Published 6/2025
Created by Christ Raharja
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 21 Lectures ( 3h 11m ) | Size: 1.62 GB
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
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