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How is the best YOLO V4 made? I believe many inexperienced people don't know what to do about it. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

YOLO series of networks have a common feature, that is, the pursuit of network accuracy and network speed, YOLO V4 on the basis of another pursuit, that is to reduce hardware requirements. The development process of YOLO V4 is interesting, with many interesting new technologies evaluated, modified, and integrated.

And it also optimizes the computational efficiency, so that the detector can complete the training well on a single GPU. YOLO V4 tunes in these areas:

Weighted residual connection (WRC)

Cross-phase partial connection (CSP)

Standardization across small batches (CmBN)

Self-confrontation training (SAT)

Mish activation

Mosaic data Enhancement

CmBN

DropBlock regularization

Loss of CIoU

Have you been surprised?

Taking a look at the comparison between YOLO V4 and some SOTA models, YOLO V4 is nearly 10 points higher than YOLO V3.

From the perspective of career development: YOLO has always been one of the most widely used mainstream algorithms, and it is not only a standard skill for engineers with a monthly salary of more than 30K, but also a technical and job-hunting weather vane, so students who can build a test model and have a deep understanding of it will be able to gain a firm foothold on the job search road!

After reading the above, have you mastered how the best YOLO V4 is made? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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