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Development Software Engineering Object Detection

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
Bestseller
Rating: 4.4 out of 54.4 (523 ratings)
2,254 students
Created by Valentyn Sichkar
Last updated 1/2021
English
English
30-Day Money-Back Guarantee

What you'll learn

  • Apply already trained YOLO v3-v4 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-v4 in Darknet framework
  • Build own PyQt graphical user interface for Objects Detection based on YOLO v3-v4 algorithm
Curated for the Udemy for Business collection

Course content

9 sections • 58 lectures • 7h 6m total length

  • Preview03:24
  • Quick Win - Step 1: Simple Object Detection by thresholding with mask
    10:48
  • Quick Win - Step 2: Simple Object Detection by thresholding with mask
    08:29
  • Activity: Let's get acquainted
    01:22
  • Installing Miniconda, Python, PyCharm, OpenCV
    03:35

  • Preview02:35
  • Objects Detection on Image with YOLO v3 and OpenCV
    23:34
  • Activity: Detect Objects on this image
    01:10
  • Objects Detection on Video with YOLO v3 and OpenCV
    14:02
  • Activity: Detect Objects on this video
    01:09
  • Objects Detection in Real Time with YOLO v3 and OpenCV
    05:56
  • Quiz: What are the best practices for
    10 questions
  • Conclusion: key takeaways for Objects Detection with dnn library in OpenCV
    01:12
  • YOLO v4: instructions
    00:16

  • Preview01:29
  • How does labelled image in YOLO format looks like?
    02:11
  • Useful resources for labelling
    03:04
  • Labelling image in YOLO format
    12:23
  • Activity: Label Objects on this image
    00:56
  • Labelling video in YOLO format
    09:50
  • Activity: Label Objects on this video
    01:03
  • Preparing files for training
    17:38
  • Quiz: What are the best practices for
    10 questions
  • Conclusion: key takeaways for labelling data in YOLO format
    01:10
  • YOLO v4: instructions
    00:04

  • Preview01:25
  • Toolkit for downloading images
    01:21
  • Downloading images from huge dataset
    09:45
  • Activity: Download Images for these classes
    01:34
  • Converting downloaded files to YOLO format
    35:27
  • Preparing files for training
    14:29
  • Joining datasets for training
    30:27
  • Quiz: What are the best practices for
    10 questions
  • Conclusion: key takeaways for creating custom dataset and converting it to YOLO
    01:35
  • YOLO v4: instructions
    00:05

  • Preview01:34
  • Downloading Traffic Signs dataset
    04:02
  • Converting downloaded Traffic Signs dataset to YOLO format
    26:09
  • Preparing files for training
    12:24
  • Quiz: What are the best practices for
    10 questions
  • Conclusion: key takeaways for converting Traffic Signs dataset in YOLO format
    01:04
  • YOLO v4: instructions
    00:05

  • Preview01:40
  • Installing Darknet
    07:08
  • Checking installation
    16:18
  • Preparing files for training
    04:00
  • Setting up configuration files
    24:48
  • Running training process
    07:04
  • When do we stop training?
    05:23
  • Activity: Test trained custom models on these images
    02:35
  • Activity: Test trained custom models on these videos
    02:26
  • Quiz: What are the best practices for
    10 questions
  • Conclusion: key takeaways for training YOLO v3 in Darknet framework
    01:06
  • YOLO v4: instructions
    00:49

  • Congratulation word and recap of learned skills
    01:54
  • What is next?
    04:38
  • Installing PyQt for building user interface
    08:10
  • Creating PyQt interface
    25:35
  • Integrating YOLO v3 into PyQt interface
    11:15
  • Running experiments with PyQt interface for Objects Detection
    00:54
  • YOLO v4: instructions
    00:06

  • Preview26:28
  • Quiz: YOLO v3
    10 questions

  • Preview05:09

Requirements

  • Basic knowledge of Objects Detection Algorithms
  • Basics on how YOLO v3-v4 works
  • Intermediate knowledge of Python v3
  • Basic knowledge of OpenCV
  • Basics on how to work with Anaconda Environments
  • Basics on how to work with PyCharm IDE or any other Python IDE
  • Basics on how to work with Terminal Window or Anaconda Prompt
  • To have Linux Ubuntu installed is optional, but recommended

Description

In this hands-on course, you'll train your own Object Detector using YOLO v3-v4 algorithm.

  1. 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.

  2. After that, you’ll label own dataset as well as create custom one by extracting needed images from huge existing dataset.

  3. 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.

  4. When datasets are ready, you’ll train and test YOLO v3-v4 Detectors in Darknet framework.

  5. 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

Featured review

Raymond Andrade
Raymond Andrade
79 courses
11 reviews
Rating: 5.0 out of 510 months ago
It seems to go by slow for those who are experienced, but he does a great job and has very useful handouts. I migrated from tensorflow to yolo because they still don't officially support object detection. Very refreshing to get this working in a day with a data set I had previously.

Instructor

Valentyn Sichkar
Computer Vision, Machine Learning, Image Processing
Valentyn Sichkar
  • 4.4 Instructor Rating
  • 523 Reviews
  • 2,254 Students
  • 1 Course

I am PhD student in Intelligent Systems. Studying Computer Vision, Machine Learning, Image Processing. Developing algorithms for safety autonomous vehicles.

I have a BSc in Manufacturing automation where I obtained knowledge on how to improve production speed and quality by integrating more efficient equipment, like non-stop filtering, velocity and temperature control in real time, as well as optical sensors for sorting and classifying different types of products.

And I have an MSc in Intelligent Systems where I obtained extensive knowledge of machine learning, computer vision, and intelligent robotics. My final project was to develop Alarm-Warning system for mobile robot that has information about distances to the objects - Safe distance, Warning distance and Alarm distance. The system creates a kind of bubble around mobile robot with green, yellow and red zones preventing collisions with obstacles.

I have published research on using different dimensions of filters for convolutional neural networks (ConvNet) for effective classification of Traffic Signs. Trained ConvNet I deployed on the Web on Linux VPS and on the basis of Flask framework in order to have opportunity to test classification online.

Professional interests: Computer Vision, Convolutional Neural Networks, Autopilot Car's System, Autonomous Robots.

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