Supplementary Figure 2: Single-image identification accuracy for different group sizes and different variations of the identification network.

Each network is trained from scratch using 3,000 temporally uncorrelated images per animal (90% for training and 10% for validation) and then tested with 300 new temporally uncorrelated images to compute the single-image identification accuracy (Supplementary Notes). We train and test each network five times. For every repetition, the individuals of the group and the images of each individual are selected randomly. Images are extracted from videos of 184 different animals recorded in isolation (Supplementary Fig. 2). Colored lines with markers represent single-image accuracies (mean ± s.d., n = 5) for network architectures with different numbers of convolutional layers (a; see Supplementary Table 2 for the architectures) and different sizes and numbers of fully connected layers (b; see Supplementary Table 3 for the architectures). The black solid line with diamond markers shows the accuracy for the network used to identify images in idtracker.ai (see Supplementary Table 1, identification convolutional neural network).