Transcription of YOLOv3: An Incremental Improvement
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YOLOv3: An Incremental Improvement
YOLOv3: An Incremental ImprovementJoseph Redmon, Ali FarhadiUniversity of WashingtonAbstractWe present some updates to YOLO! We made a bunchof little design changes to make it better. We also trainedthis new network that s pretty swell. It s a little bigger thanlast time but more accurate. It s still fast though, don tworry. At320 320 YOLOv3 runs in 22 ms at mAP,as accurate as SSD but three times faster. When we lookat the old .5 IOU mAP detection metric YOLOv3 is quitegood. It 51 ms on a Titan X, com-pared 198 ms by RetinaNet, similar perfor-mance but faster. As always, all the code is online IntroductionSometimes you just kinda phone it in for a year, youknow? I didn t do a whole lot of research this year. Spenta lot of time on Twitter. Played around with GANs a had a little momentum left over from last year [12] [1]; Imanaged to make some improvements to YOLO.
2.3. Predictions Across Scales YOLOv3 predicts boxes at 3 different scales. Our sys-tem extracts features from those scales using a similar con-cept to feature pyramid networks [8]. From our base fea-ture extractor we add several convolutional layers. The last of these predicts a 3-d tensor encoding bounding box, ob-jectness, and class predictions.
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