Influence of the training data set for our baseline CNN (left, solid lines) and ResNet (right, dashed lines). We only vary the set of positive (top) and negative (bottom) examples. Networks trained on the baseline data sets (N1+L1) are plotted in dark blue. Two of the best networks from the upper-right panel, the baseline ResNet (N1+L1) and the ResNet from C21 (N1+L7), are shown as dashed gray lines in all panels for reference (except in the right-hand panels where the colors are overlaid by the ones indicated in the legend). The thick gray curve corresponds to a random classifier. Optimal performance is obtained for ground-truth data sets comprising mock lenses with bright, deblended multiple images, and large fractions of typical non-lens contaminants. The AUROC, TPR₀ and TPR₁₀ tend to be higher for the ResNet, except for data sets containing limited numbers of tricky non-lens galaxies.