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tokens: and FRI classes. However, it performs worse overall for the FRIIs, as shown in Table 9 . This may be due to the more complex decoder confusing radio emission from the FRIIs with noise. Table 9. The average metrics (in percentages) across each of the classes in the original LOFAR data set, for the inc. decoder CapsNet model. Five runs were done in total, using 600 samples in the test set. As the default CapsNet model performs better overall compared to the other two CapsNet models, it is chosen as the basis of comparison against the two ConvNet models across the original fits and masked 4rms sigma-clipped data sets. The default CapsNet model still performs significantly worse compared to the two ConvNets, as it is beyond both their 95 per cent confidence intervals, across all metrics. The variability in metrics is higher for the original data set compared to that of the two ConvNets, as is evident in the generally increased confidence intervals of the CapsNet model, in Table 7 , particularly for the FRIIs. Fig. 8 shows the Receiver Operating Characteristic (ROC) curves across the default capsule network and ConvNet-4. ROC curves plot the true positive rate (recall) against the false positive rate. Figure 8. ROC curves for both a single run with the default CapsNet model and the ConvNet-4 model. The curves show that ConvNet-4 outperforms the default CapsNet across all the classes. In a first attempt to use the default CapsNet model (containing 58M free parameters), we observed a clear overfitting, owing to the large number of free parameters compared to the number of training images. Despite this, the model still achieved very similar results to the models using many fewer parameters quoted in this work. Table 10 shows the labels and prediction vector for some sources that the Capsule network could not reliably classify , as probabilities higher than 0.5 across two classes were attained. Table 10. The labels and corresponding probability vector of the default CapsNet network predictions, using four examples of sources shown in Fig. 10 , having probabilities greater than 0.5 across two classes. 5.2 LOFAR original and augmented images We augmented the images with translation, rotation, and flipping as outlined in Section 3.2 , keeping the distribution of FRI and FRII sources the same as in the original data set. Table 1 gives the number of original and augmented images. There are again 79 per cent and 21 per cent of the original samples used in training and testing, respectively. 5.2.1 ConvNet-4 and ConvNet-8 We applied both ConvNet-4 and ConvNet-8 models to the original and augmented data set, with the results shown in Tables 5 and 6 . The overall metrics are significantly better (Avg. Recall = 93.4 and 94.3) than was observed when the same model was used on the original images (Avg. Recall = 88.7 and 91.2 for ConvNet-4 and ConvNet-8, respectively), therefore both models benefit from data augmentation. The confidence intervals are also