The results shown in Fig. indicate that there is a trade-off between the number of confidently classified images and classification accuracy. In self-labeling, we chose a probability threshold of 0.9 to start training, for which 58% of confidently classified images were selected with 96% classification accuracy. Selflabeling is performed by re-training both the CNN encoder and classifier using selected images. For each ion image, we use one weak and one strong data augmentation (Tab. S1 and Methods), which provides two pseudo labels as the classifier outputs. A cross-entropy loss is calculated for the pseudo labels and the model parameters are updated to minimize the loss as illustrated in Fig 1 . In each epoch, we update the confidently classified images for training using the same softmax probability threshold of 0.9. As illustrated in Fig. , the loss (purple line) decreases with training time. Meanwhile we observe a significant increase in the number of confidently classified images and a slight increase in the accuracy with training time. These results demonstrate that the CNN model corrects itself during the self-labeling process, which gradually includes additional confidently classified ion images into the training and increases the overall classification accuracy.