The most popularly used method to evaluate a SSL method in computer vision (or any other pre-training method as such) is to learn a linear classifier on the frozen features of the trained backbone model (in this case it is ResNet20) and evaluate the classifier on unseen images. Other methods include fine-tuning on the source dataset or even a target dataset with 5% or 10% labels present. Practically, we can use the backbone model for any downstream task such as semantic segmentation, object detection, and so on where the backbone models are usually pre-trained with pure supervised learning.