We present YOLO, a new approach to object detection. Prior work on object detection
repurposes classifiers to perform detection. Instead, we frame object detection as a
regression problem to spatially separated bounding boxes and associated class
probabilities. A single neural network predicts 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 …

… • Improve the speed and mAP after CNN • But, It is not enough to operate real-time yet …
You only look once: Unified, real-time object detection, J Redmon et al., CVPR 2016 … You
only look once: Unified, real-time object detection, J Redmon et al., CVPR 2016 …