Object detection is the most commonly used application of computer vision, which also helps the computer recognize and classify objects inside an image. This video course will help you learn Python-based object recognition methods and develop custom object detection models.
The course begins with an introduction to the You Only Look Once (YOLO) object detection system, Python programming, and Convolutional Neural Networks (CNNs). You will learn object detection by installing Anaconda on your computer and the OpenCV library in Python. Next, you will learn to perform object detection and recognition on a single object in an image and on a real-time webcam video using YOLO pre-trained model and Common Objects in Context (COCO) dataset.
Moving ahead, you will learn the pros and cons of using a pre-trained dataset model and a custom dataset trained model. In addition, you will get an overview of the free GPU offered by Google Colab. Toward the end, you will learn to create a custom dataset and train a darknet YOLO model to detect coronavirus from an electron microscope image or video output.
By the end of this video course, you will develop the skills required to build object recognition models using predefined and custom datasets.
The code files for this course are placed here: https://github.com/PacktPublishing/Computer-Vision-YOLO-Custom-Object-D…
Become familiar with Python and OpenCV libraries
Perform object recognition using a predefined dataset
Create a custom dataset and train the You Only Look Once (YOLO) model
Use the YOLO model on a single image for object detection
Try the YOLO model for object detection from a real-time webcam video
Create a full-fledged flawless coronavirus detection model