SimCLR

Why we included this project

SimCLR showed that a simple contrastive loss can learn strong visual features without any labels, and this repository makes that idea easy to pick apart. It trains an encoder on pairs of augmented views, then lets you freeze the result and check quality with a linear classifier, so you get a concrete read on how well the representations transfer. The code is compact and driven by a config file, which makes it pleasant to read and easy to point at your own dataset, and a Colab notebook covers the evaluation step end to end. The author's blog post walks through the architecture in more depth, which helps when you want to understand what the loss is actually doing. If you are new to self-supervised vision and want a focused, runnable reference rather than a big framework, this is a good place to start.