• How to run, from scratch, a YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 program to detect 80 object classes in < 10 minutes
• How to install and train YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLO11 using Custom Dataset & perform Object Detection for image, video & Real-Time using Webcam
• How to use YOLOv7 & YOLOv8 new features: Instance Segmentation, Pose Estimation, Image Classification, Object Tracking + Real-world Projects
• 7 Real Projects: Various Vehicle Counter Web App, Person Counter, Squat Counter, Masker Detection, Weather Classification, Coffee Leaf Diseases, Cattle Counter
• YOLOv7, YOLOv8, YOLOv9, YOLOv10 architecture and how it really works
• How to find dataset
• Data annotation/labeling using LabelImg
• Automatic Dataset splitting
• How to train YOLO v7, YOLO v8, YOLO v9, YOLO v10, YOLO11 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
• Real World Project #5: Various Vehicle Counter and Speed Estimation Web App with Cool Dashboard using YOLOv9 + Streamlit
• Real World Project #6: Cattle Counter using YOLOv10 + Bytetrack
• Real World Project #7: Person Counter using YOLO11 + Bytetrack

Welcome to the YOLOv7, YOLOv8, YOLOv9, YOLOv10, & YOLOv11 Deep Learning Course, a 5 COURSES IN 1. YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 are the current five best object detection deep learning models. They are fast and very accurate. YOLOv11 is the latest 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, YOLOv10 & YOLO11 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, YOLOv9, and YOLOv10 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, YOLOv9, YOLOv10, and YOLOv11 installation

15. Train YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLO 11 on your own custom dataset

16. Visualize your training result using Tensorboard

17. Test the trained YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLO11  models on image, video, and real-time using webcam.

18. YOLOv7 New Features: Pose Estimation

19. YOLOv7 New Features: Instance Segmentation

20. YOLOv8 New Features: Instance Segmentation & Object Tracking

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

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

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

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

25. Real World Project #5: Various Vehicle Counter and Speed Estimation Web App with Cool Dashboard using YOLOv9 + Streamlit
26. Real World Project #6: Cattle Counter using YOLOv10 + Bytetrack
27. Real World Project #7: Person Counter using YOLOv11 + Bytetrack

Who this course is for:

• Professionals who want to quickly grasp and apply YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLO11 on real projects
• Undergraduate/Graduate students who are taking computer vision using deep learning as their final project
• Anyone who is interested in learning Deep Learning and How to Apply it in solving Computer Vision problem