Object Detection & Image Classification With Pytorch & Ssd

Building object detection system, image classification and image segmentation models using Pytorch, CNN, YOLOv, and SSD

What you'll learn

Learn the basic fundamentals of object detection and image classification

Learn how to build object detection system using Pytorch and SSD

Learn how to build object detection system using Pytorch and Faster R-CNN

Learn how to build object detection system using YOLOv

Learn how to build object detection system using DETR ResNet

Learn how to build manufacturing defect detection model using Keras and Convolutional Neural Network

Learn how to build manufacturing defect detection system using OpenCV

Learn how to build waste classification model using Keras and Convolutional Neural Network

Learn how to build waste classification system using OpenCV

Learn how to build broken road image segmentation model using Unet

Learn how to build broken road detection system using OpenCV

Learn how to activate camera using OpenCV

Learn how object detection system works, starting from input image processing, feature extraction, region proposal, bounding box, and class prediction

Learn how image classification system works starting from data collection, labelling, preprocessing, model selection, training, validation, predicting new image

Learn how to test object detection and image classification systems using variety of inputs like images and videos

Requirements

No previous experience in object detection is required

Basic knowledge in Python and computer vision

Description

Welcome to Object Detection & Image Classification with Pytorch & SSD course. This is a comprehensive project based course where you will learn how to build object detection system, manufacturing defect detection system, waste classification system, and broken road segmentation model using Pytorch, Keras, convolutional neural network, U net, YOLOv, single shot detector, and DETR ResNet. This course is a perfect combination between Python and computer vision, making it an ideal opportunity for you to practice your programming skills while improving your technical knowledge in software development. In the introduction session, you will learn the basic fundamentals of object detection and image classification, such as getting to know how each system works step by step. In the next section, you will learn how to find and download datasets from Kaggle, it is a platform that offers a wide range of high quality datasets from various industries. Before starting the project, you will learn the basics of computer vision like activating cameras and processing images using OpenCV. Afterward, we will start the project, firstly, we are going to build object detection system using Faster R CNN, SSD, YOLOv and Detection Transformers ResNet, those are pre trained models that enable you to detect and classify objects without the need to train them using your own data. Following that, we are going to build a manufacturing defect detection model using Keras and Convolutional Neural Network to classify whether a product is defective or in good condition based on image input. This system will enable users to automatically inspect products using camera or uploaded images, reducing the need for manual quality control checks in factories. Then, after that, we are also going to build a waste classification model using Keras and CNN to distinguish between organic and non organic waste. This system will enable users to automate waste sorting for recycling or disposal purposes by analyzing waste images and accurately identifying materials such as plastic bottles, food waste, papers. In the next section, we are going to build a broken road image segmentation model using the U Net architecture, which is widely used for pixel wise image segmentation tasks. This system will enable users to identify damaged or pothole areas on roads from images, which can assist in infrastructure maintenance and smart city planning.Lastly, at the end of the course, we will conduct testing to make sure the model accuracy is high and the system performs as expected. We will test the system using various inputs such as images, short videos, and real time camera feeds to ensure the features are fully functioning.Well, before getting into the course, we need to ask this question to ourselves, why should we build object detection and image classification models? Well, here is my answer, these models help businesses to automate tasks that were once manual and repetitive, reducing dependency on constant human supervision and improving consistency. This technology is very valuable in industries like manufacturing, waste management, agriculture, retail and transportation. By implementing these systems, businesses can significantly reduce human error and increase processing speed. This will lead to greater efficiency and cost saving.Below are things that you can expect to learn from this course:Learn the basic fundamentals of object detection and image classificationLearn how object detection system works, starting from input image processing, feature extraction, region proposal, bounding box, class prediction, and post processingLearn how image classification system works starting from data collection, labelling, preprocessing, model selection, training, validation, finetuning, and predicting new imageLearn how to activate camera using OpenCVLearn how to build object detection system using Pytorch and SSDLearn how to build object detection system using Pytorch and Faster R-CNNLearn how to build object detection system using YOLOvLearn how to build object detection system using DETR ResNetLearn how to build manufacturing defect detection model using Keras and Convolutional Neural NetworkLearn how to build manufacturing defect detection system using OpenCVLearn how to build waste classification model using Keras and Convolutional Neural NetworkLearn how to build waste classification system using OpenCVLearn how to build broken road image segmentation model using UnetLearn how to build broken road detection system using OpenCVLearn how to test object detection and image classification systems using variety of inputs like images and videos

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Introduction to Object Detection & Image Classification

Lecture 5 Introduction to Object Detection & Image Classification

Section 4: Finding & Downloading Datasets From Kaggle

Lecture 6 Finding & Downloading Datasets From Kaggle

Section 5: Activating Camera with OpenCV

Lecture 7 Activating Camera with OpenCV

Section 6: Building Object detection system with Pytorch & SSD

Lecture 8 Building Object detection system with Pytorch & SSD

Section 7: Building Object detection system with Pytorch & Faster R-CNN

Lecture 9 Building Object detection system with Pytorch & Faster R-CNN

Section 8: Building Object detection system with YOLOv

Lecture 10 Building Object detection system with YOLOv

Section 9: Building Object detection system with DETR ResNet

Lecture 11 Building Object detection system with DETR ResNet

Section 10: Building Manufacturing Defect Detection Model with Keras & CNN

Lecture 12 Building Manufacturing Defect Detection Model with Keras & CNN

Section 11: Building Manufacturing Defect Detection System with OpenCV

Lecture 13 Building Manufacturing Defect Detection System with OpenCV

Section 12: Testing Manufacturing Detection System

Lecture 14 Testing Manufacturing Detection System

Section 13: Building Waste Classification Model with Keras & CNN

Lecture 15 Building Waste Classification Model with Keras & CNN

Section 14: Building Waste Classification System with OpenCV

Lecture 16 Building Waste Classification System with OpenCV

Section 15: Testing Waste Classification System

Lecture 17 Testing Waste Classification System

Section 16: Building Broken Road Image Segmentation Model with Unet

Lecture 18 Building Broken Road Image Segmentation Model with Unet

Section 17: Building Broken Road Detection System with OpenCV

Lecture 19 Building Broken Road Detection System with OpenCV

Section 18: Testing Broken Road Detection System

Lecture 20 Testing Broken Road Detection System

Section 19: Conclusion & Summary

Lecture 21 Conclusion & Summary

Software engineers who are interested in building object detection systems using Pytorch, SSD, Faster R-CNN, YOLOv, and DETR ResNet,Machine learning engineers who are interested in building image classification system using Keras, Convolutional Neural Network, and OpenCV