[jacquesm]

I've tried the rendering trick but it didn't work well enough, the real pictures seem to give much better results when used on unseen data.

> Also, you said you're doing modified VGG and into 20k classes. That works,

Right now there are 1002 classes, the 1000 most common lego parts, 'mess' and 'other'.

> but another thing to maybe try is use binary_crossentropy as the loss function and a sigmoid (instead of softmax) on the final activation layer, to be able to do multiclass classification. Then your labels could be a vector of shape possibilities, colour possibilities, or whatever you could divide your 20k classes into.

Ok, I can try that. Thank you!

[gwern]

Tagging/multi-label classification is useful because it'll help tame your explosion of classification if you want to expand. For example, it can then handle stuck-together parts by tagging it as both parts rather than putting them into a generic 'other' classification, or you could include separate tags for colors or fakeness or damagedness, avoiding the need for 100,000 categories of 'fake damaged red square brick' etc. It might also improve learning since it's a more natural way of describing the data.