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3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   YOLO9000: Better, Faster, Stronger

   Redmon and Farhadi · arXiv (Cornell University) · 2016

   The paper presents YOLOv2, an improved real-time object detector, and YOLO9000, which is trained jointly on detection and classification data to predict over 9000 object categories.

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   1. UncheckedAt 40 frames per second, the improved YOLOv2 detector scores 78.6 mAP on VOC 2007, above Faster RCNN with ResNet and SSD while running faster.“At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster.”
   2. UncheckedTraining on detection and classification data together lets YOLO9000 predict detections for object classes that have no labelled detection data.“Our joint training allows YOLO9000 to predict detections for object classes that don't have labelled detection data.”
   3. UncheckedYOLO9000 scores 19.7 mAP on the ImageNet detection validation set, though only 44 of its 200 classes had detection training data.“YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes.”