YOLOv7 YOLOv8 YOLOv9 YOLOv10 YOLOv11 – Deep Learning Course

Train Custom Dataset, Object Detection, Pose Estimation, Instance Segmentation, Image Classification, Cool Web Dashboard

Created by: Dr. Priyanto Hidayatullah (Ph.D in AI)
Ph.D in Artificial Intelligence

Created by: Stunning Vision AI Academy
Providing high-quality education in AI and Computer Vision.

What Will I Learn?

• 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
• Object Detection, Instance Segmentation, Pose Estimation, Image Classification, Object Tracking + Real-World Projects
• 8 Real Projects: Various Vehicle Counter Web App, Person Counter, Squat Counter, Weather Classification, Leaf Diseases, Cattle Counter, X-Ray Classification
• YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLO11 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
• Real World Project #8: X-Ray Image Classification using YOLO11

Requirements

• Programming experience is an advantage but not required
• Windows laptop/PC, especially with Nvidia GPU

Target audience

• 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