| File Name: | Object Detection & Image Classification with Pytorch & SSD |
| Content Source: | https://www.udemy.com/course/object-detection-image-classification-with-pytorch-ssd/ |
| Genre / Category: | Other Tutorials |
| File Size : | 1.7 GB |
| Publisher: | Christ Raharja |
| Updated and Published: | June 28, 2025 |
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.
Below are things that you can expect to learn from this course:
- Learn the basic fundamentals of object detection and image classification
- Learn how object detection system works, starting from input image processing, feature extraction, region proposal, bounding box, class prediction, and post processing
- Learn how image classification system works starting from data collection, labelling, preprocessing, model selection, training, validation, finetuning, and predicting new image
- Learn how to activate camera using OpenCV
- 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 test object detection and image classification systems using variety of inputs like images and videos
Who this course is for:
- 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
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