Object Detection & Image Classification with Pytorch & SSD

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.