What you'll learn:
- Introduction to YOLO26: Architecture, Innovations, and Benchmarks
- Using YOLO26 for Detection, Segmentation, Pose Estimation, OBB, and YOLOE-26
- Step-by-Step YOLO26 Setup on Windows with Google Antigravity
- YOLO26 vs YOLO11: Speed and Accuracy Comparison
- YOLO26 Custom Object Detection: Dataset Creation & Model Training
- YOLO26 Instance Segmentation: Dataset Annotation & Model Training
- Fine-Tuning YOLO26 for Pose Estimation on a Custom Dataset
- Training YOLO26 for Image Classification on a Custom Dataset
- Exporting Models with Ultralytics YOLO26
- Building a Vehicle Intensity Heatmap from YOLO26 Detections
- Real-Time Bird’s Eye View (BEV) System using YOLO26 and OpenCV
- YOLOv12 architecture and how it really works
- What is Non Maximum Suppression & Mean Average Precision
- How to use YOLOv12 for Object Detection
- Evaluating YOLOv12 Model Performance on Images, Videos & on the Live Webcam Feed
- Blurring Objects with YOLOv12 and OpenCV-Python
- Data annotation/labeling using Roboflow
- Build a Tennis Analysis System with YOLO, OpenCV and PyTorch
- Training and Fine-Tuning YOLOv12 Models on Custom Datasets
- Object Detection in the Browser using YOLOv12 and Flask
This comprehensive course combines YOLOv12 and YOLO26 into one complete, real-world computer vision masterclass. You will learn how to build, train, evaluate, and deploy state-of-the-art YOLO models for real-time AI applications.
YOLOv12 introduces advanced architectural and training enhancements that improve both speed and accuracy across multiple vision tasks. YOLO26 further pushes performance with optimized backbones, improved small object detection, NMS-free inference, and faster CPU execution.
Throughout this course, you will learn:
Object Detection
Instance Segmentation
Pose Estimation
Image Classification
Oriented Bounding Boxes (OBB)
Multi-Object Tracking
What You Will Learn
Fundamentals & Architecture
Understanding YOLOv12 and YOLO26 architectures
Key improvements and performance innovations
Non-Maximum Suppression (NMS) and Mean Average Precision (mAP)
YOLO26 vs earlier YOLO versions comparison
Model Setup & Usage
Step-by-step environment setup
Running detection, segmentation, pose, OBB, and classification
Performance testing and benchmarking
Custom Dataset Creation
Finding and preparing datasets
Data annotation and labeling
Using Roboflow for detection and segmentation projects
Automatic dataset splitting
Training & Fine-Tuning
Training YOLOv12 and YOLO26 on custom datasets
Fine-tuning for detection, segmentation, pose, and classification
Model evaluation and optimization
Real-World Projects (8+ Hands-On Projects)
PPE Detection System
Pothole Detection & Segmentation Models
Advanced Multi-Object Tracking with Bot-SORT & ByteTrack
Vehicle Intensity Heatmap for congestion analysis
Real-Time Bird’s Eye View (BEV) system
Tennis Analysis System using YOLO and OpenCV
Object Blurring Applications
Custom Web Applications with Flask
Model Export and Deployment using Ultralytics
