Scaled YOLO v4 is the best neural network for object detection on MS COCO dataset
Paper: https://arxiv.org/abs/2011.08036
- Pytorch: YOLOv4-CSP, YOLOv4-P5, YOLOv4-P6, YOLOv4-P7 (main repository — use to reproduce results): https://github.com/WongKinYiu/ScaledYOLOv4
- YOLOv4-CSP
- YOLOv4-tiny
- YOLOv4-large - Darknet: YOLOv4-tiny, YOLOv4-CSP, YOLOv4x-MISH: https://github.com/AlexeyAB/darknet
- Structure of YOLOv4-CSP: https://netron.app/?url=https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg
Scaled YOLO v4 is the best neural network for object detection — the most accurate (55.8% AP Microsoft COCO test-dev) among neural network published. In addition, it is the best in terms of the ratio of speed to accuracy in the entire range of accuracy and speed from 15 FPS to 1774 FPS. …
YOLOv4 — the most accurate real-time neural network on MS COCO dataset.
Darket YOLOv4 is faster and more accurate than real-time neural networks Google TensorFlow EfficientDet and FaceBook Pytorch/Detectron RetinaNet/MaskRCNN on Microsoft COCO dataset.
Article: https://arxiv.org/abs/2004.10934
Code: https://github.com/AlexeyAB/darknet
Yolov v4 tiny discussion 1770 FPS: https://www.reddit.com/r/MachineLearning/comments/hu7lyt/p_yolov4tiny_speed_1770_fps_tensorrtbatch4/
Discussion: https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/
We demonstrate the nuances of comparing and using neural networks to detect objects. Our goal was to develop an algorithm for use in real production, and not just for moving science forward. Accuracy of YOLOv4 (608x608) — 43.5% AP / 65.7% AP50 Microsoft-COCO-testdev.
62 FPS — YOLOv4 (608x608 batch=1) on Tesla V100 —…
