A popular way to evaluate a SSL method in computer vision or for that fact any other pre-training method as such is to learn a linear classifier on the frozen features of the trained backbone model and evaluate the classifier on unseen images. Other methods often include fine-tuning on the source dataset or even a target dataset with 5% or 10% labels present. You can use the backbone we just trained for any downstream task such as image classification (like we do here) or segmentation or detection, where the backbone models are usually pre-trained with supervised learning.