YOLO9000: Better, Faster, Stronger YOLO9000: Better, Faster, Stronger
Paper summary YOLOv2 is improved YOLO; - can change image size for varying tradeoff between speed and accuracy; - uses anchor boxes to predict bounding boxes; - overcomes localization errors and lower recall not by bigger nor ensemble but using variety of ideas from past work (batch normalization, multi-scaling and etc) to keep the network simple and fast; - "With batch nor-malization we can remove dropout from the model without overfitting" - gets 78.6 mAP at 40 FPS. YOLO9000; - uses WordTree representation which enables multi-label classification as well as making classification dataset also applicable to detection; - is a model trained simultaneously both for detection on COCO and classification on ImageNet; - is validated for detecting not labeled object classes; - detects more than 9000 different object classes in real-time.
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YOLO9000: Better, Faster, Stronger
Joseph Redmon and Ali Farhadi
arXiv e-Print archive - 2016 via arXiv
Keywords: cs.CV

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