Building object detection system, image classification and image segmentation models using Pytorch, CNN, YOLOv, and SSD
Sub Category
- Data Science
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Objectives
- 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
Pre Requisites
- No previous experience in object detection is required
- Basic knowledge in Python and computer vision
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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