Table of Contents

Train YOLO26 on a Custom Dataset with YOLOE-26 Auto-Labeling

Auto-Labeling a Custom Dataset for YOLO26 Object Detection

Configuring Your Development Environment

Project Structure

Step 1: Define Custom Object Classes and Reference Images

Step 2: Draw Visual Prompt Boxes for YOLOE-26 Auto-Labeling

Step 3: Auto-Label a Custom Dataset with YOLOE-26 Visual Prompting

Step 4: Clean the Pseudo-Labels

Step 5: Export the Custom Dataset in Standard YOLO Format

Step 6: Train YOLO26 on a Custom Dataset

Step 7: Test YOLO26 with Real-Time Webcam Object Detection

Step 8: Compare YOLO26, YOLOE-26, and Fine-Tuned Object Detection Models

Why Fine-Tune YOLO26 If YOLOE-26 Already Works?

What This Workflow Covered and What It Did Not

Summary

In this lesson, you will learn how to use YOLOE-26 visual prompting to auto-label a custom dataset, clean pseudo-labels, export YOLO training data, and fine-tune YOLO26 for class-specific object detection.

This lesson is the last in a 2-part series on…