YOLO11 & YOLOv12: Object Detection & Web Apps in Python 2026

Learn Custom Object Detection, Tracking, and Pose Estimation with YOLO11 & YOLOv12, and Build Web Apps

YOLO11 and YOLOv12 are the latest state-of-the-art computer vision model architectures, surpassing previous versions in both speed and accuracy. Building on the advancements of earlier YOLO models, YOLO11 and YOLOv12 introduce significant architectural and training enhancements, making them versatile tools for a variety of computer vision tasks..

These models support a wide range of applications, including object detection, instance segmentation, image classification, pose estimation, and oriented object detection (OBB).

In this course, you will learn:

• What's New in Ultralytics YOLO11.

• How to use Ultralytics YOLO11 for Object Detection, Instance Segmentation, Pose Estimation, and Image Classification.

• Running Object Detection, Instance Segmentation Pose Estimation and Image Classification with YOLO11 on Windows/Linux.

• Evaluating YOLO11 Model Performance: Testing and Analysis

• Training a YOLO11 Object Detection Model on a Custom Dataset in Google Colab for Personal Protective Equipment (PPE) Detection.

• Step-by-Step Guide: YOLO11 Object Detection on Custom Datasets on Windows/Linux.

• Training YOLO11 Instance Segmentation on Custom Datasets for Pothole Detection.

• Fine-Tuning YOLO11 Pose Estimation for Human Activity Recognition.

• Fine-Tuning YOLO11 Image Classification for Plant Classification.

• Multi-Object Tracking with Bot-SORT and ByteTrack Algorithms.

• License Plate Detection & Recognition using YOLO11 and EasyOCR.

• Integrating YOLO11 with Flask to Build a Web App.

• Creating a Streamlit Web App for Object Detection with YOLO11.

• Car and License Plate Detection & Recognition with YOLO11 and PaddleOCR

• Introduction to YOLOv12.

• How to use YOLOv12 for Object Detection.

• Fine-Tune YOLOv12 Object Detection Model on Custom Dataset for PPE Detection.