// the find
pjreddie/darknet
Convolutional Neural Networks
Darknet is the original C/CUDA framework that Joseph Redmon used to train and run YOLO — the real-time object detector that made single-shot detection practical. It's primarily a research artifact now: people come here for the YOLO config files and pretrained weights, not to build production systems on top of it.
The C implementation is genuinely fast and has minimal dependencies — you can get inference running on bare hardware without a Python stack. The cfg file format for defining network architectures is simple and readable, which made it easy to experiment with layer configs in the YOLO era. It has working CUDA paths that were ahead of their time when written. The weight files and configs for YOLOv1 through v3 are canonical here — if you're studying the architecture evolution, this is the primary source.
The repo is effectively unmaintained — the original author stopped working on YOLO for ethical reasons in 2020, and this fork has had no meaningful commits since. YOLOv4, YOLOv7, and later variants all live in separate repos (AlexeyAB/darknet, WongKinYiu/yolov7) that this README points to without making clear that this one is obsolete. The C codebase has no tests, memory management is manual and error-prone, and building it on modern systems without a specific CUDA/cuDNN version matrix in hand is an afternoon of pain. If you actually want to run YOLO inference today, Ultralytics or the PyTorch YOLO repos are the right answer — this is a museum piece.