YOLOv4 Tutorial #2 - How to Run YOLOv4 on Images and video | OpenCV Python | Computer Vision |2020

If you want to implement the latest YOLOv4 on images and video, then check out this tutorial on how install Darknet and thus run YOLOv4 in python.

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[UPDATE]Easier Tutorial of YOLOv4 - https://youtu.be/Iw3tducClFw

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==Timecode== 0:00 - Introduction 2:03 - Downloading DarkNet 2:45 - Copying Open files into Darknet 3:57 - Changing the CuDNN version in Darknet 4:50 - Compile YOLOv4 with updated CUDA version. 5:29 - Compiling Darknet 8:27 - Run Detection on Images 10:03 - Run Detection on Videos 10:53 - Summary

Hey guys and welcome back, so in the last lecture, we spent some time setting up the prerequisites for YOLOv4, like Visual Studio, Python, CUDA, CuDnn and OpenCV. If you have not completed the previous tutorial, then I highly suggest that you do because this lecture builds upon the steps we took in the previous tutorial. So if you have completed that tutorial, then this lecture we are going to be installing Darknet Framework and thus YOLOv4. By the end of this tutorial you will be able to implement YOLOv4 on an Image and on Video.

-CMD for Images- darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights

-CMD for Videos- darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights PATH_TO_THE_VIDEO

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

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