YOLOv4 Tutorial #1 — Prerequisites for YOLOv4 Installation in 10 Steps
Introduction
Hey guys and welcome back, so in the last lecture, I spoke about how YOLOv4 works and why its so awesome! Today I’m going to show you how to install the main dependencies in 10 Steps. If you follow these steps with me you should be able to get YOLOv4 working on images, videos and webcams in the upcoming tutorials.
Let’s go through the 10 steps that we need to for YOLOv4.
- Install Python
- Git Installation
- CMake Installation
- Visual Studio Installation
- Updating GPU Driver
- CUDA installation
- CuDNN Installation
- OpenCV Installation
- CMake OpenCV Configuration, and
- Building OpenCV in Visual Studio
It may seem like a lot of steps but, it is more reliable that using Anaconda to install everything. Well at least from my experience.
Some Prerequisites that you will need for this tutorial:
- Are a mid to high range PC/Laptop
- Windows 10, we will be launching an Ubuntu version of this YOLOv4 soon., and
- A CUDA supported NVIDIA GPU, Just note that this will NOT work on any other GPU brand
Road Map
So, looking at our road map
- Tut 1 which is this one, we spend setting up the pre-requisites
- Tut 2, which is the next lecture, we will install Darknet & implement YOLOv4 on an image
- Tut 3 we take YOLOv4 into real time object detection on video, for processing and saving a video and detection on a webcam.
- Tut 4, we create a social Distancing App using YOLOv4.
Okay so let’s get into the tutorial — Click this link to get started
If you are interested in Enrolling in my best-selling courses on YOLOR and YOLOX then sign up over here when it gets released — Click Here
