Vedadet is a modular single-stage object detection framework built on PyTorch. It decomposes detectors into four parts: data pipeline, model, postprocessing and criterion for easier deployment. The toolbox supports popular single-stage architectures including RetinaNet and FCOS, with built-in support for converting models to TensorRT engines for efficient inference on NVIDIA hardware.