  We show that the YOLOv4 object detection neural network based on the CSP
approach, scales both up and down and is applicable to small and large networks
while maintaining optimal speed and accuracy. We propose a network scaling
approach that modifies not only the depth, width, resolution, but also
structure of the network. YOLOv4-large model achieves state-of-the-art results:
55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of ~16 FPS on Tesla
V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP
(73.3 AP50). To the best of our knowledge, this is currently the highest
accuracy on the COCO dataset among any published work. The YOLOv4-tiny model
achieves 22.0% AP (42.0% AP50) at a speed of 443 FPS on RTX 2080Ti, while by
using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774
FPS.
