My YouTube video on this!

In this tutorial, we will be training a custom object detector for mask detection using YOLOv4 and Darknet on our Linux(Ubuntu 18.04) system

FOLLOW THESE 10 STEPS TO TRAIN AN OBJECT DETECTOR USING YOLOv4

( But first ✅Subscribe to my YouTube channel 👉🏻 https://bit.ly/3Ap3sdi 😁😜)

1. Create yolov4 and training folders on your Desktop
2. Open a command prompt and navigate to the “yolov4” folder
3. Create and copy the darknet.exe file
4. Create & copy the files we need for training ( i.e. “obj” dataset folder, “yolov4-custom.cfg”, “obj.data”, “obj.names” and “process.py” files) to your yolov4 dir
5. Copy the “yolov4-custom.cfg”, “obj.data”, “obj.names”, and “process.py” files and the “obj” dataset folder from the yolov4 directory to the darknet directory
6. Run the process.py python script to create the train.txt & test.txt files
7. Download the pre-trained YOLOv4 weights
8. Train the…

My YouTube video on this

In this tutorial, we will be training a custom object detector for mask detection using YOLOv4-tiny and Darknet on our Windows system

FOLLOW THESE 10 STEPS TO TRAIN AN OBJECT DETECTOR USING YOLOv4-tiny

( But first ✅Subscribe to my YouTube channel 👉🏻 https://bit.ly/3Ap3sdi 😁😜)

1. Create yolov4-tiny and training folders on your Desktop
2. Open a command prompt and navigate to the “yolov4-tiny” folder
3. Create and copy the darknet.exe file
4. Create & copy the files we need for training ( i.e. “obj” dataset, “yolov4-tiny-custom.cfg”, “obj.data”, “obj.names” and “process.py” ) to your yolov4-tiny dir
5. Copy the files “yolov4-tiny-custom.cfg”, “obj.data”, “obj.names”, and “process.py” and the “obj” dataset folder from the yolov4-tiny directory to the darknet directory
6. Run the process.py python script to create the train.txt & test.txt files
7. Download the pre-trained YOLOv4-tiny weights
8. Train the detector
9. …

My YouTube video on this!

In this tutorial, we will be training a custom object detector for mask detection using YOLOv4 and Darknet on our Windows system

FOLLOW THESE 10 STEPS TO TRAIN AN OBJECT DETECTOR USING YOLOv4

( But first ✅Subscribe to my YouTube channel 👉🏻 https://bit.ly/3Ap3sdi 😁😜)

1. Create yolov4 and training folders on your Desktop
2. Open a command prompt and navigate to the “yolov4” folder
3. Create and copy the darknet.exe file
4. Create & copy the files we need for training ( i.e. “obj” dataset, “yolov4-custom.cfg”, “obj.data”, “obj.names” and “process.py” ) to your yolov4 dir
5. Copy the “yolov4-custom.cfg”, “obj.data”, “obj.names”, and “process.py” files and the “obj” folder from the yolov4 directory to the darknet directory
6. Run the process.py python script to create the train.txt & test.txt files
7. Download the pre-trained YOLOv4 weights
8. Train the detector
9. Check…

My YouTube Videos on this!

For Windows

For Linux

CONTENTS

• SECTION (A) Installing on Windows
• SECTION (B) YOLO Usage on Windows
• SECTION (C) Installing on Linux
• SECTION (D) YOLO Usage on Linux

( But first ✅Subscribe to my YouTube channel 👉🏻 https://bit.ly/3Ap3sdi 😁😜)

(A) Installing YOLOv4 on Windows

(Installation method: Using CMake)

STEP 1) Download Darknet

Download Darknet zip-archive with the latest commit and uncompress it: master.zip

STEP 2) Install MSVC (Microsoft Visual Studio)

Go to MSVC: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community. This will download the MSVC Community edition setup executable file for you. Run it. Select the “Desktop development with C++” package as shown below and click on install.

STEP 3) Install CMake

Next, we need to install CMake GUI for Windows. (Link given below)

CMake GUI: Windows win64-x64 Installerhttps://cmake.org/download/

Select…

CONTENTS

• CUDA & CUDNN FOR WINDOWS
• CUDA & CUDNN FOR LINUX

( But first ✅Subscribe to my YouTube channel 👉🏻 https://bit.ly/3Ap3sdi 😁😜)

CUDA & CUDNN FOR WINDOWS

STEP 1) Download and install CUDA Toolkit

Go to https://developer.nvidia.com/cuda-downloads to download the latest CUDA Toolkit.

You can also download previous versions from the Archive of Previous CUDA Releases under the Resources section on the same page given above.

( For this Tutorial, I will download and install CUDA 11.0. You can the latest CUDA toolkit and its corresponding cuDNN file. This is just for demonstration )

Go to Archive of Previous CUDA Releases and click on CUDA Toolkit 11.0 Update1.

• On the next page, first, choose your operating system as Windows.
• Next, choose…

What is YOLO?

YOLO stands for You Only Look Once. YOLO is a state-of-the-art, real-time object detection system. It was developed by Joseph Redmon. It is a real-time object recognition system that can recognize multiple objects in a single frame. YOLO has evolved into newer versions over time, viz., YOLOv2, YOLOv3, and YOLOv4.

YOLO uses a totally different approach than other previous detection systems. It applies a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.

The basic idea of…