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Training YOLO v3 for Objects Detection with Custom Data

Training YOLO v3 for Objects Detection with Custom Data, Build your own detector by labelling, training and testing on image, video and in real time with camera: YOLO v3 and v4

training-yolo-v3-for-objects-detection-with-custom-data

  • Bestseller
  • Created by Valentyn Sichkar
  • English
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Description

In this hands-on course, you'll train your own Object Detector using YOLO v3-v4 algorithm.
As for beginning, you’ll implement already trained YOLO v3-v4 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-v4 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.
Content Organization. Each Section of the course contains:
Videos
Code Practices
Code Templates
Activities
Quizzes
Downloadable Instructions
Discussion Opportunities
Who this course is for:

Students who study Computer Vision and want to know how to use YOLO v3-v4 for Objects Detection
Students who know basics of YOLO but want to know how to Train YOLO v3-v4 with New Data
Students who study Object Detection Algorithms and want to Label Own Data in YOLO format
Students who use already existing datasets for Objects Detection but want to Convert them in YOLO format
Young Researchers who study different Objects Detection Algorithms and want to Train YOLO v3-v4 with Custom Data and Compare results with different approaches

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