While deep learning has seen consistent improvements in medical imaging applications, one of the biggest hurdles for further gains is the need for large amounts of data. This data is not always readily available for different reasons, e.g., due to a lack of experts to annotate the samples. Equivariant neural networks have proven to be a way to increase data efficiency by, e.g., forgoing the need for data augmentation. This study extends previous research on group-equivariant networks applied to transmission electron microscopy (TEM) images of different types of viruses. It is shown that group equivariant networks, when compared to baseline convolutional networks, obtain higher accuracies by 1.4 to 2.3% when increasing the training epochs from 300 to 2400. Furthermore, data augmentation strategies by rotations and reflections, as well as pre-training on ImageNet, can be skipped. When turning off batch normalization, the performance of the equivariant networks drops about 25% while the baseline fails to converge, implying that equivariance and batch normalization extract improved information from data by different mechanisms. The VGG16 architecture outperforms a smaller custom architecture by 3.9 to 4.4%, while the choice of symmetry group does not impact performance significantly. This study contributes to predictable scaling, which is already of great importance in, e.g., the training of large language models. Predictable scaling is expected to increase in significance in the field of biomedical image analysis since the model and dataset sizes continue to grow.