YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 2026

• Understand the evolution of YOLO from YOLOv5 to YOLO26
• Learn the architecture and innovations behind YOLO26
• Set up and run YOLO models in Google Colab and local environments (Windows/Linux)
• Perform object detection using YOLOv5, YOLOv8, YOLO11, YOLOv12, and YOLO26
• Apply instance segmentation using YOLO models
• Implement pose estimation models for human activity recognition
• Understand and use oriented bounding boxes (OBB) in object detection
• Train custom YOLO models on your own datasets
• Annotate and label datasets using Roboflow
• Prepare datasets for training including splitting and preprocessing
• Fine-tune YOLO models for specific real-world use cases
• Evaluate model performance using appropriate metrics and testing techniques
• Compare performance between different YOLO versions (YOLO26 vs YOLO11, YOLOv8, etc.)
• Build real-world projects such as pothole detection systems
• Develop PPE (Personal Protective Equipment) detection models
• Create wildlife detection systems using custom datasets
• Train image classification models using YOLO frameworks
• Implement multi-object tracking using DeepSORT, Bot-SORT, and ByteTrack
• Build traffic analysis systems including vehicle counting and speed estimation
• Generate traffic heatmaps and visualize object detection outputs
• Implement Bird’s Eye View (BEV) transformation for advanced traffic analytics
• Develop license plate detection and recognition systems using PaddleOCR
• Perform segmentation and tracking simultaneously on video streams
• Build real-time computer vision applications using webcams and videos
• Deploy trained YOLO models for real-world applications
• Export YOLO models into different formats for deployment
• Create interactive web applications using Flask and Streamlit
• Integrate YOLO models into end-to-end AI pipelines
• Work with real-world datasets like VisDrone and KITTI
• Gain practical experience building scalable computer vision systems

YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 (2026 Edition)

Master the complete evolution of YOLO (You Only Look Once) — from YOLOv5 to YOLO26, including the newly added YOLOv12, and build real-world, production-ready computer vision systems.

This course is a comprehensive, hands-on bootcamp designed to take you from fundamentals to advanced applications in object detection, segmentation, pose estimation, tracking, and deployment using the latest Ultralytics frameworks.

What Makes This Course Unique?

• Covers YOLO26 (latest 2026 model)

• Hands-on training across multiple YOLO generations (v5 → v26)

• Real-world projects: traffic analysis, PPE detection, wildlife detection, license plates, and more

• Complete pipeline: dataset creation → training → evaluation → deployment

Course Structure

This course is divided into five major parts:

Part 1: YOLO26 (Next-Gen Vision AI)

Learn the latest breakthrough in edge-first AI models.

Key Topics:

• YOLO26 architecture, innovations & benchmarks

• Google Colab & Windows setup (Google Antigravity)

Multi-task capabilities:

• Object Detection

• Instance Segmentation

• Image Classification

• Pose Estimation

• Oriented Bounding Boxes (OBB)

• YOLOE-26

Hands-On Training:

• Dataset annotation with Roboflow

Training models for:

• Pothole detection

• Instance segmentation

• Wildlife detection

• Human activity recognition

• Plant classification

Advanced Applications:

• Model export & deployment

• Traffic heatmaps & vehicle analytics

• Bird’s Eye View (BEV) transformation

Comparison:

• YOLO26 vs YOLO11 (speed & accuracy)

Part 2: YOLOv12

Topics Covered:

• Introduction to YOLOv12

• What’s new in YOLOv12

• Running YOLOv12 in Google Colab

• Training YOLOv12 on custom datasets

Hands-On Project:

• PPE (Personal Protective Equipment) detection using YOLOv12

Part 3: YOLO11 (Advanced Ultralytics Pipeline)

Deep dive into modern YOLO workflows.

Key Topics:

• YOLO11 features & improvements

• Implementation (Windows, Linux, Colab)

• Model evaluation & performance analysis

Training Tasks:

• Object detection (PPE)

• Instance segmentation (potholes)

• Pose estimation (human activity)

• Image classification (plants)

Advanced Systems:

• Multi-object tracking (Bot-SORT, ByteTrack)

• Streamlit web applications

• License plate detection with PaddleOCR

Real-World Datasets:

• VisDrone (aerial detection)

• KITTI dataset

• Wildlife detection

• Car parts segmentation

Part 4: YOLOv8 (Production-Level Applications)

Build industry-ready AI systems.

Fundamentals:

• YOLO vs CNN, RCNN family

• YOLOv8 architecture & improvements

• YOLOv7 vs YOLOv8 comparison

Implementation & Training:

• Running on Windows & Colab

• Dataset preparation & annotation

• Custom training

Projects:

• Pothole detection

• PPE detection

• Object detection use-cases

Tracking & Analytics:

• DeepSORT tracking

• Traffic counting & speed estimation

• Vehicle entry/exit monitoring

Segmentation & Advanced Applications:

• Segmentation + tracking

• Traffic lights, cracks, helmet detection

• Face detection & analytics

• License plate recognition

• Object blurring

Web Development:

• Flask integration

• Full web app deployment

• Live webcam applications

Part 5: YOLOv5 (Foundations)

Understand the base of modern YOLO systems.

Topics:

• YOLOv5 implementation (Google Colab)

• Training on custom datasets (PPE)

• Wildlife detection project

Tools & Technologies Covered

• Ultralytics YOLO (v5, v8, v11, v12, v26)

• Python, OpenCV

• Roboflow

• DeepSORT, Bot-SORT, ByteTrack

• PaddleOCR

• Flask & Streamlit

• Google Colab & local environments

What You’ll Build

• Real-time object detection systems

• Traffic analysis & monitoring solutions

• License plate recognition systems

• Pose estimation & activity recognition models

• End-to-end AI pipelines

• Deployable web applications

Who This Course is For

• Beginners in computer vision & AI

• Machine Learning engineers

• Developers building AI applications

• Researchers exploring latest YOLO models

By the End of This Course

You will be able to:

• Work with all major YOLO versions (v5 → v26)

• Train and fine-tune custom models

• Build real-world AI applications

• Deploy scalable computer vision systems