Object detection is one of the fundamental problems of computer vision. Instead of region detection and object classification separately in two stage detectors, object classification and bounding-box regression are done directly without using pre-generated region proposals in one stage detector. YOLO (you only looks once) is one of the representative models of one-stage architecture. The YOLO family has continued to evolve since 2016, this summer we've got its latest update to version 7. If you are trying to learn how to train your model on a custom dataset from the very beginning, there are already many tutorials, notebooks and videos available online. In Nilvana, we really care about its real-world performance on the embedded devices, especially Nvidia Jetson family.