SUPPNet performs fully automated precise continuum normalization of merged echelle spectra and offers flexible manual fine-tuning, if necessary. The code uses a fully convolutional deep neural network (SUPP Network) trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that give access to regressed knots, which are useful for optional manual corrections. The active learning technique controls possible biases that may arise from training with synthetic spectra and extends the applicability of the method to features absent in this kind of spectra.