Yolov4 Object Detection - How it Works & Why it's So Amazing! | OpenCV Python | Computer Vision

Find out what makes YOLOv4 Object Detection — Superior, Faster & More Accurate in Object Detection. This Computer Vision tutorial is based in OpenCV Python

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==Timecode== 0:00 - Introduction to yolo v4 object detection 3:19 - Object Detector Architectures 4:13 - Selection of Architecture 5:20 - Training Optimizations 8:02 - Additional Improvements 8:32 - Experimental Setup 10:50 - Results 11:29 - Summary

So guess what, YOLOv4 has just been released a few days ago, and I must say I am really really excited by this release. Why? Well Yolo version 3 was quite popular, robust and quick, and now YOLOv4 in comparison I feel is a significant upgrade in terms of speed and performance. So, this article I am going to dissect the paper YOLOv4: Optimal Speed and Accuracy of Object Detection by Alexey Bochkovsky, Chien Yao and Hon-Yuan.

Wait — hold it… what happened to the original creators of Yolo v1–3 Joseph Redmon and Ali Farhadi — Well Joseph or Joe tweeted in Feb 2020 that he will stop Computer vision research because of how the technology was being used for military applications and that the privacy concerns were having a societal impact.

Okay so back to YOLOv4, I am not going to cover YOLO v2 and Yolo v3 in this video because I already cover it in another video of mine which you can check out on my YouTube Channel.

I’ll be dissecting the YOLOv4 paper and help you understand this great technology without too much technical jargon, to uncover: 1)How it works, 2) How it was developed, 3) What approached they used, 4) Why they used particular methods, 5)As well how it performs in comparison to competing object detection models, 6) and Finally, why it’s so awesome!

Okay so if you are ready to get started with AI, Computer vision and YOLOv4! 😉

References: https://medium.com/@riteshkanjee/yolov4-superior-faster-more-accurate-object-detection-7e8194bf1872

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