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YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 2026

Name: YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 2026
Rating: 4.1 (697 reviews)

YOLOv5, YOLOv8, YOLO11, YOLOv12 & YOLO26: Custom Object Detection, Segmentation, Tracking & Pose Estimation

Role Play

Created byMuhammad Moin

Last updated 3/2026

English

What you'll learn

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

Course content

44 sections • 83 lectures • 26h 4m total length

Introduction to YOLO26: Architecture, Innovation, and Benchmarks21:54
This lecture introduces YOLO26, the latest release in the Ultralytics YOLO object detection series. YOLO26 delivers faster and more accurate real-time performance across images and videos, powered by architectural improvements and refined training strategies that push practical performance even further.
Key Highlights of YOLO26
Improved detection of small objects.
Up to 43% faster CPU inference compared to previous versions
End-to-End, NMS-free inference for cleaner and faster predictions
Multi-task support, including:
Object Detection
Instance Segmentation
Pose Estimation
Image Classification
Oriented Bounding Boxes (OBB)
Optimized backbone and training pipeline for improved stability and accuracy

How to Annotate/ Label a Custom Dataset Using Roboflow21:30
In this lecture, you will learn how to annotate and label a custom dataset using Roboflow for object detection tasks. We will cover image upload, creating classes, drawing bounding boxes, organizing data, and exporting the dataset in YOLO format. By the end of this lecture, you will have a fully prepared dataset ready for training your YOLO26 object detection model.
Train YOLO26 on a Custom Pothole Dataset for Pothole Detection19:57
In this lecture, you will learn how to train a YOLO26 object detection model on a custom pothole dataset. We will cover dataset configuration, training setup, model parameters, and performance monitoring. You will also learn how to evaluate the trained model and run inference to detect potholes in new images and videos.

How to Annotate & Label a Custom Dataset for Instance Segmentation with Roboflow16:55
In this lecture, you will learn how to annotate and label a custom dataset for instance segmentation using Roboflow. We will cover creating classes, drawing masks, organizing data, and exporting it in YOLO26 format. By the end, your dataset will be fully prepared for training a YOLO26 instance segmentation model.
Train a YOLO26 Instance Segmentation Model on a Custom Pothole Dataset14:50
In this lecture, you will learn how to train a YOLO26 instance segmentation model on a custom pothole dataset. The lecture covers dataset configuration, model setup, training, performance evaluation, and running inference to detect and segment potholes in images and videos. By the end, you will be able to build a custom instance segmentation model using YOLO26.

Requirements

Mac / Windows / Linux - all operating systems work with this course!

Description

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

Who this course is for:

Anyone who is interested in Computer Vision
Anyone who study Computer Vision and want to know how to use YOLO for Object Detection, Instance Segmentation, Pose Estimation and Image Classification
Anyone who aims to build Deep learning Apps with Computer Vision

YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 2026

What you'll learn

Explore related topics

Course content

YOLO26: Setting a New Global Standard for Edge-First Vision AI1 lecture • 22min

YOLO26 Hands-On in Google Colab: Detection, Segmentation, Pose, OBB & YOLOE-261 lecture • 32min

YOLO26 Implementation on Windows (Google Antigravity)1 lecture • 21min

Ultralytics YOLO26 vs YOLO11: Performance Comparison1 lecture • 12min

YOLO26 Custom Object Detection: Dataset Creation & Model Training2 lectures • 41min

YOLO26 Instance Segmentation: Dataset Annotation & Model Training2 lectures • 32min

Training YOLO26 for Object Detection on a Custom Dataset1 lecture • 22min

Training YOLO26 Instance Segmentation on a Custom Dataset1 lecture • 24min

Fine-Tuning YOLO26 Pose Estimation with a Custom Dataset1 lecture • 24min

Training YOLO26 for Image Classification on a Custom Dataset1 lecture • 14min

Requirements

Description

Who this course is for: