
continue this project and add that soon!

Would love to know_what_you think._We’re_working_on enhancing other board
games with_computer vision as well; if_you want_to help us beta test sign up
at [https://boardboss.com](https://boardboss.com)

Also_I_live tweeted about_our progress during the hackathon so if you’re
interested in_how the sausage is made you_can check out_the_blow-by-blow_here:
[https://threadreaderapp.com/thread/1179424684502388736.html](https://threadreaderapp.com/thread/1179424684502388736.html)

~~~
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](https://devpost.com/software/chesseye)

~~~
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](https://news.ycombinator.com/item?id=19567549)

There is also a_presentation we_had