YOLOv7 YOLOv8 YOLOv9 YOLOv10 YOLOv11 - Deep Learning Course

🚀 [Train Custom Dataset, Object Detection, Pose Estimation, Instance Segmentation, Image Classification] 📚🎓

Welcome to the Future of AI-Driven Vision Tasks! 🌟

YOLOv7 YOLOv8 YOLOv9 Deep Learning Course: A Comprehensive Journey into Advanced Object Detection Models ✨

Course Headline: Train Custom Dataset, Master Object Detection, Pose Estimation, Instance Segmentation, Image Classification, and Build a Cool Web Dashboard with YOLOv7, YOLOv8, & YOLOv9!

Course Overview 🔍

Dive into the world of AI and explore the latest advancements in object detection with our 3 COURSES IN 1 package. This course is designed to equip you with the knowledge and skills to master YOLOv7, YOLOv8, and YOLOv9 - the most cutting-edge models for object detection, image classification, and more.

What You Will Learn 🚀

1. Efficiency Unleashed: Run a YOLO model from scratch to detect 80 types of objects in under 10 minutes!
2. YOLO Evolution: Understand the progression from YOLO v1 to the latest release, YOLOv8.
3. Performance Comparison: Conduct real experiments to see how YOLO models stack up against each other.
4. Advantages of YOLO: Discover what sets YOLO apart from other deep learning models.
5. YOLO Innovations: Learn about the new features introduced in YOLOv7 and YOLOv8.
6. Neural Networks Fundamentals: Gain a deep understanding of artificial neural networks, including neurons, perceptrons, and layers.
7. Activation Functions Mastery: Explore different activation functions like Sigmoid, tanh, ReLu, Leaky ReLu, Mish, and SiLU.
8. Convolutional Neural Networks (CNNs): Delve into the convolution process, pooling layers, flattening, and more.
9. Computer Vision Explained: Tackle various computer vision problems such as image classification, object localization, and instance segmentation.
10. YOLO Architecture Deep Dive: Analyze the architecture of YOLOv7, YOLOv8, and YOLOv9 in meticulous detail.
11. Dataset Acquisition: Learn how to find and source datasets for your projects.
12. Data Annotation Made Easy: Use LabelImg to annotate your dataset accurately.
13. Dataset Management: Automatically split your dataset for training and testing.
14. Step-by-Step Installation Guide: Follow a detailed guide for YOLOv7, YOLOv8, and YOLOv9 installation.
15. Custom Dataset Training: Train YOLO models on your own custom datasets.
16. Tensorboard Visualization: Learn to visualize your training progress with Tensorboard.
17. Model Testing: Apply the trained YOLOv7, YOLOv8, and YOLOv9 models in real-world scenarios using images, videos, and webcams.
18. YOLOv7 Enhancements: Explore Pose Estimation and Instance Segmentation features.
19. YOLOv8 Enhancements: Discover the newInstance Segmentation & Object Tracking capabilities. 20-24. Real World Projects: Work on hands-on projects to apply your knowledge, including a Robust mask detector, Weather YOLOv8 classification app, Coffee Leaf Diseases Segmentation app, and a Squat Counter app. Cap off your learning with a Various Vehicle Counter and Speed Estimation Web App with a Cool Dashboard using YOLOv9 + Streamlit.

Join us on this journey to master one of the most powerful object detection models ever created! With Dr. Priyanto Hidayatullah (Ph.D in AI) as your guide, you'll not only learn but also apply advanced AI techniques to real-world problems. 🤖🌐

Don't just follow the crowd - be a leader in AI-driven computer vision applications! 🏆✨

It seems you've compiled a range of feedback from different learners who have taken Professor Hidayatullah's course on YOLOv7 and YOLOv8. The feedback covers various aspects of the course, including its clarity, comprehensiveness, repetitiveness, and the instructor's teaching style and English proficiency. Here's a summary of the points raised:

Positive Feedback:

• The course provides a detailed explanation of how to implement YOLOv7 and YOLOv8 models.
• It covers both local and cloud (Google Colab) environments, which is valuable for learners with different setups.
• The instructor's knowledge in the field is acknowledged as being comprehensive.
• The course structure is well put together and up to date with the latest YOLO versions.
• Professor Priyanto is considered one of the best instructors due to his ability to keep things simple and explain concepts clearly.
• The course includes steps for setting up your own dataset, which is a valuable skill.
• The course's responsive support for questions and concerns is highly appreciated.

Areas for Improvement:

• Some learners found the repetition of certain steps in different videos to be unnecessary and would prefer more concise content.
• There are suggestions to update the course with the latest tools, such as using Label Studio instead of labelImg and incorporating YOLOv9 if available at the time of updating the course.
• A few learners pointed out that the English language delivery was not a concern, but others found it challenging, with transcripts not accurately reflecting the spoken lectures.
• Some learners felt that there could be more clarity on when to use YOLOv7 versus YOLOv8, and potentially a flowchart or diagram to illustrate their differences and use cases.

Overall, the course seems to have been well-received for its educational content and the instructor's approach to teaching. However, learners suggest that updates and improvements could be made to enhance clarity and efficiency, especially in terms of using the most current tools and addressing any language barriers more effectively. The feedback indicates a strong foundation in YOLO model implementation but also room for improvement based on learner experiences and needs.