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YOLOv3: An Incremental Improvement

About

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at https://pjreddie.com/yolo/

Joseph Redmon, Ali Farhadi• 2018

Related benchmarks

TaskDatasetResultRank
Object DetectionCOCO 2017 (val)
AP55.3
2454
Object DetectionCOCO (test-dev)
mAP33
1195
Object DetectionPASCAL VOC 2007 (test)
mAP58.4
821
Object DetectionMS COCO (test-dev)
mAP@.562.4
677
Object DetectionCOCO v2017 (test-dev)
mAP36.2
499
Video Object DetectionImageNet VID (val)
mAP (%)68.59
341
Object DetectionDOTA 1.0 (test)--
256
Object DetectionNDDA stop sign images (test)
Detection Rate100
90
Object DetectionMS-COCO (test)--
81
Object DetectionVOC 2007 (test)
AP@5080.2
52
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Other info

Code

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