YOLOv2, or YOLO9000, is a single-stage real-time object detection model. It improves upon YOLOv1 in several ways, including the use of Darknet-19 as a backbone, batch normalization, use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.
Source: YOLO9000: Better, Faster, StrongerPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 31 | 35.23% |
Real-Time Object Detection | 5 | 5.68% |
Autonomous Driving | 3 | 3.41% |
License Plate Recognition | 3 | 3.41% |
Quantization | 3 | 3.41% |
Image Classification | 3 | 3.41% |
General Classification | 3 | 3.41% |
3D Object Detection | 3 | 3.41% |
Human Detection | 2 | 2.27% |