[owldimoon]

Hey! I'm working on exactly this same problem for an undergraduate Computer Vision course right now. It's not going well! Chess piece recognition is hard. I definitely think the electronics approach described in the other comment is a much more reliable way to go.

[joemi]

If you ever do get it working, I think it'll be fun to push it to over-recognize chess games everywhere, like the deepdream stuff over-recognizing animals (or whatever) everywhere.

[danielvf]

Sounds like a blast!

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.

[Ayeceedee]

We weren't given many constraints related to the chess piece recognition itself. The course instead asks us to implement a CV research paper, and we chose an existing research project which focused on chess piece recognition.

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.

[yeldarb]

What challenges are you running into? What sort of data augmentation are you doing?