YOLO26 & The Legacy : 6 in 1 YOLO Masterclass 2026 (Udemy.com)

This is a good intro, maybe not quite the right fit for me. I have extensive programming experience. I didn't get much from the repeated "here's how you copy files in Windows" bits. Speeding up playback in those sections helped. Also, the description of YOLO architecture could have been more in-depth than just listing computational blocks. All that said, though, I like the course's thoroughness.

Dr. Priyanto Hidayatullah has done an outstanding job with this course! The best part is that he consistently updates the content, ensuring it stays relevant and cutting-edge. His explanations are clear, detailed, and well-structured, making complex deep-learning concepts accessible to learners at all levels. While some have mentioned that he repeats certain steps, such as installing libraries, I find this approach extremely beneficial. It allows learners to jump directly into specific sections—whether it's data augmentation, hyperparameter optimization, or model training—without missing any critical setup steps. This structure makes it feel like each section is a self-contained, unique tutorial, which is great for those looking for targeted learning. I'm also applying these techniques in my PhD research, and this course has been a valuable resource in deepening my understanding of YOLOv7, YOLOv8, and beyond. Thank you so much, Dr. Priyanto, for your dedication to improving this course and sharing your knowledge with the community!

Good points: You will learn YOLO and he also tried a lot use Google Colab (Jupyter Notebook which GPU for students with machines CPU only) But 4 star because of 2 main reasons: 1. labelImg is deprecated, it is now Label Studio which is better and runs in docker, the course problaby was made before it, but also yolo v9 and for this has an update and custom labelling is to important. 2. There is a lot of no necessary repetation inside a lot of videos (the same step made again in videos), and even some videos in lessons are the excatly same then another. It was wast time and could just be a link (ex: there is a lot of steps equal to v7, v8 and v9, there is no need have the same video 3 times). Is a good teacher, but in these times a short and directly course is better them a long one, and the argument "learning by repetation" has no sense in videos, because the student can replay anytime.

The course was very well put together. The course does go through similar concepts using different tools, namely using YoloV7 and Yolov8 on both a PC framework and Google Colab framework. It is quite up to date given both YoloV7 and V8 have come out in late 2022 and early 2023 respectively. Some have noted the use of the different concepts/tools is repetitive, however I feel it's more comprehensive. If you know a certain stream you know you'll follow for example you have access to a GPU you may not be interested in Colab. Or more likely, if you don't have access to a GPU and want to run some of these models using Google's resources you may only use Colab. However having both explained clearly is very helpful. One thing that would be good for improvement would be a bit more of a flowchart in terms if there are certain things that make YoloV7 superior to YoloV8 and vice versa. The course does start off with explaining the history of Yolo's development, but one thing I found with the comprehensive nature of the course I didn't necessarily feel the need to go through both YoloV7 and V8 for my use case, but appreciate having both explanations given the differences in the syntax to run the models. At the end of the course my overall feeling is there is a V8 model for most use cases and higher version seems to be superior, but I may be oversimplifying something or missing something. For those who complained about English quality, I found Prof. Priyanto's English perfectly understandable and his accent was not distracting as some claimed. I do understand that if English is a student's second language a different accent may be hard to understand but as a native English speaker I found the criticism of Prof. Priyanto's English and accent unfounded and unfair. All in all I really enjoyed the course, appreciated the time Prof Priyanto put into it, and felt it was good value. It explained the steps of making your own dataset which is something most courses are missing as far as I could find which is very useful if you want to work with your own data or set up your own models. Thank you, Prof. Priyanto. I will look forward to more courses from you!

Excellent course. Before this course I came in with a little experience but a lot of loopholes in the coding and setup of using yolo. I had no knowledge in using colab or the anaconda prompt as much as I've seen here. Best of all the response to questions and concerns are very quick and assuring that whenever i need help with any issue i can ask.

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.

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!!!

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.

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.

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.