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YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 2026

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  • 5,915 Students
  • Updated 3/2026
4.1
(688 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Short Course
Study Mode
Online
Duration
4 Hour(s) 45 Minute(s)
Language
English
Taught by
Muhammad Moin
Rating
4.1
(688 Ratings)

Course Overview

YOLO Object Detection Bootcamp: YOLOv5 to YOLO26 2026

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

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

See more details

Course Content

  • 44 section(s)
  • 83 lecture(s)
  • Section 1 YOLO26: Setting a New Global Standard for Edge-First Vision AI
  • Section 2 YOLO26 Hands-On in Google Colab: Detection, Segmentation, Pose, OBB & YOLOE-26
  • Section 3 YOLO26 Implementation on Windows (Google Antigravity)
  • Section 4 Ultralytics YOLO26 vs YOLO11: Performance Comparison
  • Section 5 YOLO26 Custom Object Detection: Dataset Creation & Model Training
  • Section 6 YOLO26 Instance Segmentation: Dataset Annotation & Model Training
  • Section 7 Training YOLO26 for Object Detection on a Custom Dataset
  • Section 8 Training YOLO26 Instance Segmentation on a Custom Dataset
  • Section 9 Fine-Tuning YOLO26 Pose Estimation with a Custom Dataset
  • Section 10 Training YOLO26 for Image Classification on a Custom Dataset
  • Section 11 Model Export with Ultralytics YOLO26
  • Section 12 YOLO26 Vehicle Detection: Visualizing Traffic Intensity
  • Section 13 YOLO26 Bird’s Eye View (BEV) Transformation for Traffic Analysis
  • Section 14 Introduction to YOLOv12
  • Section 15 YOLOv12 Implementation | Google Colab
  • Section 16 Training Custom YOLOv12
  • Section 17 YOLO11: New Features and Improvements
  • Section 18 YOLO11 Implementation | Google Colab
  • Section 19 YOLO11 Implementation | Windows & Linux
  • Section 20 Evaluating YOLO11 Model Performance: Testing and Analysis
  • Section 21 Training Custom YOLO11
  • Section 22 Train YOLO11 Instance Segmentation Model on a Custom Dataset
  • Section 23 Image Classification with YOLO11 on a Custom Dataset
  • Section 24 Human Activity Recognition with YOLO11: Fine-Tune YOLO11 Pose Estimation Model
  • Section 25 Multi-Object Tracking with Ultralytics YOLO11
  • Section 26 YOLO11 Streamlit Application
  • Section 27 Car and License Plate Detection & Recognition with YOLO11 and PaddleOCR
  • Section 28 Training Ultralytics YOLO11 on the VisDrone Dataset for Aerial Detection
  • Section 29 Training Ultralytics YOLO11 on the KITTI Dataset
  • Section 30 African Wildlife Animals Detection Using Ultralytics YOLO11
  • Section 31 Train a YOLO11 Instance Segmentation Model on the Car Parts Dataset
  • Section 32 YOLOv8 Introduction
  • Section 33 Advanced Computer Vision with Ultralytics YOLO11 & YOLOv8
  • Section 34 YOLOv8 Implementation
  • Section 35 Training Custom YOLOv8
  • Section 36 YOLOv8 Object Tracking
  • Section 37 YOLOv8 Object Segmentation and Tracking
  • Section 38 Training YOLOv8 Segmentation Model on Custom Dataset
  • Section 39 YOLOv8 Apps
  • Section 40 YOLOv8 WebApp Development
  • Section 41 YOLOv5 Implementation | Google Colab
  • Section 42 Training Custom YOLOv5
  • Section 43 African Wildlife Animals Detection Using YOLOv5
  • Section 44 Hands-On Computer Vision: Interactive Role Play
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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
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Reviews

  • S
    Sakthivel R
    1.0

    its one of the worst course i ever invested my money! it could be great if i get a refund

  • Y
    Yaşar
    1.0

    He doesn't explain the software in detail and doesn't say where it downloaded the data from. It just says it wrote it beforehand.

  • O
    Okpe Amos
    2.0

    Too many repetitions😪😪

  • H
    Henry Rose
    1.0

    It’s hard to understand what the instructor is trying to say. I don’t know what are his objectives. His explanation was not thorough.

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