Valentyn Sichkar
In this hands–on course, you’ll train your own Object Detector using YOLO v3–v4 algorithms.
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. The code templates you can integrate later in your own future projects and use them for your own trained YOLO detectors.
After that, you’ll label individual 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:
Video Lectures
Coding Activities
Code Templates
Quizzes
Downloadable Instructions
Discussion Opportunities
Video Lectures of the course have SMART objectives:
S – specific (the lecture has specific objectives)
Instructor Details
Courses : 1
Specification: Train YOLO for Object Detection with Custom Data
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18 reviews for Train YOLO for Object Detection with Custom Data
Add a review
| Price | $10.99 |
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| Provider | |
| Duration | 7 hours |
| Year | 2021 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
$10.99
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.
Aswathi M –
Excellent. Explains each line of code. Resources is perfect.
Nearthlab –
just scratching the surface
Arun –
So far very interesting
Radha Krishna Chaitanya Valluru –
Great Course. Everything went well as promised at the beginning of the course
Yashwanth –
Yes
Khushpreet Sandhu –
no tensorflow implementation
Ryosuke Yamazaki –
great sample codes kind explanation for biginners
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!
Ricardo Sanchez –
Hands down the best course I’ve taken/purchased on Udemy
Benedict –
Great start!
Harshith Valluru –
It was really good. Instructor explanations are very clear
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.
Josh Baynes –
It is good so far
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.
Daniel Trevino Sanchez –
Tuve algunos problemas de inicio con el programa de python
Marlon Reis –
I liked very much!
Adib Bachtiar –
great!