YOLO: Custom Object Detection & Web App in Python

YOLO: Custom Object Detection & Web App in Python, available at $69.99, has an average rating of 4.54, with 76 lectures, based on 279 reviews, and has 2214 subscribers.

You will learn about Python based YOLO Object Detection using Custom Trained Dataset Models. YOLO Custom Training YOLO V5 Object Detection Train multiple objects Essential concepts of Streamlit Develop Web App with Python This course is ideal for individuals who are Beginner and Professional who want to develop custom object detection model form scratch. It is particularly useful for Beginner and Professional who want to develop custom object detection model form scratch.

Enroll now: YOLO: Custom Object Detection & Web App in Python

Summary

Title: YOLO: Custom Object Detection & Web App in Python

Price: $69.99

Average Rating: 4.54

Number of Lectures: 76

Number of Published Lectures: 76

Number of Curriculum Items: 76

Number of Published Curriculum Objects: 76

Original Price: $139.99

Quality Status: approved

Status: Live

What You Will Learn

Python based YOLO Object Detection using Custom Trained Dataset Models.
YOLO Custom Training
YOLO V5 Object Detection
Train multiple objects
Essential concepts of Streamlit
Develop Web App with Python

Who Should Attend

Beginner and Professional who want to develop custom object detection model form scratch.

Target Audiences

Beginner and Professional who want to develop custom object detection model form scratch.

Welcome to ‘YOLO: Custom Object Detection & Web App in Python’

Object Detection is the most used applications of Computer Vision, where computer/machine can able to locate and classify the object in an image.

In this course we specifically using YOLO (You Only Look Once) and powerful and popular unified object detection model. YOLO uses neural networks to provide real-time object detection. This algorithm is popular because of its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals.

This course is divided into two halves. The first half deals with object detection with custom dataset where we will locate 20 classes of objects. And in second half we will create an web app and give the Graphical User Interphase experience to the use. Not only that we will also deploy our model in Cloud platform.

Now let us see the topics in the course

Introductory theory session about YOLO Object Detection
1. Here in this section I will explain history of Object Detection
2. Object Detection Metrics like IoU (Intersection Over Union), Precision, mean Average Precision (mAP) etc.
3. Then we will see the mathematical concept behind YOLO
4. Also I will cover how YOLO improved from each version

After that, we are ready to proceed with preparing our computer for Python coding by downloading and installing the Python package and will check and see if everything is installed fine.

2. Data Preparation for YOLO model

In this section we will put every we learn in to practice. This section is completely hands-on where we will do python code and use pandas dataframes to prepare the data.

a. Thumb rules to follow in Collect Data

b. Label image for object detection: Here we will use LabelImg tool which is an open source tool to label the label.

c. Parse data from XML files and extract information like filename, size, bounding box info like (xmin, xmax, ymin, ymax)

d. Process the data from XML in pandas dataframe. And then split the image and save the respective label information information in train and test.

3. Train YOLO v5 Model

4. Develop Web App in Python

That’s all about the topics which are currently included in this quick course. The code, images and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Understanding Object Detection

Lecture 3: Install Python

Lecture 4: Install Virtual Environment

Lecture 5: Install Python Packages

Lecture 6: Do's & Don'ts in Data Collection and Labeling

Lecture 7: Download Complete Python & Data

Chapter 2: Data Preparation

Lecture 1: What we will do in this section

Lecture 2: Collect Data

Lecture 3: Labeling

Lecture 4: Get List of XML files in Python

Lecture 5: Read & Extract Labels Data from XML files

Lecture 6: Read & Extract Labels Data from XML files part2

Lecture 7: Convert Labels information into Pandas Dataframe

Lecture 8: Labels for YOLO model

Lecture 9: Create Labes for YOLO model in Python

Lecture 10: Split Data/Images into train and test sets

Lecture 11: LABEL ENCODING TO OBJECTS

Lecture 12: Create Train & Test Folder

Lecture 13: Create Function and Move Train image, and label text in train folder

Lecture 14: Move Test images, and label text in test folder

Chapter 3: Training YOLO Model

Lecture 1: Create YAML file

Lecture 2: Google Drive Resources

Lecture 3: Setting Up Google Colab

Lecture 4: Get YOLO v5 repository

Lecture 5: Training YOLO v5 model

Lecture 6: New Update in Training Command

Lecture 7: Save YOLO model

Lecture 8: New update in export model

Lecture 9: How to Resume training

Lecture 10: Results & Evaluation

Chapter 4: Prediction from YOLO Model

Lecture 1: Download Resources

Lecture 2: What we will do

Lecture 3: Step-1, Load data.yaml file

Lecture 4: Step-2: Load YOLO model with OpenCV

Lecture 5: Step-3: Get detection from YOLO model

Lecture 6: Understand YOLO model output detections

Lecture 7: Non Maximum Suppression – part 1

Lecture 8: Non Maximum Suppression – part 2

Lecture 9: Draw Bounding Box

Lecture 10: Create YOLO Predictions Module

Lecture 11: Final Object Detection from Image with YOLO

Lecture 12: Real Time Object Detection with YOLO

Lecture 13: Error !!!

Chapter 5: Next Steps

Lecture 1: Web App

Chapter 6: Streamlit Crash Course for Web App

Lecture 1: Streamlit Crash Course

Lecture 2: Install Visual Studio Code

Lecture 3: Activate Virtual Environment in VS Code

Lecture 4: Your First Streamlit App

Lecture 5: Streamlit Basics part – 1

Lecture 6: Streamlit Basics part – 2

Lecture 7: Streamlit Basics part – 3

Lecture 8: Streamlit Layouts – page configuration

Lecture 9: Streamlit Layouts – Side Bar

Lecture 10: Streamlit Layouts – Columns

Lecture 11: Streamlit Layouts – Tabs

Lecture 12: Streamlit Widgets

Lecture 13: Streamlit Widgets – Radio Button

Lecture 14: Streamlit Widget – Selectbox

Lecture 15: Streamlit Widget – Slider

Lecture 16: Streamlit Widgets – Text Inputs

Lecture 17: Streamlit Widgets – File Upload

Lecture 18: Streamlit Widgets – File Upload part – 2

Chapter 7: Project: Develop Web App for YOLO Model

Lecture 1: Download Resources

Lecture 2: Setting Up App

Lecture 3: Home Page

Lecture 4: YOLO App – Load YOLO model into app

Lecture 5: YOLO App – part – 2

Lecture 6: YOLO App – part -3

Lecture 7: YOLO App – part – 4

Lecture 8: YOLO App – part – 5

Lecture 9: YOLO App – part – 6

Lecture 10: YOLO for Images App

Lecture 11: YOLO for Video/Real Time Object Detection Part – 1

Lecture 12: YOLO for Video/Real Time Object Detection Part – 1

Chapter 8: BONUS

Lecture 1: Bonus Lecture

Instructors

G Sudheer
Instructor
datascience Anywhere
Team of Engineers
Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 4 votes
2 stars: 4 votes
3 stars: 26 votes
4 stars: 89 votes
5 stars: 156 votes

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