Summary
Most of the image datasets that exist in nature or industry are unlabeled image datasets. Think of X-ray images generated by diagnostic labs, or MRI or dental scans, and many more. Pictures generated on Amazon reviews or images from Google Street View or e-commerce websites like EBay are also mostly unlabelled; also a large proportion of Facebook, Instagram, or WhatsApp images are never tagged and are therefore unlabelled as well. A lot of these image datasets remain unused with untapped potential due to current modelling techniques requiring large amounts of manually labelled sets. Removing the need for large, labelled datasets and expanding the realm of what is possible is Self-Supervised Learning.
We have seen in this chapter how PyTorch Lightning can be used to quickly create Self-Supervised Learning models such as contrastive learning. In fact, PyTorch Lightning is the first framework to provide out-of-the-box support for many Self-Supervised Learning models. We implemented...
