A computer-implemented method of generating ground truth training data and training a neural network-based template generator for cluster metadata determination task, the method comprising: accessing a series of image sets generated during a sequencing run, each image set in the series generated during a respective sequencing cycle of the sequencing run, each image in the series depicting clusters and their surrounding background, each image in the series having pixels in a pixel domain, and each of the pixels is divided into a plurality of subpixels in a subpixel domain; obtaining, from a base caller, a base call classifying each of the subpixels as one of four bases (A, C, T, and G), thereby producing a base call sequence for each of the subpixels across a plurality of sequencing cycles of the sequencing run; generating a cluster map that identifies the clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence; determining cluster metadata based on the disjointed regions in the cluster map, wherein the cluster metadata includes cluster centers, cluster shapes, cluster sizes, cluster background, and/or cluster boundaries; using the cluster metadata to generate ground truth training data for training a neural network-based template generator for cluster metadata determination task, wherein the ground truth training data comprises a decay map, a ternary map, or a binary map, wherein the decay map is calculated based on distance between a subpixel to a center of mass of a disjointed region to which the subpixel belongs, wherein the ternary map is calculated by classifying subpixels that are in the disjointed regions as cluster interior subpixels, subpixels that are at centers of mass of the disjointed regions as cluster center subpixels, and subpixels that do not belong to any of the disjointed regions as background subpixels, and wherein the binary map is calculated by classifying the subpixels that are at the centers of mass of the disjointed regions as cluster center subpixels and all other subpixels as non-center subpixels; and training the neural network-based template generator to produce the decay map, the ternary map, or the binary map as output based on the ground truth training data. accessing a series of image sets generated during a sequencing run, each image set in the series generated during a respective sequencing cycle of the sequencing run, each image in the series depicting clusters and their surrounding background, each image in the series having pixels in a pixel domain, and each of the pixels is divided into a plurality of subpixels in a subpixel domain; obtaining, from a base caller, a base call classifying each of the subpixels as one of four bases (A, C, T, and G), thereby producing a base call sequence for each of the subpixels across a plurality of sequencing cycles of the sequencing run; generating a cluster map that identifies the clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence; determining cluster metadata based on the disjointed regions in the cluster map, wherein the cluster metadata includes cluster centers, cluster shapes, cluster sizes, cluster background, and/or cluster boundaries; using the cluster metadata to generate ground truth training data for training a neural network-based template generator for cluster metadata determination task, wherein the ground truth training data comprises a decay map, a ternary map, or a binary map, wherein the decay map is calculated based on distance between a subpixel to a center of mass of a disjointed region to which the subpixel belongs, wherein the ternary map is calculated by classifying subpixels that are in the disjointed regions as cluster interior subpixels, subpixels that are at centers of mass of the disjointed regions as cluster center subpixels, and subpixels that do not belong to any of the disjointed regions as background subpixels, and wherein the binary map is calculated by classifying the subpixels that are at the centers of mass of the disjointed regions as cluster center subpixels and all other subpixels as non-center subpixels; and wherein the ground truth training data comprises a decay map, a ternary map, or a binary map, wherein the decay map is calculated based on distance between a subpixel to a center of mass of a disjointed region to which the subpixel belongs, wherein the ternary map is calculated by classifying subpixels that are in the disjointed regions as cluster interior subpixels, subpixels that are at centers of mass of the disjointed regions as cluster center subpixels, and subpixels that do not belong to any of the disjointed regions as background subpixels, and wherein the binary map is calculated by classifying the subpixels that are at the centers of mass of the disjointed regions as cluster center subpixels and all other subpixels as non-center subpixels; and training the neural network-based template generator to produce the decay map, the ternary map, or the binary map as output based on the ground truth training data.