Papers With Code links machine learning research papers to the code implementations that accompany them. Each paper page includes a summary, the abstract, links to the paper PDF, and — where available — a link to the GitHub repository with the official or community implementation. The site also tracks benchmark results across tasks like image classification, object detection, and machine translation, showing which models currently lead on each benchmark.

For practitioners, this solves a common problem: reading a paper and then spending hours searching for working code. For researchers, it provides a way to see how their work compares to others on standard benchmarks. The site covers thousands of papers and is updated continuously. It's become a standard reference for anyone working in deep learning and related fields.