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## (neural networks for object detection)
Paper YOLO v4: https://arxiv.org/abs/2004.10934
* **Paper YOLOv7**: https://arxiv.org/abs/2207.02696
Paper Scaled YOLO v4: * **[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html)**: use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
* **source code YOLOv7 - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7
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* **Paper YOLOv4**: https://arxiv.org/abs/2004.10934
* **source code YOLOv4 - Darknet (use to reproduce results):** https://github.com/AlexeyAB/darknet
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* **Paper Scaled-YOLOv4 (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
* **source code Scaled-YOLOv4 - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/ScaledYOLOv4
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### YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
* **Paper**: https://arxiv.org/abs/2207.02696
* **source code - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7
YOLOv7 is more accurate and faster than YOLOv5 by **120%** FPS, than YOLOX by **180%** FPS, than Dual-Swin-T by **1200%** FPS, than ConvNext by **550%** FPS, than SWIN-L by **500%** FPS, than PPYOLOE-X by **150%** FPS.
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1.
* YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+500%` FPS faster than SWIN-L C-M-RCNN (53.9% AP, 9.2 FPS A100 b=1)
* YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+550%` FPS faster than ConvNeXt-XL C-M-RCNN (55.2% AP, 8.6 FPS A100 b=1)
* YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+120%` FPS faster than YOLOv5-X6-r6.1 (55.0% AP, 38 FPS V100 b=1)
* YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+1200%` FPS faster than Dual-Swin-T C-M-RCNN (53.6% AP, 6.5 FPS V100 b=1)
* YOLOv7x (52.9% AP, 114 FPS V100 b=1) by `+150%` FPS faster than PPYOLOE-X (51.9% AP, 45 FPS V100 b=1)
* YOLOv7 (51.2% AP, 161 FPS V100 b=1) by `+180%` FPS faster than YOLOX-X (51.1% AP, 58 FPS V100 b=1)
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![image](https://user-images.githubusercontent.com/4096485/177674970-0c633f50-405d-4a20-a8d9-bd467d09d6ab.png)
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![image](https://user-images.githubusercontent.com/4096485/177675030-a929ee00-0eba-4d93-95c2-225231d0fd61.png)
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