You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon∗, Santosh Divvala∗†, Ross Girshick¶, Ali Farhadi∗† University of Washington∗, Allen Institute for AI†, Facebook AI Research¶ http://pjreddie.com/yolo/ Abstract We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to per- form detection. Instead, we frame object detection as a re- gression problem to spatially separated bounding boxes and associated class probabilities. A single neural network pre- dicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the m
