PepNet is a fully convolutional neural network that directly outputs a predicted peptide sequence. The input spectrum is represented as a 20,480 × 4 input matrix, where the first dimension is a discretized m/z axis with a bin size of 0.1 m/z, the second dimension represents the peak intensities and corresponding m/z values, and these dimensions are repeated in both forward and reverse order. The core of the model is a series of five temporal convolutional blocks , each of which operates at a different resolution. The overall model thus aims to capture both local and global structures, which are then integrated in a final merging branch. The output of the model is produced via a final softmax layer, yielding a probability matrix of size 32 × 23, where 32 is the maximum peptide length and 23 is the alphabet size. Unlike DeepNovo and related methods, no post-processing is employed to force the predicted peptide to match the observed precursor mass.