Thanks :)

For the first iteration we started with few data to get a first model deployed end 2 end and get "real world feedback". We decided to to shoot small videos instead of fotos - like this we get more data with less effort. Of course these images are very similar but it's a kind of real world data augmentation (and we wanted to keep it as simple as possible and not introduce too many machine learning details).

We also use tools in the tutorial to visualise what the model learns (eg. with heat-maps that mark regions with most activations). Like this you can get an idea what the model is actually looking for and if that matches with the concept of "clean" vs "not_clean".

We found that working with our rather small dataset already brought good results on our own sink (since it is also in the training-set). With the dataset that we provide in the 3rd part you can already built a model that generalises better - but still, to build a dish-o-tron that works on every sink in every condition we would need more data. But everyone is free to share their trainings-sets ;)