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YOLO9000:Better, Faster, StrongerJoseph Redmon , Ali Farhadi University of Washington , Allen Institute for AI introduce YOLO9000, a state-of-the-art, real-timeobject detection system that can detect over 9000 objectcategories. First we propose various improvements to theYOLO detection method, both novel and drawn from priorwork. The improved model, YOLOv2, is state-of-the-art onstandard detection tasks likePASCALVOC and COCO. Us-ing a novel, multi-scale training method the same YOLOv2model can run at varying sizes, offering an easy tradeoffbetween speed and accuracy. At 67 FPS, YOLOv2 mAP on VOC 2007. At 40 FPS, YOLOv2 gets , outperforming state-of-the-art methods like Faster R-CNN with ResNet and SSD while still running significantlyfaster. Finally we propose a method to jointly train on ob-ject detection and classification. Using this method we trainYOLO9000 simultaneously on the COCO detection datasetand the ImageNet classification dataset.
High Resolution Classifier. All state-of-the-art detec-tion methods use classifier pre-trained on ImageNet [16]. Starting with AlexNet most classifiers operate on input im-ages smaller than 256 256 [8]. The original YOLO trains the classifier network at 224 224 and increases the reso-lution to 448 for detection. This means the network has to
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