
Collect diverse training data by capturing screenshots from YouTube videos and other sources, label frames with text boxes and message boxes, and organize pre images for a custom yolo detector.
Split data into training and testing sets for a YOLO v3 detector, using training and testing prefixes to force key examples into both pools and improve model performance.
Verify a yolov3 model works locally by running it against provided sample frames and a video feed, placing artifacts in the correct folders, and inspecting detections and labeling for accuracy.
Conclude by exploring automatic text tracking and auto labeling, using a real-time visualizer to show detections and a fallback labeler to reduce false positives.
Have you ever wanted to have a step by step guide that can help you create your own Yolo V3 Object Detector? Go no further!
This course will teach you how to create a custom object detector that will be used to detect text boxes across Video games and Breaking news banners.
This Course will teach you various ways to collect data from free sources including Youtube Videos.
Next we explore the Software Vott and learn how to setup the project and label images like a pro.
After this, you learn how to build your Object Detector for Free using Google Colab Free GPU.
Finally we wrap this up by verifying that your object detector works on both images and video feed.
By completing this course you gain the ability to find a new way to analyze footage with computer vision. There is plenty of information on the screen that can be detected and extracted for processing.
This fundamental course will prepare you for when you want to take on Automated Text Extract and labeling course.
In the followup course, you will learn how to take this skill to the next level and open the door to applications not tapped into by many.