Udemy – Object Detection & Image Classification with Pytorch & SSD 2025-6

Udemy – Object Detection & Image Classification with Pytorch & SSD 2025-6 Downloadly IRSpace

Aug 17, 2025 - 02:40 Updated: Aug 19, 2025 - 14:35

0 1

Udemy – Object Detection & Image Classification with Pytorch & SSD 2025-6

Object Detection & Image Classification with Pytorch & SSD Course. This comprehensive, project-based course teaches participants how to develop object detection and image classification systems using tools such as Pytorch, SSD, Keras, Convolutional Neural Networks, YOLOv, U Net, and DETR ResNet. During the course, participants complete four hands-on projects: building an object detection system with pre-trained models such as Faster R-CNN and YOLOv, designing a manufacturing defect detection system using CNN to classify defective and healthy products, creating an organic and inorganic waste classification model, and developing a broken road image segmentation system with the U Net architecture. The course begins with the basics of machine vision, working with Kaggle datasets, and processing images with OpenCV. Finally, the models are tested with a variety of inputs including images, videos, and live camera feeds to ensure the accuracy and proper performance of the systems. This course combines Python and machine vision, providing an ideal opportunity to enhance your programming skills and technical knowledge in developing intelligent software.

What you will learn

Basics of object recognition and image classification.
Building an object recognition system using Pytorch and SSD.
Building an object recognition system using Pytorch and Faster R-CNN.
Building an object recognition system using YOLOv.
Building an object recognition system using DETR ResNet.
Building a manufacturing defect detection model using Keras and convolutional neural networks.
Building a manufacturing defect detection system using OpenCV.
Building a garbage classification model using Keras and a convolutional neural network.
Building a waste classification system using OpenCV.
Building an image segmentation model of broken roads using Unet.
Building a broken road detection system using OpenCV.
Camera activation using OpenCV.
How an object recognition system works from input image processing, feature extraction, region suggestion, bounding box, and class prediction.
How an image classification system works, from data collection, labeling, preprocessing, model selection, training, validation, and prediction of new images.
How to test object recognition and image classification systems using a variety of inputs such as images and videos.

This course is suitable for people who:

Software engineers interested in building object recognition systems using Pytorch, SSD, Faster R-CNN, YOLOv, and DETR ResNet.
Machine learning engineers interested in building image classification systems using Keras, Convolutional Neural Networks, and OpenCV.