Computer Vision Projects | Part 1 | Euron
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Unlock the secrets of object detection with our comprehensive "Master YOLOv5: End-to-End Object Detection Guide." Perfect for beginners and experienced coders alike, this tutorial walks you through every step of building a complete object detection pipeline.
In this video, you'll:
Learn the fundamentals of Python and object-oriented programming.
Explore practical computer vision concepts and object detection basics.
Build a modular pipeline using YOLOv5 for waste detection.
Annotate datasets and configure the model for custom training.
Deploy your project using tools like AWS and GitHub.
What’s inside:
A hands-on walkthrough from dataset preparation to live deployment.
Clear explanations of coding logic and best practices.
Guidance on using tools like Google Drive for data storage and annotation platforms like LabelImg.
Tips for optimizing training performance with YOLOv5.
This guide is perfect for those looking to kickstart their programming journey or expand their knowledge in computer vision. Hit play and code along with us to build a functional object detection system from scratch!
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#pytorch #opencvdnn #datascienceproject #computervision #yoloobjectdetection
CHAPTERS:
00:00 - Introduction
00:49 - Prerequisite
01:40 - What is Waste Detection
03:22 - Demo
06:59 - Creating GitHub Repository
11:07 - Importance of Template File
13:43 - Understanding __init__.py
19:04 - Defining Constants
20:40 - Exception Handling
20:54 - Logger Functionality
21:00 - Understanding Pipeline
21:21 - Utility Functions Overview
21:40 - Creating Required Folders and Files
23:10 - Using Pathlib for Cross-Platform Paths
25:40 - Folder Creation Process
28:10 - File Creation Process
31:30 - Testing Template File
36:06 - Project Setup and requirements.txt
40:20 - Creating setup.py
43:18 - Logging Utilities and Exception Handling
44:46 - Preparing Dataset for YOLO Model
47:31 - Installing and Using LabelImg
53:30 - Creating Data File for YOLO Model
56:44 - Starting Notebook Experiment
58:00 - Cloning YOLOv5 Repository
1:02:32 - Downloading Data from Google Drive
1:04:14 - Data Download from Google Drive
1:10:18 - Unzipping Data Files
1:12:12 - Reading EML Data File
1:13:07 - Preparing Configuration File
1:14:05 - Custom YOLOv5 Model Overview
1:17:14 - Training Custom YOLOv5 Model
1:21:50 - Visualizing Training Results
1:29:22 - Custom Logger Implementation
1:36:00 - Custom Exception Handling
1:39:40 - Utility Functions
1:43:00 - Project Workflow Overview
1:45:35 - Data Injection Stage Explained
1:48:30 - Understanding Constants
1:52:05 - Preparing Data in Configuration
1:55:43 - Data Injection Component Overview
2:00:39 - Integrating Data Injection in Main
2:00:55 - Preparing Training Pipeline
2:04:24 - Testing Training Pipeline
2:07:01 - Committing Changes to GitHub
2:09:15 - Data Validation Process - 1
2:10:34 - Data Validation Process - 2
2:11:14 - Updating Constants
2:12:15 - Updating Configuration Entity
2:13:38 - Updating Components
2:19:08 - Adding Data Validation to Pipeline
2:25:55 - Model Trainer Overview - 1
2:26:12 - Model Trainer Overview - 2
2:27:01 - Model Trainer Overview - 3
2:28:55 - Model Trainer Overview - 4
2:30:44 - Initiating Model Trainer
2:37:34 - Adding Model Trainer to Run Pipeline
2:46:21 - Writing app.py for User Application
2:46:40 - Adding Constants to app.py
2:48:39 - Creating Flask Application
2:49:35 - Creating index.html for Flask App
2:51:38 - Flask App for Image Prediction
2:54:38 - Flask App for Live Prediction
2:57:54 - Creating Docker File and GitHub Workflow
3:18:59 - Continuous Deployment Overview
3:21:40 - AWS Deployment Complete
3:22:17 - Azure Deployment Overview
3:22:47 - Azure Deployment Process
3:24:17 - Creating Container Registry
3:28:09 - Creating Web App for Container
3:30:05 - Building Docker Image
3:31:59 - Pushing Image to Container Registry
3:35:38 - Continuous Deployment via GitHub Actions
3:37:27 - Deployment Process Started
3:39:14 - Deployment Completed Successfully
3:39:29 - Deleting Resource Group
3:40:09 - Final Output Overview
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