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Research map / Detection and segmentation / CNN features

YOLO9000: Better, Faster, Stronger

CVPR 2017 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors · 19,336 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add batch normalization (ablation)YOLO · VOC 2007mAP: 63.4 -> 65.8Helped
fine-tune the classifier at 448 × 448 before detection (ablation)YOLO · VOC 2007mAP: 65.8 -> 69.5Helped
replace direct box prediction with convolutional anchor-box prediction (ablation)YOLO · VOC 2007mAP: 69.5 -> 69.2Hurt
replace direct box prediction with convolutional anchor-box prediction (ablation)YOLO · VOC 2007recall: 81% -> 88%Helped
add dimension priors and direct location prediction (ablation)YOLO with anchor boxes · VOC 2007mAP: 69.2 -> 74.4Helped
add passthrough fine-grained features (ablation)YOLOv2 · VOC 2007mAP: 74.4 -> 75.4Helped
add multi-scale training (ablation)YOLOv2 · VOC 2007mAP: 75.4 -> 76.8Helped
use high-resolution detection (ablation)YOLOv2 · VOC 2007mAP: 76.8 -> 78.6Helped
use IOU-based k-means priors rather than hand-picked anchor boxes (ablation)YOLOv2 · VOC 2007average IOU to closest prior: 60.9 -> 67.2Helped
evaluate the same multi-scale-trained YOLOv2 at lower input resolutionYOLOv2 · VOC 2007FPS: 40 -> 91 FPS; 78.6 -> 69.0 mAPMixed
jointly train detection and classification using COCO and ImageNetYOLO9000 · ImageNet detection validation, 156 classes without COCO detection labelsmAP: 16.0Helped

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