Hi, yes I’ve tried regression problems for cnns. If your output is bounded you can simply scale the outputs from the model. This would allow the model to keep its small weights which is good for L2-weight regularisation. Some solutions could be linear scaling, log/exp scaling or predicting the offset from a typical value.
output = model(input) * 100 # Linear scaling
output = model(input) + guess_output # Add expected value

I haven’t done this in a while so I’d advise you to look for second opinions on google  Good luck!