0
$\begingroup$

I have theorical question that I couldnt decide how to approach. I have tons of grayscaled shape pictures and my goal is seperate these images to good printed and bad printed. For this, I look at roughness of images because some of those is corrupted while printing (e.g I expected triangle shape but I saw some corruption in its edges. So its bad printed). Should I train my model to give each of them spesific label names (like good_printed_triangle, bad_printed_triangle, good_printed_square etc.) or just good and bad is ok? I was thinking about second option but how can CNN find correlation between different shapes, it makes me confuse. I am open for all ideas and thank you in advance.

$\endgroup$
1
$\begingroup$

In general in this situation you should be training to just the specific problem you are trying to solve - which from the sounds of it is just whether edges of any kind on the shape are blurry or cleanly printed.

Unless you specifically need the shape labelled as a square triangle etc you should drop it as a label as will just add unnecessary complexity and overhead to the model.

| improve this answer | |
$\endgroup$
  • $\begingroup$ Thank you for answer. Problem is when I tried to do like that my accuracy remain soo low around 0.3 which is quite bad. $\endgroup$ – justRandomLearner Feb 13 at 11:25
  • $\begingroup$ Quite hard to say much more without seeing the data etc $\endgroup$ – Philip Feb 13 at 13:41

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.