The C framework that points you to newer YOLOs

What it does Darknet is an open source neural network framework written in C and CUDA. It supports both CPU and GPU computation and claims to be fast and easy to install. The README dedicates most of its space to benchmarking YOLOv7, YOLOv4, and Scaled-YOLOv4, yet the source-code links for those models point to external PyTorch repositories and the AlexeyAB fork rather than this tree.

The interesting bit In an ecosystem dominated by Python and PyTorch, Darknet remains a bare-metal C framework with optional CUDA kernels. That design keeps the runtime dependency-free and compact, a rarity among deep-learning stacks that typically drag in heavy Python environments.

Key highlights

• Ships as plain C with CUDA acceleration; no Python interpreter required
• Supports both CPU and GPU computation
• README acts as an index for the YOLO family, linking papers and external source repos
• Claims to be “fast, easy to install” relative to heavier ML frameworks

Caveats

• The README prominently features YOLOv7, yet the actual implementation links lead to PyTorch repos and the AlexeyAB fork, leaving it unclear which model versions (if any) live inside this repository itself
• The repository feels more like a legacy framework and portal than an active monorepo for latest YOLO research

Verdict Worth a look if you want a minimal, dependency-free neural network runtime in C. Skip it if you expect the latest YOLOv7 source to live here; the README sends you to other repositories.