You Only Look Once (YOLO) Object Detection Models

YOLOv1

Code

Paper: JOseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, “You Only Look Once: Unified, Real-Time Object Detection“, 2016

Code: https://github.com/AlexeyAB/darknet

YOLOv2

Paper: “YOLO9000:Better, Faster, Stronger“

Code: https://pjreddie.com/darknet/yolov2/

YOLOv3

Paper: YOLOv3: An Incremental Improvement

Code: https://pjreddie.com/darknet/yolo/

YOLOv4

Paper:

• YOLOv4: Optimal Speed and Accuracy of Object Detection
• Scaled-YOLOv4: Scaling Cross Stage Partial Network

Code:

• https://github.com/AlexeyAB/darknet
• https://github.com/WongKinYiu/ScaledYOLOv4

YOLOv5

Paper: none yet

Code: https://github.com/ultralytics/yolov5

YOLOv6

Paper: none yet

Code: https://github.com/meituan/YOLOv6

YOLOv7

Official YOLOv7 (state-of-the-art real-time detector) is more accurate and faster than:

• – YOLOv5 by 120% FPS
• – YOLOX by 180% FPS
• – Dual-Swin-T by 1200% FPS
• – ConvNext by 550% FPS
• – SWIN-L CM-RCNN by 500% FPS
• – PPYOLOE-X by 150% FPS

Paper: Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 2022

Code: https://github.com/WongKinYiu/yolov7/releases

References

• A Brief History of YOLO Object Detection Models From YOLOv1 to YOLOv5