YOLOv3: An Incremental Improvement

8 Apr 2018  ·  Joseph Redmon, Ali Farhadi ·

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/

PDF Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Robust Object Detection Cityscapes Photometric distortion mPC [AP] 16.9 # 5
Object Detection COCO-O YOLOv3 (DarkNet-53) Average mAP 14.8 # 41
Effective Robustness -0.37 # 37
Object Detection COCO test-dev YOLOv3 + Darknet-53 box mAP 33.0 # 225
Hardware Burden 0G # 1
Operations per network pass 146.0G # 1
Classification InDL Darknet53 Average Recall 88.53% # 7
One-stage Anchor-free Oriented Object Detection SKU110K-R YOLOv3-Rotate AP 49.1 # 5
AP@75 51.1 # 5

Methods