YOLOv7 YOLOv8 YOLOv9 - Deep Learning Course

YOLO evolution, Train Custom Dataset, Object Detection, Pose Estimation, Instance Segmentation, Image Classification

4.65 (457 reviews)

Udemy

platform

English

language

Other

What you will learn

How to run, from scratch, a YOLOv7, YOLOv8, & YOLOv9 program to detect 80 object classes in < 10 minutes

How to install and train YOLOv7, YOLOv8, & YOLOv9 using your own Custom Dataset and perform Object Detection for image, video and Real-Time using Webcam/Camera

How to use YOLOv7 & YOLOv8 new features: Instance Segmentation, Pose Estimation, Image Classification, Object Tracking + Real-world Projects

4 Real-World Projects: Masker Detection, Weather Image/Video Classification, Coffee Leaf Diseases Segmentation Squat Counter

YOLOv7 & YOLOv8 architecture and how it really works

How to find dataset

Data annotation/labeling using LabelImg

Automatic Dataset splitting

How to train YOLOv7, YOLOv8, and YOLOv9 using custom dataset, transfer learning and resume training.

How to visualize training performance using TensorBoard

Easily understand The Fundametal Theory of Deep Learning and How exactly Convolutional Neural Networks Work

Real-World Project #1: Masker detection using YOLOv7 & YOLOv8

Real-World Project #2: Weather Image/Video Classification using YOLOv8

Real-World Project #3: Coffee Leaf Diseases Segmentation using YOLOv8

Real-World Project #4: Squat Counter based on YOLOv7 Pose Estimation

Why take this course?

Welcome to the YOLOv7, YOLOv8, & YOLOv9 Deep Learning Course, a 3 COURSES IN 1. YOLOv7, YOLOv8, and YOLOv9 are the current three best object detection deep learning models. They are fast and very accurate. YOLOv9 is the latest official version of YOLO whereas YOLOv8 is the most popular YOLO version of all.

What will you learn:

1. How to run, from scratch, a YOLOv7, YOLOv8, & YOLOv9 program to detect 80 types of objects in < 10 minutes.

2. YOLO evolution from YOLO v1 to YOLO v8

3. What is the real performance comparison, based on our experiment

4. What are the advantages of YOLO compares to other deep learning models

5. What’s new in YOLOv7 and YOLOv8

6. How artificial neural networks work (neuron, perceptron, feed-forward network, hidden layers, fully connected layers, etc)

7. Different Activation functions and how they work (Sigmoid, tanh, ReLu, Leaky ReLu, Mish, and SiLU)

8. How convolutional neural networks work (convolution process, pooling layer, flattening, etc)

9. Different computer vision problems (image classification, object localization, object detection, instance segmentation, semantic segmentation)

10. YOLOv7 & YOLOv8 architecture in detail

11. How to find the dataset

12. How to perform data annotation using LabelImg

13. How to automatically split a dataset

14. A detailed step-by-step YOLOv7, YOLOv8, and YOLOv9 installation

15. Train YOLOv7, YOLOv8, and YOLOv9 on your own custom dataset

16. Visualize your training result using Tensorboard

17. Test the trained YOLOv7, YOLOv8, and YOLOv9 models on image, video, and webcam.

18. YOLOv7 New Features: Pose Estimation

19. YOLOv7 New Features: Instance Segmentation

20. YOLOv8 New Features: Instance Segmentation & Object Tracking

20. Real World Project #1: Robust mask detector using YOLOv7 and YOLOv8

21. Real World Project #2: Weather YOLOv8 classification application

22. Real World Project #3: Coffee Leaf Diseases Segmentation application

23. Real World Project #4: YOLOv7 Squat Counter application

Screenshots

YOLOv7 YOLOv8 YOLOv9 - Deep Learning Course - Screenshot_01

YOLOv7 YOLOv8 YOLOv9 - Deep Learning Course - Screenshot_02

YOLOv7 YOLOv8 YOLOv9 - Deep Learning Course - Screenshot_03

YOLOv7 YOLOv8 YOLOv9 - Deep Learning Course - Screenshot_04

Reviews

Akiti

October 1, 2023

works great. i had no idea how yolo works but after viewing i now think i can go ahead and fix real world problems using yolo

Iheb

September 23, 2023

When I initially enrolled in this course, my expectations were centered around gaining an in-depth understanding of YOLO (You Only Look Once) and acquiring the practical skills required to implement it effectively. Unfortunately, my experience with the course has been somewhat different from what I had anticipated. While I was eager to delve into the inner workings of YOLO and explore its nuances, a substantial portion of the course appeared to be focused on the process of downloading the necessary components. This aspect, I must admit, felt somewhat redundant, as key information was reiterated multiple times. Additionally, the requirement to download several models and datasets on multiple occasions proved to be quite time and energy-consuming. Regrettably, some of these resources were left unused, leaving me with a sense of inefficiency. I understand the importance of clear and deliberate explanations in an educational context, and it is possible that the intention was to provide comprehensive guidance. However, I couldn't help but feel that the pace of the course was slower than expected. It led me to contemplate whether the same content could have been covered more efficiently, perhaps in a condensed timeframe. It's my belief that a course of this nature could potentially be condensed into a shorter duration, allowing learners to achieve the same level of understanding within a more streamlined timeframe, possibly through alternative platforms like YouTube. I genuinely appreciate the effort put forth by the course instructor and the potential for improvement in future iterations. With a more focused approach and an emphasis on optimizing the learning experience, I believe that future iterations of this course can offer a more efficient and engaging educational journey.

Manoj

September 21, 2023

An excellent course on YOLOv7 and YOLOv8 by Professor Hidayatullah, highlighting each and every step of using these algorithms in various scenarios. Each step is painstakingly reproduced to help the student. The Professor strongly believes in REINFORCEMENT LEARNING (pun intended, ?) since he keeps repeating the same steps over and over again in various sections. To some it may appear bothersome but works GREAT for me because I believe in the same philosophy wherein something has to be repeatedly drilled into one’s mind so that it becomes second nature. The course also can act as template for one to use to create and bring their own projects to fruition since it provides all the necessary detail, including image annotation, installing cuDNN for CUDA etc. Thoroughly enjoyed the course and look forward to other courses from him on related topics. PS: The only gripe I have is maybe he could have discussed the Python source code to some of the earlier sections like he did for the last section on squat counter, though I agree that would have the course longer. Still, great job, Professor!!!

XnneHang

September 11, 2023

The course is easy to follow and understand.Except at the first I can't find the weights file and yolov7-main python file. Senei you can add the website of your github at the begining of the vedio so that it can be easy to find. Above all,it make me understand how to use and train yolo.

Rashen

May 25, 2023

Even though he say Mac users are welcome he does not explain how to use it on mac, he only explains for windows users only. Course structure and materials are great.

清水

April 5, 2023

The lecture itself was very good. It would have been better if there was a PDF file explaining the commands and options.

Mardhiyah

April 3, 2023

Great course prepared by Dr. Hidayatullah. Very useful for students learning how to use yolov7 and yolov8 algorithms in real-life application. Dr. Hidayatullah is very knowledgeable in this field. I would recommend this course for people who want to learn more about Yolo's capability and how to apply to other problems in the future.

Bahar

March 18, 2023

Mohon menambahkan detail terkait aplikasi Pose Estimation dengan gerakan olahraga lainnya, terima kasih

Muhammad

March 16, 2023

Covered topics such as environment setup and installing libraries in detail. Covered how to implement the code for different applications using yolov7 and explained what each function does. Covered training of yolov7 on a custom dataset with easy-to-follow steps. One suggestion would be to have more videos like the last video in the course, where the code inside the detection algorithm is explained. Overall, the course is excellent for learning the application of yolov7, and now yolov8. But I would like it if it included more explanation of the detection code.

Raihan

January 29, 2023

This course is very easy to understand for beginners who want to learn AI object recognition, because the explanation is to the point and the tutorial is not confusing, so it can be followed easily.

Max

January 24, 2023

This course is informative, and helpful for getting started with yolov7. The instructor does a good job of outline the steps required to get yolov7 and addition requirements downloaded onto your local machine or on google colab. It is my opinion that this course is mainly for beginners.

Zahri

January 14, 2023

This course taught me a practical way to operate YoloV7 in the best and most detailed way. The creator is active when students find a problem. Go through the Q&A section, and you'll find you're problem is solved. Even though the theory about YoloV7 itself is minimal, but I think it's the best way for a quick learning mechanism like this.

Nikhil

January 9, 2023

because in the video detection section it doesn't detect every vehicles in that video also the labeling is not correct.The model detecting a car as truck.

Andrea

December 24, 2022

I have not been able to install onnxruntime. No whl available fo x64 windows. non mentio of that problen. The Rquirement.txt do not contain all libraries needed

Nisa

December 15, 2022

This course helps me to understand YOLOv7 more. I can follow the instructions to put it into practice. It is easy to learn for a beginner like me. Thank you.

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