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Training YOLO v3 for Objects Detection with Custom Data

Training YOLO v3 for Objects Detection with Custom Data
Training YOLO v3 for Objects Detection with Custom Data
Link : bestseller udemycode coupon YOLO v3
Custom object training and detection with YOLOv3, Darknet and OpenCV. YOLO is a state-of-the-art, real-time object detection system. It looks at the whole image at test time so its predictions are informed by global context in the image. Bestseller
 by Valentyn Sichkar

What you'll learn

  • Apply already trained YOLO v3 for Objects Detection on image, video and in real time with camera
  • Label own dataset and structure files in YOLO format
  • Create custom dataset in YOLO format
  • Convert existing dataset of Traffic Signs in YOLO format
  • Train YOLO v3 in Darknet framework
  • Build own PyQt graphical user interface for Objects Detection based on YOLO v3 algorithm

In this hands-on course, you'll train your own Object Detector using YOLO v3 algorithm.

  • As for beginning, you’ll implement already trained YOLO v3 on COCO dataset. You’ll detect objects on image, video and in real time by OpenCV deep learning library. Those code templates you can integrate later in your own future projects and use them for your own trained models.
  • After that, you’ll label own dataset as well as create custom one by extracting needed images from huge existing dataset.
  • Next, you’ll convert Traffic Signs dataset into YOLO format. Code templates for converting you can modify and apply for other datasets in your future work.
  • When datasets are ready, you’ll train and test YOLO v3 Detectors in Darknet framework.
  • As for Bonus part, you’ll build graphical user interface for Object Detection by YOLO and by the help of PyQt. This project you can represent as your results to your supervisor or to make a presentation in front of classmates or even mention it in your resume.
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