DSOD implements an object detection architecture published at ICCV 2017, enabling object detection models to be trained from scratch without relying on ImageNet pre-trained weights. The method uses deep supervision techniques to improve training convergence when training detectors from scratch. The project includes model implementations and training code for object detection on standard benchmarks.