Sounds like a great learning opportunity for beginners and people who haven't even started with machine learning yet, like me. I'm looking forward to it. If you're so inclined, a new blog post focusing on the machine learning process would be much appreciated, it's always interesting to read about the process of how somebody solved a real problem and implemented the solution.
Just so I understand the process correctly, did you manually sort some pieces to get a labeled training set, feed those through the machine, train the NN with that, and then manually correct the errors when sorting unknown pieces, added all those pictures to the same training set and then finally run the full training again? How many labeled images do you need to start getting acceptable performance? Are you training the NN continuously with every new image, or from scratch with an increasing data set?
Do you think a stereo camera would improve the classification in a meaningful way, or maybe a second camera from a different angle?
Just so I understand the process correctly, did you manually sort some pieces to get a labeled training set, feed those through the machine, train the NN with that, and then manually correct the errors when sorting unknown pieces, added all those pictures to the same training set and then finally run the full training again? How many labeled images do you need to start getting acceptable performance? Are you training the NN continuously with every new image, or from scratch with an increasing data set?
Do you think a stereo camera would improve the classification in a meaningful way, or maybe a second camera from a different angle?