awesome-yolo-object-detection

Why we included this project

This index is worth a bookmark for anyone building with YOLO-family detectors, since it pulls together hundreds of implementations, deployment toolkits, and datasets in one place. Instead of sorting purely by model version, it groups projects by what you actually need: language ports from PyTorch and C to Rust, inference engines like TensorRT and ONNX, NPU and FPGA targets, and compression tricks such as pruning and quantization. It also branches into application areas like autonomous driving, industrial defect inspection, medical imaging, agriculture, and anti-UAV work, so you can skip straight to the projects relevant to your field. A substantial dataset section sits alongside the code, letting you find training data for a given task without a second search. For a team scoping out a detection project, it is a useful map before you commit to a framework.