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- 85% Training YOLO v3 for Objects Detection with Custom Data

Training YOLO v3 for Objects Detection with Custom Data

$99.99 $14.99Track price

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8.8/10 (Our Score)
Product is rated as #109 in category Python

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.

Content Organization. Each Section of the course contains:

Videos

Code Practices

Code Templates

Activities

Quizzes

Downloadable Instructions

Discussion Opportunities

Instructor Details

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.

Specification: Training YOLO v3 for Objects Detection with Custom Data

Duration 7 hours
Year 2020
Level Intermediate
Certificate Yes
Quizzes Yes

15 reviews for Training YOLO v3 for Objects Detection with Custom Data

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  1. Avatar

    Harleen Singh

    I find the code writing and explanation very lucid and clean. I must appreciate the efforts taken by Valentyn to provide us required comment and helpful PDF’s to go ahead. Definitely the course helped and further recommended.

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  2. Avatar

    Aswathi M

    Excellent. Explains each line of code. Resources is perfect.

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  3. Avatar

    Nearthlab

    just scratching the surface

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  4. Avatar

    Arun

    So far very interesting

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  5. Avatar

    Radha Krishna Chaitanya Valluru

    Great Course. Everything went well as promised at the beginning of the course

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  6. Avatar

    Yashwanth

    Yes

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  7. Avatar

    Khushpreet Sandhu

    no tensorflow implementation

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  8. Avatar

    Ryosuke Yamazaki

    great sample codes kind explanation for biginners

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  9. Avatar

    Radu Goguta

    Amazing course, now I can train my own models from scratch on my own dataset where I personally label the points of interest. The instructor is also amazing, very responsive to all my questions! 5 stars and I recommend it to everyone who wants to create his own DNN for object detection!

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  10. Avatar

    Ricardo Sanchez

    Hands down the best course I’ve taken/purchased on Udemy

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  11. Avatar

    Benedict

    Great start!

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  12. Avatar

    Harshith Valluru

    It was really good. Instructor explanations are very clear

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  13. Avatar

    Michelle Sainos Vizuett

    It’s a great course, everything is very clear and works perfectly fine. This course really exceeded my expectations. I was looking for copper and I found gold.

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    Josh Baynes

    It is good so far

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    Jos Delfosse

    Good course with nice videos, clear PDF instructions and good Python code templates to use pre trained YOLO v3 or train it on custom data using Darknet. Section 1 to 7 are about preparing data on images, videos or camera (labelImg, ffmpeg,…). Section 8 is the interesting one and is about how to configure Darknet to train your custom data. However, since I have a macbook pro without NVidia GPU (no CUDA), the CPU only training would have been too long. I had to stop it. Fortunately, the computed weights (for custom data we use in the course) are provided so you can still have a look to the final results. Last section gives some explanations about how YOLO work. However, if you really want to put your hands in the YOLO algorithm, you need to take a Deep Learning course.

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    Training YOLO v3 for Objects Detection with Custom Data
    Training YOLO v3 for Objects Detection with Custom Data

    $99.99 $14.99

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