YOLO vs RT-DETR 2025

In this video, we compare two leading object detection models in 2025: YOLO and RT-DETR. Watch as we test their performance side-by-side on real-world video footage, analyzing speed, accuracy, total detections, and confidence levels. Whether you're into computer vision, AI development, or just curious about object detection technology, this comparison will help you decide which model is best for your projects.

We break down the setup, inference process, and results using Python and Ultralytics. You'll see real-time stats, FPS comparisons, detection counts, and visual outputs for both YOLO V8 and RT-DETR L/X models.

🔍 What you’ll learn:
How to install and configure YOLO and RT-DETR
How to run inference and compare models side-by-side
Key metrics: inference time, FPS, detection count, confidence
Which model performs better in different scenarios

📁 Get the code and notebooks on GitHub:
Blog: Labellerr’s GitHub:https://github.com/Labellerr/Hands-On...

Notebook:https://github.com/Labellerr/Hands-On...

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Chapters:
0:00 Introduction: YOLO vs RT-DETR in 2025
0:07 YOLO: Real-Time Single-Shot Detection
0:19 RT-DETR: Transformer-Based Detection
0:35 Video Comparison Preview
1:08 Notebook Setup for Model Comparison
1:34 Installing Required Libraries
2:03 Importing Libraries & Model Configuration
2:41 Comparator Class Initialization
3:13 Video Inference & Reporting Methods
3:46 Test 1: YOLOv8-L vs RT-DETR-L
4:11 Highway Traffic Sample Video
5:02 L-Model Results Analysis
6:27 Performance Metrics Visualization
7:07 Side-by-Side Detection Comparison
7:43 Test 2: YOLOv8-X vs RT-DETR-X
8:53 X-Model Results Analysis
10:11 Final Model Performance Summary
10:49 GitHub Repository & Resources

Interested in learning more about our services?
Website: https://www.labellerr.com
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